system

Abstract

The system according to the embodiment aims to automatically make suggestions tailored to a user's lifestyle based on a huge amount of activity logs. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects behavior logs. The analysis unit analyzes the behavior logs collected by the collection unit. The proposal unit makes proposals based on the analysis results obtained by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not been sufficient in automatically making useful suggestions from huge amounts of behavioral logs, and there is room for improvement.

[0005] The system according to the embodiment aims to automatically make suggestions tailored to a user's lifestyle based on a huge amount of activity logs. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects behavior logs. The analysis unit analyzes the behavior logs collected by the collection unit. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically make suggestions tailored to a user's lifestyle based on a vast amount of activity logs. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The lifestyle suggestion system according to an embodiment of the present invention is an application in which AI automatically analyzes a massive amount of activity logs collected through IoT devices and makes suggestions tailored to the user's lifestyle. This application is designed to be used naturally without the user even being aware of the AI ​​and is provided as a package service that includes the IoT devices. First, the IoT devices collect the user's activity logs. For example, smart home devices collect data such as the user's wake-up time, meal times, and exercise time. This data is sent to the cloud via the IoT devices and analyzed by AI. Next, the AI ​​analyzes the collected activity logs to understand the user's lifestyle. For example, the AI ​​may recognize a user's pattern of waking up at 7:00 a.m. every morning and eating breakfast at 8:00 a.m. Based on this information, the AI ​​makes optimal suggestions to the user. For example, if the user desires to live a healthy lifestyle, the AI ​​analyzes the user's exercise time and dietary habits and suggests an appropriate exercise plan and meal menu. Similarly, if the user desires to relax, the AI ​​analyzes the user's stress level and suggests relaxing activities. This application is designed to be used naturally without the user even being aware of the AI. For example, when a user takes a specific action, the application automatically makes suggestions. This allows users to receive suggestions tailored to their lifestyle, leading to a more comfortable life. Furthermore, this application is provided as a package service that includes IoT devices. For example, smart home devices and wearable devices are included in the package, and by using these devices, users can collect more detailed behavioral logs and receive more accurate suggestions. In this way, an application that analyzes the massive amount of behavioral logs collected through IoT devices and automatically makes suggestions tailored to a user's lifestyle using AI is designed so that users can use it naturally, without even being aware of the AI, and is provided as a package service that includes IoT devices. This allows the lifestyle suggestion system to automatically make suggestions tailored to a user's lifestyle.

[0029] A lifestyle suggestion system according to an embodiment includes a collection unit, an analysis unit, and a suggestion unit. The collection unit collects a user's behavior log. The behavior log includes, but is not limited to, wake-up time, meal times, and exercise time, for example. The collection unit collects the user's behavior log using, for example, a smart home device. Smart home devices include smart speakers, smart lights, smart thermostats, and the like. The collection unit can also collect the user's behavior log using a wearable device. Wearable devices include smart watches, fitness trackers, smart glasses, and the like. For example, the collection unit records the user's wake-up time using a smart speaker. The collection unit can also record the user's bedtime using a smart light. The collection unit can also record the user's room temperature adjustment pattern using a smart thermostat. The analysis unit analyzes the behavior log collected by the collection unit. For example, the analysis unit analyzes the behavior log using AI to understand the user's lifestyle. The AI ​​analyzes the behavior log using a machine learning algorithm, for example. For example, the analysis unit understands the user's lifestyle patterns based on data such as the user's wake-up time, meal times, and exercise time. The analysis unit can also analyze the user's activity log over time to understand changes in lifestyle. For example, the analysis unit recognizes that the user wakes up at 7:00 a.m. every morning and eats breakfast at 8:00 a.m. The analysis unit can also analyze how often the user exercises on weekends. The suggestion unit makes suggestions based on the analysis results obtained by the analysis unit. The suggestion unit makes optimal suggestions for the user using, for example, AI. The AI ​​generates suggestions using, for example, natural language processing technology. For example, the suggestion unit suggests an exercise plan and a meal menu if the user wishes to live a healthy lifestyle. The suggestion unit can also suggest relaxing activities if the user wishes to relax. For example, the suggestion unit analyzes the user's exercise time and meal content to suggest appropriate exercise plans and meal menus. The suggestion unit can also analyze the user's stress level to suggest relaxing activities.As a result, the lifestyle suggestion system according to the embodiment can automatically make suggestions that are suited to the user's lifestyle.

[0030] The collection unit can collect data on the user's wake-up time, meal time, and exercise time using a smart home device or a wearable device. The collection unit, for example, uses a smart home device to collect data on the user's wake-up time, meal time, and exercise time. Smart home devices include smart speakers, smart lights, smart thermostats, etc. For example, the collection unit can use a smart speaker to record the user's wake-up time. The collection unit can also use a smart light to record the user's bedtime. The collection unit can also use a smart thermostat to record the user's room temperature adjustment pattern. The collection unit, for example, uses a wearable device to collect data on the user's wake-up time, meal time, and exercise time. Wearable devices include a smart watch, fitness tracker, smart glasses, etc. For example, the collection unit can use a smart watch to record the user's exercise time. The collection unit can also use a fitness tracker to record the user's step count. The collection unit can also use smart glasses to record the user's visual information. This makes it possible to collect detailed activity logs using smart home devices and wearable devices. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input data acquired from smart home devices and wearable devices into AI and have the AI ​​collect the data.

[0031] The analysis unit can analyze the collected behavior log and understand the user's lifestyle. The analysis unit, for example, uses AI to analyze the collected behavior log and understand the user's lifestyle. The AI ​​analyzes the behavior log, for example, using a machine learning algorithm. For example, the analysis unit understands the user's lifestyle patterns based on data such as the user's wake-up time, meal times, and exercise time. The analysis unit can also analyze the user's behavior log in chronological order to understand changes in the user's lifestyle. For example, the analysis unit recognizes a pattern in which the user wakes up at 7:00 a.m. every morning and has breakfast at 8:00 a.m. The analysis unit can also analyze how often the user exercises on weekends. In this way, the user's lifestyle can be understood by analyzing the behavior log. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected behavior log into AI and have the AI ​​understand the user's lifestyle.

