System

Abstract

An object of a system according to an embodiment is to distribute an advertisement suitable for a life scene of a consumer.SOLUTION: A system includes a life scene analysis unit, an advertisement selection unit, and an advertisement distribution unit. The life scene analysis unit analyzes a life scene of a person. The advertisement selection unit selects an optimum advertisement on the basis of the life scene analyzed by the life scene analysis unit. The advertisement distribution part distributes the advertisement selected by the advertisement selection part to the device of the consumer.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies may deliver advertisements that are not suitable for the consumer's daily life, which may reduce the effectiveness of the advertisements.

[0005] The system according to the embodiment aims to deliver advertisements suited to the lifestyles of consumers. [Means for solving the problem]

[0006] The system according to the embodiment includes a life scene analysis unit, an advertisement selection unit, and an advertisement distribution unit. The life scene analysis unit analyzes the life scenes of a consumer. The advertisement selection unit selects an optimal advertisement based on the life scenes analyzed by the life scene analysis unit. The advertisement distribution unit distributes the advertisement selected by the advertisement selection unit to the consumer's device. [Effects of the Invention]

[0007] The system according to the embodiment can deliver advertisements suited to the lifestyles of consumers. [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) An advertisement targeting system according to an embodiment of the present invention is a system that analyzes the lifestyles of consumers, selects optimal advertisements using a generation AI, and delivers them to the consumers' devices. As a result, the advertisement targeting system delivers advertisements that are suited to the lifestyles of consumers, thereby improving engagement rates.

[0029] An advertisement targeting system according to an embodiment includes a life scene analysis unit, an advertisement selection unit, and an advertisement distribution unit. The life scene analysis unit analyzes the life scenes of a consumer. For example, the life scene analysis unit analyzes the consumer's behavioral data, location information, and time period to identify the consumer's current life scene. The life scene analysis unit can also analyze the life scenes using a generation AI. For example, the generation AI identifies the life scene based on the consumer's behavioral data. The advertisement selection unit selects an optimal advertisement based on the life scene analyzed by the life scene analysis unit. For example, the advertisement selection unit selects advertisements related to coffee and breakfast during the morning commute. The advertisement selection unit can also select advertisements for relaxation goods and movies during evening relaxation time. For example, the generation AI selects the optimal advertisement based on the life scene. The advertisement distribution unit distributes the advertisements selected by the advertisement selection unit to the consumer's devices. For example, the advertisement distribution unit distributes advertisements to smartphones, tablets, and PCs. The advertisement distribution unit can also distribute advertisements at appropriate times. For example, the generation AI determines the timing to distribute advertisements based on the life scene. As a result, the advertisement targeting system according to the embodiment can deliver advertisements suited to lifestyle scenes and improve the engagement rate.

[0030] The lifestyle scene analysis unit can analyze voice data and environmental sounds to identify lifestyle scenes. The lifestyle scene analysis unit, for example, analyzes voice data collected from a consumer's smartphone or smart speaker to identify lifestyle scenes. For example, the morning commute often includes train sounds and car engine sounds. The lifestyle scene analysis unit also analyzes environmental sounds in the consumer's home to identify lifestyle scenes. For example, it can estimate mealtimes and relaxation times from cooking sounds in the kitchen and television sounds. The lifestyle scene analysis unit also analyzes voice data in the consumer's office environment to identify scenes during work and breaks. For example, it can grasp the progress of work from keyboard typing sounds and conversation content. This allows for more accurate identification of lifestyle scenes by analyzing voice data and environmental sounds.

[0031] The lifestyle scene analysis unit can infer lifestyle scenes by analyzing the content of social media posts and messages. For example, the lifestyle scene analysis unit analyzes a consumer's social media posts and infers lifestyle scenes from the content of the posts and hashtags. For example, it can identify scenes based on hashtags such as "#breakfast" and "#commute." The lifestyle scene analysis unit can also analyze the content of conversations on messaging apps to infer lifestyle scenes. For example, it can identify a lunch break scene from a message such as "I'm going to lunch now." The lifestyle scene analysis unit can also analyze a consumer's photos posted on social media and infer lifestyle scenes using image recognition technology. For example, it can identify the current scene from a photo of a meal or a photo of a landscape. This allows for more accurate lifestyle scene inference by analyzing the content of social media posts and messages.

[0032] The lifestyle scene analysis unit can analyze home appliance usage data and identify lifestyle scenes. The lifestyle scene analysis unit can, for example, analyze usage data of smart home appliances and identify lifestyle scenes. For example, a morning scene can be identified from the usage time of a coffee maker. The lifestyle scene analysis unit can also analyze usage data of smart lighting and identify lifestyle scenes. For example, wake-up time and bedtime can be estimated from the on / off times of the lights. The lifestyle scene analysis unit can also analyze opening and closing data of a smart refrigerator and identify lifestyle scenes. For example, meal preparation and snack time can be estimated from the frequency of opening and closing the refrigerator. This allows for more accurate identification of lifestyle scenes by analyzing home appliance usage data.

