system

Abstract

The system according to this embodiment aims to enable elderly people and people with physical limitations to easily access everyday shopping and appropriate goods and services. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a recommendation unit, an agency unit, and a support unit. The data collection unit collects the user's schedule and health data. The analysis unit analyzes the data collected by the data collection unit. The recommendation unit recommends products and services based on the analysis results obtained by the analysis unit. The agency unit purchases and delivers the products recommended by the recommendation unit on behalf of the user. The support unit assists with collaboration with local communities and communication with family.

Description

Technical Field

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

Background Art

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

[0007] The system according to this embodiment can enable elderly people and people with physical limitations to easily access everyday shopping and appropriate goods and services. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI agent service according to an embodiment of the present invention is a system targeted at the elderly and people with physical limitations, which substitutes for daily shopping and recommends appropriate products and services. This system has unique schedule management, health data linkage, and community support functions. The AI agent service collects the user's schedule and health data, which is analyzed by AI. Next, based on the analysis results, the optimal products and services are recommended to the user. Furthermore, a shopping proxy service is provided to purchase and deliver the specified products. It also has functions for cooperation with the local community and support for communication with family members. For example, the AI agent service collects detailed data such as the user's meal records and medical information. For example, if the user records the daily meal content and the AI analyzes this data, products containing the necessary nutrients can be recommended. Next, the collected data is analyzed by AI. The AI takes into account the user's health status and schedule and recommends the optimal products and services. For example, if the user needs a specific nutrient, foods containing that nutrient are recommended. Also, in accordance with the user's schedule, products and medicines are recommended at an appropriate timing. Furthermore, a shopping proxy service is provided. The AI selects and purchases and delivers the products specified by the user on their behalf. For example, the user lists daily necessities, and based on that list, the AI selects the products and purchases and delivers them on their behalf. It also has functions for cooperation with the local community and support for communication with family members. For example, it has a cooperation function with neighboring volunteers and support groups to ensure support in case of emergency. As a result, the user can reduce the sense of social isolation and live with peace of mind. With this mechanism, an environment is realized in which the elderly and people with physical limitations can live an independent life and actively participate in society. The user can intuitively use the service without performing complicated operations. Also, with voice recognition support and QR code (registered trademark) support, the operation is simple and easy for anyone to use. For example, when the user inputs "I want to buy milk" by voice, the AI recommends the optimal product and purchases and delivers it on their behalf. Also, by using the QR code, product information can be easily obtained and the support procedures can be carried out smoothly.In this way, by using AI agent services, it is possible to create an environment where elderly people and those with physical limitations can live independently and actively participate in society, thereby improving their quality of life and health. Thus, AI agent services can realize an environment where elderly people and those with physical limitations can live independently and actively participate in society.

[0029] The AI ​​agent service according to this embodiment comprises a data collection unit, an analysis unit, a recommendation unit, an agency unit, and a support unit. The data collection unit collects the user's schedule and health data. For example, the data collection unit can collect the user's calendar appointments, event schedules, and daily tasks. The data collection unit can also collect the user's health data, such as heart rate, blood pressure, body temperature, and exercise level. For example, the data collection unit can obtain health data from the user's smartwatch or fitness tracker. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit recommends the most suitable products and services, taking into account the user's health condition and schedule. For example, if the user needs a specific nutrient, the analysis unit will recommend foods containing that nutrient. The analysis unit can also recommend products and medicines at the appropriate time according to the user's schedule. The recommendation unit recommends products and services based on the analysis results obtained by the analysis unit. The Recommendation Department makes recommendations based on user preferences, past purchase history, health status, etc. For example, it analyzes products and services previously purchased by the user and recommends similar products and services. The Recommendation Department can also recommend foods containing specific nutrients or health supplements according to the user's health status. The Agency Department purchases and delivers products recommended by the Recommendation Department on behalf of the user. For example, the Agency Department purchases and delivers products specified by the user from online stores. The Agency Department can also purchase and deliver products specified by the user from physical stores. For example, the Agency Department selects products based on a list of daily necessities that the user has compiled, and purchases and delivers them on behalf of the user. The Support Department supports collaboration with local communities and communication with family. For example, the Support Department has functions to collaborate with nearby volunteers and support organizations to ensure support in emergencies. The Support Department also has functions to support communication with family. For example, the support department provides features such as messaging, video calls, and notifications for family members.As a result, the AI ​​agent service according to this embodiment can support the user's life by collecting the user's schedule and health data, recommending products and services based on the analysis results, and purchasing and delivering them on their behalf.

[0030] The data collection unit collects user schedules and health data. For example, it can collect user calendar appointments, event schedules, and daily tasks. Specifically, it integrates with the user's calendar and task management applications to obtain schedule and task information from these applications. This allows for a detailed understanding of the user's schedule. The data collection unit can also collect health data such as the user's heart rate, blood pressure, body temperature, and exercise level. For example, it can obtain health data from the user's smartwatch or fitness tracker. These devices monitor the user's physical activity and health status in real time and collect data. The collected data is sent to a cloud server and managed centrally. Furthermore, the data collection unit can also collect user diet and sleep data. For example, it integrates with the user's meal logging applications and sleep trackers to obtain data from these applications. This allows for a comprehensive understanding of the user's health status. The data collection unit centrally manages this data and can integrate with other systems and departments as needed. For example, the collected data can be made accessible to the analysis and recommendation departments. 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.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit recommends optimal products and services, taking into account the user's health status and schedule. Specifically, it uses AI to analyze the collected data and makes recommendations based on the user's health status and lifestyle. For example, it analyzes the user's heart rate and blood pressure data to assess stress levels and health risks. This allows it to recommend foods containing specific nutrients if the user needs them. It can also recommend products and medications at the appropriate time according to the user's schedule. For example, if a user has a busy schedule, the analysis unit will recommend foods and supplements suitable for energy replenishment according to the user's schedule. Furthermore, the analysis unit analyzes the user's past data and behavioral patterns to support long-term health management and lifestyle improvement. For example, it analyzes the user's exercise data to assess the risks of inactivity or excessive exercise and proposes an appropriate exercise plan. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to comprehensively analyze the user's health status and lifestyle and recommend the most suitable products and services.

[0032] The recommendation unit recommends products and services based on the analysis results obtained by the analysis unit. The recommendation unit makes recommendations based on factors such as the user's preferences, past purchase history, and health status. Specifically, it analyzes the user's past purchase history and recommends similar products and services. For example, it recommends products with similar effects based on health foods and supplements the user has purchased in the past. The recommendation unit can also recommend foods and health supplements containing specific nutrients depending on the user's health status. For example, if the user has high blood pressure, it recommends foods and supplements that help lower blood pressure. Furthermore, the recommendation unit can make recommendations tailored to the user's lifestyle and preferences. For example, if the user has a busy schedule, it recommends easily consumable health foods and supplements. Also, if the user has an allergy to a specific ingredient, it recommends products that do not contain that ingredient. The recommendation unit notifies the user of these recommendations on their smartphone or computer, making them easily accessible. This allows the recommendation system to recommend products and services that are best suited to the user's health condition and lifestyle, thereby supporting the user's life.

[0033] The purchasing department purchases and delivers products recommended by the recommendation department. For example, the purchasing department can purchase and deliver products specified by the user from an online store. Specifically, when a user selects a recommended product, the purchasing department handles the purchase process at the online store and delivers the product to the address specified by the user. The purchasing department can also purchase and deliver products specified by the user from a physical store. For example, a user can list their daily necessities, and the purchasing department can select products based on that list and purchase and deliver them on their behalf. The purchasing department can perform these purchase procedures quickly and accurately, saving the user time and effort. Furthermore, the purchasing department can manage product inventory and delivery status in real time and notify the user. For example, if a product is out of stock, the purchasing department will suggest an alternative product to the user and respond quickly. It can also track the delivery status in real time and notify the user of the estimated delivery time. This allows the purchasing department to respond quickly and flexibly to the user's needs and ensure that products are delivered reliably.