[0032] The suggestion unit can analyze the user's exercise time or meal content and suggest an exercise plan or meal menu. The suggestion unit can analyze the user's exercise time and meal content using, for example, AI, and suggest an appropriate exercise plan or meal menu. The AI ​​can generate suggestions using, for example, natural language processing technology. For example, the suggestion unit can suggest an exercise plan or meal menu if the user desires to live a healthy lifestyle. The suggestion unit can also suggest relaxing activities if the user desires to relax. For example, the suggestion unit can analyze the user's exercise time and meal content and suggest an appropriate exercise plan or meal menu. The suggestion unit can also analyze the user's stress level and suggest relaxing activities. In this way, by analyzing the user's exercise time and meal content, it is possible to suggest an appropriate exercise plan or meal menu. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's exercise time and meal content into AI and have the AI ​​execute the exercise plan and meal menu suggestions.

[0033] The suggestion unit can analyze the user's stress level and suggest activities for relaxation. The suggestion unit can, for example, use AI to analyze the user's stress level and suggest relaxing activities. The AI ​​can generate suggestions using natural language processing technology, for example. For example, if the user desires to relax, the suggestion unit can suggest relaxing activities. The suggestion unit can also analyze the user's stress level and suggest relaxing activities. For example, the suggestion unit can analyze the user's stress level and suggest relaxing activities. The suggestion unit can also analyze the user's stress level and suggest relaxing activities. In this way, relaxing activities can be suggested by analyzing the user's stress level. Some or all of the above-described processing in the suggestion unit can be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's stress level into AI and have the AI ​​suggest relaxing activities.

[0034] The suggestion unit can automatically make a suggestion when the user takes a specific action. The suggestion unit can automatically make a suggestion when the user takes a specific action, for example, using AI. The AI ​​generates the suggestion using natural language processing technology, for example. For example, the suggestion unit can automatically make a suggestion when the user takes a specific action. The suggestion unit can also automatically make a suggestion when the user takes a specific action. For example, the suggestion unit can automatically make a suggestion when the user takes a specific action. The suggestion unit can also automatically make a suggestion when the user takes a specific action. In this way, by automatically making a suggestion when the user takes a specific action, the user can naturally use the AI ​​without being aware of it. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input user behavior data into AI and cause the AI ​​to execute suggestions based on the specific action.

[0035] The collection unit can analyze the user's past behavior log and select the optimal collection method. The collection unit can, for example, use AI to analyze the user's past behavior log and select the optimal collection method. The AI ​​can, for example, analyze the behavior log using a machine learning algorithm. For example, the collection unit can customize the collection method based on the user's frequent behavior in the past. The collection unit can also analyze the user's past behavior patterns and determine the optimal collection timing. The collection unit can also select a collection method that prioritizes the use of a specific device from the user's past behavior log. In this way, the optimal collection method can be selected by analyzing the user's past behavior log. Some or all of the above-described processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the user's past behavior log into AI and have the AI ​​select the optimal collection method.

[0036] The collection unit can perform filtering based on the user's current living situation or areas of interest when collecting the behavior log. The collection unit, for example, uses AI to perform filtering based on the user's current living situation or areas of interest when collecting the behavior log. The AI ​​performs filtering using, for example, a machine learning algorithm. For example, if the user is interested in health, the collection unit can prioritize collecting data related to exercise and diet. Furthermore, if the user is concentrating on work, the collection unit can prioritize collecting work-related data. Furthermore, if the user is relaxing, the collection unit can prioritize collecting relaxation-related data. In this way, by performing filtering based on the user's living situation and areas of interest, highly relevant data can be collected. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data on the user's living situation and areas of interest into AI and have the AI ​​perform filtering.

[0037] When collecting action logs, the collection unit can prioritize collecting highly relevant logs based on the user's geographical location information. When collecting action logs, the collection unit, for example, uses AI to prioritize collecting highly relevant logs based on the user's geographical location information. The AI ​​performs filtering using, for example, a machine learning algorithm. For example, when the user is in a specific location, the collection unit prioritizes collecting action logs related to that location. Furthermore, when the user is traveling, the collection unit can prioritize collecting action logs related to the travel. Furthermore, when the user is at home, the collection unit can prioritize collecting action logs at home. In this way, by taking the user's geographical location information into consideration, highly relevant action logs can be collected preferentially. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to AI and cause the AI ​​to collect highly relevant logs.

[0038] The collection unit can analyze the user's social media activities and collect related logs when collecting the behavior logs. The collection unit can, for example, use AI to analyze the user's social media activities and collect related logs when collecting the behavior logs. The AI ​​can, for example, analyze the social media activities using a machine learning algorithm. For example, the collection unit can collect related behavior logs based on information shared by the user on social media. The collection unit can also adjust the collection timing based on the frequency of the user's social media activities. The collection unit can also collect related behavior logs based on the user's areas of interest on social media. In this way, related behavior logs can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input data on the user's social media activities into AI and cause the AI ​​to collect related logs.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the behavior log during analysis. The analysis unit, for example, uses AI to adjust the level of detail of the analysis based on the importance of the behavior log during analysis. The AI ​​evaluates the importance of the behavior log using, for example, a machine learning algorithm. For example, the analysis unit performs a detailed analysis on important behavior logs. The analysis unit can also perform a simplified analysis on less important behavior logs. The analysis unit can also determine the priority of the analysis based on the importance of the behavior log. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the behavior log. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input importance data of the behavior log into AI and have the AI ​​adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the action log during analysis. The analysis unit, for example, uses AI to apply different analysis algorithms depending on the category of the action log during analysis. The AI ​​analyzes the action log using, for example, a machine learning algorithm. For example, the analysis unit applies a health analysis algorithm to an action log related to health. The analysis unit can also apply a work analysis algorithm to an action log related to work. The analysis unit can also apply a relaxation analysis algorithm to an action log related to relaxation. In this way, applying different analysis algorithms depending on the category of the action log enables more accurate analysis. Some 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 category data of the action log into AI and cause the AI ​​to apply different analysis algorithms.

