System

Abstract

To provide a system that provides personalized proposals based on a user's preferences and circumstances.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects a user's preferences and circumstances. The analysis unit analyzes the data collected by the collection unit. The proposal unit generates personalized proposals based on analysis results obtained by the analysis unit. The provision unit provides the proposals generated by the proposal unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have the drawback of making it difficult to efficiently provide personalized suggestions based on a user's preferences and circumstances.

[0005] The system of the embodiment aims to provide personalized suggestions based on the user's preferences and circumstances. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, and a provision unit. The collection unit collects user preferences and situations. The analysis unit analyzes the data collected by the collection unit. The suggestion unit generates personalized suggestions based on the analysis results obtained by the analysis unit. The provision unit provides the suggestions generated by the suggestion unit to the user. Effect of the Invention

[0007] An embodiment of the system can provide personalized suggestions based on a user's preferences and circumstances. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

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

[0028] (Example 1) A concierge AI system according to an embodiment of the present invention is a system that provides personalized suggestions and guidance linked to an individual's lifestyle. The system collects the user's preferences and circumstances, analyzes the collected data, generates customized suggestions based on the user's preferences and circumstances, and provides the generated suggestions and guidance to the user. This mechanism allows the user to receive customized advice in areas such as food, fashion, and entertainment. For example, the user's past behavior history and current situation are collected. For example, data such as restaurants the user has visited in the past, fashion items purchased, and entertainment events attended are collected. This makes it possible to grasp the user's preferences and circumstances. Next, the collected data is analyzed. The AI ​​analyzes the collected data and determines the user's preferences and circumstances. For example, data on restaurants the user has visited in the past can be analyzed to identify the user's food preferences. This makes it possible to make suggestions tailored to the user's preferences. Furthermore, customized suggestions are generated based on the analysis results. The AI ​​generates customized suggestions in areas such as food, fashion, and entertainment based on the user's preferences and circumstances. For example, it can suggest the next restaurant to visit based on the user's food preferences. Finally, the generated suggestions and guidance are provided to the user. Suggestions and guidance are provided in a user-friendly way, such as through notifications or in-app displays, allowing users to receive suggestions and guidance intuitively, enabling the concierge AI system to provide personalized suggestions and guidance based on the user's preferences and circumstances.

[0029] The concierge AI system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, and a provision unit. The collection unit collects the user's preferences and situation. The collection unit collects, for example, the user's past behavior history and current situation. For example, the collection unit can collect data such as restaurants visited by the user in the past, fashion items purchased, and entertainment events attended by the user. The collection unit can also obtain, for example, the user's location information and grasp the user's current activity situation. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data using, for example, AI to determine the user's preferences and situation. For example, the analysis unit can analyze data of restaurants visited by the user in the past and identify the user's food preferences. The analysis unit can also analyze, for example, the user's purchase history and identify the user's fashion preferences. The suggestion unit generates customized suggestions based on the analysis results obtained by the analysis unit. The suggestion unit generates customized suggestions in areas such as food, fashion, and entertainment based on the user's preferences and situation using, for example, AI. For example, the suggestion unit can suggest a restaurant to visit next based on the user's food preferences. The suggestion unit can also suggest a fashion item to be purchased next based on, for example, the user's fashion preferences. The provision unit provides the proposal generated by the suggestion unit to the user. The provision unit provides the generated proposal and guidance in a manner that is easy for the user to understand, such as, for example, by notification or display within an app. For example, the provision unit can send a push notification to the user's smartphone to display the proposal. The provision unit can also provide the proposal by, for example, displaying a pop-up within an app. In this way, the concierge AI system according to the embodiment can provide personalized proposals and guidance based on the user's preferences and circumstances.

[0030] The collection unit collects the user's preferences and situations. Specifically, it collects the user's past behavior history and current situation. For example, data such as restaurants the user has visited in the past, fashion items purchased, and entertainment events attended can be collected. This includes collecting data from the user's smartphone and wearable devices. The GPS function of the smartphone can be used to obtain the user's location information in real time and grasp the current activity status. In addition, data is also collected from the user's social media accounts and online shopping history to understand the user's interests and concerns in more detail. For example, by collecting photos and reviews of restaurants shared by the user on SNS and purchase history at online stores, the user's preferences can be more accurately identified. The collection unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the analysis unit and proposal unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection unit to efficiently and effectively collect data and improve the performance of the entire system.

[0031] The analysis unit analyzes the data collected by the collection unit. Specifically, the analysis unit uses AI to analyze the collected data and determine the user's preferences and situation. For example, the data of restaurants visited by the user in the past can be analyzed to identify the user's food preferences. The AI ​​uses natural language processing technology to analyze the user's reviews and comments and extract positive and negative evaluations. The AI ​​can also analyze the user's purchase history to identify the user's fashion preferences. The AI ​​uses image recognition technology to identify the style and brand of the purchased fashion items and grasp the user's preferences in detail. Furthermore, the analysis unit can analyze the user's location information and activity status to grasp the current situation and needs in real time. For example, the analysis unit provides data to make appropriate suggestions based on the user's current location and time of day. The analysis unit can also utilize past data and statistical information to analyze long-term trends and patterns and predict future needs. This allows the analysis unit to quickly and accurately analyze the collected data and grasp the user's preferences and situation in real time.

[0032] The suggestion unit generates customized suggestions based on the analysis results obtained by the analysis unit. Specifically, the suggestion unit uses AI to generate customized suggestions in areas such as food, fashion, and entertainment based on the user's preferences and situation. For example, it can suggest the next restaurant to visit based on the user's food preferences. The AI ​​analyzes the user's past visit history and reviews to identify restaurants with the user's preferred cuisine and atmosphere. It can also suggest the next fashion item to purchase based on the user's fashion preferences. The AI ​​analyzes the user's purchase history and brand preferences, and suggests the most suitable items taking into account the latest trends and sales information. Furthermore, it can also suggest the next event to attend and the next movie to watch based on the user's entertainment preferences. The AI ​​analyzes the user's past participation history and evaluations to identify events and movies that the user may be interested in. The suggestion unit also takes into account the user's current situation and needs when generating these suggestions. For example, it makes appropriate suggestions based on the user's current location and time of day. This allows the suggestion unit to quickly and accurately generate the most suitable suggestions for the user.

[0033] The providing unit provides the user with the proposal generated by the proposing unit. Specifically, the generated proposal and guidance are provided in a manner that is easy for the user to understand, such as through notifications or display within the app. For example, the providing unit can send a push notification to the user's smartphone and display the proposal. The push notification includes a concise and easy-to-understand message so that the user can immediately check it. In addition, the providing unit can provide the proposal by displaying a pop-up within the app. The pop-up display allows the user to immediately check important proposals and guidance while using the app. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and effectiveness of the proposal content. For example, whether the user accepted the proposal, and collect evaluations and comments on the proposal, and reflect them in the next proposal. In addition, the providing unit can reliably transmit information using multiple communication means. For example, important information is reliably delivered by using not only smartphone notifications but also voice calls, SMS, emails, and the like. As a result, the providing unit can quickly and reliably provide proposals to the user and improve the user's satisfaction.