[0034] The support department assists with collaboration with local communities and communication with family. For example, the support department has functions to coordinate with nearby volunteers and support organizations to ensure support in emergencies. Specifically, the support department collaborates with local volunteer and support organizations to ensure that users can receive assistance in emergencies. For example, if a user faces a health problem or emergency, the support department will notify nearby volunteers and request prompt assistance. The support department also has functions to support communication with family. For example, the support department provides messaging, video call, and notification functions to family members. This allows users to easily stay in touch with their families and live with peace of mind. Furthermore, the support department notifies family members of the user's living situation and health status, so that family members can stay informed about the user's situation. For example, it shares the user's health data and schedule with family members, making it easier for family members to manage the user's health and provide support for their daily life. In this way, the support department can comprehensively support the user's life and provide an environment in which they can live with peace of mind.

[0035] The data collection unit can collect detailed data such as the user's meal records and medical information. For example, the data collection unit can collect data when the user records the contents of their daily meals. The data collection unit can also collect the user's medical information and diagnostic results. For example, the data collection unit can collect the user's medical history and diagnostic results to understand their health status. The data collection unit can also collect data on the user's calorie intake and nutrients. For example, the data collection unit can collect data when the user records the calories and nutrients of the food they consume. This allows the data collection unit to perform more accurate analysis by collecting detailed data such as the user's meal records and medical information. Detailed data includes, but is not limited to, the contents of meals, calorie intake, medical history, and diagnostic results. Some or all of the above processing 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 meal records into a generating AI and have the generating AI perform an analysis of the meal contents.

[0036] The analysis unit can analyze the collected data and recommend the most suitable products and services, taking into account the user's health condition and schedule. For example, the analysis unit can analyze the user's health condition and, if the user needs a specific nutrient, recommend foods containing that nutrient. The analysis unit can also recommend products and medications at the appropriate time, according to the user's schedule. For example, the analysis unit can recommend necessary products and services, taking into account the user's schedule. The analysis unit can recommend the most suitable products and services based on the user's health condition and schedule. This allows the analysis unit to make suggestions that meet the user's needs by recommending the most suitable products and services, taking into account the user's health condition and schedule. Optimal products and services include, but are not limited to, products tailored to health conditions and services tailored to schedules. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform recommendations based on the user's health condition and schedule.

[0037] The recommendation unit can recommend foods containing specific nutrients if the user needs those nutrients. For example, if the user needs vitamins, the recommendation unit will recommend foods containing vitamins. The recommendation unit can also recommend foods containing minerals if the user needs minerals. For example, if the user needs protein, the recommendation unit will recommend foods containing protein. The recommendation unit can recommend foods containing specific nutrients according to the user's health condition. In this way, the recommendation unit can support the user's health by recommending foods containing specific nutrients if the user needs them. Specific nutrients include, but are not limited to, vitamins, minerals, and proteins. Some or all of the above processing in the recommendation unit may be performed using, for example, AI, or without AI. For example, the recommendation unit can input the user's health condition into a generating AI and have the generating AI recommend foods containing specific nutrients.

[0038] The agency unit can purchase and deliver products specified by the user. For example, the agency unit can purchase food specified by the user from an online store and deliver it. The agency unit can also purchase daily necessities specified by the user from a physical store and deliver them. For example, the agency unit can purchase medicine specified by the user from a pharmacy and deliver it. The agency unit can purchase and deliver products specified by the user. In this way, the agency unit can assist the user with their shopping by purchasing and delivering products specified by the user. Specified products include, but are not limited to, food, daily necessities, and medicines. Some or all of the above processing in the agency unit may be performed using, for example, AI, or not using AI. For example, the agency unit can input a list of products specified by the user into a generating AI and have the generating AI select and purchase the products.

[0039] The support department has the function of coordinating with local volunteers and support organizations to ensure emergency support. For example, the support department can coordinate with local volunteers to provide a rapid response in an emergency. The support department can also coordinate with support organizations to provide emergency assistance. For example, the support department can provide contact information for emergency response. By ensuring emergency support, the support department can enhance users' sense of security. Emergency support includes, but is not limited to, emergency response, emergency contact, and evacuation assistance. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input emergency support information into a generating AI and have the generating AI execute an appropriate response method.

[0040] The support unit can be equipped with family communication support functions. For example, the support unit can provide a messaging function to family members. It can also provide a video call function to family members. For example, the support unit can provide a notification function to family members to share important information. By equipping the support unit with family communication support functions, the support unit can strengthen the user's social connections. Communication support functions include, but are not limited to, messaging, video calls, and notification functions. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input family communication data into a generating AI and have the generating AI execute an appropriate support method.

[0041] The data collection unit can be easy to operate through voice recognition and QR code support. The data collection unit collects data, for example, when the user gives a voice command. The data collection unit can also collect data by reading a QR code. For example, if the user gives a voice command, the data collection unit will collect the data using voice recognition technology. The data collection unit's ease of operation through voice recognition and QR code support allows users to intuitively use the service. Voice recognition support includes, but is not limited to, the types of voice commands, recognition accuracy, and supported languages. QR code support includes, but is not limited to, the methods for generating and reading QR codes and the types of information they correspond to. Some or all of the above-described processes in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input voice data into a generating AI and have the generating AI perform voice recognition and data collection.

[0042] The data collection unit can analyze the user's past health data and schedule history to select the optimal data collection method. For example, the data collection unit can set the timing of regular health checks based on the user's past health data. The data collection unit can also analyze the user's schedule history and collect data while avoiding busy times. For example, the data collection unit can suggest a specific data collection method (e.g., voice input, text input) according to the user's health condition. The data collection unit can select the optimal data collection method by analyzing the user's past health data and schedule history. The optimal data collection method includes, but is not limited to, the type of sensor data, collection frequency, and data accuracy. Some or all of the above processing 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 past health data into a generating AI and have the generating AI select the optimal data collection method.

[0043] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, if the user is interested in health, the data collection unit will prioritize collecting health-related data. Furthermore, if the user is busy, the data collection unit can collect only important data and omit unnecessary data. For example, the data collection unit can adjust the frequency and method of data collection according to the user's lifestyle. By filtering data based on the user's lifestyle and areas of interest, the data collection unit can prioritize the collection of important data. Filtering includes, but is not limited to, criteria for selecting high-priority data and filtering algorithms. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can input the user's lifestyle data into a generating AI and have the generating AI perform data filtering.

[0044] 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 is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also collect data about nearby services and products based on the user's location information. For example, the data collection unit will collect highly relevant data by considering the user's travel history. By prioritizing the collection of highly relevant data by considering the user's geographical location information, the data collection unit can provide information that is useful to the user. Geographical location information includes, but is not limited to, GPS data, location information services, and map data. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0045] 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 analyze a user's social media posts and collect data on products and services of interest. The data collection unit can also collect relevant data by referring to the activities of the user's followers and friends. For example, the data collection unit can analyze a user's social media trends and collect relevant data. By analyzing a user's social media activity, the data collection unit can collect data tailored to the user's interests. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. 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 social media data into a generating AI and have the generating AI collect relevant data.

[0046] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on high-importance data to improve accuracy. Conversely, the analysis unit can perform a simplified analysis on low-importance data to reduce processing time. For example, the analysis unit can determine the priority of the analysis according to the importance of the data and proceed with processing efficiently. The analysis unit can perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Data importance includes, but is not limited to, data reliability, relevance, and urgency. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data importance into a generating AI and have the generating AI perform the analysis with a specified level of detail.

[0047] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a medical-specific analysis algorithm to health data. It can also apply a schedule management-specific analysis algorithm to schedule data. For example, the analysis unit can apply a trend analysis algorithm to social media data. By applying different analysis algorithms depending on the data category, the analysis unit can perform highly accurate analysis. Data categories include, but are not limited to, health data, behavioral data, and environmental data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI apply an appropriate analysis algorithm.

[0048] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis of the latest data to provide real-time information. The analysis unit can also analyze trends and make future predictions by referring to past data. For example, the analysis unit can adjust the priority of analysis according to the data collection timing to proceed with processing efficiently. The analysis unit can provide real-time information by determining the priority of analysis based on the data collection timing. The data collection timing includes, but is not limited to, real-time data, historical data, and seasonal data. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI execute the analysis priority.

