system

Abstract

The system according to the embodiment aims to target advertisements based on life scenes. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a distribution unit. The collection unit collects data related to life scenes. The analysis unit analyzes the data collected by the collection unit and determines the life scenes. The generation unit generates advertisements based on the life scenes determined by the analysis unit. The distribution unit distributes the advertisements generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, advertisements based on the attributes of consumers may not be suited to their daily lives, which may reduce the effectiveness of the advertisements.

[0005] The system according to the embodiment aims to target advertisements based on life scenes. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a distribution unit. The collection unit collects data related to life scenes. The analysis unit analyzes the data collected by the collection unit and determines the life scenes. The generation unit generates advertisements based on the life scenes determined by the analysis unit. The distribution unit distributes the advertisements generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can target advertisements based on life scenes. [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 advertising targeting system according to an embodiment of the present invention targets advertising based on "life scenes" rather than on consumers' "attributes." This advertising targeting system collects data on consumers' life scenes, analyzes the collected data, generates advertisements based on the life scenes, and delivers the generated advertisements tailored to the consumers' current life scenes. For example, the advertising targeting system collects detailed data, such as the activities consumers engage in and their locations. This allows the system to understand the consumers' current life scenes. The system then analyzes the collected data and generates advertisements based on the life scenes. AI is used for the analysis to select advertisements that are most suited to the consumers' current life scenes. For example, for a consumer commuting to work, advertisements for commuting-related products and services can be generated. This generates advertisements that are highly relevant to the consumers. The generated advertisements are delivered tailored to the consumers' current life scenes. For example, if a consumer is commuting to work, an advertisement appropriate for the commute is displayed on the consumer's smartphone. This allows advertisements to be displayed at an appropriate time for the consumer, improving engagement rates. This system reduces advertisements that consumers find annoying and improves advertising effectiveness. When consumers receive advertisements that fit their lifestyles, they become more interested in the advertisements and their engagement rate improves. For example, if a consumer is relaxing at home, they will be shown advertisements for products and services related to relaxation, which is likely to lead to a positive response from the consumer. This allows the ad targeting system to target advertisements based on the consumer's lifestyle, thereby improving their engagement rate.

[0029] An advertisement targeting system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a distribution unit. The collection unit collects data related to lifestyle scenes. The data related to lifestyle scenes includes, for example, detailed data on what activities a consumer is engaged in and where they are located, but is not limited to such examples. The collection unit collects data, for example, on whether the consumer is commuting to work or relaxing at home. The collection unit can also collect activity details and location information of the consumer. For example, the collection unit collects data on whether the consumer is exercising, eating, shopping, etc. The analysis unit analyzes the data collected by the collection unit to determine the lifestyle scene. For example, statistical analysis or a machine learning algorithm is used for the analysis, but is not limited to such examples. For example, the analysis unit determines what lifestyle scene the consumer is currently in based on the collected data. The generation unit generates an advertisement based on the lifestyle scene determined by the analysis unit. For example, a generation AI (generation AI or LLM) is used for the generation, but is not limited to such examples. For example, the generation unit generates an advertisement for commuting-related products and services for a consumer commuting to work. Furthermore, the generation unit can generate advertisements for products and services related to relaxation for consumers who are relaxing at home. The distribution unit distributes the advertisements generated by the generation unit in accordance with the consumer's current lifestyle. Devices such as, but not limited to, smartphones, personal computers, and televisions are used for distribution. For example, if a consumer is commuting to work, the distribution unit can display an advertisement suitable for commuting on a smartphone. Furthermore, if a consumer is relaxing at home, the distribution unit can display an advertisement suitable for relaxation on a personal computer or television. As a result, the advertisement targeting system according to the embodiment can improve the engagement rate by targeting advertisements based on the consumer's lifestyle.