[0034] The collection unit can collect the user's past behavior history and current situation. The collection unit, for example, collects the user's past behavior history. For example, the collection unit can collect data such as restaurants the user has visited in the past, fashion items purchased, and entertainment events attended by the user. The collection unit can also collect the user's current situation. For example, the collection unit can obtain the user's location information and grasp the current activity situation. This allows for more accurate suggestions by collecting the user's past behavior history and current situation. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's behavior history data into AI and cause AI to collect the data.

[0035] The analysis unit can analyze the collected data and determine the user's preferences and situation. The analysis unit can, for example, analyze the collected data and determine the user's preferences and situation. For example, the analysis unit can analyze data on restaurants visited by the user in the past and identify the user's food preferences. The analysis unit can also analyze the user's purchase history and identify the user's fashion preferences. Furthermore, the analysis unit can analyze the user's entertainment activities and identify events that interest the user. In this way, the collected data can be analyzed to accurately determine the user's preferences and situation. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the collected data to AI and have the AI ​​execute the analysis of the data.

[0036] The suggestion unit can generate personalized suggestions in the areas of food, fashion, and entertainment based on the user's preferences and circumstances. The suggestion unit generates personalized suggestions in the areas of food, fashion, and entertainment based on the user's preferences and circumstances, for example. For example, the suggestion unit can suggest a restaurant to visit next based on the user's food preferences. The suggestion unit can also suggest a fashion item to purchase next based on the user's fashion preferences. Furthermore, the suggestion unit can also suggest an event to attend next based on the user's entertainment preferences. This allows the user to be provided with optimal suggestions by generating customized suggestions based on the user's preferences and circumstances. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's preferences and situation data into AI and cause the AI ​​to generate suggestions.

[0037] The providing unit can provide the generated proposal or guidance in a manner that is easy for the user to understand, such as by notification or display within the app. The providing unit provides the generated proposal or guidance in a manner that is easy for the user to understand, such as by notification or display within the app. For example, the providing unit can send a push notification to the user's smartphone and display the proposal. The providing unit can also provide the proposal by displaying a pop-up within the app. Furthermore, the providing unit can select an optimal display method according to the user's device. For example, the providing unit can adjust the display method according to the device, such as a smartphone, tablet, or PC. As a result, the generated proposal or guidance is provided in a manner that is easy for the user to understand, so that the user can intuitively receive the proposal or guidance. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the proposal data to AI and cause the AI ​​to execute the optimal providing method.

[0038] The collection unit can analyze the user's past behavior history and select the optimal data collection method. The collection unit, for example, analyzes the user's past behavior history and selects the optimal data collection method. For example, the collection unit determines the priority of data collection based on places the user has frequently visited in the past. The collection unit can also analyze the user's past purchase history and collect related data preferentially. Furthermore, the collection unit can collect data on events of interest based on the user's past entertainment activities. This makes it possible to select the optimal data collection method by analyzing the user's past behavior history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's behavior history data into AI and cause AI to select the optimal data collection method.

[0039] The collection unit can perform filtering based on the user's current living situation and field of interest when collecting data. The collection unit performs filtering based on the user's current living situation and field of interest when collecting data, for example. For example, if the user is currently on a diet, the collection unit preferentially collects health-related data. In addition, if the user starts a new hobby, the collection unit can also collect data related to the hobby. Furthermore, if the user plans to participate in a specific event, the collection unit can also collect data related to the event. This allows more relevant data to be collected by filtering data based on the user's current living situation and field of interest. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's living situation and field of interest data into AI and have the AI ​​perform filtering of the data.

[0040] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user when collecting data. For example, the collection unit preferentially collects highly relevant data in consideration of the geographical location information of the user when collecting data. For example, when the user is in a specific area, the collection unit preferentially collects restaurant information in that area. In addition, when the user is traveling, the collection unit can also preferentially collect tourist information of the travel destination. Furthermore, when the user is at home, the collection unit can also preferentially collect event information in the vicinity. In this way, by considering the geographical location information of the user, highly relevant data can be preferentially collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's location information data to AI and cause AI to execute collection of highly relevant data.

[0041] The collection unit can analyze the social media activity of the user at the time of data collection and collect related data. For example, the collection unit analyzes the social media activity of the user at the time of data collection and collects related data. For example, the collection unit collects related restaurant data based on restaurant information shared by the user on social media. The collection unit can also collect information on brands that the user follows on social media. Furthermore, the collection unit can collect event information that the user plans to attend on social media. In this way, by analyzing the social media activity of the user, related data can be efficiently collected. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the social media data of the user to AI and cause the AI ​​to collect related data.

[0042] The analysis unit can optimize the analysis algorithm by referring to the user's past behavior history during data analysis. The analysis unit optimizes the analysis algorithm by referring to the user's past behavior history during data analysis, for example. For example, the analysis unit analyzes food preferences based on data of restaurants visited by the user in the past. The analysis unit can also analyze fashion preferences based on the user's past purchase history. Furthermore, the analysis unit can analyze events of interest based on the user's past entertainment activities. This makes it possible to optimize the analysis algorithm by referring to the user's past behavior history and provide highly accurate analysis results. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's behavior history data into AI and cause AI to execute optimization of the analysis algorithm.

[0043] The analysis unit can customize the analysis method based on the user's current living situation when analyzing data. The analysis unit customizes the analysis method based on the user's current living situation when analyzing data. For example, if the user is currently on a diet, the analysis unit prioritizes health-related data analysis. In addition, if the user has started a new hobby, the analysis unit can also perform data analysis related to the hobby. Furthermore, if the user is planning to participate in a specific event, the analysis unit can also perform data analysis related to the event. This allows for customizing the analysis method based on the user's current living situation to provide more relevant analysis results. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's living situation data into AI and cause AI to customize the analysis method.

[0044] The analysis unit can perform the analysis while considering the geographical location information of the user when analyzing data. The analysis unit performs the analysis while considering the geographical location information of the user, for example, when analyzing data. For example, when the user is in a specific area, the analysis unit preferentially analyzes restaurant information in that area. In addition, when the user is traveling, the analysis unit can also preferentially analyze tourist information of the travel destination. Furthermore, when the user is at home, the analysis unit can also preferentially analyze event information in the vicinity. In this way, by considering the geographical location information of the user, it is possible to provide a more relevant analysis result. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's location information data to AI and have the AI ​​execute the analysis.

[0045] The analysis unit can improve the accuracy of the analysis by analyzing the user's social media activities during data analysis. The analysis unit can improve the accuracy of the analysis by analyzing the user's social media activities during data analysis, for example. For example, the analysis unit analyzes food preferences based on restaurant information shared by the user on social media. The analysis unit can also analyze fashion preferences based on information on brands the user follows on social media. Furthermore, the analysis unit can analyze events of interest based on event information that the user plans to attend on social media. This allows the accuracy of the analysis to be improved by analyzing the user's social media activities. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input the user's social media data into AI and cause AI to perform improvement of the accuracy of the analysis.