[0049] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to provide highly accurate results. Alternatively, the analysis unit can efficiently proceed with the analysis by postponing the analysis of less relevant data. For example, the analysis unit can adjust the order of analysis according to the relevance of the data to provide optimal results. By adjusting the order of analysis based on the relevance of the data, the analysis unit can perform efficient analysis. The relevance of the data includes, but is not limited to, correlation analysis, identification of causal relationships, and relevance scores. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI execute the order of analysis.

[0050] The recommendation unit can adjust the level of detail in its recommendations based on the importance of the products. For example, the recommendation unit can provide detailed recommendations for high-importance products to deepen the user's understanding. Conversely, it can provide simplified recommendations for low-importance products to expedite processing. For example, the recommendation unit can adjust the level of detail in its recommendations according to the importance of the products to provide optimal results. By adjusting the level of detail in recommendations based on the importance of the products, the recommendation unit can provide efficient recommendations. The importance of a product includes, but is not limited to, user needs, product popularity, and urgency. Some or all of the above processing in the recommendation unit may be performed using, for example, AI, or not using AI. For example, the recommendation unit can input the importance of the products into a generating AI and have the generating AI execute the recommendation detail.

[0051] The recommendation unit can apply different recommendation algorithms depending on the product category when making recommendations. For example, for food products, the recommendation unit can apply a recommendation algorithm that takes into account nutritional value and health benefits. For daily necessities, the recommendation unit can also apply a recommendation algorithm that takes into account frequency of use and convenience. For example, for services, the recommendation unit can apply a recommendation algorithm that takes into account user needs and usage history. By applying different recommendation algorithms depending on the product category, the recommendation unit can make highly accurate recommendations. Product categories include, but are not limited to, food, daily necessities, and pharmaceuticals. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the product category into a generating AI and have the generating AI apply an appropriate recommendation algorithm.

[0052] The recommendation unit can determine the priority of recommendations based on the product submission date. For example, the recommendation unit will prioritize recommending products that are urgent. The recommendation unit can also quickly recommend products that are due soon. For example, the recommendation unit can adjust the recommendation priority according to the product submission date to provide the best results. By determining the recommendation priority based on the product submission date, the recommendation unit can prioritize recommending products that are urgent. The product submission date includes, but is not limited to, the product release date, the user's purchase history, and seasonality. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input the product submission date into a generating AI and have the generating AI execute the recommendation priority.

[0053] The recommendation unit can adjust the order of recommendations based on the relevance of the products. For example, the recommendation unit can prioritize recommending highly relevant products to meet user needs. Alternatively, the recommendation unit can efficiently recommend products by delaying less relevant ones. For example, the recommendation unit can adjust the order of recommendations according to the relevance of the products to provide optimal results. By adjusting the order of recommendations based on the relevance of the products, the recommendation unit can provide recommendations that meet user needs. Product relevance includes, but is not limited to, correlation analysis, user preferences, and past purchase history. Some or all of the above processing in the recommendation unit may be performed using, for example, AI, or not using AI. For example, the recommendation unit can input product relevance into a generating AI and have the generating AI execute the recommendation order.

[0054] The proxy service can analyze the user's past purchase history to select the optimal proxy service method during the proxy service process. For example, the proxy service can prioritize the proxy service for products that the user frequently purchases based on their past purchase history. The proxy service can also prioritize the proxy service for specific brands or products based on the user's purchase history. For example, the proxy service can analyze the user's purchase history and suggest the optimal timing for purchase. The proxy service can select the optimal proxy service method by analyzing the user's past purchase history. The optimal proxy service method may include, but is not limited to, the user's needs, past purchase history, and product type. 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 the user's past purchase history into a generating AI and have the generating AI select the optimal proxy service method.

[0055] The proxy service can customize the means of proxy service based on the user's current living situation at the time of service. For example, if the user is busy, the proxy service can provide a quick proxy service. Alternatively, if the user is relaxed, the proxy service can provide a proxy service that includes detailed explanations. For example, the proxy service can adjust the means of proxy service according to the user's living situation. By customizing the means of proxy service based on the user's living situation, the proxy service can provide the optimal proxy service for the user. The means of proxy service include, but are not limited to, delivery, purchasing, and procedural services. Some or all of the above processing in the proxy service may be performed using, for example, AI, or not using AI. For example, the proxy service can input the user's living situation data into a generating AI and have the generating AI execute the proxy service.

[0056] The proxy service unit can select the optimal proxy service method when performing a proxy service, taking into account the user's geographical location information. For example, if the user is in a specific region, the proxy service unit will provide a proxy service method relevant to that region. The proxy service unit can also perform proxy services related to nearby services and products based on the user's location information. For example, the proxy service unit will select the optimal proxy service method by considering the user's travel history. By selecting the optimal proxy service method while considering the user's geographical location information, the proxy service unit can provide a proxy service that is beneficial to the user. The optimal proxy service method may include, but is not limited to, the user's geographical location information, the type of product, and the delivery method. Some or all of the above processing in the proxy service unit may be performed using, for example, AI, or not using AI. For example, the proxy service unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal proxy service method.

[0057] The proxy service can analyze the user's social media activity and propose proxy services during the proxy service process. For example, the proxy service can analyze the user's social media posts and perform proxy services related to products and services of interest. The proxy service can also propose proxy services by referring to the activities of the user's followers and friends. For example, the proxy service can analyze the user's social media trends and propose proxy services. By analyzing the user's social media activity, the proxy service can provide proxy services tailored to the user's interests. Proxy services include, but are not limited to, delivery services, purchase services, and procedural services. Some or all of the above-described processes in the proxy service may be performed using, for example, AI, or not using AI. For example, the proxy service can input the user's social media data into a generating AI and have the generating AI execute the proxy services.

[0058] The support unit can analyze the user's past communication history to select the optimal support method during support. For example, the support unit can prioritize providing frequently used support methods based on the user's past communication history. The support unit can also prioritize providing specific support methods based on the user's communication history. For example, the support unit can analyze the user's communication history and suggest the optimal support timing. The support unit can select the optimal support method by analyzing the user's past communication history. The optimal support method includes, but is not limited to, the user's needs, past communication history, and type of support. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's communication history into a generating AI and have the generating AI select the optimal support method.

[0059] The support unit can customize the means of support based on the user's current living situation during support. For example, if the user is busy, the support unit can provide quick support. Alternatively, if the user is relaxed, the support unit can provide support that includes detailed explanations. For example, the support unit adjusts the means of support according to the user's living situation. By customizing the means of support based on the user's living situation, the support unit can provide the optimal means of support for the user. Means of support include, but are not limited to, communication methods, emergency response methods, and daily living support methods. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input user living situation data into a generating AI and have the generating AI execute the means of support.

[0060] The support unit can select the optimal support method when providing support, taking into account the user's geographical location. For example, if the user is in a specific region, the support unit can provide support methods relevant to that region. The support unit can also provide support regarding nearby services and products based on the user's location information. For example, the support unit can select the optimal support method by considering the user's travel history. By selecting the optimal support method while considering the user's geographical location information, the support unit can provide support that is beneficial to the user. The optimal support method includes, but is not limited to, the user's geographical location information, the type of support, and the means of response. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal support method.

[0061] The support department can analyze a user's social media activity and propose support methods during support sessions. For example, the support department can analyze a user's social media posts and provide support regarding products and services of interest. The support department can also propose support methods by referencing the activities of the user's followers and friends. For example, the support department can analyze a user's social media trends and propose support methods. By analyzing a user's social media activity, the support department can provide support methods tailored to the user's interests. Support methods include, but are not limited to, communication methods, emergency response methods, and daily life support methods. Some or all of the above-described processes in the support department may be performed using AI, for example, or without AI. For example, the support department can input a user's social media data into a generating AI and have the generating AI execute support methods.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The data collection unit can analyze the user's past health data and schedule history to select the optimal data collection method. For example, it can set the timing for regular health checks based on the user's past health data. It can also analyze the user's schedule history and collect data while avoiding busy times. Furthermore, it can suggest specific data collection methods (e.g., voice input, text input) according to the user's health condition. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past health data and schedule history.