[0033] The lifestyle scene analysis unit can analyze purchase history and infer lifestyle scenes. The lifestyle scene analysis unit, for example, analyzes online shopping purchase history and infers lifestyle scenes. For example, if breakfast food is purchased, a morning scene can be identified. The lifestyle scene analysis unit can also analyze purchase history at physical stores and infer lifestyle scenes. For example, if sports equipment is purchased, a scene during exercise can be identified. The lifestyle scene analysis unit can also analyze the history of regular purchases and infer lifestyle scenes. For example, it can understand a consumer's routine from monthly regular purchases and identify scenes. This allows lifestyle scenes to be more accurately inferred by analyzing purchase history.

[0034] The advertisement selection unit can analyze the advertisement click history and select advertisements suitable for each lifestyle scene. For example, the advertisement selection unit can analyze a consumer's past advertisement click history and select advertisements suitable for each lifestyle scene based on advertisements with high click rates. For example, advertisements clicked during morning commute hours can be prioritized. The advertisement selection unit can also analyze the advertisement effectiveness for each lifestyle scene based on the advertisement click history and select the most suitable advertisement. For example, advertisements clicked during relaxation time can be delivered during relaxation time. The advertisement selection unit can also cluster the consumer's advertisement click history and select advertisements suitable for each cluster. For example, advertisements related to commuting can be delivered to the commuting cluster. In this way, advertisements suitable for each lifestyle scene can be selected by analyzing the past advertisement click history.

[0035] The advertisement selection unit can analyze the music playback history and select advertisements that match the lifestyle scene. For example, the advertisement selection unit analyzes the consumer's music playback history, infers the lifestyle scene from the genre and time of the music being played, and selects an appropriate advertisement. For example, it can deliver advertisements for relaxation goods during times when relaxing music is being played. The advertisement selection unit also analyzes the advertising effectiveness for each lifestyle scene based on the music playback history and selects the optimal advertisement. For example, it can deliver advertisements for sports equipment to match music played during exercise. The advertisement selection unit also clusters the consumer's music playback history and selects advertisements that are appropriate for each cluster. For example, it can deliver advertisements related to commuting to a commuting cluster. In this way, by analyzing the music playback history, advertisements that match the lifestyle scene can be selected.

[0036] The advertisement selection unit can analyze the reading history and select advertisements appropriate for each lifestyle scene. For example, the advertisement selection unit analyzes the consumer's e-book reading history, infers the lifestyle scene from the reading genre and time period, and selects an appropriate advertisement. For example, advertisements for relaxation goods can be delivered during evening reading hours. The advertisement selection unit also analyzes the advertising effectiveness for each lifestyle scene based on the reading history and selects the optimal advertisement. For example, business-related advertisements can be delivered to business books read during commute times. The advertisement selection unit also clusters the consumer's reading history and selects advertisements appropriate for each cluster. For example, relaxation-related advertisements can be delivered to the relaxation cluster. In this way, by analyzing the reading history, advertisements appropriate for each lifestyle scene can be selected.

[0037] The advertisement selection unit can analyze exercise data and select advertisements that suit lifestyle scenes. For example, the advertisement selection unit analyzes exercise data collected from a consumer's wearable device, infers the lifestyle scene from the type of exercise and time of day, and selects an appropriate advertisement. For example, an advertisement for a sports drink can be delivered while the consumer is running. The advertisement selection unit also analyzes the advertising effectiveness for each lifestyle scene based on the exercise data and selects the optimal advertisement. For example, a relaxation-related advertisement can be delivered while the consumer is doing yoga. The advertisement selection unit also clusters the consumer's exercise data and selects advertisements that are appropriate for each cluster. For example, fitness-related advertisements can be delivered to a fitness cluster. In this way, advertisements that suit lifestyle scenes can be selected by analyzing the exercise data.

[0038] The ad delivery unit can analyze device usage patterns and deliver ads at the optimal timing. The ad delivery unit, for example, analyzes a consumer's smartphone usage pattern and delivers ads at the optimal timing. For example, ads can be displayed during commuting time or break time. The ad delivery unit also analyzes the advertising effectiveness for each lifestyle scene based on the device usage history and delivers ads at the optimal timing. For example, ads for relaxation goods can be delivered during evening relaxation time. The ad delivery unit also clusters consumer device usage patterns and delivers ads at the optimal timing for each cluster. For example, ads can be delivered to the commuting cluster during commuting time. In this way, by analyzing device usage patterns, ads can be delivered at the optimal timing.

[0039] The ad distribution unit can analyze app usage history and distribute ads within appropriate apps. For example, the ad distribution unit can analyze a consumer's app usage history and distribute ads within frequently used apps. For example, ads can be displayed in news apps and social networking apps. The ad distribution unit can also analyze the advertising effectiveness for each lifestyle scene based on the app usage history and distribute ads within the most appropriate app. For example, ads for relaxation goods can be distributed to apps used during relaxation time. The ad distribution unit can also cluster the consumer's app usage history and distribute ads within apps appropriate for each cluster. For example, ads can be distributed to apps used during commuting time for a commuting cluster. In this way, by analyzing app usage history, ads can be distributed within appropriate apps.

[0040] The advertisement distribution unit can distribute advertisements through smart home devices. For example, the advertisement distribution unit distributes audio advertisements through a consumer's smart home device (e.g., a smart speaker). For example, an advertisement for coffee can be distributed by audio in the morning. The advertisement distribution unit also distributes visual advertisements using the display of the smart home device. For example, an advertisement for relaxation goods can be displayed on a smart display in the living room. The advertisement distribution unit also analyzes usage data of the smart home device and distributes advertisements at the optimal timing. For example, an advertisement for cooking utensils can be distributed while cooking in the kitchen. In this way, by distributing advertisements through smart home devices, advertisements that fit with lifestyle situations can be provided.