[0041] The analysis unit can determine the analysis priority based on the collection time of the behavior log during analysis. The analysis unit, for example, uses AI to determine the analysis priority based on the collection time of the behavior log during analysis. The AI ​​evaluates the collection time of the behavior log using, for example, a machine learning algorithm. For example, the analysis unit prioritizes analysis of recently collected behavior logs. The analysis unit can also prioritize analysis of behavior logs collected when an important event occurred. The analysis unit can also prioritize analysis of behavior logs collected when a user performed a specific behavior. This enables efficient analysis by determining the analysis priority based on the collection time of the behavior log. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data on the collection time of the behavior log into AI and have the AI ​​determine the analysis priority.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the behavior logs during analysis. The analysis unit, for example, uses AI to adjust the order of analysis based on the relevance of the behavior logs during analysis. The AI ​​evaluates the relevance of the behavior logs using, for example, a machine learning algorithm. For example, the analysis unit prioritizes analysis of highly relevant behavior logs. The analysis unit can also postpone analysis of less relevant behavior logs. The analysis unit can also determine the order of analysis according to the relevance of the behavior logs. This enables efficient analysis by adjusting the order of analysis based on the relevance of the behavior logs. Some 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 relevance data of the behavior logs to AI and have the AI ​​adjust the order of analysis.

[0043] The suggestion unit can select the optimal suggestion method by analyzing the user's past behavior log when making a suggestion. The suggestion unit can, for example, use AI to analyze the user's past behavior log and select the optimal suggestion method when making a suggestion. The AI ​​can, for example, analyze the behavior log using a machine learning algorithm. For example, the suggestion unit selects the optimal suggestion method based on the suggestion methods the user has preferred in the past. The suggestion unit can also analyze the user's past behavior patterns and determine the optimal suggestion timing. The suggestion unit can also select a suggestion method that prioritizes the use of a specific device from the user's past behavior log. In this way, the optimal suggestion method can be selected by analyzing the user's past behavior log. Some or all of the above-described processing in the suggestion unit can be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past behavior log into AI and have the AI ​​select the optimal suggestion method.

[0044] The suggestion unit can adjust the proposed means based on the user's current living situation when making a suggestion. The suggestion unit, for example, uses AI to adjust the proposed means based on the user's current living situation when making a suggestion. The AI ​​evaluates the living situation using, for example, a machine learning algorithm. For example, if the user is interested in health, the suggestion unit can make suggestions related to exercise and diet. Furthermore, if the user is concentrating on work, the suggestion unit can make suggestions related to work. Furthermore, if the user is relaxing, the suggestion unit can make suggestions related to relaxation. In this way, by adjusting the proposed means based on the user's current living situation, more appropriate suggestions can be made. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's living situation data into AI and cause the AI ​​to adjust the proposed means.

[0045] The suggestion unit can select the optimal suggestion method based on the user's geographical location information when making a suggestion. The suggestion unit, for example, uses AI to select the optimal suggestion method based on the user's geographical location information when making a suggestion. The AI ​​evaluates the geographical location information using, for example, a machine learning algorithm. For example, if the user is in a specific location, the suggestion unit makes suggestions related to the location. Also, if the user is traveling, the suggestion unit can make travel-related suggestions. Also, if the user is at home, the suggestion unit can make home suggestions. In this way, by taking the user's geographical location information into consideration, highly relevant suggestions can be made. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's geographical location information into AI and cause the AI ​​to select the optimal suggestion method.

[0046] The suggestion unit, when making a suggestion, can analyze the user's social media activity and suggest a suggestion means. The suggestion unit, when making a suggestion, can use, for example, AI to analyze the user's social media activity and suggest a suggestion means. The AI ​​can analyze the social media activity using, for example, a machine learning algorithm. For example, the suggestion unit makes relevant suggestions based on information shared by the user on social media. The suggestion unit can also adjust the timing of the suggestion based on the frequency of the user's social media activity. The suggestion unit can also make relevant suggestions based on the user's areas of interest on social media. In this way, relevant suggestions can be made by analyzing the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the user's social media activity into AI and cause the AI ​​to suggest suggestion means.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] When analyzing a user's behavior log, the analysis unit can predict future behavior based on the user's past behavior patterns. For example, if the user has a habit of exercising every weekend, the analysis unit recognizes that pattern and suggests exercising the following weekend. Also, if the user tends to behave in a particular way during a particular season, the analysis unit can predict that behavior as that season approaches and make related suggestions. Furthermore, if the user behaves in a particular way around a particular event (e.g., a birthday or anniversary), the analysis unit can predict that behavior as that event approaches and make related suggestions. This allows for more appropriate suggestions to be made by predicting future behavior based on the user's past behavior patterns.

[0049] When collecting a user's behavior log, the collection unit can adjust the collection method taking into account the user's device usage status. For example, if the user frequently uses a smartphone, the collection unit can collect the behavior log through the smartphone. Alternatively, if the user frequently uses a wearable device, the collection unit can collect the behavior log through the wearable device. Furthermore, if the user does not use a specific device, the collection unit can collect the behavior log through another device. This allows for more detailed behavior logs to be collected by adjusting the collection method based on the user's device usage status.

[0050] When analyzing a user's behavior log, the analysis unit can integrate data from the user's social network. For example, by taking into account the behavioral patterns of the user's friends and family, the analysis can be more accurate. The analysis unit can also understand the user's interests based on the information the user has shared on social media. Furthermore, the analysis unit can evaluate the user's influence within the social network and make special suggestions to influential users. In this way, by integrating data from the user's social network, more accurate analysis and suggestions are possible.