[0064] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, it can perform a detailed analysis on high-importance data to improve accuracy, and a simplified analysis on low-importance data to reduce processing time. Furthermore, it can determine the priority of the analysis according to the importance of the data to proceed with processing efficiently. In this way, the analysis unit can perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0065] The recommendation unit can adjust the level of detail in recommendations based on the importance of the product. For example, it can provide detailed recommendations for high-importance products to deepen the user's understanding, and simplified recommendations for low-importance products to expedite processing. Furthermore, it can adjust the level of detail in recommendations according to the importance of the product to provide the optimal result. In this way, the recommendation unit can perform efficient recommendations by adjusting the level of detail in recommendations based on the importance of the product.

[0066] The purchasing department can analyze the user's past purchase history to select the most suitable purchasing method. For example, it can prioritize purchasing frequently purchased items based on the user's past purchase history. It can also prioritize purchasing specific brands or products based on the user's purchase history. Furthermore, it can analyze the user's purchase history and suggest the optimal timing for purchase. In this way, the purchasing department can select the most suitable purchasing method by analyzing the user's past purchase history.

[0067] The support department can analyze a user's past communication history to select the most appropriate support method during support. For example, it can prioritize providing frequently used support methods based on the user's past communication history. It can also prioritize providing specific support methods based on the user's communication history. Furthermore, it can analyze the user's communication history and suggest the optimal timing for support. In this way, the support department can select the most appropriate support method by analyzing the user's past communication history.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The data collection unit collects the user's schedule and health data. For example, it can collect the user's calendar appointments, event schedules, and daily tasks. The data collection unit can also collect health data such as the user's heart rate, blood pressure, body temperature, and exercise level. The data collection unit obtains health data from the user's smartwatch or fitness tracker. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, it recommends the most suitable products and services considering the user's health status and schedule. If the user needs a specific nutrient, it recommends foods containing that nutrient. It can also recommend products and medications at the appropriate time according to the user's schedule. Step 3: The recommendation unit recommends products and services based on the analysis results obtained by the analysis unit. For example, it makes recommendations based on the user's preferences, past purchase history, health status, etc. It analyzes products and services that the user has purchased in the past and recommends similar products and services. It can also recommend foods containing specific nutrients or health supplements according to the user's health status. Step 4: The proxy service department purchases and delivers the products recommended by the recommendation department. For example, it can purchase and deliver products specified by the user from an online store. It can also purchase and deliver products specified by the user from a physical store. The user creates a list of daily necessities, and the service department selects products based on that list and purchases and delivers them on their behalf. Step 5: The support department assists with collaboration with the local community and communication with family. For example, it has functions to coordinate with local volunteers and support groups to ensure support in emergencies. It also provides messaging, video call, and notification functions for family members.

[0070] (Example of form 2) The AI agent service according to an embodiment of the present invention is a system targeted at the elderly and people with physical limitations, which substitutes for daily shopping and recommends appropriate products and services. This system has unique schedule management, health data linkage, and community support functions. The AI agent service collects the user's schedule and health data, which is analyzed by the AI. Next, based on the analysis results, it recommends the optimal products and services for the user. Furthermore, it provides a shopping proxy service to purchase and deliver the specified products. It also has a function to support communication with the local community and family. For example, the AI agent service collects detailed data such as the user's meal records and medical information. For example, if the user records their daily meal content and the AI analyzes this data, it can recommend products containing the necessary nutrients. Next, the AI analyzes the collected data. The AI takes into account the user's health status and schedule and recommends the optimal products and services. For example, if the user needs a specific nutrient, it recommends foods containing that nutrient. Also, in accordance with the user's schedule, it recommends products and medications at an appropriate timing. Furthermore, it provides a shopping proxy service. It purchases and delivers the products specified by the user. For example, if the user lists their daily necessities and the AI selects products based on that list and purchases and delivers them on their behalf. It also has a function to support communication with the local community and family. For example, it has a cooperation function with neighboring volunteers and support groups and ensures support in case of emergencies. As a result, the user can reduce their sense of social isolation and live with peace of mind. With this mechanism, it realizes an environment where the elderly and people with physical limitations can live an independent life and actively participate in society. The user can intuitively use the service without performing complex operations. Also, with voice recognition support and QR code support, the operation is simple and easy for anyone to use. For example, when the user inputs "I want to buy milk" by voice, the AI recommends the optimal product and purchases and delivers it on their behalf. Also, by using the QR code, product information can be easily obtained and the support procedure can be smoothly carried out.In this way, by using AI agent services, it is possible to create an environment where elderly people and those with physical limitations can live independently and actively participate in society, thereby improving their quality of life and health. Thus, AI agent services can realize an environment where elderly people and those with physical limitations can live independently and actively participate in society.

[0071] The AI ​​agent service according to this embodiment comprises a data collection unit, an analysis unit, a recommendation unit, an agency unit, and a support unit. The data collection unit collects the user's schedule and health data. For example, the data collection unit can collect the user's calendar appointments, event schedules, and daily tasks. The data collection unit can also collect the user's health data, such as heart rate, blood pressure, body temperature, and exercise level. For example, the data collection unit can obtain health data from the user's smartwatch or fitness tracker. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit recommends the most suitable products and services, taking into account the user's health condition and schedule. For example, if the user needs a specific nutrient, the analysis unit will recommend foods containing that nutrient. The analysis unit can also recommend products and medicines at the appropriate time according to the user's schedule. The recommendation unit recommends products and services based on the analysis results obtained by the analysis unit. The Recommendation Department makes recommendations based on user preferences, past purchase history, health status, etc. For example, it analyzes products and services previously purchased by the user and recommends similar products and services. The Recommendation Department can also recommend foods containing specific nutrients or health supplements according to the user's health status. The Agency Department purchases and delivers products recommended by the Recommendation Department on behalf of the user. For example, the Agency Department purchases and delivers products specified by the user from online stores. The Agency Department can also purchase and deliver products specified by the user from physical stores. For example, the Agency Department selects products based on a list of daily necessities that the user has compiled, and purchases and delivers them on behalf of the user. The Support Department supports collaboration with local communities and communication with family. For example, the Support Department has functions to collaborate with nearby volunteers and support organizations to ensure support in emergencies. The Support Department also has functions to support communication with family. For example, the support department provides features such as messaging, video calls, and notifications for family members.As a result, the AI ​​agent service according to this embodiment can support the user's life by collecting the user's schedule and health data, recommending products and services based on the analysis results, and purchasing and delivering them on their behalf.

[0072] The data collection unit collects user schedules and health data. For example, it can collect user calendar appointments, event schedules, and daily tasks. Specifically, it integrates with the user's calendar and task management applications to obtain schedule and task information from these applications. This allows for a detailed understanding of the user's schedule. The data collection unit can also collect health data such as the user's heart rate, blood pressure, body temperature, and exercise level. For example, it can obtain health data from the user's smartwatch or fitness tracker. These devices monitor the user's physical activity and health status in real time and collect data. The collected data is sent to a cloud server and managed centrally. Furthermore, the data collection unit can also collect user diet and sleep data. For example, it integrates with the user's meal logging applications and sleep trackers to obtain data from these applications. This allows for a comprehensive understanding of the user's health status. The data collection unit centrally manages this data and can integrate with other systems and departments as needed. For example, the collected data can be made accessible to the analysis and recommendation departments. 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.

[0073] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit recommends optimal products and services, taking into account the user's health status and schedule. Specifically, it uses AI to analyze the collected data and makes recommendations based on the user's health status and lifestyle. For example, it analyzes the user's heart rate and blood pressure data to assess stress levels and health risks. This allows it to recommend foods containing specific nutrients if the user needs them. It can also recommend products and medications at the appropriate time according to the user's schedule. For example, if a user has a busy schedule, the analysis unit will recommend foods and supplements suitable for energy replenishment according to the user's schedule. Furthermore, the analysis unit analyzes the user's past data and behavioral patterns to support long-term health management and lifestyle improvement. For example, it analyzes the user's exercise data to assess the risks of inactivity or excessive exercise and proposes an appropriate exercise plan. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to comprehensively analyze the user's health status and lifestyle and recommend the most suitable products and services.