[0041] The advertisement distribution unit can distribute advertisements through a wearable device. For example, the advertisement distribution unit distributes advertisements in the form of notifications through a consumer's wearable device (e.g., a smart watch). For example, an advertisement for a sports drink can be displayed during exercise. The advertisement distribution unit also distributes visual advertisements using the display of the wearable device. For example, an advertisement for relaxation goods can be displayed during relaxation time. The advertisement distribution unit also analyzes usage data of the wearable device and distributes advertisements at optimal times. For example, an advertisement for running shoes can be distributed while running. In this way, by distributing advertisements through the wearable device, advertisements that fit with lifestyle situations can be provided.

[0042] The engagement measurement unit can analyze behavioral data and measure the engagement rate. For example, the engagement measurement unit can analyze behavioral data (e.g., clicks, purchases) of consumers after viewing an advertisement and measure the engagement rate. For example, it can analyze the purchase rate after clicking on an advertisement. The engagement measurement unit also builds a system that measures the engagement rate based on behavioral data after viewing an advertisement. For example, it can analyze the time spent on a website after viewing an advertisement. The engagement measurement unit can also cluster behavioral data of consumers after viewing an advertisement and measure the engagement rate of each cluster. For example, it can analyze the engagement rate in a specific lifestyle scene. This makes it possible to accurately measure the engagement rate by analyzing behavioral data after viewing an advertisement.

[0043] The engagement measurement unit can analyze comments and reviews and measure the engagement rate. For example, the engagement measurement unit can analyze consumer comments and reviews on advertisements and measure the engagement rate. For example, it can analyze the proportion of positive comments. The engagement measurement unit can also analyze text data of comments and reviews and build a system to measure the engagement rate. For example, it can measure the proportion of positive responses using sentiment analysis. The engagement measurement unit can also cluster consumer comments and reviews on advertisements and measure the engagement rate of each cluster. For example, it can analyze the content of comments in specific life situations. This makes it possible to accurately measure the engagement rate by analyzing comments and reviews on advertisements.

[0044] The engagement measurement unit can analyze purchase data and measure the effectiveness of advertising. The engagement measurement unit can, for example, analyze consumer purchase data and measure the effectiveness of advertising. For example, it can analyze the purchase rate after viewing an advertisement. The engagement measurement unit also builds a system that measures the effectiveness of advertising based on the purchase data. For example, it can analyze the purchase amount after viewing an advertisement. The engagement measurement unit also clusters consumer purchase data and measures the advertising effectiveness of each cluster. For example, it can analyze purchasing behavior in specific life situations. This makes it possible to accurately measure the effectiveness of advertising by analyzing the purchase data.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The ad targeting system can also analyze a user's health data and select the most appropriate advertisements based on their health condition. For example, it can analyze heart rate and sleep data collected from a user's wearable device to identify their health condition. If their heart rate is high, it can select advertisements related to relaxation, and if lack of sleep is detected, it can select advertisements for sleep-improving products. It can also analyze a user's food records and select advertisements for health foods based on nutritional balance. This allows for a more personalized advertising experience by providing advertisements tailored to the user's health condition.

[0047] The ad targeting system can also analyze a user's hobbies and interests and select advertisements based on those hobbies. For example, the system can analyze a user's social media posts and search history to identify hobbies and interests. If a user is interested in outdoor activities, advertisements for outdoor equipment can be selected. If a user is interested in cooking, advertisements for cooking equipment and recipes can be selected. Furthermore, the system can analyze a user's purchasing history and select advertisements related to products previously purchased. This can increase the relevance of advertisements by providing advertisements that match the user's hobbies and interests.

[0048] The ad targeting system can also analyze a user's travel history and select advertisements based on travel destinations. For example, the system can analyze a user's past travel destinations and reservation history to select advertisements related to the user's next travel destination. If the user traveled to a beach resort, beach-related advertisements can be selected. If the user enjoyed urban sightseeing, advertisements related to urban sightseeing can be selected. Furthermore, the system can analyze the user's activity data during the trip and select advertisements based on the activities. This can enrich the travel experience by providing advertisements based on the user's travel history.

[0049] The ad targeting system can also analyze a user's exercise data and select advertisements based on their exercise status. For example, the system can analyze exercise data collected from a user's wearable device to identify the type and intensity of exercise. If the user is running, advertisements for running shoes or sports drinks can be selected. If the user is doing yoga, advertisements for yoga mats or relaxation-related products can be selected. Furthermore, based on the user's exercise data, advertisements for post-exercise recovery products can be selected. This can increase the relevance of advertisements by providing advertisements that correspond to the user's exercise status.

[0050] The ad targeting system can also analyze a user's reading history and select advertisements based on the reading genre and time of day. For example, the user's e-book reading history can be analyzed to identify the reading genre and time of day. If the user reads at night to relax, advertisements for relaxation goods can be selected. If the user reads a business book during their commute, business-related advertisements can be selected. Furthermore, based on the user's reading history, advertisements for accessories and gadgets related to reading can be selected. This can increase the relevance of advertisements by providing advertisements that correspond to the user's reading history.