[0051] When collecting a user's behavior log, the collection unit can adjust the collection method taking into account changes in the user's living environment. For example, if the user moves, the collection unit can collect a behavior log to adapt to the new environment. Also, if the user introduces a new device, data from the device can be collected. Furthermore, if the user starts a new lifestyle, the collection unit can collect a behavior log related to that lifestyle. In this way, by adjusting the collection method in response to changes in the user's living environment, more appropriate behavior logs can be collected.

[0052] When analyzing a user's activity log, the analysis unit can integrate the user's health data for analysis. For example, by taking the user's heart rate and sleep data into consideration, more accurate analysis can be performed. In addition, the analysis unit can evaluate nutritional balance based on the user's dietary data. Furthermore, the analysis unit can evaluate the effectiveness of exercise based on the user's exercise data and propose an appropriate exercise plan. Integrating the user's health data allows for more accurate analysis and proposals.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The collection unit collects the user's behavior log. The behavior log includes wake-up time, meal times, exercise time, etc. The collection unit collects the behavior log using smart home devices and wearable devices. For example, a smart speaker can be used to record wake-up times, a smart light can be used to record bedtimes, and a smart thermostat can be used to record room temperature adjustment patterns. Step 2: The analysis unit analyzes the behavioral logs collected by the collection unit. The analysis unit uses AI to analyze the behavioral logs and understand the user's lifestyle. For example, it uses a machine learning algorithm to understand the user's lifestyle patterns based on data such as the user's wake-up time, meal times, and exercise time, and analyzes them over time to understand changes in lifestyle. Step 3: The suggestion unit makes suggestions based on the analysis results obtained by the analysis unit. The suggestion unit uses AI to make optimal suggestions for the user, generating suggestions using natural language processing technology, for example. If the user wants to live a healthy lifestyle, the suggestion unit will suggest exercise plans and meal menus, and if the user wants to relax, the suggestion unit will suggest relaxing activities.

[0055] (Example 2) The lifestyle suggestion system according to an embodiment of the present invention is an application in which AI automatically analyzes a massive amount of activity logs collected through IoT devices and makes suggestions tailored to the user's lifestyle. This application is designed to be used naturally without the user even being aware of the AI ​​and is provided as a package service that includes the IoT devices. First, the IoT devices collect the user's activity logs. For example, smart home devices collect data such as the user's wake-up time, meal times, and exercise time. This data is sent to the cloud via the IoT devices and analyzed by AI. Next, the AI ​​analyzes the collected activity logs to understand the user's lifestyle. For example, the AI ​​may recognize a user's pattern of waking up at 7:00 a.m. every morning and eating breakfast at 8:00 a.m. Based on this information, the AI ​​makes optimal suggestions to the user. For example, if the user desires to live a healthy lifestyle, the AI ​​analyzes the user's exercise time and dietary habits and suggests an appropriate exercise plan and meal menu. Similarly, if the user desires to relax, the AI ​​analyzes the user's stress level and suggests relaxing activities. This application is designed to be used naturally without the user even being aware of the AI. For example, when a user takes a specific action, the application automatically makes suggestions. This allows users to receive suggestions tailored to their lifestyle, leading to a more comfortable life. Furthermore, this application is provided as a package service that includes IoT devices. For example, smart home devices and wearable devices are included in the package, and by using these devices, users can collect more detailed behavioral logs and receive more accurate suggestions. In this way, an application that analyzes the massive amount of behavioral logs collected through IoT devices and automatically makes suggestions tailored to a user's lifestyle using AI is designed so that users can use it naturally, without even being aware of the AI, and is provided as a package service that includes IoT devices. This allows the lifestyle suggestion system to automatically make suggestions tailored to a user's lifestyle.

[0056] A lifestyle suggestion system according to an embodiment includes a collection unit, an analysis unit, and a suggestion unit. The collection unit collects a user's behavior log. The behavior log includes, but is not limited to, wake-up time, meal times, and exercise time, for example. The collection unit collects the user's behavior log using, for example, a smart home device. Smart home devices include smart speakers, smart lights, smart thermostats, and the like. The collection unit can also collect the user's behavior log using a wearable device. Wearable devices include smart watches, fitness trackers, smart glasses, and the like. For example, the collection unit records the user's wake-up time using a smart speaker. The collection unit can also record the user's bedtime using a smart light. The collection unit can also record the user's room temperature adjustment pattern using a smart thermostat. The analysis unit analyzes the behavior log collected by the collection unit. For example, the analysis unit analyzes the behavior log using AI to understand the user's lifestyle. The AI ​​analyzes the behavior log using a machine learning algorithm, for example. For example, the analysis unit understands the user's lifestyle patterns based on data such as the user's wake-up time, meal times, and exercise time. The analysis unit can also analyze the user's activity log over time to understand changes in lifestyle. For example, the analysis unit recognizes that the user wakes up at 7:00 a.m. every morning and eats breakfast at 8:00 a.m. The analysis unit can also analyze how often the user exercises on weekends. The suggestion unit makes suggestions based on the analysis results obtained by the analysis unit. The suggestion unit makes optimal suggestions for the user using, for example, AI. The AI ​​generates suggestions using, for example, natural language processing technology. For example, the suggestion unit suggests an exercise plan and a meal menu if the user wishes to live a healthy lifestyle. The suggestion unit can also suggest relaxing activities if the user wishes to relax. For example, the suggestion unit analyzes the user's exercise time and meal content to suggest appropriate exercise plans and meal menus. The suggestion unit can also analyze the user's stress level to suggest relaxing activities.As a result, the lifestyle suggestion system according to the embodiment can automatically make suggestions that are suited to the user's lifestyle.