[0074] The recommendation unit recommends products and services based on the analysis results obtained by the analysis unit. The recommendation unit makes recommendations based on factors such as the user's preferences, past purchase history, and health status. Specifically, it analyzes the user's past purchase history and recommends similar products and services. For example, it recommends products with similar effects based on health foods and supplements the user has purchased in the past. The recommendation unit can also recommend foods and health supplements containing specific nutrients depending on the user's health status. For example, if the user has high blood pressure, it recommends foods and supplements that help lower blood pressure. Furthermore, the recommendation unit can make recommendations tailored to the user's lifestyle and preferences. For example, if the user has a busy schedule, it recommends easily consumable health foods and supplements. Also, if the user has an allergy to a specific ingredient, it recommends products that do not contain that ingredient. The recommendation unit notifies the user of these recommendations on their smartphone or computer, making them easily accessible. This allows the recommendation system to recommend products and services that are best suited to the user's health condition and lifestyle, thereby supporting the user's life.

[0075] The purchasing department purchases and delivers products recommended by the recommendation department. For example, the purchasing department can purchase and deliver products specified by the user from an online store. Specifically, when a user selects a recommended product, the purchasing department handles the purchase process at the online store and delivers the product to the address specified by the user. The purchasing department can also purchase and deliver products specified by the user from a physical store. For example, a user can list their daily necessities, and the purchasing department can select products based on that list and purchase and deliver them on their behalf. The purchasing department can perform these purchase procedures quickly and accurately, saving the user time and effort. Furthermore, the purchasing department can manage product inventory and delivery status in real time and notify the user. For example, if a product is out of stock, the purchasing department will suggest an alternative product to the user and respond quickly. It can also track the delivery status in real time and notify the user of the estimated delivery time. This allows the purchasing department to respond quickly and flexibly to the user's needs and ensure that products are delivered reliably.

[0076] The support department assists with collaboration with local communities and communication with family. For example, the support department has functions to coordinate with nearby volunteers and support organizations to ensure support in emergencies. Specifically, the support department collaborates with local volunteer and support organizations to ensure that users can receive assistance in emergencies. For example, if a user faces a health problem or emergency, the support department will notify nearby volunteers and request prompt assistance. The support department also has functions to support communication with family. For example, the support department provides messaging, video call, and notification functions to family members. This allows users to easily stay in touch with their families and live with peace of mind. Furthermore, the support department notifies family members of the user's living situation and health status, so that family members can stay informed about the user's situation. For example, it shares the user's health data and schedule with family members, making it easier for family members to manage the user's health and provide support for their daily life. In this way, the support department can comprehensively support the user's life and provide an environment in which they can live with peace of mind.

[0077] The data collection unit can collect detailed data such as the user's meal records and medical information. For example, the data collection unit can collect data when the user records the contents of their daily meals. The data collection unit can also collect the user's medical information and diagnostic results. For example, the data collection unit can collect the user's medical history and diagnostic results to understand their health status. The data collection unit can also collect data on the user's calorie intake and nutrients. For example, the data collection unit can collect data when the user records the calories and nutrients of the food they consume. This allows the data collection unit to perform more accurate analysis by collecting detailed data such as the user's meal records and medical information. Detailed data includes, but is not limited to, the contents of meals, calorie intake, medical history, and diagnostic results. Some or all of the above processing 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 meal records into a generating AI and have the generating AI perform an analysis of the meal contents.

[0078] The analysis unit can analyze the collected data and recommend the most suitable products and services, taking into account the user's health condition and schedule. For example, the analysis unit can analyze the user's health condition and, if the user needs a specific nutrient, recommend foods containing that nutrient. The analysis unit can also recommend products and medications at the appropriate time, according to the user's schedule. For example, the analysis unit can recommend necessary products and services, taking into account the user's schedule. The analysis unit can recommend the most suitable products and services based on the user's health condition and schedule. This allows the analysis unit to make suggestions that meet the user's needs by recommending the most suitable products and services, taking into account the user's health condition and schedule. Optimal products and services include, but are not limited to, products tailored to health conditions and services tailored to schedules. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform recommendations based on the user's health condition and schedule.

[0079] The recommendation unit can recommend foods containing specific nutrients if the user needs those nutrients. For example, if the user needs vitamins, the recommendation unit will recommend foods containing vitamins. The recommendation unit can also recommend foods containing minerals if the user needs minerals. For example, if the user needs protein, the recommendation unit will recommend foods containing protein. The recommendation unit can recommend foods containing specific nutrients according to the user's health condition. In this way, the recommendation unit can support the user's health by recommending foods containing specific nutrients if the user needs them. Specific nutrients include, but are not limited to, vitamins, minerals, and proteins. Some or all of the above processing in the recommendation unit may be performed using, for example, AI, or without AI. For example, the recommendation unit can input the user's health condition into a generating AI and have the generating AI recommend foods containing specific nutrients.

[0080] The agency unit can purchase and deliver products specified by the user. For example, the agency unit can purchase food specified by the user from an online store and deliver it. The agency unit can also purchase daily necessities specified by the user from a physical store and deliver them. For example, the agency unit can purchase medicine specified by the user from a pharmacy and deliver it. The agency unit can purchase and deliver products specified by the user. In this way, the agency unit can assist the user with their shopping by purchasing and delivering products specified by the user. Specified products include, but are not limited to, food, daily necessities, and medicines. Some or all of the above processing in the agency unit may be performed using, for example, AI, or not using AI. For example, the agency unit can input a list of products specified by the user into a generating AI and have the generating AI select and purchase the products.

[0081] The support department has the function of coordinating with local volunteers and support organizations to ensure emergency support. For example, the support department can coordinate with local volunteers to provide a rapid response in an emergency. The support department can also coordinate with support organizations to provide emergency assistance. For example, the support department can provide contact information for emergency response. By ensuring emergency support, the support department can enhance users' sense of security. Emergency support includes, but is not limited to, emergency response, emergency contact, and evacuation assistance. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input emergency support information into a generating AI and have the generating AI execute an appropriate response method.

[0082] The support unit can be equipped with family communication support functions. For example, the support unit can provide a messaging function to family members. It can also provide a video call function to family members. For example, the support unit can provide a notification function to family members to share important information. By equipping the support unit with family communication support functions, the support unit can strengthen the user's social connections. Communication support functions include, but are not limited to, messaging, video calls, and notification functions. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input family communication data into a generating AI and have the generating AI execute an appropriate support method.

[0083] The data collection unit can be easy to operate through voice recognition and QR code support. The data collection unit collects data, for example, when the user gives a voice command. The data collection unit can also collect data by reading a QR code. For example, if the user gives a voice command, the data collection unit will collect the data using voice recognition technology. The data collection unit's ease of operation through voice recognition and QR code support allows users to intuitively use the service. Voice recognition support includes, but is not limited to, the types of voice commands, recognition accuracy, and supported languages. QR code support includes, but is not limited to, the methods for generating and reading QR codes and the types of information they correspond to. Some or all of the above-described processes in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input voice data into a generating AI and have the generating AI perform voice recognition and data collection.

[0084] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. Conversely, if the user is relaxed, the data collection unit can collect more detailed data to obtain more accurate information. For example, if the user is in a hurry, the data collection unit can collect only the minimum necessary data to proceed quickly. By adjusting the timing of data collection based on the user's emotions, the data collection unit can reduce the user's burden. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the user's emotion data into the generative AI and have the generative AI execute the timing of data collection.

[0085] The data collection unit can analyze the user's past health data and schedule history to select the optimal data collection method. For example, the data collection unit can set the timing of regular health checks based on the user's past health data. The data collection unit can also analyze the user's schedule history and collect data while avoiding busy times. For example, the data collection unit can suggest a specific data collection method (e.g., voice input, text input) according to the user's health condition. The data collection unit can select the optimal data collection method by analyzing the user's past health data and schedule history. The optimal data collection method includes, but is not limited to, the type of sensor data, collection frequency, and data accuracy. Some or all of the above processing 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 past health data into a generating AI and have the generating AI select the optimal data collection method.

[0086] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, if the user is interested in health, the data collection unit will prioritize collecting health-related data. Furthermore, if the user is busy, the data collection unit can collect only important data and omit unnecessary data. For example, the data collection unit can adjust the frequency and method of data collection according to the user's lifestyle. By filtering data based on the user's lifestyle and areas of interest, the data collection unit can prioritize the collection of important data. Filtering includes, but is not limited to, criteria for selecting high-priority data and filtering algorithms. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can input the user's lifestyle data into a generating AI and have the generating AI perform data filtering.