[0030] The collection unit can collect activity details and location information of the resident. The collection unit, for example, collects information on what activities the resident is engaged in. For example, the collection unit collects data on whether the resident is exercising, eating, shopping, etc. The collection unit can also collect location information of the resident. For example, the collection unit uses GPS data or Wi-Fi location information to determine where the resident is located. This allows for more accurate determination of lifestyle scenes by collecting detailed data on the resident's activities and locations. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the resident's activity details and location information into AI, which then performs analysis.

[0031] The analysis unit can analyze the collected data and determine the life scene. For example, the analysis unit determines what life scene the consumer is currently in based on the collected data. For example, the analysis unit can analyze the data using statistical analysis and determine the life scene. The analysis unit can also analyze the data using a machine learning algorithm and determine the life scene. For example, the analysis unit inputs the collected data into a machine learning algorithm, and the algorithm determines the life scene. In this way, the life scene can be accurately determined by analyzing the collected data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into AI, and the AI ​​can determine the life scene.

[0032] The generation unit can generate advertisements suitable for the consumer based on the determined life scene. The generation unit generates advertisements that are most suitable for the consumer based on, for example, the determined life scene. For example, the generation unit generates advertisements for commuting-related products and services for a consumer commuting to work. The generation unit can also generate advertisements for relaxation-related products and services for a consumer relaxing at home. This improves the relevance of advertisements by generating optimal advertisements based on life scenes. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI (generation AI or LLM), or may be performed without using a generation AI. For example, the generation unit can input the determined life scene into the generation AI, which then generates the advertisement.

[0033] The distribution unit can distribute the generated advertisement based on the consumer's current lifestyle scene. The distribution unit, for example, distributes the generated advertisement in accordance with the consumer's current lifestyle scene. For example, if the consumer is commuting, the distribution unit can display an advertisement suitable for the commute on a smartphone. Furthermore, if the consumer is relaxing at home, the distribution unit can also display an advertisement suitable for relaxation on a computer or television. In this way, by distributing advertisements in accordance with lifestyle scenes, the engagement rate improves. Some or all of the above-described processing in the distribution unit may be performed using, or without, AI, for example. For example, the distribution unit can input the generated advertisement into AI, which then distributes the advertisement.

[0034] The collection unit can analyze the consumer's past behavioral history and select an appropriate data collection method. For example, the collection unit prioritizes data collection at places the consumer has frequently visited in the past. The collection unit can also select a relevant data collection method based on specific activities the consumer has performed in the past. Furthermore, the collection unit can analyze the consumer's past behavioral patterns and suggest the most efficient data collection method. This allows the optimal data collection method to be selected by analyzing the past behavioral history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the consumer's past behavioral history into AI, which can select the optimal data collection method.

[0035] The collection unit can filter data based on the consumer's current health condition and mood when collecting data. For example, if the consumer is in good health, the collection unit collects detailed data. Furthermore, if the consumer is in poor health, the collection unit can refrain from collecting data and attempt to collect again at a later date. Furthermore, if the consumer is in good mood, the collection unit can prioritize collecting positive data. This enables more appropriate data collection by filtering data based on the consumer's health condition and mood. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the consumer's health condition and mood data into AI, which can then filter the data.

[0036] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the resident. For example, when the resident is in a specific location, the collection unit prioritizes collecting data related to that location. Furthermore, when the resident is on the move, the collection unit can also collect data related to the resident's travel route. Furthermore, when the resident is at home, the collection unit can also collect data related to domestic activities. In this way, highly relevant data can be prioritized by taking the geographical location information into account. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the resident into AI, which can then prioritize collecting highly relevant data.

[0037] The collection unit can analyze the social media activities of consumers and collect related data when collecting data. For example, the collection unit collects related data based on information shared by consumers on social media. The collection unit can also analyze the activities of accounts followed by consumers on social media and collect related data. Furthermore, the collection unit can collect related data based on events in which consumers participate on social media. This allows for efficient collection of related data by analyzing social media activities. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the social media activity data of consumers into AI, which then collects related data.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data of high importance. The analysis unit can also perform a concise analysis on data of low importance. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail on data of medium importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to AI, which can then adjust the level of detail of the analysis.