[0057] The collection unit can collect data on the user's wake-up time, meal time, and exercise time using a smart home device or a wearable device. The collection unit, for example, uses a smart home device to collect data on the user's wake-up time, meal time, and exercise time. Smart home devices include smart speakers, smart lights, smart thermostats, etc. For example, the collection unit can use a smart speaker to record the user's wake-up time. The collection unit can also use a smart light to record the user's bedtime. The collection unit can also use a smart thermostat to record the user's room temperature adjustment pattern. The collection unit, for example, uses a wearable device to collect data on the user's wake-up time, meal time, and exercise time. Wearable devices include a smart watch, fitness tracker, smart glasses, etc. For example, the collection unit can use a smart watch to record the user's exercise time. The collection unit can also use a fitness tracker to record the user's step count. The collection unit can also use smart glasses to record the user's visual information. This makes it possible to collect detailed activity logs using smart home devices and wearable devices. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input data acquired from smart home devices and wearable devices into AI and have the AI ​​collect the data.

[0058] The analysis unit can analyze the collected behavior log and understand the user's lifestyle. The analysis unit, for example, uses AI to analyze the collected behavior log and understand the user's lifestyle. The AI ​​analyzes the behavior log, for example, using a machine learning algorithm. For example, the analysis unit understands the user's lifestyle patterns based on data such as the user's wake-up time, meal times, and exercise time. The analysis unit can also analyze the user's behavior log in chronological order to understand changes in the user's lifestyle. For example, the analysis unit recognizes a pattern in which the user wakes up at 7:00 a.m. every morning and has breakfast at 8:00 a.m. The analysis unit can also analyze how often the user exercises on weekends. In this way, the user's lifestyle can be understood by analyzing the behavior log. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected behavior log into AI and have the AI ​​understand the user's lifestyle.

[0059] The suggestion unit can analyze the user's exercise time or meal content and suggest an exercise plan or meal menu. The suggestion unit can analyze the user's exercise time and meal content using, for example, AI, and suggest an appropriate exercise plan or meal menu. The AI ​​can generate suggestions using, for example, natural language processing technology. For example, the suggestion unit can suggest an exercise plan or meal menu if the user desires to live a healthy lifestyle. The suggestion unit can also suggest relaxing activities if the user desires to relax. For example, the suggestion unit can analyze the user's exercise time and meal content and suggest an appropriate exercise plan or meal menu. The suggestion unit can also analyze the user's stress level and suggest relaxing activities. In this way, by analyzing the user's exercise time and meal content, it is possible to suggest an appropriate exercise plan or meal menu. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's exercise time and meal content into AI and have the AI ​​execute the exercise plan and meal menu suggestions.

[0060] The suggestion unit can analyze the user's stress level and suggest activities for relaxation. The suggestion unit can, for example, use AI to analyze the user's stress level and suggest relaxing activities. The AI ​​can generate suggestions using natural language processing technology, for example. For example, if the user desires to relax, the suggestion unit can suggest relaxing activities. The suggestion unit can also analyze the user's stress level and suggest relaxing activities. For example, the suggestion unit can analyze the user's stress level and suggest relaxing activities. The suggestion unit can also analyze the user's stress level and suggest relaxing activities. In this way, relaxing activities can be suggested by analyzing the user's stress level. Some or all of the above-described processing in the suggestion unit can be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's stress level into AI and have the AI ​​suggest relaxing activities.

[0061] The suggestion unit can automatically make a suggestion when the user takes a specific action. The suggestion unit can automatically make a suggestion when the user takes a specific action, for example, using AI. The AI ​​generates the suggestion using natural language processing technology, for example. For example, the suggestion unit can automatically make a suggestion when the user takes a specific action. The suggestion unit can also automatically make a suggestion when the user takes a specific action. For example, the suggestion unit can automatically make a suggestion when the user takes a specific action. The suggestion unit can also automatically make a suggestion when the user takes a specific action. In this way, by automatically making a suggestion when the user takes a specific action, the user can naturally use the AI ​​without being aware of it. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input user behavior data into AI and cause the AI ​​to execute suggestions based on the specific action.

[0062] The collection unit can estimate the user's emotions and adjust the timing of collecting the behavior log based on the estimated user emotions. The collection unit can estimate the user's emotions using, for example, AI and adjust the timing of collecting the behavior log based on the estimated user emotions. The AI ​​can estimate emotions using, for example, an emotion estimation algorithm. For example, if the user is feeling stressed, the collection unit can reduce the collection timing to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can increase the collection timing to collect detailed data. Furthermore, if the user is in a hurry, the collection unit can minimize the collection timing and collect only important data. In this way, by adjusting the collection timing based on the user's emotions, the user's burden can be reduced and detailed data can be collected. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotional data into the AI ​​and have the AI ​​adjust the collection timing.

[0063] The collection unit can analyze the user's past behavior log and select the optimal collection method. The collection unit can, for example, use AI to analyze the user's past behavior log and select the optimal collection method. The AI ​​can, for example, analyze the behavior log using a machine learning algorithm. For example, the collection unit can customize the collection method based on the user's frequent behavior in the past. The collection unit can also analyze the user's past behavior patterns and determine the optimal collection timing. The collection unit can also select a collection method that prioritizes the use of a specific device from the user's past behavior log. In this way, the optimal collection method can be selected by analyzing the user's past behavior log. Some or all of the above-described processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the user's past behavior log into AI and have the AI ​​select the optimal collection method.

[0064] The collection unit can perform filtering based on the user's current living situation or areas of interest when collecting the behavior log. The collection unit, for example, uses AI to perform filtering based on the user's current living situation or areas of interest when collecting the behavior log. The AI ​​performs filtering using, for example, a machine learning algorithm. For example, if the user is interested in health, the collection unit can prioritize collecting data related to exercise and diet. Furthermore, if the user is concentrating on work, the collection unit can prioritize collecting work-related data. Furthermore, if the user is relaxing, the collection unit can prioritize collecting relaxation-related data. In this way, by performing filtering based on the user's living situation and areas of interest, highly relevant data can be collected. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data on the user's living situation and areas of interest into AI and have the AI ​​perform filtering.