[0087] 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 is stressed, the data collection unit will prioritize collecting high-priority data. Conversely, if the user is relaxed, the data collection unit can collect detailed data to improve the accuracy of the analysis. For example, if the user is in a hurry, the data collection unit will quickly collect only the minimum necessary data. By prioritizing data collection based on the user's emotions, the data collection unit can prioritize the collection of important data. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI prioritize the data.

[0088] 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 is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also collect data about nearby services and products based on the user's location information. For example, the data collection unit will collect highly relevant data by considering the user's travel history. By prioritizing the collection of highly relevant data by considering the user's geographical location information, the data collection unit can provide information that is useful to the user. Geographical location information includes, but is not limited to, GPS data, location information services, and map data. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0089] 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 analyze a user's social media posts and collect data on products and services of interest. The data collection unit can also collect relevant data by referring to the activities of the user's followers and friends. For example, the data collection unit can analyze a user's social media trends and collect relevant data. By analyzing a user's social media activity, the data collection unit can collect data tailored to the user's interests. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. 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 social media data into a generating AI and have the generating AI collect relevant data.

[0090] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit provides simple and easy-to-understand analysis results. Conversely, if the user is relaxed, the analysis unit can provide detailed analysis results to facilitate deeper understanding. For example, if the user is in a hurry, the analysis unit provides concise analysis results that get straight to the point. By adjusting the presentation of the analysis based on the user's emotions, the analysis unit can provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI execute the presentation of the analysis.

[0091] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on high-importance data to improve accuracy. Conversely, the analysis unit can perform a simplified analysis on low-importance data to reduce processing time. For example, the analysis unit can determine the priority of the analysis according to the importance of the data and proceed with processing efficiently. The analysis unit can perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Data importance includes, but is not limited to, data reliability, relevance, and urgency. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data importance into a generating AI and have the generating AI perform the analysis with a specified level of detail.

[0092] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a medical-specific analysis algorithm to health data. It can also apply a schedule management-specific analysis algorithm to schedule data. For example, the analysis unit can apply a trend analysis algorithm to social media data. By applying different analysis algorithms depending on the data category, the analysis unit can perform highly accurate analysis. Data categories include, but are not limited to, health data, behavioral data, and environmental data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI apply an appropriate analysis algorithm.

[0093] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. Conversely, if the user is relaxed, the analysis unit can provide a detailed analysis to facilitate deeper understanding. For example, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. By adjusting the length of the analysis based on the user's emotions, the analysis unit can provide an appropriate analysis result for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI perform the analysis to determine the length.

[0094] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis of the latest data to provide real-time information. The analysis unit can also analyze trends and make future predictions by referring to past data. For example, the analysis unit can adjust the priority of analysis according to the data collection timing to proceed with processing efficiently. The analysis unit can provide real-time information by determining the priority of analysis based on the data collection timing. The data collection timing includes, but is not limited to, real-time data, historical data, and seasonal data. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI execute the analysis priority.

[0095] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to provide highly accurate results. Alternatively, the analysis unit can efficiently proceed with the analysis by postponing the analysis of less relevant data. For example, the analysis unit can adjust the order of analysis according to the relevance of the data to provide optimal results. By adjusting the order of analysis based on the relevance of the data, the analysis unit can perform efficient analysis. The relevance of the data includes, but is not limited to, correlation analysis, identification of causal relationships, and relevance scores. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI execute the order of analysis.

[0096] The recommendation unit can estimate the user's emotions and adjust the way recommendations are presented based on those emotions. For example, if the user is stressed, the recommendation unit can provide simple and easy-to-understand recommendations. Conversely, if the user is relaxed, the recommendation unit can provide detailed recommendations to promote deeper understanding. For example, if the user is in a hurry, the recommendation unit can provide concise recommendations that get straight to the point. By adjusting the way recommendations are presented based on the user's emotions, the recommendation unit can provide recommendations that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input user emotion data into the generative AI and have the generative AI execute the recommendation presentation method.

[0097] The recommendation unit can adjust the level of detail in its recommendations based on the importance of the products. For example, the recommendation unit can provide detailed recommendations for high-importance products to deepen the user's understanding. Conversely, it can provide simplified recommendations for low-importance products to expedite processing. For example, the recommendation unit can adjust the level of detail in its recommendations according to the importance of the products to provide optimal results. By adjusting the level of detail in recommendations based on the importance of the products, the recommendation unit can provide efficient recommendations. The importance of a product includes, but is not limited to, user needs, product popularity, and urgency. Some or all of the above processing in the recommendation unit may be performed using, for example, AI, or not using AI. For example, the recommendation unit can input the importance of the products into a generating AI and have the generating AI execute the recommendation detail.

[0098] The recommendation unit can apply different recommendation algorithms depending on the product category when making recommendations. For example, for food products, the recommendation unit can apply a recommendation algorithm that takes into account nutritional value and health benefits. For daily necessities, the recommendation unit can also apply a recommendation algorithm that takes into account frequency of use and convenience. For example, for services, the recommendation unit can apply a recommendation algorithm that takes into account user needs and usage history. By applying different recommendation algorithms depending on the product category, the recommendation unit can make highly accurate recommendations. Product categories include, but are not limited to, food, daily necessities, and pharmaceuticals. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the product category into a generating AI and have the generating AI apply an appropriate recommendation algorithm.

[0099] The recommendation unit can estimate the user's emotions and adjust the length of recommendations based on the estimated emotions. For example, if the user is in a hurry, the recommendation unit can provide short, concise recommendations. Conversely, if the user is relaxed, the recommendation unit can provide detailed recommendations to promote deeper understanding. For example, if the user is excited, the recommendation unit can provide recommendations with visually stimulating effects. By adjusting the length of recommendations based on the user's emotions, the recommendation unit can provide recommendations that are appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input user emotion data into the generative AI and have the generative AI determine the length of the recommendations.

[0100] The recommendation unit can determine the priority of recommendations based on the product submission date. For example, the recommendation unit will prioritize recommending products that are urgent. The recommendation unit can also quickly recommend products that are due soon. For example, the recommendation unit can adjust the recommendation priority according to the product submission date to provide the best results. By determining the recommendation priority based on the product submission date, the recommendation unit can prioritize recommending products that are urgent. The product submission date includes, but is not limited to, the product release date, the user's purchase history, and seasonality. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input the product submission date into a generating AI and have the generating AI execute the recommendation priority.

[0101] The recommendation unit can adjust the order of recommendations based on the relevance of the products. For example, the recommendation unit can prioritize recommending highly relevant products to meet user needs. Alternatively, the recommendation unit can efficiently recommend products by delaying less relevant ones. For example, the recommendation unit can adjust the order of recommendations according to the relevance of the products to provide optimal results. By adjusting the order of recommendations based on the relevance of the products, the recommendation unit can provide recommendations that meet user needs. Product relevance includes, but is not limited to, correlation analysis, user preferences, and past purchase history. Some or all of the above processing in the recommendation unit may be performed using, for example, AI, or not using AI. For example, the recommendation unit can input product relevance into a generating AI and have the generating AI execute the recommendation order.

[0102] The proxy unit can estimate the user's emotions and adjust the proxy method based on the estimated emotions. For example, if the user is stressed, the proxy unit can provide a quick and easy proxy method. If the user is relaxed, the proxy unit can also provide a proxy method that includes detailed explanations. For example, if the user is in a hurry, the proxy unit can provide a method that completes the proxy in the shortest possible time. By adjusting the proxy method based on the user's emotions, the proxy unit can provide the optimal proxy method for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proxy unit may be performed using AI, for example, or not using AI. For example, the proxy unit can input the user's emotion data into the generative AI and have the generative AI execute the proxy method.