[0051] The ad targeting system can also analyze a user's music playback history and select advertisements based on the music genre and time period. For example, the system analyzes a user's music playback history to identify the genre and time period of the music being played. If the user is playing relaxing music during a certain time period, an advertisement for relaxation goods can be selected. If the user is playing energetic music while exercising, an advertisement for sports equipment can be selected. Furthermore, based on the user's music playback history, an advertisement for accessories or gadgets related to the music can be selected. This can increase the relevance of advertisements by providing advertisements that correspond to the user's music playback history.

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

[0053] Step 1: The lifestyle scene analysis unit analyzes the lifestyle scenes of the consumer. For example, the lifestyle scene analysis unit analyzes the consumer's behavioral data, location information, and time of day to identify the current lifestyle scene. Life scenes can also be analyzed using a generation AI. The generation AI identifies lifestyle scenes based on the consumer's behavioral data. Step 2: The advertisement selection unit selects the optimal advertisement based on the lifestyle scene analyzed by the lifestyle scene analysis unit. For example, the advertisement selection unit can select advertisements related to coffee and breakfast during the morning commute, and advertisements for relaxation goods and movies during evening relaxation time. The generation AI selects the optimal advertisement based on the lifestyle scene. Step 3: The ad distribution unit distributes the ads selected by the ad selection unit to consumers' devices. For example, the ad distribution unit can distribute ads to smartphones, tablets, and PCs at appropriate times. The generation AI determines the timing of ad distribution based on the consumer's lifestyle.

[0054] (Example 2) An advertisement targeting system according to an embodiment of the present invention is a system that analyzes the lifestyles of consumers, selects optimal advertisements using a generation AI, and delivers them to the consumers' devices. As a result, the advertisement targeting system delivers advertisements that are suited to the lifestyles of consumers, thereby improving engagement rates.

[0055] An advertisement targeting system according to an embodiment includes a life scene analysis unit, an advertisement selection unit, and an advertisement distribution unit. The life scene analysis unit analyzes the life scenes of a consumer. For example, the life scene analysis unit analyzes the consumer's behavioral data, location information, and time period to identify the consumer's current life scene. The life scene analysis unit can also analyze the life scenes using a generation AI. For example, the generation AI identifies the life scene based on the consumer's behavioral data. The advertisement selection unit selects an optimal advertisement based on the life scene analyzed by the life scene analysis unit. For example, the advertisement selection unit selects advertisements related to coffee and breakfast during the morning commute. The advertisement selection unit can also select advertisements for relaxation goods and movies during evening relaxation time. For example, the generation AI selects the optimal advertisement based on the life scene. The advertisement distribution unit distributes the advertisements selected by the advertisement selection unit to the consumer's devices. For example, the advertisement distribution unit distributes advertisements to smartphones, tablets, and PCs. The advertisement distribution unit can also distribute advertisements at appropriate times. For example, the generation AI determines the timing to distribute advertisements based on the life scene. As a result, the advertisement targeting system according to the embodiment can deliver advertisements suited to lifestyle scenes and improve the engagement rate.

[0056] The lifestyle scene analysis unit can analyze voice data and environmental sounds to identify lifestyle scenes. The lifestyle scene analysis unit, for example, analyzes voice data collected from a consumer's smartphone or smart speaker to identify lifestyle scenes. For example, the morning commute often includes train sounds and car engine sounds. The lifestyle scene analysis unit also analyzes environmental sounds in the consumer's home to identify lifestyle scenes. For example, it can estimate mealtimes and relaxation times from cooking sounds in the kitchen and television sounds. The lifestyle scene analysis unit also analyzes voice data in the consumer's office environment to identify scenes during work and breaks. For example, it can grasp the progress of work from keyboard typing sounds and conversation content. This allows for more accurate identification of lifestyle scenes by analyzing voice data and environmental sounds.

[0057] The lifestyle scene analysis unit can infer lifestyle scenes by analyzing the content of social media posts and messages. For example, the lifestyle scene analysis unit analyzes a consumer's social media posts and infers lifestyle scenes from the content of the posts and hashtags. For example, it can identify scenes based on hashtags such as "#breakfast" and "#commute." The lifestyle scene analysis unit can also analyze the content of conversations on messaging apps to infer lifestyle scenes. For example, it can identify a lunch break scene from a message such as "I'm going to lunch now." The lifestyle scene analysis unit can also analyze a consumer's photos posted on social media and infer lifestyle scenes using image recognition technology. For example, it can identify the current scene from a photo of a meal or a photo of a landscape. This allows for more accurate lifestyle scene inference by analyzing the content of social media posts and messages.

[0058] The life scene analysis unit can use the emotion estimation function to analyze the emotional state and identify life scenes based on the emotion. The life scene analysis unit, for example, analyzes the facial expressions of a consumer to estimate the emotional state. For example, it can determine whether the consumer is relaxed or focused from a smile or a serious expression. The life scene analysis unit can also analyze the tone of the consumer's voice to estimate the emotional state. For example, it can determine the consumer's stress level or relaxed level from the tone and speed of the voice. The life scene analysis unit can also analyze the text of the consumer's social media posts to estimate the emotional state. For example, it can determine the consumer's current emotional state from the frequency of positive and negative words. As a result, the emotion estimation function can be used to identify life scenes based on emotions.