[0065] The collection unit can estimate the user's emotions and determine the priority of the action logs to be collected based on the estimated user emotions. The collection unit can estimate the user's emotions using, for example, AI and determine the priority of the action logs to be collected based on the estimated user emotions. The AI ​​can estimate emotions using, for example, an emotion estimation algorithm. For example, if the user is feeling stressed, the collection unit can prioritize collecting action logs related to stress reduction. Also, if the user is relaxed, the collection unit can prioritize collecting action logs related to relaxation. Also, if the user is in a hurry, the collection unit can prioritize collecting important action logs. In this way, by prioritizing the action logs based on the user's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotional data into the AI ​​and have the AI ​​determine the priority of the behavior log.

[0066] When collecting action logs, the collection unit can prioritize collecting highly relevant logs based on the user's geographical location information. When collecting action logs, the collection unit, for example, uses AI to prioritize collecting highly relevant logs based on the user's geographical location information. The AI ​​performs filtering using, for example, a machine learning algorithm. For example, when the user is in a specific location, the collection unit prioritizes collecting action logs related to that location. Furthermore, when the user is traveling, the collection unit can prioritize collecting action logs related to the travel. Furthermore, when the user is at home, the collection unit can prioritize collecting action logs at home. In this way, by taking the user's geographical location information into consideration, highly relevant action logs can be collected preferentially. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to AI and cause the AI ​​to collect highly relevant logs.

[0067] The collection unit can analyze the user's social media activities and collect related logs when collecting the behavior logs. The collection unit can, for example, use AI to analyze the user's social media activities and collect related logs when collecting the behavior logs. The AI ​​can, for example, analyze the social media activities using a machine learning algorithm. For example, the collection unit can collect related behavior logs based on information shared by the user on social media. The collection unit can also adjust the collection timing based on the frequency of the user's social media activities. The collection unit can also collect related behavior logs based on the user's areas of interest on social media. In this way, related behavior logs can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input data on the user's social media activities into AI and cause the AI ​​to collect related logs.

[0068] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. The analysis unit can estimate the user's emotion using, for example, AI and adjust the presentation method of the analysis based on the estimated user's emotion. The AI ​​can estimate the emotion using, for example, an emotion estimation algorithm. For example, if the user is stressed, the analysis unit can provide a simple and visually easy-to-understand analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is in a hurry, the analysis unit can also provide a summary analysis result. This allows the analysis result to be easily understood by adjusting the presentation method of the analysis based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotional data into the AI ​​and have the AI ​​adjust the way the analysis is expressed.

[0069] The analysis unit can adjust the level of detail of the analysis based on the importance of the behavior log during analysis. The analysis unit, for example, uses AI to adjust the level of detail of the analysis based on the importance of the behavior log during analysis. The AI ​​evaluates the importance of the behavior log using, for example, a machine learning algorithm. For example, the analysis unit performs a detailed analysis on important behavior logs. The analysis unit can also perform a simplified analysis on less important behavior logs. The analysis unit can also determine the priority of the analysis based on the importance of the behavior log. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the behavior log. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input importance data of the behavior log into AI and have the AI ​​adjust the level of detail of the analysis.

[0070] The analysis unit can apply different analysis algorithms depending on the category of the action log during analysis. The analysis unit, for example, uses AI to apply different analysis algorithms depending on the category of the action log during analysis. The AI ​​analyzes the action log using, for example, a machine learning algorithm. For example, the analysis unit applies a health analysis algorithm to an action log related to health. The analysis unit can also apply a work analysis algorithm to an action log related to work. The analysis unit can also apply a relaxation analysis algorithm to an action log related to relaxation. In this way, applying different analysis algorithms depending on the category of the action log enables more accurate analysis. Some 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 category data of the action log into AI and cause the AI ​​to apply different analysis algorithms.

[0071] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit can, for example, use AI to estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The AI ​​can, for example, use an emotion estimation algorithm to estimate the emotions. For example, the analysis unit can provide a short and concise analysis result when the user is in a hurry. The analysis unit can also provide a detailed analysis result when the user is relaxed. The analysis unit can also provide a visually stimulating analysis result when the user is excited. This allows the analysis length to be adjusted based on the user's emotions, thereby providing an analysis result of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into AI and have the AI ​​adjust the length of the analysis.

[0072] The analysis unit can determine the analysis priority based on the collection time of the behavior log during analysis. The analysis unit, for example, uses AI to determine the analysis priority based on the collection time of the behavior log during analysis. The AI ​​evaluates the collection time of the behavior log using, for example, a machine learning algorithm. For example, the analysis unit prioritizes analysis of recently collected behavior logs. The analysis unit can also prioritize analysis of behavior logs collected when an important event occurred. The analysis unit can also prioritize analysis of behavior logs collected when a user performed a specific behavior. This enables efficient analysis by determining the analysis priority based on the collection time of the behavior log. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data on the collection time of the behavior log into AI and have the AI ​​determine the analysis priority.

[0073] The analysis unit can adjust the order of analysis based on the relevance of the behavior logs during analysis. The analysis unit, for example, uses AI to adjust the order of analysis based on the relevance of the behavior logs during analysis. The AI ​​evaluates the relevance of the behavior logs using, for example, a machine learning algorithm. For example, the analysis unit prioritizes analysis of highly relevant behavior logs. The analysis unit can also postpone analysis of less relevant behavior logs. The analysis unit can also determine the order of analysis according to the relevance of the behavior logs. This enables efficient analysis by adjusting the order of analysis based on the relevance of the behavior logs. Some 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 relevance data of the behavior logs to AI and have the AI ​​adjust the order of analysis.