[0103] The proxy service can analyze the user's past purchase history to select the optimal proxy service method during the proxy service process. For example, the proxy service can prioritize the proxy service for products that the user frequently purchases based on their past purchase history. The proxy service can also prioritize the proxy service for specific brands or products based on the user's purchase history. For example, the proxy service can analyze the user's purchase history and suggest the optimal timing for purchase. The proxy service can select the optimal proxy service method by analyzing the user's past purchase history. The optimal proxy service method may include, but is not limited to, the user's needs, past purchase history, and product type. 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 the user's past purchase history into a generating AI and have the generating AI select the optimal proxy service method.

[0104] The proxy service can customize the means of proxy service based on the user's current living situation at the time of service. For example, if the user is busy, the proxy service can provide a quick proxy service. Alternatively, if the user is relaxed, the proxy service can provide a proxy service that includes detailed explanations. For example, the proxy service can adjust the means of proxy service according to the user's living situation. By customizing the means of proxy service based on the user's living situation, the proxy service can provide the optimal proxy service for the user. The means of proxy service include, but are not limited to, delivery, purchasing, and procedural services. Some or all of the above processing in the proxy service may be performed using, for example, AI, or not using AI. For example, the proxy service can input the user's living situation data into a generating AI and have the generating AI execute the proxy service.

[0105] The proxy unit can estimate the user's emotions and determine the priority of its actions based on those emotions. For example, if the user is stressed, the proxy unit will prioritize high-priority actions. Conversely, if the user is relaxed, the proxy unit can perform more detailed actions to facilitate a deeper understanding. For example, if the user is in a hurry, the proxy unit will quickly perform only the minimum necessary actions. By determining the priority of actions based on the user's emotions, the proxy unit can prioritize high-priority actions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proxy unit may be performed using AI or not. For example, the proxy unit can input user emotion data into a generative AI and have the generative AI prioritize actions.

[0106] The proxy service unit can select the optimal proxy service method when performing a proxy service, taking into account the user's geographical location information. For example, if the user is in a specific region, the proxy service unit will provide a proxy service method relevant to that region. The proxy service unit can also perform proxy services related to nearby services and products based on the user's location information. For example, the proxy service unit will select the optimal proxy service method by considering the user's travel history. By selecting the optimal proxy service method while considering the user's geographical location information, the proxy service unit can provide a proxy service that is beneficial to the user. The optimal proxy service method may include, but is not limited to, the user's geographical location information, the type of product, and the delivery method. Some or all of the above processing in the proxy service unit may be performed using, for example, AI, or not using AI. For example, the proxy service unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal proxy service method.

[0107] The proxy service can analyze the user's social media activity and propose proxy services during the proxy service process. For example, the proxy service can analyze the user's social media posts and perform proxy services related to products and services of interest. The proxy service can also propose proxy services by referring to the activities of the user's followers and friends. For example, the proxy service can analyze the user's social media trends and propose proxy services. By analyzing the user's social media activity, the proxy service can provide proxy services tailored to the user's interests. Proxy services include, but are not limited to, delivery services, purchase services, and procedural services. Some or all of the above-described processes in the proxy service may be performed using, for example, AI, or not using AI. For example, the proxy service can input the user's social media data into a generating AI and have the generating AI execute the proxy services.

[0108] The support unit can estimate the user's emotions and adjust its support methods based on the estimated emotions. For example, if the user is stressed, the support unit can provide quick and easy support. If the user is relaxed, the support unit can also provide support methods that include detailed explanations. For example, if the user is in a hurry, the support unit can provide a way to complete the support in the shortest possible time. By adjusting its support methods based on the user's emotions, the support unit can provide the optimal support method for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI execute the support methods.

[0109] The support unit can analyze the user's past communication history to select the optimal support method during support. For example, the support unit can prioritize providing frequently used support methods based on the user's past communication history. The support unit can also prioritize providing specific support methods based on the user's communication history. For example, the support unit can analyze the user's communication history and suggest the optimal support timing. The support unit can select the optimal support method by analyzing the user's past communication history. The optimal support method includes, but is not limited to, the user's needs, past communication history, and type of support. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's communication history into a generating AI and have the generating AI select the optimal support method.

[0110] The support unit can customize the means of support based on the user's current living situation during support. For example, if the user is busy, the support unit can provide quick support. Alternatively, if the user is relaxed, the support unit can provide support that includes detailed explanations. For example, the support unit adjusts the means of support according to the user's living situation. By customizing the means of support based on the user's living situation, the support unit can provide the optimal means of support for the user. Means of support include, but are not limited to, communication methods, emergency response methods, and daily living support methods. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input user living situation data into a generating AI and have the generating AI execute the means of support.

[0111] The support unit can estimate the user's emotions and determine the priority of support based on the estimated emotions. For example, if the user is stressed, the support unit will prioritize high-priority support. Conversely, if the user is relaxed, the support unit can provide detailed support to facilitate a deeper understanding. For example, if the user is in a hurry, the support unit will provide only the necessary minimum support quickly. By determining the priority of support based on the user's emotions, the support unit can prioritize high-priority support. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can input user emotion data into a generative AI and have the generative AI prioritize support.

[0112] The support unit can select the optimal support method when providing support, taking into account the user's geographical location. For example, if the user is in a specific region, the support unit can provide support methods relevant to that region. The support unit can also provide support regarding nearby services and products based on the user's location information. For example, the support unit can select the optimal support method by considering the user's travel history. By selecting the optimal support method while considering the user's geographical location information, the support unit can provide support that is beneficial to the user. The optimal support method includes, but is not limited to, the user's geographical location information, the type of support, and the means of response. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal support method.

[0113] The support department can analyze a user's social media activity and propose support methods during support sessions. For example, the support department can analyze a user's social media posts and provide support regarding products and services of interest. The support department can also propose support methods by referencing the activities of the user's followers and friends. For example, the support department can analyze a user's social media trends and propose support methods. By analyzing a user's social media activity, the support department can provide support methods tailored to the user's interests. Support methods include, but are not limited to, communication methods, emergency response methods, and daily life support methods. Some or all of the above-described processes in the support department may be performed using AI, for example, or without AI. For example, the support department can input a user's social media data into a generating AI and have the generating AI execute support methods.

[0114] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0115] 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 stressed, the unit reduces the frequency of data collection to lessen the user's burden. Conversely, if the user is relaxed, it can collect more detailed data to obtain more accurate information. Furthermore, if the user is in a hurry, it can collect only the minimum necessary data to expedite processing. In this way, the data collection unit can reduce the user's burden and achieve efficient data collection by adjusting the timing of data collection based on the user's emotions.

[0116] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is stressed, the analysis unit provides simple and easy-to-understand results. If the user is relaxed, it can provide detailed results to facilitate a deeper understanding. Furthermore, if the user is in a hurry, it can provide concise results that get straight to the point. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the presentation of the analysis based on the user's emotions.

[0117] The recommendation system can estimate the user's emotions and adjust the way recommendations are presented based on those emotions. For example, if a user is stressed, the recommendation system will provide simple and easy-to-understand recommendations. If the user is relaxed, it can provide detailed recommendations to encourage deeper understanding. Furthermore, if the user is in a hurry, it can provide concise recommendations that get straight to the point. In this way, the recommendation system can provide recommendations that are easy for the user to understand by adjusting the way recommendations are presented based on the user's emotions.

[0118] The proxy function can estimate the user's emotions and adjust the proxy method based on those emotions. For example, if the user is stressed, the proxy function can provide a quick and easy proxy method. If the user is relaxed, it can provide a proxy method that includes detailed explanations. Furthermore, if the user is in a hurry, it can provide a method that completes the proxy in the shortest possible time. In this way, the proxy function can provide the optimal proxy method for the user by adjusting the proxy method based on the user's emotions.

[0119] The support department can estimate the user's emotions and adjust its support methods based on those estimates. For example, if the user is stressed, the support department can provide quick and easy support. If the user is relaxed, it can provide support that includes detailed explanations. Furthermore, if the user is in a hurry, it can provide a method that completes the support in the shortest possible time. In this way, the support department can provide the optimal support method for the user by adjusting its support methods based on the user's emotions.

[0120] The data collection unit can analyze the user's past health data and schedule history to select the optimal data collection method. For example, it can set the timing for regular health checks based on the user's past health data. It can also analyze the user's schedule history and collect data while avoiding busy times. Furthermore, it can suggest specific data collection methods (e.g., voice input, text input) according to the user's health condition. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past health data and schedule history.