[0039] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a behavior analysis algorithm to behavioral data of a resident. The analysis unit can also apply a location analysis algorithm to location data of a resident. Furthermore, the analysis unit can also apply a health analysis algorithm to health data of a resident. This improves the accuracy of analysis by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category into AI, which then applies an appropriate analysis algorithm.

[0040] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit, for example, prioritizes analysis of the most recent data. The analysis unit can also analyze current data while referring to past data. Furthermore, the analysis unit can prioritize analysis of data collected during a specific period. In this way, by determining the analysis priority based on the time when the data was collected, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into AI, and the AI ​​can determine the analysis priority.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI, which can then adjust the order of analysis.

[0042] The generation unit can adjust the level of detail of the advertisement based on the importance of the life scene when generating the advertisement. For example, the generation unit generates a detailed advertisement for a life scene with high importance. The generation unit can also generate a concise advertisement for a life scene with low importance. Furthermore, the generation unit can generate an advertisement with an appropriate level of detail for a life scene with medium importance. In this way, by adjusting the level of detail of the advertisement based on the importance of the life scene, an appropriate advertisement can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the importance of the life scene into AI, and the AI ​​can adjust the level of detail of the advertisement.

[0043] When generating an advertisement, the generation unit can apply different generation algorithms depending on the category of the life scene. For example, the generation unit can apply a commuting-related advertisement generation algorithm to a life scene during commuting. The generation unit can also apply a relaxation-related advertisement generation algorithm to a life scene during relaxation at home. Furthermore, the generation unit can also apply an outing-related advertisement generation algorithm to a life scene during outing. This makes it possible to generate an appropriate advertisement depending on the category of the life scene. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the category of the life scene into AI, which then applies an appropriate generation algorithm.

[0044] When generating advertisements, the generation unit can determine the priority of advertisements based on the time when the life scenes were collected. The generation unit, for example, generates advertisements based on the most recent life scenes. The generation unit can also generate advertisements based on current life scenes while referring to past life scenes. Furthermore, the generation unit can also generate advertisements based on life scenes collected during a specific period. In this way, by determining the priority of advertisements based on the time when they were collected, advertisements based on the most recent life scenes can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the time when the life scenes were collected into AI, which can then determine the priority of advertisements.

[0045] The generation unit can adjust the order of advertisements based on the relevance of life scenes when generating advertisements. The generation unit, for example, generates advertisements based on highly relevant life scenes. The generation unit can also generate advertisements based on less relevant life scenes. Furthermore, the generation unit can dynamically adjust the order of advertisements according to the relevance of life scenes. This allows the order of advertisements to be adjusted based on relevance, thereby generating advertisements that are optimal for consumers. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the relevance of life scenes into AI, which can then adjust the order of advertisements.

[0046] When delivering an ad, the delivery unit can select the optimal delivery method by analyzing the consumer's past ad response history. For example, the delivery unit prioritizes delivery methods for ads to which the consumer has responded favorably in the past. The delivery unit can also avoid delivery methods for ads that the consumer has ignored in the past. Furthermore, the delivery unit can analyze the consumer's past ad response history and suggest the most effective delivery method. In this way, the optimal delivery method can be selected by analyzing the past ad response history. Some or all of the above-mentioned processing in the delivery unit may be performed using, for example, AI, or may be performed without using AI. For example, the delivery unit can input the consumer's past ad response history into AI, which can select the optimal delivery method.

[0047] The distribution unit can customize the distribution means based on the consumer's current activity status when distributing advertisements. For example, if the consumer is on the move, the distribution unit distributes advertisements to a smartphone. If the consumer is at home, the distribution unit can also distribute advertisements to a television or computer. Furthermore, if the consumer is out, the distribution unit can also distribute advertisements to digital signage. In this way, by customizing the distribution means based on the consumer's current activity status, advertisements can be delivered in a manner optimal for the consumer. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input the consumer's current activity status into AI, which then customizes the distribution means.