[0074] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is presented based on the estimated user's emotion. The suggestion unit can estimate the user's emotion using, for example, AI and adjust the way the suggestion is presented based on the estimated user's emotion. The AI ​​can estimate the emotion using, for example, an emotion estimation algorithm. For example, if the user is stressed, the suggestion unit can provide a simple and visually easy-to-understand suggestion. If the user is relaxed, the suggestion unit can also provide a detailed suggestion. If the user is in a hurry, the suggestion unit can also provide a suggestion that focuses on the main points. This allows the suggestion to be easily understood by adjusting the way the suggestion is presented based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input the user's emotion data into AI and cause the AI ​​to adjust the way the suggestion is presented.

[0075] The suggestion unit can select the optimal suggestion method by analyzing the user's past behavior log when making a suggestion. The suggestion unit can, for example, use AI to analyze the user's past behavior log and select the optimal suggestion method when making a suggestion. The AI ​​can, for example, analyze the behavior log using a machine learning algorithm. For example, the suggestion unit selects the optimal suggestion method based on the suggestion methods the user has preferred in the past. The suggestion unit can also analyze the user's past behavior patterns and determine the optimal suggestion timing. The suggestion unit can also select a suggestion method that prioritizes the use of a specific device from the user's past behavior log. In this way, the optimal suggestion method can be selected by analyzing the user's past behavior log. Some or all of the above-described processing in the suggestion unit can be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past behavior log into AI and have the AI ​​select the optimal suggestion method.

[0076] The suggestion unit can adjust the proposed means based on the user's current living situation when making a suggestion. The suggestion unit, for example, uses AI to adjust the proposed means based on the user's current living situation when making a suggestion. The AI ​​evaluates the living situation using, for example, a machine learning algorithm. For example, if the user is interested in health, the suggestion unit can make suggestions related to exercise and diet. Furthermore, if the user is concentrating on work, the suggestion unit can make suggestions related to work. Furthermore, if the user is relaxing, the suggestion unit can make suggestions related to relaxation. In this way, by adjusting the proposed means based on the user's current living situation, more appropriate suggestions can be made. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's living situation data into AI and cause the AI ​​to adjust the proposed means.

[0077] The suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated user emotions. The suggestion unit can estimate the user's emotions using, for example, AI and prioritize the suggestions based on the estimated user emotions. The AI ​​can estimate the emotions using, for example, an emotion estimation algorithm. For example, if the user is feeling stressed, the suggestion unit can prioritize suggestions related to stress reduction. Also, if the user is relaxed, the suggestion unit can prioritize suggestions related to relaxation. Also, if the user is in a hurry, the suggestion unit can prioritize important suggestions. In this way, by prioritizing suggestions based on the user's emotions, important suggestions can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input the user's emotion data into AI and have the AI ​​determine the priority of suggestions.

[0078] The suggestion unit can select the optimal suggestion method based on the user's geographical location information when making a suggestion. The suggestion unit, for example, uses AI to select the optimal suggestion method based on the user's geographical location information when making a suggestion. The AI ​​evaluates the geographical location information using, for example, a machine learning algorithm. For example, if the user is in a specific location, the suggestion unit makes suggestions related to the location. Also, if the user is traveling, the suggestion unit can make travel-related suggestions. Also, if the user is at home, the suggestion unit can make home suggestions. In this way, by taking the user's geographical location information into consideration, highly relevant suggestions can be made. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's geographical location information into AI and cause the AI ​​to select the optimal suggestion method.

[0079] The suggestion unit, when making a suggestion, can analyze the user's social media activity and suggest a suggestion means. The suggestion unit, when making a suggestion, can use, for example, AI to analyze the user's social media activity and suggest a suggestion means. The AI ​​can analyze the social media activity using, for example, a machine learning algorithm. For example, the suggestion unit makes relevant suggestions based on information shared by the user on social media. The suggestion unit can also adjust the timing of the suggestion based on the frequency of the user's social media activity. The suggestion unit can also make relevant suggestions based on the user's areas of interest on social media. In this way, relevant suggestions can be made by analyzing the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the user's social media activity into AI and cause the AI ​​to suggest suggestion means. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and suggestion unit described above 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 a user's behavior log using the camera 42 and microphone 38B of the smart device 14 and transmits the collected behavior log to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected behavior log to understand the user's lifestyle. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes optimal suggestions to the user based on the analysis results. The suggestion unit may be realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and suggestion unit described above 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 a user's behavior log using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected behavior log to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected behavior log to understand the user's lifestyle. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes optimal suggestions to the user based on the analysis results. The suggestion unit may be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and suggestion unit described above 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 a user's action log using the camera 42 and microphone 238 of the headset type terminal 314 and transmits the collected action log to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected action log to understand the user's lifestyle. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes optimal suggestions to the user based on the analysis results. The suggestion unit may be realized, for example, by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and suggestion unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects a user's behavior log using the camera 42 and microphone 238 of the robot 414 and transmits the collected behavior log to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected behavior log to understand the user's lifestyle. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes optimal suggestions to the user based on the analysis results. The suggestion unit may be realized, for example, by the control unit 46A of the robot 414.

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

[0081] When analyzing a user's behavior log, the analysis unit can predict future behavior based on the user's past behavior patterns. For example, if the user has a habit of exercising every weekend, the analysis unit recognizes that pattern and suggests exercising the following weekend. Also, if the user tends to behave in a particular way during a particular season, the analysis unit can predict that behavior as that season approaches and make related suggestions. Furthermore, if the user behaves in a particular way around a particular event (e.g., a birthday or anniversary), the analysis unit can predict that behavior as that event approaches and make related suggestions. This allows for more appropriate suggestions to be made by predicting future behavior based on the user's past behavior patterns.