[0046] The suggestion unit can generate an optimal suggestion by referring to the user's past behavioral history when generating a suggestion. The suggestion unit generates an optimal suggestion by referring to the user's past behavioral history, for example, when generating a suggestion. For example, the suggestion unit suggests a restaurant to be visited next based on data of restaurants visited by the user in the past. The suggestion unit can also suggest a fashion item to be purchased next based on the user's past purchase history. Furthermore, the suggestion unit can also suggest an event to be attended next based on the user's past entertainment activities. In this way, an optimal suggestion can be generated by referring to the user's past behavioral history. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's behavioral history data into an AI and cause the AI ​​to generate an optimal suggestion.

[0047] The suggestion unit can customize the proposal content based on the user's current living situation when generating the proposal. The suggestion unit customizes the proposal content based on the user's current living situation when generating the proposal. For example, if the user is currently on a diet, the suggestion unit provides a health suggestion. In addition, if the user has started a new hobby, the suggestion unit can also provide a suggestion related to the hobby. Furthermore, if the user is planning to participate in a specific event, the suggestion unit can also provide a suggestion related to the event. This allows the suggestion content to be customized based on the user's current living situation, thereby providing a more appropriate suggestion. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's living situation data into AI and cause AI to customize the proposal content.

[0048] The suggestion unit can generate an optimal suggestion by considering the geographical location information of the user when generating a suggestion. The suggestion unit generates an optimal suggestion by considering the geographical location information of the user, for example, when generating a suggestion. For example, when the user is in a specific area, the suggestion unit can suggest restaurants in that area. In addition, when the user is traveling, the suggestion unit can also suggest tourist spots at the travel destination. Furthermore, when the user is at home, the suggestion unit can also suggest nearby events. In this way, by considering the geographical location information of the user, more relevant suggestions can be provided. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's location information data into AI and cause AI to generate an optimal suggestion.

[0049] The suggestion unit can adjust the suggestion content by analyzing the user's social media activity when generating a suggestion. For example, the suggestion unit adjusts the suggestion content by analyzing the user's social media activity when generating a suggestion. For example, the suggestion unit can suggest a restaurant to visit next based on restaurant information shared by the user on social media. The suggestion unit can also suggest a fashion item to purchase next based on information on a brand the user follows on social media. Furthermore, the suggestion unit can also suggest an event to attend next based on event information the user plans to attend on social media. This makes it possible to provide more appropriate suggestions by analyzing the user's social media activity. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's social media data into AI and cause AI to adjust the suggestion content.

[0050] The providing unit can select the optimal providing method by referring to the user's past behavior history when providing a proposal. For example, the providing unit selects the optimal providing method by referring to the user's past behavior history when providing a proposal. For example, the providing unit preferentially uses a providing method (notification, email, etc.) that the user has used in the past. The providing unit can also provide a proposal at the optimal timing based on the user's past behavior history. Furthermore, the providing unit can analyze the user's past reactions and select the most effective providing method. In this way, the optimal providing method can be selected by referring to the user's past behavior history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's behavior history data to AI and cause the AI ​​to select the optimal providing method.

[0051] The providing unit can customize the providing means based on the user's current living situation when providing a proposal. The providing unit customizes the providing means based on the user's current living situation when providing a proposal. For example, if the user is currently on a diet, the providing unit preferentially provides health-related proposals. In addition, if the user has started a new hobby, the providing unit can also provide proposals related to the hobby. Furthermore, if the user is planning to participate in a specific event, the providing unit can also provide proposals related to the event. In this way, by customizing the providing means based on the user's current living situation, the proposal can be provided in a more appropriate manner. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's living situation data into AI and cause the AI ​​to customize the providing means.

[0052] The providing unit can select the optimal providing method in consideration of the device information of the user when providing a proposal. For example, the providing unit selects the optimal providing method in consideration of the device information of the user when providing a proposal. For example, when the user is using a smartphone, the providing unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. Furthermore, when the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This makes it possible to select the optimal providing method by considering the device information of the user. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the device information of the user to the AI ​​and cause the AI ​​to select the optimal providing method.

[0053] The providing unit can adjust the means of providing the proposal by analyzing the social media activity of the user when providing the proposal. For example, the providing unit adjusts the means of providing the proposal by analyzing the social media activity of the user when providing the proposal. For example, the providing unit can suggest a restaurant to be visited next based on restaurant information shared by the user on social media. The providing unit can also suggest a fashion item to be purchased next based on information on a brand the user follows on social media. Furthermore, the providing unit can also suggest an event to be attended next based on event information that the user plans to attend on social media. In this way, by analyzing the user's social media activity, the proposal can be provided in a more appropriate manner. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's social media data into AI and cause the AI ​​to adjust the means of providing.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The concierge AI system can further collect health data of the user and customize suggestions based on the health condition. For example, the collection unit can collect data such as heart rate and exercise amount from the user's fitness tracker or smartwatch. The analysis unit analyzes these data and determines the user's health condition. The suggestion unit can generate appropriate diet and exercise suggestions based on the user's health condition. The provision unit can provide these suggestions to the user via notifications or within the app. This makes it possible to provide personalized suggestions according to the user's health condition.

[0056] The collection unit may also analyze the user's social media activity to understand the user's interests. For example, the collection unit collects data on posts shared by the user on social media and accounts followed by the user. The analysis unit analyzes this data to identify the user's interests. The suggestion unit may suggest related events or products based on the user's interests. The provision unit may provide these suggestions to the user via notifications or within the app. This may provide personalized suggestions based on the user's social media activity.

[0057] The suggestion unit can also predict future behavior based on the user's past behavior history and generate suggestions based on the prediction. For example, the suggestion unit can suggest places to visit and events to participate in the next season based on data on places the user visited in a particular season in the past and events the user participated in. The suggestion unit can also predict and suggest the next product to be purchased based on the user's past purchase history. This makes it possible to provide personalized suggestions that predict the user's future behavior.

[0058] The collection unit can also prioritize collecting highly relevant data by taking into account the geographical location information of the user. For example, when the user is in a specific area, the collection unit prioritizes collecting restaurant information in that area. Also, when the user is traveling, the collection unit can prioritize collecting tourist information about the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting event information in the vicinity. In this way, by taking into account the geographical location information of the user, highly relevant data can be prioritized.

[0059] The suggestion unit can also customize the suggestion content based on the user's current living situation. For example, if the user is currently on a diet, the suggestion unit can provide health-related suggestions. If the user has started a new hobby, the suggestion unit can provide suggestions related to the hobby. If the user is planning to participate in a specific event, the suggestion unit can provide suggestions related to the event. In this way, by customizing the suggestion content based on the user's current living situation, more appropriate suggestions can be provided.