[0059] The lifestyle scene analysis unit can analyze home appliance usage data and identify lifestyle scenes. The lifestyle scene analysis unit can, for example, analyze usage data of smart home appliances and identify lifestyle scenes. For example, a morning scene can be identified from the usage time of a coffee maker. The lifestyle scene analysis unit can also analyze usage data of smart lighting and identify lifestyle scenes. For example, wake-up time and bedtime can be estimated from the on / off times of the lights. The lifestyle scene analysis unit can also analyze opening and closing data of a smart refrigerator and identify lifestyle scenes. For example, meal preparation and snack time can be estimated from the frequency of opening and closing the refrigerator. This allows for more accurate identification of lifestyle scenes by analyzing home appliance usage data.

[0060] The lifestyle scene analysis unit can analyze purchase history and infer lifestyle scenes. The lifestyle scene analysis unit, for example, analyzes online shopping purchase history and infers lifestyle scenes. For example, if breakfast food is purchased, a morning scene can be identified. The lifestyle scene analysis unit can also analyze purchase history at physical stores and infer lifestyle scenes. For example, if sports equipment is purchased, a scene during exercise can be identified. The lifestyle scene analysis unit can also analyze the history of regular purchases and infer lifestyle scenes. For example, it can understand a consumer's routine from monthly regular purchases and identify scenes. This allows lifestyle scenes to be more accurately inferred by analyzing purchase history.

[0061] The life scene analysis unit can use the emotion estimation function to track changes in life scenes based on emotions in real time. The life scene analysis unit, for example, analyzes the emotional state of a consumer in real time and tracks changes in life scenes. For example, it can detect a change from a stressed state to a relaxed state. The life scene analysis unit also builds a system that tracks changes in life scenes in real time based on emotion estimation data. For example, it can identify scenes based on fluctuations in emotion scores. The life scene analysis unit also monitors the consumer's emotional reactions in real time and tracks changes in life scenes. For example, if positive emotions increase, it can determine that the consumer has transitioned to a relaxation period. This makes it possible to track changes in life scenes based on emotions in real time.

[0062] The advertisement selection unit can analyze the advertisement click history and select advertisements suitable for each lifestyle scene. For example, the advertisement selection unit can analyze a consumer's past advertisement click history and select advertisements suitable for each lifestyle scene based on advertisements with high click rates. For example, advertisements clicked during morning commute hours can be prioritized. The advertisement selection unit can also analyze the advertisement effectiveness for each lifestyle scene based on the advertisement click history and select the most suitable advertisement. For example, advertisements clicked during relaxation time can be delivered during relaxation time. The advertisement selection unit can also cluster the consumer's advertisement click history and select advertisements suitable for each cluster. For example, advertisements related to commuting can be delivered to the commuting cluster. In this way, advertisements suitable for each lifestyle scene can be selected by analyzing the past advertisement click history.

[0063] The advertisement selection unit can analyze the music playback history and select advertisements that match the lifestyle scene. For example, the advertisement selection unit analyzes the consumer's music playback history, infers the lifestyle scene from the genre and time of the music being played, and selects an appropriate advertisement. For example, it can deliver advertisements for relaxation goods during times when relaxing music is being played. The advertisement selection unit also analyzes the advertising effectiveness for each lifestyle scene based on the music playback history and selects the optimal advertisement. For example, it can deliver advertisements for sports equipment to match music played during exercise. The advertisement selection unit also clusters the consumer's music playback history and selects advertisements that are appropriate for each cluster. For example, it can deliver advertisements related to commuting to a commuting cluster. In this way, by analyzing the music playback history, advertisements that match the lifestyle scene can be selected.

[0064] The advertisement selection unit can select the optimal advertisement based on the emotional state using the emotion estimation function. For example, the advertisement selection unit analyzes the emotional state of a consumer and selects an advertisement appropriate for when the consumer is in a positive emotional state. For example, it can deliver an advertisement for relaxation goods when the consumer is relaxing. The advertisement selection unit also analyzes the advertising effectiveness for each lifestyle scene based on the emotion estimation data and selects the optimal advertisement. For example, it can deliver a relaxation-related advertisement when the consumer is in a stressful state. The advertisement selection unit also monitors the consumer's emotional response in real time and selects an advertisement based on the emotional state. For example, it can deliver an entertainment-related advertisement when the consumer is feeling a strong positive emotion. In this way, the emotion estimation function can be used to select the optimal advertisement based on the emotional state.

[0065] The advertisement selection unit can analyze the reading history and select advertisements appropriate for each lifestyle scene. For example, the advertisement selection unit analyzes the consumer's e-book reading history, infers the lifestyle scene from the reading genre and time period, and selects an appropriate advertisement. For example, advertisements for relaxation goods can be delivered during evening reading hours. The advertisement selection unit also analyzes the advertising effectiveness for each lifestyle scene based on the reading history and selects the optimal advertisement. For example, business-related advertisements can be delivered to business books read during commute times. The advertisement selection unit also clusters the consumer's reading history and selects advertisements appropriate for each cluster. For example, relaxation-related advertisements can be delivered to the relaxation cluster. In this way, by analyzing the reading history, advertisements appropriate for each lifestyle scene can be selected.