[0121] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, it can perform a detailed analysis on high-importance data to improve accuracy, and a simplified analysis on low-importance data to reduce processing time. Furthermore, it can determine the priority of the analysis according to the importance of the data to proceed with processing efficiently. In this way, the analysis unit can perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0122] The recommendation unit can adjust the level of detail in recommendations based on the importance of the product. For example, it can provide detailed recommendations for high-importance products to deepen the user's understanding, and simplified recommendations for low-importance products to expedite processing. Furthermore, it can adjust the level of detail in recommendations according to the importance of the product to provide the optimal result. In this way, the recommendation unit can perform efficient recommendations by adjusting the level of detail in recommendations based on the importance of the product.

[0123] The purchasing department can analyze the user's past purchase history to select the most suitable purchasing method. For example, it can prioritize purchasing frequently purchased items based on the user's past purchase history. It can also prioritize purchasing specific brands or products based on the user's purchase history. Furthermore, it can analyze the user's purchase history and suggest the optimal timing for purchase. In this way, the purchasing department can select the most suitable purchasing method by analyzing the user's past purchase history.

[0124] The support department can analyze a user's past communication history to select the most appropriate support method during support. For example, it can prioritize providing frequently used support methods based on the user's past communication history. It can also prioritize providing specific support methods based on the user's communication history. Furthermore, it can analyze the user's communication history and suggest the optimal timing for support. In this way, the support department can select the most appropriate support method by analyzing the user's past communication history.

[0125] The following briefly describes the processing flow for example form 2.

[0126] Step 1: The data collection unit collects the user's schedule and health data. For example, it can collect the user's calendar appointments, event schedules, and daily tasks. The data collection unit can also collect health data such as the user's heart rate, blood pressure, body temperature, and exercise level. The data collection unit obtains health data from the user's smartwatch or fitness tracker. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, it recommends the most suitable products and services considering the user's health status and schedule. If the user needs a specific nutrient, it recommends foods containing that nutrient. It can also recommend products and medications at the appropriate time according to the user's schedule. Step 3: The recommendation unit recommends products and services based on the analysis results obtained by the analysis unit. For example, it makes recommendations based on the user's preferences, past purchase history, health status, etc. It analyzes products and services that the user has purchased in the past and recommends similar products and services. It can also recommend foods containing specific nutrients or health supplements according to the user's health status. Step 4: The proxy service department purchases and delivers the products recommended by the recommendation department. For example, it can purchase and deliver products specified by the user from an online store. It can also purchase and deliver products specified by the user from a physical store. The user creates a list of daily necessities, and the service department selects products based on that list and purchases and delivers them on their behalf. Step 5: The support department assists with collaboration with the local community and communication with family. For example, it has functions to coordinate with local volunteers and support groups to ensure support in emergencies. It also provides messaging, video call, and notification functions for family members.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the data collection unit, analysis unit, recommendation unit, proxy unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects the user's schedule and health data using the camera 42 and microphone 38B of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The recommendation unit is implemented in the specific processing unit 290 of the data processing unit 12 and recommends products and services based on the analysis results. The proxy unit is implemented in the specific processing unit 46A of the smart device 14 and purchases and delivers the recommended products on behalf of the user. The support unit is implemented in the specific processing unit 46A of the smart device 14 and supports collaboration with the local community and communication with family. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0131] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the data collection unit, analysis unit, recommendation unit, proxy unit, and support unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects the user's schedule and health data using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The recommendation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and recommends products and services based on the analysis results. The proxy unit is implemented, for example, by the control unit 46A of the smart glasses 214 and purchases and delivers the recommended products on behalf of the user. The support unit is implemented, for example, by the control unit 46A of the smart glasses 214 and supports collaboration with the local community and communication with family. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0147] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the data collection unit, analysis unit, recommendation unit, proxy unit, and support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects the user's schedule and health data using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The recommendation unit is implemented in the specific processing unit 290 of the data processing unit 12 and recommends products and services based on the analysis results. The proxy unit is implemented in the specific processing unit 46A of the headset terminal 314 and purchases and delivers the recommended products on behalf of the user. The support unit is implemented in the specific processing unit 46A of the headset terminal 314 and supports collaboration with the local community and communication with family. 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.

[0163] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] Each of the multiple elements described above, including the data collection unit, analysis unit, recommendation unit, proxy unit, and support unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects the user's schedule and health data using the camera 42 and microphone 238 of the robot 414 and transmits it to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The recommendation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and recommends products and services based on the analysis results. The proxy unit is implemented, for example, by the control unit 46A of the robot 414 and purchases and delivers the recommended products on behalf of the user. The support unit is implemented, for example, by the control unit 46A of the robot 414 and supports collaboration with the local community and communication with family. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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."

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] (Note 1) A data collection unit that collects users' schedules and health data, An analysis unit analyzes the data collected by the aforementioned collection unit, A recommendation unit that recommends products and services based on the analysis results obtained by the aforementioned analysis unit, The agency unit purchases and delivers the products recommended by the aforementioned recommendation unit, It includes a support department that assists with collaboration with local communities and communication with families. A system characterized by the following features. (Note 2) The aforementioned collection unit is It collects detailed data such as users' meal records and medical information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed, and the system recommends the most suitable products and services, taking into account the user's health status and schedule. The system described in Appendix 1, characterized by the features described herein. (Note 4) The recommendation unit is, If a user needs a specific nutrient, the system will recommend foods containing that nutrient. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned agency unit, We purchase and deliver products specified by the user. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned support unit is It has a function to coordinate with local volunteers and support groups, ensuring support in emergencies. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned support unit is It includes features to support communication with family members. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is With voice recognition and QR code support, it's easy to use. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze the user's past health data and schedule history to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, 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 12) 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 13) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The recommendation unit is, It estimates the user's emotions and adjusts the way recommendations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The recommendation unit is, When making recommendations, adjust the level of detail based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 23) The recommendation unit is, When making recommendations, different recommendation algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 24) The recommendation unit is, It estimates the user's emotions and adjusts the length of recommendations based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The recommendation unit is, When making recommendations, the priority of recommendations is determined based on when the product was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 26) The recommendation unit is, When making recommendations, the order of recommendations is adjusted based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned agency unit, It estimates the user's emotions and adjusts the proxy method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned agency unit, When providing the service, we analyze the user's past purchase history to select the most suitable method of service. The system described in Appendix 1, characterized by the features described herein. (Note 29) 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 30) The aforementioned agency unit, It estimates the user's emotions and determines the priority of the proxy based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) 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 32) 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. (Note 33) The aforementioned support unit is It estimates the user's emotions and adjusts the support method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned support unit is During support, we analyze the user's past communication history to select the most appropriate support method. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned support unit is During support, customize the support methods based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned support unit is The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned support unit is During support, the optimal support method will be selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned support unit is During support, we analyze the user's social media activity and suggest support methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0199] 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 data collection unit that collects users' schedules and health data, An analysis unit analyzes the data collected by the aforementioned collection unit, A recommendation unit that recommends products and services based on the analysis results obtained by the aforementioned analysis unit, A proxy service unit that purchases and delivers the products recommended by the aforementioned recommendation unit, It includes a support department that assists with collaboration with local communities and communication with families. A system characterized by the following features.

2. The aforementioned collection unit is It collects detailed data such as users' meal records and medical information. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed, and the system recommends the most suitable products and services, taking into account the user's health status and schedule. The system according to feature 1.

4. The recommendation unit is, If a user needs a specific nutrient, the system will recommend foods containing that nutrient. The system according to feature 1.

5. The aforementioned agency unit, We purchase and deliver products specified by the user. The system according to feature 1.

6. The aforementioned support unit is It has a function to coordinate with local volunteers and support groups, ensuring support in emergencies. The system according to feature 1.

7. The aforementioned support unit is It includes features to support communication with family members. The system according to feature 1.

8. The aforementioned collection unit is With voice recognition support and QR code (registered trademark) assistance, it is easy to operate. The system according to feature 1.

9. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is Analyze the user's past health data and schedule history to select the optimal data collection method. The system according to feature 1.

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