[0048] When delivering advertisements, the delivery unit can select the optimal delivery method by taking into account the geographical location information of the consumer. For example, if the consumer is in a specific location, the delivery unit delivers an advertisement related to that location. Furthermore, if the consumer is on the move, the delivery unit can also deliver an advertisement related to the consumer's route. Furthermore, if the consumer is at home, the delivery unit can also deliver an advertisement related to domestic activities. In this way, the optimal delivery method can be selected by taking into account the geographical location information. Some or all of the above-mentioned processing in the delivery unit may be performed using, for example, AI, or may be performed without using AI. For example, the delivery unit can input the consumer's geographical location information into AI, which can then select the optimal delivery method.

[0049] When delivering advertisements, the distribution unit can analyze consumers' social media activities and suggest distribution methods. For example, the distribution unit distributes relevant advertisements based on information shared by consumers on social media. The distribution unit can also analyze the activities of accounts followed by consumers on social media and distribute relevant advertisements. Furthermore, the distribution unit can distribute relevant advertisements based on events in which consumers are participating on social media. In this way, by analyzing social media activities, the optimal distribution method can be proposed. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input consumers' social media activity data into AI, which then proposes the optimal distribution method.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The advertisement targeting system may further include a purchase history analysis unit that analyzes a user's purchase history. The purchase history analysis unit collects data on products and services purchased by the user in the past and analyzes the user's purchasing trends based on this data. For example, it can identify product categories that the user has frequently purchased in the past and generate advertisements related to those categories. The purchase history analysis unit can also identify products that the user tends to purchase in connection with specific seasons or events and generate advertisements tailored to those timings. Furthermore, the purchase history analysis unit can generate advertisements that recommend products that are compatible with products the user has purchased in the past. This makes it possible to provide more personalized advertisements based on the user's purchase history.

[0052] The collection unit can analyze the user's social media activity to understand the user's interests. For example, the collection unit can analyze posts that the user has "liked" or "shared" on social media to identify the user's interests. The collection unit can also analyze the accounts the user follows and groups the user joins to understand the user's areas of interest. Furthermore, the collection unit can analyze hashtags the user uses on social media to identify topics of interest to the user. This makes it possible to generate more relevant advertisements based on the user's social media activity.

[0053] The generation unit can analyze a user's past ad click history and extract characteristics of ads with high click rates. For example, the generation unit can analyze the design and message of ads that the user has clicked on in the past and generate a new ad based on this. The generation unit can also identify ads that users tend to click during specific time periods and generate ads tailored to those time periods. Furthermore, the generation unit can identify ads that users tend to click on using specific devices and generate ads optimized for those devices. This makes it possible to provide more effective ads based on the user's ad click history.

[0054] The collection unit can collect the user's health data and generate advertisements based on the user's health condition. For example, if the user is exercising, advertisements for sports equipment and health foods can be generated. If the user is in poor health, advertisements for medicines and health management services can be generated. Furthermore, if the user provides data based on the results of a health check, advertisements for products and services useful for improving health can be generated based on the results. This makes it possible to increase the relevance of advertisements by providing advertisements that are tailored to the user's health condition.

[0055] The analysis unit can analyze the user's past behavioral history and customize the content of advertisements based on the user's behavioral patterns. For example, it can generate advertisements related to places the user has frequently visited in the past. It can also generate advertisements appropriate for a specific time period based on the user's activities during that time period. It can also generate advertisements tailored to the timing of the user's activities related to a specific season or event. This makes it possible to provide more personalized advertisements based on the user's behavioral history.