[0066] The advertisement selection unit can analyze exercise data and select advertisements that suit lifestyle scenes. For example, the advertisement selection unit analyzes exercise data collected from a consumer's wearable device, infers the lifestyle scene from the type of exercise and time of day, and selects an appropriate advertisement. For example, an advertisement for a sports drink can be delivered while the consumer is running. The advertisement selection unit also analyzes the advertising effectiveness for each lifestyle scene based on the exercise data and selects the optimal advertisement. For example, a relaxation-related advertisement can be delivered while the consumer is doing yoga. The advertisement selection unit also clusters the consumer's exercise data and selects advertisements that are appropriate for each cluster. For example, fitness-related advertisements can be delivered to a fitness cluster. In this way, advertisements that suit lifestyle scenes can be selected by analyzing the exercise data.

[0067] The advertisement selection unit can select advertisements based on emotions in real time using the emotion estimation function. For example, the advertisement selection unit analyzes the emotional state of consumers in real time and selects optimal advertisements based on their emotions. For example, entertainment-related advertisements can be delivered when positive emotions are strong. The advertisement selection unit also analyzes the advertising effectiveness for each lifestyle scene in real time based on the emotion estimation data and selects optimal advertisements. For example, relaxation-related advertisements can be delivered when a consumer is under stress. The advertisement selection unit also monitors consumers' emotional responses in real time and builds a system that selects advertisements based on their emotional state. For example, advertisements can be dynamically adjusted in response to fluctuations in the emotion score. This allows for real-time selection of advertisements based on emotions, maximizing advertising effectiveness.

[0068] The ad delivery unit can analyze device usage patterns and deliver ads at the optimal timing. The ad delivery unit, for example, analyzes a consumer's smartphone usage pattern and delivers ads at the optimal timing. For example, ads can be displayed during commuting time or break time. The ad delivery unit also analyzes the advertising effectiveness for each lifestyle scene based on the device usage history and delivers ads at the optimal timing. For example, ads for relaxation goods can be delivered during evening relaxation time. The ad delivery unit also clusters consumer device usage patterns and delivers ads at the optimal timing for each cluster. For example, ads can be delivered to the commuting cluster during commuting time. In this way, by analyzing device usage patterns, ads can be delivered at the optimal timing.

[0069] The ad distribution unit can analyze app usage history and distribute ads within appropriate apps. For example, the ad distribution unit can analyze a consumer's app usage history and distribute ads within frequently used apps. For example, ads can be displayed in news apps and social networking apps. The ad distribution unit can also analyze the advertising effectiveness for each lifestyle scene based on the app usage history and distribute ads within the most appropriate app. For example, ads for relaxation goods can be distributed to apps used during relaxation time. The ad distribution unit can also cluster the consumer's app usage history and distribute ads within apps appropriate for each cluster. For example, ads can be distributed to apps used during commuting time for a commuting cluster. In this way, by analyzing app usage history, ads can be distributed within appropriate apps.

[0070] The ad delivery unit can adjust the timing of ad delivery based on the emotional state using the emotion estimation function. The ad delivery unit, for example, analyzes the emotional state of a consumer in real time and adjusts the timing of ad delivery based on the emotion. For example, it can deliver ads when positive emotions are strong. The ad delivery unit also analyzes the advertising effectiveness for each lifestyle scene based on the emotion estimation data and delivers ads at the optimal timing. For example, it can deliver relaxation-related ads when a consumer is under stress. The ad delivery unit also monitors the emotional responses of consumers in real time and builds a system that adjusts the timing of ad delivery based on the emotional state. For example, it can dynamically adjust ads in response to fluctuations in the emotion score. This allows the advertising effectiveness to be maximized by adjusting the timing of ad delivery based on the emotional state.

[0071] The advertisement distribution unit can distribute advertisements through smart home devices. For example, the advertisement distribution unit distributes audio advertisements through a consumer's smart home device (e.g., a smart speaker). For example, an advertisement for coffee can be distributed by audio in the morning. The advertisement distribution unit also distributes visual advertisements using the display of the smart home device. For example, an advertisement for relaxation goods can be displayed on a smart display in the living room. The advertisement distribution unit also analyzes usage data of the smart home device and distributes advertisements at the optimal timing. For example, an advertisement for cooking utensils can be distributed while cooking in the kitchen. In this way, by distributing advertisements through smart home devices, advertisements that fit with lifestyle situations can be provided.

[0072] The advertisement distribution unit can distribute advertisements through a wearable device. For example, the advertisement distribution unit distributes advertisements in the form of notifications through a consumer's wearable device (e.g., a smart watch). For example, an advertisement for a sports drink can be displayed during exercise. The advertisement distribution unit also distributes visual advertisements using the display of the wearable device. For example, an advertisement for relaxation goods can be displayed during relaxation time. The advertisement distribution unit also analyzes usage data of the wearable device and distributes advertisements at optimal times. For example, an advertisement for running shoes can be distributed while running. In this way, by distributing advertisements through the wearable device, advertisements that fit with lifestyle situations can be provided.

[0073] The ad delivery unit can select an ad delivery channel based on emotion using the emotion estimation function. For example, the ad delivery unit analyzes the emotional state of a consumer and selects the optimal ad delivery channel based on the emotion. For example, it can deliver ads through social media when positive emotions are strong. The ad delivery unit also analyzes the advertising effectiveness for each lifestyle scenario based on the emotion estimation data and selects the optimal delivery channel. For example, it can deliver relaxation-related ads through a smart speaker when a consumer is under stress. The ad delivery unit also monitors the consumer's emotional responses in real time and builds a system that selects an ad delivery channel based on the consumer's emotional state. For example, it can dynamically adjust the ad delivery channel according to fluctuations in the emotion score. This maximizes advertising effectiveness by selecting an ad delivery channel based on emotion.