[0060] The providing unit can also select the optimal providing method by taking into account the device information of the user. For example, when the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Also, when the user is using a tablet, the providing unit can provide a display method that is optimized for a large screen. Furthermore, when the user is using a smartwatch, the providing unit can provide a display method that is simple and has high visibility. In this way, the optimal providing method can be selected by taking into account the device information of the user.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The collection unit collects the user's preferences and circumstances. For example, it collects the user's past behavior history and current circumstances. Specifically, it can collect data such as restaurants the user has visited in the past, fashion items purchased, and entertainment events the user has attended. It can also obtain the user's location information and grasp the current activity status. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze the collected data and determine the user's preferences and situation. Specifically, the analysis unit can analyze data on restaurants the user has visited in the past to identify the user's food preferences. The analysis unit can also analyze the user's purchase history to identify the user's fashion preferences. Step 3: The suggestion unit generates customized suggestions based on the analysis results obtained by the analysis unit. For example, using AI, customized suggestions are generated in areas such as food, fashion, and entertainment based on the user's preferences and circumstances. Specifically, it can suggest the next restaurant to visit based on the user's food preferences, or the next fashion item to purchase based on the user's fashion preferences. Step 4: The providing unit provides the user with the proposal generated by the proposing unit. For example, the generated proposal or guidance is provided in a manner that is easy for the user to understand, such as by notification or displaying within the app. Specifically, the proposal can be provided by sending a push notification to the user's smartphone to display the proposal or by displaying a pop-up within the app.

[0063] (Example 2) A concierge AI system according to an embodiment of the present invention is a system that provides personalized suggestions and guidance linked to an individual's lifestyle. The system collects the user's preferences and circumstances, analyzes the collected data, generates customized suggestions based on the user's preferences and circumstances, and provides the generated suggestions and guidance to the user. This mechanism allows the user to receive customized advice in areas such as food, fashion, and entertainment. For example, the user's past behavior history and current situation are collected. For example, data such as restaurants the user has visited in the past, fashion items purchased, and entertainment events attended are collected. This makes it possible to grasp the user's preferences and circumstances. Next, the collected data is analyzed. The AI ​​analyzes the collected data and determines the user's preferences and circumstances. For example, data on restaurants the user has visited in the past can be analyzed to identify the user's food preferences. This makes it possible to make suggestions tailored to the user's preferences. Furthermore, customized suggestions are generated based on the analysis results. The AI ​​generates customized suggestions in areas such as food, fashion, and entertainment based on the user's preferences and circumstances. For example, it can suggest the next restaurant to visit based on the user's food preferences. Finally, the generated suggestions and guidance are provided to the user. Suggestions and guidance are provided in a user-friendly way, such as through notifications or in-app displays, allowing users to receive suggestions and guidance intuitively, enabling the concierge AI system to provide personalized suggestions and guidance based on the user's preferences and circumstances.

[0064] The concierge AI system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, and a provision unit. The collection unit collects the user's preferences and situation. The collection unit collects, for example, the user's past behavior history and current situation. For example, the collection unit can collect data such as restaurants visited by the user in the past, fashion items purchased, and entertainment events attended by the user. The collection unit can also obtain, for example, the user's location information and grasp the user's current activity situation. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data using, for example, AI to determine the user's preferences and situation. For example, the analysis unit can analyze data of restaurants visited by the user in the past and identify the user's food preferences. The analysis unit can also analyze, for example, the user's purchase history and identify the user's fashion preferences. The suggestion unit generates customized suggestions based on the analysis results obtained by the analysis unit. The suggestion unit generates customized suggestions in areas such as food, fashion, and entertainment based on the user's preferences and situation using, for example, AI. For example, the suggestion unit can suggest a restaurant to visit next based on the user's food preferences. The suggestion unit can also suggest a fashion item to be purchased next based on, for example, the user's fashion preferences. The provision unit provides the proposal generated by the suggestion unit to the user. The provision unit provides the generated proposal and guidance in a manner that is easy for the user to understand, such as, for example, by notification or display within an app. For example, the provision unit can send a push notification to the user's smartphone to display the proposal. The provision unit can also provide the proposal by, for example, displaying a pop-up within an app. In this way, the concierge AI system according to the embodiment can provide personalized proposals and guidance based on the user's preferences and circumstances.

[0065] The collection unit collects the user's preferences and situations. Specifically, it collects the user's past behavior history and current situation. For example, data such as restaurants the user has visited in the past, fashion items purchased, and entertainment events attended can be collected. This includes collecting data from the user's smartphone and wearable devices. The GPS function of the smartphone can be used to obtain the user's location information in real time and grasp the current activity status. In addition, data is also collected from the user's social media accounts and online shopping history to understand the user's interests and concerns in more detail. For example, by collecting photos and reviews of restaurants shared by the user on SNS and purchase history at online stores, the user's preferences can be more accurately identified. The collection unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the analysis unit and proposal unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection unit to efficiently and effectively collect data and improve the performance of the entire system.

[0066] The analysis unit analyzes the data collected by the collection unit. Specifically, the analysis unit uses AI to analyze the collected data and determine the user's preferences and situation. For example, the data of restaurants visited by the user in the past can be analyzed to identify the user's food preferences. The AI ​​uses natural language processing technology to analyze the user's reviews and comments and extract positive and negative evaluations. The AI ​​can also analyze the user's purchase history to identify the user's fashion preferences. The AI ​​uses image recognition technology to identify the style and brand of the purchased fashion items and grasp the user's preferences in detail. Furthermore, the analysis unit can analyze the user's location information and activity status to grasp the current situation and needs in real time. For example, the analysis unit provides data to make appropriate suggestions based on the user's current location and time of day. The analysis unit can also utilize past data and statistical information to analyze long-term trends and patterns and predict future needs. This allows the analysis unit to quickly and accurately analyze the collected data and grasp the user's preferences and situation in real time.

[0067] The suggestion unit generates customized suggestions based on the analysis results obtained by the analysis unit. Specifically, the suggestion unit uses AI to generate customized suggestions in areas such as food, fashion, and entertainment based on the user's preferences and situation. For example, it can suggest the next restaurant to visit based on the user's food preferences. The AI ​​analyzes the user's past visit history and reviews to identify restaurants with the user's preferred cuisine and atmosphere. It can also suggest the next fashion item to purchase based on the user's fashion preferences. The AI ​​analyzes the user's purchase history and brand preferences, and suggests the most suitable items taking into account the latest trends and sales information. Furthermore, it can also suggest the next event to attend and the next movie to watch based on the user's entertainment preferences. The AI ​​analyzes the user's past participation history and evaluations to identify events and movies that the user may be interested in. The suggestion unit also takes into account the user's current situation and needs when generating these suggestions. For example, it makes appropriate suggestions based on the user's current location and time of day. This allows the suggestion unit to quickly and accurately generate the most suitable suggestions for the user.