[0082] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can immediately suggest a relaxing activity. Also, if the user is relaxed, the suggestion unit can suggest a new hobby or activity. Furthermore, if the user is in a hurry, the suggestion unit can make suggestions that can be carried out in a short time. In this way, by adjusting the timing of suggestions based on the user's emotions, more effective suggestions can be made.

[0083] When collecting a user's behavior log, the collection unit can adjust the collection method taking into account the user's device usage status. For example, if the user frequently uses a smartphone, the collection unit can collect the behavior log through the smartphone. Alternatively, if the user frequently uses a wearable device, the collection unit can collect the behavior log through the wearable device. Furthermore, if the user does not use a specific device, the collection unit can collect the behavior log through another device. This allows for more detailed behavior logs to be collected by adjusting the collection method based on the user's device usage status.

[0084] The suggestion unit can estimate the user's emotions and customize the content of suggestions based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can suggest relaxing activities or ways to reduce stress. If the user is relaxed, the suggestion unit can suggest new hobbies or activities. If the user is excited, the suggestion unit can suggest energetic activities. In this way, by customizing the content of suggestions based on the user's emotions, more effective suggestions can be made.

[0085] When analyzing a user's behavior log, the analysis unit can integrate data from the user's social network. For example, by taking into account the behavioral patterns of the user's friends and family, the analysis can be more accurate. The analysis unit can also understand the user's interests based on the information the user has shared on social media. Furthermore, the analysis unit can evaluate the user's influence within the social network and make special suggestions to influential users. In this way, by integrating data from the user's social network, more accurate analysis and suggestions are possible.

[0086] The suggestion unit can estimate the user's emotions and adjust the format of the suggestions based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can make a simple and visually easy-to-understand suggestion. If the user is relaxed, the suggestion unit can make a suggestion that includes detailed information. Furthermore, if the user is in a hurry, the suggestion unit can make a suggestion that can be understood in a short time. As a result, by adjusting the format of the suggestion based on the user's emotions, more effective suggestions can be made.

[0087] When collecting a user's behavior log, the collection unit can adjust the collection method taking into account changes in the user's living environment. For example, if the user moves, the collection unit can collect a behavior log to adapt to the new environment. Also, if the user introduces a new device, data from the device can be collected. Furthermore, if the user starts a new lifestyle, the collection unit can collect a behavior log related to that lifestyle. In this way, by adjusting the collection method in response to changes in the user's living environment, more appropriate behavior logs can be collected.

[0088] The suggestion unit can estimate the user's emotions and adjust the frequency of suggestions based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can frequently suggest relaxing activities. Also, if the user is relaxed, the suggestion unit can regularly suggest new hobbies or activities. Furthermore, if the user is in a hurry, the suggestion unit can frequently make suggestions that can be done in a short time. Thus, by adjusting the frequency of suggestions based on the user's emotions, more effective suggestions can be made.

[0089] When analyzing a user's activity log, the analysis unit can integrate the user's health data for analysis. For example, by taking the user's heart rate and sleep data into consideration, more accurate analysis can be performed. In addition, the analysis unit can evaluate nutritional balance based on the user's dietary data. Furthermore, the analysis unit can evaluate the effectiveness of exercise based on the user's exercise data and propose an appropriate exercise plan. Integrating the user's health data allows for more accurate analysis and proposals.

[0090] The suggestion unit can estimate the user's emotions and personalize the content of suggestions based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can suggest relaxing activities or ways to reduce stress. Also, if the user is relaxed, the suggestion unit can suggest new hobbies or activities. Furthermore, if the user is excited, the suggestion unit can suggest energetic activities. In this way, by personalizing the content of suggestions based on the user's emotions, more effective suggestions can be made.

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

[0092] Step 1: The collection unit collects the user's behavior log. The behavior log includes wake-up time, meal times, exercise time, etc. The collection unit collects the behavior log using smart home devices and wearable devices. For example, a smart speaker can be used to record wake-up times, a smart light can be used to record bedtimes, and a smart thermostat can be used to record room temperature adjustment patterns. Step 2: The analysis unit analyzes the behavioral logs collected by the collection unit. The analysis unit uses AI to analyze the behavioral logs and understand the user's lifestyle. For example, it uses a machine learning algorithm to understand the user's lifestyle patterns based on data such as the user's wake-up time, meal times, and exercise time, and analyzes them over time to understand changes in lifestyle. Step 3: The suggestion unit makes suggestions based on the analysis results obtained by the analysis unit. The suggestion unit uses AI to make optimal suggestions for the user, generating suggestions using natural language processing technology, for example. If the user wants to live a healthy lifestyle, the suggestion unit will suggest exercise plans and meal menus, and if the user wants to relax, the suggestion unit will suggest relaxing activities.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

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

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] 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.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] 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.

[0106] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] 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.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] 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.

[0121] 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.

[0122] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0127] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] 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.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0137] 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0147] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0148] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0149] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0150] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0151] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0152] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0153] 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.

[0154] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0155] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0156] 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.

[0157] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0158] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0159] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0160] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0161] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0162] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0163] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0164] [Explanation of symbols]

[0165] 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 behavior logs; an analysis unit that analyzes the action log collected by the collection unit; a proposal unit that makes a proposal based on the analysis result obtained by the analysis unit; Equipped with A system characterized by:

2. The collecting unit Using smart home or wearable devices to collect data on users' wake-up times, meal times, and exercise times 2. The system of claim 1.

3. The analysis unit Analyze collected behavioral logs to understand users' lifestyles 2. The system of claim 1.

4. The proposal unit Analyzing the user's exercise time or meal content and proposing an exercise plan or meal menu 2. The system of claim 1.

5. The proposal unit Analyzes the user's stress level and suggests activities to help them relax 2. The system of claim 1.

6. The proposal unit Automatically make suggestions when users take certain actions 2. The system of claim 1.

7. The collecting unit Estimates user emotions and adjusts the timing of collecting behavior logs based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze users' past behavior logs and select the optimal collection method 2. The system of claim 1.

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