[0074] The engagement measurement unit can analyze behavioral data and measure the engagement rate. For example, the engagement measurement unit can analyze behavioral data (e.g., clicks, purchases) of consumers after viewing an advertisement and measure the engagement rate. For example, it can analyze the purchase rate after clicking on an advertisement. The engagement measurement unit also builds a system that measures the engagement rate based on behavioral data after viewing an advertisement. For example, it can analyze the time spent on a website after viewing an advertisement. The engagement measurement unit can also cluster behavioral data of consumers after viewing an advertisement and measure the engagement rate of each cluster. For example, it can analyze the engagement rate in a specific lifestyle scene. This makes it possible to accurately measure the engagement rate by analyzing behavioral data after viewing an advertisement.

[0075] The engagement measurement unit can analyze comments and reviews and measure the engagement rate. For example, the engagement measurement unit can analyze consumer comments and reviews on advertisements and measure the engagement rate. For example, it can analyze the proportion of positive comments. The engagement measurement unit can also analyze text data of comments and reviews and build a system to measure the engagement rate. For example, it can measure the proportion of positive responses using sentiment analysis. The engagement measurement unit can also cluster consumer comments and reviews on advertisements and measure the engagement rate of each cluster. For example, it can analyze the content of comments in specific life situations. This makes it possible to accurately measure the engagement rate by analyzing comments and reviews on advertisements.

[0076] The engagement measurement unit can analyze emotional responses using an emotion estimation function and measure the engagement rate. For example, the engagement measurement unit analyzes the facial expressions of consumers while they are watching an advertisement and measures the engagement rate based on their emotional responses. For example, it can detect expressions of smiles and surprise. The engagement measurement unit can also analyze the tone of voice while they are watching an advertisement and measure the engagement rate based on their emotional responses. For example, it can estimate their emotional state from the tone and speed of their voice. The engagement measurement unit can also monitor the emotional responses of consumers while they are watching an advertisement in real time and build a system to measure the engagement rate. For example, it can calculate the engagement rate based on fluctuations in the emotion score. This makes it possible to accurately measure the engagement rate by analyzing their emotional responses while they are watching an advertisement.

[0077] The engagement measurement unit can analyze purchase data and measure the effectiveness of advertising. The engagement measurement unit can, for example, analyze consumer purchase data and measure the effectiveness of advertising. For example, it can analyze the purchase rate after viewing an advertisement. The engagement measurement unit also builds a system that measures the effectiveness of advertising based on the purchase data. For example, it can analyze the purchase amount after viewing an advertisement. The engagement measurement unit also clusters consumer purchase data and measures the advertising effectiveness of each cluster. For example, it can analyze purchasing behavior in specific life situations. This makes it possible to accurately measure the effectiveness of advertising by analyzing the purchase data.

[0078] The engagement measurement unit can use the emotion estimation function to track emotion-based fluctuations in the engagement rate in real time. The engagement measurement unit, for example, analyzes the emotional state of consumers in real time and tracks emotion-based fluctuations in the engagement rate. For example, it can measure the engagement rate when positive emotions are strong. The engagement measurement unit also builds a system that tracks engagement rate fluctuations in real time based on the emotion estimation data. For example, it can calculate the engagement rate according to fluctuations in the emotion score. The engagement measurement unit also monitors consumers' emotional responses in real time and tracks emotion-based engagement rate fluctuations. For example, it can dynamically adjust the engagement rate based on fluctuations in the emotion score. This allows advertising effectiveness to be maximized by tracking emotion-based engagement rate fluctuations in real time.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The ad targeting system can also analyze a user's health data and select the most appropriate advertisements based on their health condition. For example, it can analyze heart rate and sleep data collected from a user's wearable device to identify their health condition. If their heart rate is high, it can select advertisements related to relaxation, and if lack of sleep is detected, it can select advertisements for sleep-improving products. It can also analyze a user's food records and select advertisements for health foods based on nutritional balance. This allows for a more personalized advertising experience by providing advertisements tailored to the user's health condition.

[0081] The ad targeting system can also analyze a user's hobbies and interests and select advertisements based on those hobbies. For example, the system can analyze a user's social media posts and search history to identify hobbies and interests. If a user is interested in outdoor activities, advertisements for outdoor equipment can be selected. If a user is interested in cooking, advertisements for cooking equipment and recipes can be selected. Furthermore, the system can analyze a user's purchasing history and select advertisements related to products previously purchased. This can increase the relevance of advertisements by providing advertisements that match the user's hobbies and interests.

[0082] The ad targeting system can also analyze a user's travel history and select advertisements based on travel destinations. For example, the system can analyze a user's past travel destinations and reservation history to select advertisements related to the user's next travel destination. If the user traveled to a beach resort, beach-related advertisements can be selected. If the user enjoyed urban sightseeing, advertisements related to urban sightseeing can be selected. Furthermore, the system can analyze the user's activity data during the trip and select advertisements based on the activities. This can enrich the travel experience by providing advertisements based on the user's travel history.