[0068] The providing unit provides the user with the proposal generated by the proposing unit. Specifically, the generated proposal and guidance are provided in a manner that is easy for the user to understand, such as through notifications or display within the app. For example, the providing unit can send a push notification to the user's smartphone and display the proposal. The push notification includes a concise and easy-to-understand message so that the user can immediately check it. In addition, the providing unit can provide the proposal by displaying a pop-up within the app. The pop-up display allows the user to immediately check important proposals and guidance while using the app. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and effectiveness of the proposal content. For example, whether the user accepted the proposal, and collect evaluations and comments on the proposal, and reflect them in the next proposal. In addition, the providing unit can reliably transmit information using multiple communication means. For example, important information is reliably delivered by using not only smartphone notifications but also voice calls, SMS, emails, and the like. As a result, the providing unit can quickly and reliably provide proposals to the user and improve the user's satisfaction.

[0069] The collection unit can collect the user's past behavior history and current situation. The collection unit, for example, collects the user's past behavior history. For example, the collection unit can collect data such as restaurants the user has visited in the past, fashion items purchased, and entertainment events attended by the user. The collection unit can also collect the user's current situation. For example, the collection unit can obtain the user's location information and grasp the current activity situation. This allows for more accurate suggestions by collecting the user's past behavior history and current situation. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's behavior history data into AI and cause AI to collect the data.

[0070] The analysis unit can analyze the collected data and determine the user's preferences and situation. The analysis unit can, for example, analyze the collected data and determine the user's preferences and situation. For example, the analysis unit can analyze data on restaurants visited by the user in the past and identify the user's food preferences. The analysis unit can also analyze the user's purchase history and identify the user's fashion preferences. Furthermore, the analysis unit can analyze the user's entertainment activities and identify events that interest the user. In this way, the collected data can be analyzed to accurately determine the user's preferences and situation. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the collected data to AI and have the AI ​​execute the analysis of the data.

[0071] The suggestion unit can generate personalized suggestions in the areas of food, fashion, and entertainment based on the user's preferences and circumstances. The suggestion unit generates personalized suggestions in the areas of food, fashion, and entertainment based on the user's preferences and circumstances, for example. For example, the suggestion unit can suggest a restaurant to visit next based on the user's food preferences. The suggestion unit can also suggest a fashion item to purchase next based on the user's fashion preferences. Furthermore, the suggestion unit can also suggest an event to attend next based on the user's entertainment preferences. This allows the user to be provided with optimal suggestions by generating customized suggestions based on the user's preferences and circumstances. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's preferences and situation data into AI and cause the AI ​​to generate suggestions.

[0072] The providing unit can provide the generated proposal or guidance in a manner that is easy for the user to understand, such as by notification or display within the app. The providing unit provides the generated proposal or guidance in a manner that is easy for the user to understand, such as by notification or display within the app. For example, the providing unit can send a push notification to the user's smartphone and display the proposal. The providing unit can also provide the proposal by displaying a pop-up within the app. Furthermore, the providing unit can select an optimal display method according to the user's device. For example, the providing unit can adjust the display method according to the device, such as a smartphone, tablet, or PC. As a result, the generated proposal or guidance is provided in a manner that is easy for the user to understand, so that the user can intuitively receive the proposal or guidance. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the proposal data to AI and cause the AI ​​to execute the optimal providing method.

[0073] The collection unit can estimate the user's emotion and adjust the timing of data collection based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and adjusts the timing of data collection based on the estimated user's emotion. For example, when the user is stressed, the collection unit refrains from collecting data and collects data when the user is relaxed. In addition, when the user is relaxed, the collection unit can actively collect data and obtain detailed information. Furthermore, when the user is busy, the collection unit can minimize data collection and collect detailed information later. This allows data to be collected at a more appropriate timing by adjusting the timing of data collection based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to an AI and cause the AI ​​to adjust the timing of data collection.

[0074] The collection unit can analyze the user's past behavior history and select the optimal data collection method. The collection unit, for example, analyzes the user's past behavior history and selects the optimal data collection method. For example, the collection unit determines the priority of data collection based on places the user has frequently visited in the past. The collection unit can also analyze the user's past purchase history and collect related data preferentially. Furthermore, the collection unit can collect data on events of interest based on the user's past entertainment activities. This makes it possible to select the optimal data collection method by analyzing the user's past behavior history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's behavior history data into AI and cause AI to select the optimal data collection method.

[0075] The collection unit can perform filtering based on the user's current living situation and field of interest when collecting data. The collection unit performs filtering based on the user's current living situation and field of interest when collecting data, for example. For example, if the user is currently on a diet, the collection unit preferentially collects health-related data. In addition, if the user starts a new hobby, the collection unit can also collect data related to the hobby. Furthermore, if the user plans to participate in a specific event, the collection unit can also collect data related to the event. This allows more relevant data to be collected by filtering data based on the user's current living situation and field of interest. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's living situation and field of interest data into AI and have the AI ​​perform filtering of the data.

[0076] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and determines the priority of data to be collected based on the estimated user's emotion. For example, when the user is excited, the collection unit preferentially collects entertainment-related data. In addition, when the user is relaxed, the collection unit can also preferentially collect relaxation-related data. Furthermore, when the user is stressed, the collection unit can also preferentially collect data that is useful for stress reduction. In this way, by determining the priority of data to be collected based on the user's emotion, more appropriate data can be preferentially collected. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to an AI and cause the AI ​​to execute the determination of the priority of the data.

[0077] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user when collecting data. For example, the collection unit preferentially collects highly relevant data in consideration of the geographical location information of the user when collecting data. For example, when the user is in a specific area, the collection unit preferentially collects restaurant information in that area. In addition, when the user is traveling, the collection unit can also preferentially collect tourist information of the travel destination. Furthermore, when the user is at home, the collection unit can also preferentially collect event information in the vicinity. In this way, by considering the geographical location information of the user, highly relevant data can be preferentially collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's location information data to AI and cause AI to execute collection of highly relevant data.

[0078] The collection unit can analyze the social media activity of the user at the time of data collection and collect related data. For example, the collection unit analyzes the social media activity of the user at the time of data collection and collects related data. For example, the collection unit collects related restaurant data based on restaurant information shared by the user on social media. The collection unit can also collect information on brands that the user follows on social media. Furthermore, the collection unit can collect event information that the user plans to attend on social media. In this way, by analyzing the social media activity of the user, related data can be efficiently collected. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the social media data of the user to AI and cause the AI ​​to collect related data.

[0079] The analysis unit can estimate the user's emotion and adjust the data analysis method based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the data analysis method based on the estimated user's emotion. For example, when the user is relaxed, the analysis unit performs detailed data analysis to provide highly accurate results. In addition, when the user is in a hurry, the analysis unit can also perform quick data analysis to provide results immediately. Furthermore, when the user is excited, the analysis unit can also provide visually attractive data analysis results. As a result, by adjusting the data analysis method based on the user's emotion, more appropriate analysis results can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the AI ​​and cause the AI ​​to adjust the data analysis method.

[0080] The analysis unit can optimize the analysis algorithm by referring to the user's past behavior history during data analysis. The analysis unit optimizes the analysis algorithm by referring to the user's past behavior history during data analysis, for example. For example, the analysis unit analyzes food preferences based on data of restaurants visited by the user in the past. The analysis unit can also analyze fashion preferences based on the user's past purchase history. Furthermore, the analysis unit can analyze events of interest based on the user's past entertainment activities. This makes it possible to optimize the analysis algorithm by referring to the user's past behavior history and provide highly accurate analysis results. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's behavior history data into AI and cause AI to execute optimization of the analysis algorithm.

[0081] The analysis unit can customize the analysis method based on the user's current living situation when analyzing data. The analysis unit customizes the analysis method based on the user's current living situation when analyzing data. For example, if the user is currently on a diet, the analysis unit prioritizes health-related data analysis. In addition, if the user has started a new hobby, the analysis unit can also perform data analysis related to the hobby. Furthermore, if the user is planning to participate in a specific event, the analysis unit can also perform data analysis related to the event. This allows for customizing the analysis method based on the user's current living situation to provide more relevant analysis results. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's living situation data into AI and cause AI to customize the analysis method.

[0082] The analysis unit can estimate the user's emotion and determine the priority of the analysis results based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and determines the priority of the analysis results based on the estimated user's emotion. For example, when the user is excited, the analysis unit preferentially provides entertainment-related analysis results. In addition, when the user is relaxed, the analysis unit can also preferentially provide relaxation-related analysis results. Furthermore, when the user is stressed, the analysis unit can also preferentially provide analysis results that are useful for stress reduction. In this way, by determining the priority of the analysis results based on the user's emotion, more appropriate analysis results can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute the determination of the priority of the analysis results.

[0083] The analysis unit can perform the analysis while considering the geographical location information of the user when analyzing data. The analysis unit performs the analysis while considering the geographical location information of the user, for example, when analyzing data. For example, when the user is in a specific area, the analysis unit preferentially analyzes restaurant information in that area. In addition, when the user is traveling, the analysis unit can also preferentially analyze tourist information of the travel destination. Furthermore, when the user is at home, the analysis unit can also preferentially analyze event information in the vicinity. In this way, by considering the geographical location information of the user, it is possible to provide a more relevant analysis result. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's location information data to AI and have the AI ​​execute the analysis.

[0084] The analysis unit can improve the accuracy of the analysis by analyzing the user's social media activities during data analysis. The analysis unit can improve the accuracy of the analysis by analyzing the user's social media activities during data analysis, for example. For example, the analysis unit analyzes food preferences based on restaurant information shared by the user on social media. The analysis unit can also analyze fashion preferences based on information on brands the user follows on social media. Furthermore, the analysis unit can analyze events of interest based on event information that the user plans to attend on social media. This allows the accuracy of the analysis to be improved by analyzing the user's social media activities. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input the user's social media data into AI and cause AI to perform improvement of the accuracy of the analysis.

[0085] The suggestion unit can estimate the user's emotion and adjust the way of expressing the proposal based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the way of expressing the proposal based on the estimated user's emotion. For example, the suggestion unit provides a detailed suggestion when the user is relaxed. In addition, the suggestion unit can also provide a concise suggestion when the user is in a hurry. Furthermore, the suggestion unit can also provide a visually attractive suggestion when the user is excited. This allows a more appropriate suggestion to be provided by adjusting the way of expressing the proposal based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data to the AI ​​and cause the AI ​​to adjust the way of expressing the proposal.

[0086] The suggestion unit can generate an optimal suggestion by referring to the user's past behavioral history when generating a suggestion. The suggestion unit generates an optimal suggestion by referring to the user's past behavioral history, for example, when generating a suggestion. For example, the suggestion unit suggests a restaurant to be visited next based on data of restaurants visited by the user in the past. The suggestion unit can also suggest a fashion item to be purchased next based on the user's past purchase history. Furthermore, the suggestion unit can also suggest an event to be attended next based on the user's past entertainment activities. In this way, an optimal suggestion can be generated by referring to the user's past behavioral history. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's behavioral history data into an AI and cause the AI ​​to generate an optimal suggestion.

[0087] The suggestion unit can customize the proposal content based on the user's current living situation when generating the proposal. The suggestion unit customizes the proposal content based on the user's current living situation when generating the proposal. For example, if the user is currently on a diet, the suggestion unit provides a health suggestion. In addition, if the user has started a new hobby, the suggestion unit can also provide a suggestion related to the hobby. Furthermore, if the user is planning to participate in a specific event, the suggestion unit can also provide a suggestion related to the event. This allows the suggestion content to be customized based on the user's current living situation, thereby providing a more appropriate suggestion. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's living situation data into AI and cause AI to customize the proposal content.

[0088] The suggestion unit can estimate the user's emotion and determine the priority of the suggestions based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and determines the priority of the suggestions based on the estimated user's emotion. For example, when the user is excited, the suggestion unit can provide entertainment-related suggestions preferentially. Furthermore, when the user is relaxed, the suggestion unit can also provide relaxation-related suggestions preferentially. Furthermore, when the user is stressed, the suggestion unit can also provide suggestions that are useful for stress reduction preferentially. In this way, by determining the priority of the suggestions based on the user's emotion, more appropriate suggestions can be provided. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute the determination of the priority of the suggestions.

[0089] The suggestion unit can generate an optimal suggestion by considering the geographical location information of the user when generating a suggestion. The suggestion unit generates an optimal suggestion by considering the geographical location information of the user, for example, when generating a suggestion. For example, when the user is in a specific area, the suggestion unit can suggest restaurants in that area. In addition, when the user is traveling, the suggestion unit can also suggest tourist spots at the travel destination. Furthermore, when the user is at home, the suggestion unit can also suggest nearby events. In this way, by considering the geographical location information of the user, more relevant suggestions can be provided. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's location information data into AI and cause AI to generate an optimal suggestion.

[0090] The suggestion unit can adjust the suggestion content by analyzing the user's social media activity when generating a suggestion. For example, the suggestion unit adjusts the suggestion content by analyzing the user's social media activity when generating a suggestion. For example, the suggestion unit can suggest a restaurant to visit next based on restaurant information shared by the user on social media. The suggestion unit can also suggest a fashion item to purchase next based on information on a brand the user follows on social media. Furthermore, the suggestion unit can also suggest an event to attend next based on event information the user plans to attend on social media. This makes it possible to provide more appropriate suggestions by analyzing the user's social media activity. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's social media data into AI and cause AI to adjust the suggestion content.

[0091] The providing unit can estimate the user's emotion and adjust the method of providing the suggestion based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the method of providing the suggestion based on the estimated user's emotion. For example, the providing unit provides a detailed suggestion when the user is relaxed. Also, the providing unit can provide a concise suggestion when the user is in a hurry. Furthermore, the providing unit can provide a visually attractive suggestion when the user is excited. Thereby, by adjusting the method of providing the suggestion based on the user's emotion, the suggestion can be provided in a more appropriate manner. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the AI ​​and cause the AI ​​to adjust the method of providing the suggestion.