[0083] The ad targeting system can also use a user emotion estimation function to select advertisements based on emotions. For example, the system can analyze a user's facial expressions and voice tone to estimate their emotional state. If the user is feeling stressed, it can select advertisements related to relaxation. If the user is feeling happy, it can select advertisements related to entertainment. It can also analyze the text of a user's social media posts to estimate their emotional state. This can maximize the effectiveness of advertisements by providing advertisements that match the user's emotional state.

[0084] The ad targeting system can also analyze a user's exercise data and select advertisements based on their exercise status. For example, the system can analyze exercise data collected from a user's wearable device to identify the type and intensity of exercise. If the user is running, advertisements for running shoes or sports drinks can be selected. If the user is doing yoga, advertisements for yoga mats or relaxation-related products can be selected. Furthermore, based on the user's exercise data, advertisements for post-exercise recovery products can be selected. This can increase the relevance of advertisements by providing advertisements that correspond to the user's exercise status.

[0085] The ad targeting system can further use a user emotion estimation function to adjust the timing of ad delivery based on emotions. For example, the system analyzes the user's emotional state in real time and adjusts the timing of ad delivery based on emotions. If the user has positive emotions, it can deliver ads immediately. Also, if the user has negative emotions, it can temporarily refrain from delivering ads. Furthermore, it can dynamically adjust the timing of ad delivery according to fluctuations in the user's emotion score. In this way, adjusting the timing of ad delivery based on emotions can maximize the effectiveness of ads.

[0086] The ad targeting system can also analyze a user's reading history and select advertisements based on the reading genre and time of day. For example, the user's e-book reading history can be analyzed to identify the reading genre and time of day. If the user reads at night to relax, advertisements for relaxation goods can be selected. If the user reads a business book during their commute, business-related advertisements can be selected. Furthermore, based on the user's reading history, advertisements for accessories and gadgets related to reading can be selected. This can increase the relevance of advertisements by providing advertisements that correspond to the user's reading history.

[0087] The ad targeting system can also use a user emotion estimation function to measure the effectiveness of emotion-based advertising in real time. For example, the system can analyze a user's facial expressions and voice tone while watching an ad and measure the effectiveness of the ad based on their emotional response. If the user shows a smile or a surprised expression while watching an ad, it can be determined that the ad is highly effective. The system can also estimate the user's emotional state from the user's voice tone and speed to measure the effectiveness of the ad. Furthermore, the system can analyze the text of a user's social media posts and measure the effectiveness of the ad based on their emotional response. This allows the system to optimize advertising strategies by measuring the effectiveness of emotion-based advertising in real time.

[0088] The ad targeting system can also analyze a user's music playback history and select advertisements based on the music genre and time period. For example, the system analyzes a user's music playback history to identify the genre and time period of the music being played. If the user is playing relaxing music during a certain time period, an advertisement for relaxation goods can be selected. If the user is playing energetic music while exercising, an advertisement for sports equipment can be selected. Furthermore, based on the user's music playback history, an advertisement for accessories or gadgets related to the music can be selected. This can increase the relevance of advertisements by providing advertisements that correspond to the user's music playback history.

[0089] The ad targeting system can further use a user emotion estimation function to select an ad delivery channel based on emotion. For example, it can analyze a user's emotional state and select the optimal ad delivery channel based on emotion. If the user has positive emotions, it can deliver ads through social media. If the user is feeling stressed, it can deliver relaxation-related ads through a smart speaker. Furthermore, it can dynamically adjust the ad delivery channel according to fluctuations in the user's emotion score. This allows for the effectiveness of ads to be maximized by selecting an ad delivery channel based on emotion.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The lifestyle scene analysis unit analyzes the lifestyle scenes of the consumer. For example, the lifestyle scene analysis unit analyzes the consumer's behavioral data, location information, and time of day to identify the current lifestyle scene. Life scenes can also be analyzed using a generation AI. The generation AI identifies lifestyle scenes based on the consumer's behavioral data. Step 2: The advertisement selection unit selects the optimal advertisement based on the lifestyle scene analyzed by the lifestyle scene analysis unit. For example, the advertisement selection unit can select advertisements related to coffee and breakfast during the morning commute, and advertisements for relaxation goods and movies during evening relaxation time. The generation AI selects the optimal advertisement based on the lifestyle scene. Step 3: The ad distribution unit distributes the ads selected by the ad selection unit to consumers' devices. For example, the ad distribution unit can distribute ads to smartphones, tablets, and PCs at appropriate times. The generation AI determines the timing of ad distribution based on the consumer's lifestyle.

[0092] 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.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0121] 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.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0137] 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.

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0159] 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 life scene analysis unit that analyzes the life scenes of consumers; an advertisement selection unit that selects an optimal advertisement based on the life scene analyzed by the life scene analysis unit; an advertisement distribution unit that distributes the advertisements selected by the advertisement selection unit to devices of consumers; A system characterized by:

2. The life scene analysis unit Analyzing voice data and environmental sounds to identify living situations 2. The system of claim 1.

3. The life scene analysis unit Analyze the content of the SNS posts and messages to estimate the living situation 2. The system of claim 1.

4. The life scene analysis unit Analyzing emotional states and identifying emotion-based life scenes 2. The system of claim 1.

5. The life scene analysis unit Analyzing home appliance usage data and identifying lifestyle situations 2. The system of claim 1.

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