[0092] The providing unit can select the optimal providing method by referring to the user's past behavior history when providing a proposal. For example, the providing unit selects the optimal providing method by referring to the user's past behavior history when providing a proposal. For example, the providing unit preferentially uses a providing method (notification, email, etc.) that the user has used in the past. The providing unit can also provide a proposal at the optimal timing based on the user's past behavior history. Furthermore, the providing unit can analyze the user's past reactions and select the most effective providing method. In this way, the optimal providing method can be selected by referring to the user's past behavior history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's behavior history data to AI and cause the AI ​​to select the optimal providing method.

[0093] The providing unit can customize the providing means based on the user's current living situation when providing a proposal. The providing unit customizes the providing means based on the user's current living situation when providing a proposal. For example, if the user is currently on a diet, the providing unit preferentially provides health-related proposals. In addition, if the user has started a new hobby, the providing unit can also provide proposals related to the hobby. Furthermore, if the user is planning to participate in a specific event, the providing unit can also provide proposals related to the event. In this way, by customizing the providing means based on the user's current living situation, the proposal can be provided in a more appropriate manner. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's living situation data into AI and cause the AI ​​to customize the providing means.

[0094] The providing unit can estimate the user's emotion and adjust the timing of providing the suggestion based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the timing of providing the suggestion based on the estimated user's emotion. For example, the providing unit provides a detailed suggestion when the user is relaxed. Also, the providing unit can provide a concise suggestion when the user is in a hurry. Furthermore, the providing unit can provide a visually attractive suggestion when the user is excited. Thereby, by adjusting the timing of providing the suggestion based on the user's emotion, the suggestion can be provided at a more appropriate timing. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the AI ​​and cause the AI ​​to adjust the timing of providing the suggestion.

[0095] The providing unit can select the optimal providing method in consideration of the device information of the user when providing a proposal. For example, the providing unit selects the optimal providing method in consideration of the device information of the user when providing a proposal. For example, when the user is using a smartphone, the providing unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. Furthermore, when the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This makes it possible to select the optimal providing method by considering the device information of the user. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the device information of the user to the AI ​​and cause the AI ​​to select the optimal providing method.

[0096] The providing unit can adjust the means of providing the proposal by analyzing the social media activity of the user when providing the proposal. For example, the providing unit adjusts the means of providing the proposal by analyzing the social media activity of the user when providing the proposal. For example, the providing unit can suggest a restaurant to be visited next based on restaurant information shared by the user on social media. The providing unit can also suggest a fashion item to be purchased next based on information on a brand the user follows on social media. Furthermore, the providing unit can also suggest an event to be attended next based on event information that the user plans to attend on social media. In this way, by analyzing the user's social media activity, the proposal can be provided in a more appropriate manner. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's social media data into AI and cause the AI ​​to adjust the means of providing.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The concierge AI system can further collect health data of the user and customize suggestions based on the health condition. For example, the collection unit can collect data such as heart rate and exercise amount from the user's fitness tracker or smartwatch. The analysis unit analyzes these data and determines the user's health condition. The suggestion unit can generate appropriate diet and exercise suggestions based on the user's health condition. The provision unit can provide these suggestions to the user via notifications or within the app. This makes it possible to provide personalized suggestions according to the user's health condition.

[0099] The collection unit may also analyze the user's social media activity to understand the user's interests. For example, the collection unit collects data on posts shared by the user on social media and accounts followed by the user. The analysis unit analyzes this data to identify the user's interests. The suggestion unit may suggest related events or products based on the user's interests. The provision unit may provide these suggestions to the user via notifications or within the app. This may provide personalized suggestions based on the user's social media activity.

[0100] The analysis unit can also estimate the user's emotions and adjust the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can prioritize providing suggestions that will help the user relax. Also, if the user is excited, the analysis unit can prioritize providing entertainment-related suggestions. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. This makes it possible to provide appropriate analysis results according to the user's emotions.

[0101] The suggestion unit can also predict future behavior based on the user's past behavior history and generate suggestions based on the prediction. For example, the suggestion unit can suggest places to visit and events to participate in the next season based on data on places the user visited in a particular season in the past and events the user participated in. The suggestion unit can also predict and suggest the next product to be purchased based on the user's past purchase history. This makes it possible to provide personalized suggestions that predict the user's future behavior.

[0102] The providing unit can also estimate the user's emotion and adjust the way in which the suggestions are provided based on the estimated emotion. For example, the providing unit can provide detailed suggestions when the user is relaxed. Also, the providing unit can provide concise suggestions when the user is in a hurry. Furthermore, the providing unit can provide visually appealing suggestions when the user is excited. In this way, by adjusting the way in which the suggestions are provided based on the user's emotion, the suggestions can be provided in a more appropriate manner.

[0103] The collection unit can also prioritize collecting highly relevant data by taking into account the geographical location information of the user. For example, when the user is in a specific area, the collection unit prioritizes collecting restaurant information in that area. Also, when the user is traveling, the collection unit can prioritize collecting tourist information about the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting event information in the vicinity. In this way, by taking into account the geographical location information of the user, highly relevant data can be prioritized.

[0104] The analysis unit can also estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, when the user is relaxed, the analysis unit performs detailed data analysis to provide highly accurate results. When the user is in a hurry, the analysis unit can perform quick data analysis to provide results immediately. Furthermore, when the user is excited, the analysis unit can provide visually appealing data analysis results. In this way, by adjusting the analysis method based on the user's emotions, more appropriate analysis results can be provided.

[0105] The suggestion unit can also customize the suggestion content based on the user's current living situation. For example, if the user is currently on a diet, the suggestion unit can provide health-related suggestions. If the user has started a new hobby, the suggestion unit can provide suggestions related to the hobby. If the user is planning to participate in a specific event, the suggestion unit can provide suggestions related to the event. In this way, by customizing the suggestion content based on the user's current living situation, more appropriate suggestions can be provided.

[0106] The providing unit can also estimate the user's emotion and adjust the timing of providing the suggestion based on the estimated emotion. For example, the providing unit can provide detailed suggestions when the user is relaxed. Also, when the user is in a hurry, the providing unit can provide concise suggestions. Furthermore, when the user is excited, the providing unit can provide visually appealing suggestions. In this way, by adjusting the timing of providing the suggestion based on the user's emotion, the suggestion can be provided at a more appropriate timing.

[0107] The providing unit can also select the optimal providing method by taking into account the device information of the user. For example, when the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Also, when the user is using a tablet, the providing unit can provide a display method that is optimized for a large screen. Furthermore, when the user is using a smartwatch, the providing unit can provide a display method that is simple and has high visibility. In this way, the optimal providing method can be selected by taking into account the device information of the user.

[0108] The process flow of the second embodiment will be briefly described below.

[0109] Step 1: The collection unit collects the user's preferences and circumstances. For example, it collects the user's past behavior history and current circumstances. Specifically, it can collect data such as restaurants the user has visited in the past, fashion items purchased, and entertainment events the user has attended. It can also obtain the user's location information and grasp the current activity status. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze the collected data and determine the user's preferences and situation. Specifically, the analysis unit can analyze data on restaurants the user has visited in the past to identify the user's food preferences. The analysis unit can also analyze the user's purchase history to identify the user's fashion preferences. Step 3: The suggestion unit generates customized suggestions based on the analysis results obtained by the analysis unit. For example, using AI, customized suggestions are generated in areas such as food, fashion, and entertainment based on the user's preferences and circumstances. Specifically, it can suggest the next restaurant to visit based on the user's food preferences, or the next fashion item to purchase based on the user's fashion preferences. Step 4: The providing unit provides the user with the proposal generated by the proposing unit. For example, the generated proposal or guidance is provided in a manner that is easy for the user to understand, such as by notification or displaying within the app. Specifically, the proposal can be provided by sending a push notification to the user's smartphone to display the proposal or by displaying a pop-up within the app.

[0110] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0112] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements including the above-mentioned collection unit, analysis unit, suggestion unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the user's behavior history and current situation using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and determines the user's preferences and situation. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a customized suggestion based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the generated suggestion and guidance to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0121] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0124] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0126] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements including the above-mentioned collection unit, analysis unit, suggestion unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the user's behavior history and current situation using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to determine the user's preferences and situation. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a customized suggestion based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the generated suggestion and guidance to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0132] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0137] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0140] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements including the above-mentioned collection unit, analysis unit, suggestion unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects the user's behavior history and current situation using the camera 42 and microphone 238 of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to determine the user's preferences and situation. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a customized suggestion based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the generated suggestion and guidance to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0150] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0152] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0154] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0155] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0157] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] Each of the multiple elements including the above-mentioned collection unit, analysis unit, suggestion unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the user's behavior history and current situation using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to determine the user's preferences and situation. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a customized suggestion based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the generated suggestion and guidance to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0163] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0164] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0165] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0166] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0167] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0168] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0171] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0172] In addition, 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 upon request from the data processing device 12.

[0173] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0174] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0175] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0176] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0177] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0178] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0179] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0180] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0181] (Appendix 1) A collection unit that collects user preferences and situations; an analysis unit that analyzes the data collected by the collection unit; a suggestion unit that generates personalized suggestions based on the analysis results obtained by the analysis unit; a providing unit that provides a user with the proposal generated by the proposing unit; Equipped A system characterized by: (Appendix 2) The collecting unit includes: Collect user's past behavior history and current situation 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze collected data to determine user preferences and circumstances 2. The system of claim 1, (Appendix 4) The suggestion unit, Generate personalized suggestions in the areas of food, fashion and entertainment based on the user's preferences and circumstances 2. The system of claim 1, (Appendix 5) The providing unit is Provide generated suggestions and guidance to users in an easy-to-understand manner, either through notifications or in-app displays 2. The system of claim 1, (Appendix 6) The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1, (Appendix 7) The collecting unit includes: Analyze users' past behavioral history and select the most appropriate data collection method 2. The system of claim 1, (Appendix 8) The collecting unit includes: Filter data collection based on the user's current life situation and interests 2. The system of claim 1, (Appendix 9) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 10) The collecting unit includes: When collecting data, prioritize collecting data that is more relevant by taking into account the user's geographic location. 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting data, analyze your social media activity and collect relevant data 2. The system of claim 1, (Appendix 12) The analysis unit is Infer user sentiment and adjust data analysis methods based on the inferred user sentiment 2. The system of claim 1, (Appendix 13) The analysis unit is When analyzing data, optimize the analysis algorithm by referring to the user's past behavior history 2. The system of claim 1, (Appendix 14) The analysis unit is When analyzing data, customize the analysis method based on the user's current life situation 2. The system of claim 1, (Appendix 15) The analysis unit is Infer user emotions and prioritize analysis results based on the estimated user emotions. 2. The system of claim 1, (Appendix 16) The analysis unit is When analyzing data, the analysis takes into account the user's geographic location information. 2. The system of claim 1, (Appendix 17) The analysis unit is When analyzing data, analyze users' social media activities to improve the accuracy of the analysis 2. The system of claim 1, (Appendix 18) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 19) The suggestion unit, When generating proposals, the system generates optimal proposals by referring to the user's past behavior history. 2. The system of claim 1, (Appendix 20) The suggestion unit, Customize suggestions based on the user's current life situation when generating them 2. The system of claim 1, (Appendix 21) The suggestion unit, Estimate user sentiment and prioritize suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 22) The suggestion unit, When generating suggestions, the system considers the user's geographic location information to generate optimal suggestions. 2. The system of claim 1, (Appendix 23) The suggestion unit, When generating suggestions, the system analyzes the user's social media activity to tailor the suggestions. 2. The system of claim 1, (Appendix 24) The providing unit is Inferring a user's emotions and adjusting how suggestions are provided based on the inferred user emotions 2. The system of claim 1, (Appendix 25) The providing unit is When providing a proposal, the optimal method of providing the proposal is selected by referring to the user's past behavior history. 2. The system of claim 1, (Appendix 26) The providing unit is When providing suggestions, tailor the delivery method based on the user’s current life situation 2. The system of claim 1, (Appendix 27) The providing unit is Estimate user emotions and adjust the timing of providing suggestions based on the estimated user emotions 2. The system of claim 1, (Appendix 28) The providing unit is When providing a proposal, the optimal method of providing the proposal is selected taking into account the user's device information. 2. The system of claim 1, (Appendix 29) The providing unit is When providing suggestions, analyze users' social media activity to adjust the method of providing them 2. The system of claim 1, [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects user preferences and situations; an analysis unit that analyzes the data collected by the collection unit; a suggestion unit that generates personalized suggestions based on the analysis results obtained by the analysis unit; a providing unit that provides a user with the proposal generated by the proposing unit; Equipped A system characterized by:

2. The collecting unit includes: Collect user's past behavior history and current situation 2. The system of claim 1 .

3. The analysis unit is Analyzing the data collected by the collection unit to determine the user's preferences and circumstances 2. The system of claim 1 .

4. The suggestion unit, Generate personalized suggestions in the areas of food, fashion and entertainment based on the user's preferences and circumstances 2. The system of claim 1 .

5. The providing unit is The suggestions and guidance generated by the suggestion unit are provided to the user in a manner that is easy for the user to understand by notification or display within the app.

2. The system of claim 1 .

6. The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1 .

7. The collecting unit includes: Analyze users' past behavioral history and select the most appropriate data collection method 2. The system of claim 1 .

8. The collecting unit includes: Filter data collection based on the user's current life situation and interests 2. The system of claim 1 .

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