[0056] The distribution unit can customize the advertisement distribution method by taking into account the user's geographical location information. For example, if the user is in a specific location, advertisements related to that location can be distributed. If the user is traveling, advertisements related to the user's route can be distributed. Furthermore, if the user is at home, advertisements related to activities at home can be distributed. In this way, by taking into account the geographical location information, advertisements can be distributed in the most optimal way for the user.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The collection unit collects data related to lifestyle scenes. Data related to lifestyle scenes includes detailed data such as what activities consumers are engaged in and where they are located. For example, the collection unit collects data such as whether consumers are commuting to work, relaxing at home, exercising, eating, or shopping. Step 2: The analysis unit analyzes the data collected by the collection unit and determines the lifestyle scene. Statistical analysis and machine learning algorithms are used for the analysis. For example, the analysis unit determines the lifestyle scene the consumer is currently in based on the collected data. Step 3: The generation unit generates advertisements based on the lifestyle scenes determined by the analysis unit. A generation AI (generative AI or LLM) is used for generation. For example, the generation unit generates advertisements for commuting-related products and services for people commuting, and for people relaxing at home, generates advertisements for relaxation-related products and services. Step 4: The distribution unit distributes the advertisements generated by the generation unit according to the consumer's current lifestyle. Devices such as smartphones, PCs, and televisions are used for distribution. For example, if the consumer is commuting, an advertisement suitable for commuting is displayed on the smartphone, and if the consumer is relaxing at home, an advertisement suitable for relaxation is displayed on the PC or television.

[0059] (Example 2) An advertising targeting system according to an embodiment of the present invention targets advertising based on "life scenes" rather than on consumers' "attributes." This advertising targeting system collects data on consumers' life scenes, analyzes the collected data, generates advertisements based on the life scenes, and delivers the generated advertisements tailored to the consumers' current life scenes. For example, the advertising targeting system collects detailed data, such as the activities consumers engage in and their locations. This allows the system to understand the consumers' current life scenes. The system then analyzes the collected data and generates advertisements based on the life scenes. AI is used for the analysis to select advertisements that are most suited to the consumers' current life scenes. For example, for a consumer commuting to work, advertisements for commuting-related products and services can be generated. This generates advertisements that are highly relevant to the consumers. The generated advertisements are delivered tailored to the consumers' current life scenes. For example, if a consumer is commuting to work, an advertisement appropriate for the commute is displayed on the consumer's smartphone. This allows advertisements to be displayed at an appropriate time for the consumer, improving engagement rates. This system reduces advertisements that consumers find annoying and improves advertising effectiveness. When consumers receive advertisements that fit their lifestyles, they become more interested in the advertisements and their engagement rate improves. For example, if a consumer is relaxing at home, they will be shown advertisements for products and services related to relaxation, which is likely to lead to a positive response from the consumer. This allows the ad targeting system to target advertisements based on the consumer's lifestyle, thereby improving their engagement rate.

[0060] An advertisement targeting system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a distribution unit. The collection unit collects data related to lifestyle scenes. The data related to lifestyle scenes includes, for example, detailed data on what activities a consumer is engaged in and where they are located, but is not limited to such examples. The collection unit collects data, for example, on whether the consumer is commuting to work or relaxing at home. The collection unit can also collect activity details and location information of the consumer. For example, the collection unit collects data on whether the consumer is exercising, eating, shopping, etc. The analysis unit analyzes the data collected by the collection unit to determine the lifestyle scene. For example, statistical analysis or a machine learning algorithm is used for the analysis, but is not limited to such examples. For example, the analysis unit determines what lifestyle scene the consumer is currently in based on the collected data. The generation unit generates an advertisement based on the lifestyle scene determined by the analysis unit. For example, a generation AI (generation AI or LLM) is used for the generation, but is not limited to such examples. For example, the generation unit generates an advertisement for commuting-related products and services for a consumer commuting to work. Furthermore, the generation unit can generate advertisements for products and services related to relaxation for consumers who are relaxing at home. The distribution unit distributes the advertisements generated by the generation unit in accordance with the consumer's current lifestyle. Devices such as, but not limited to, smartphones, personal computers, and televisions are used for distribution. For example, if a consumer is commuting to work, the distribution unit can display an advertisement suitable for commuting on a smartphone. Furthermore, if a consumer is relaxing at home, the distribution unit can display an advertisement suitable for relaxation on a personal computer or television. As a result, the advertisement targeting system according to the embodiment can improve the engagement rate by targeting advertisements based on the consumer's lifestyle.

[0061] The collection unit can collect activity details and location information of the resident. The collection unit, for example, collects information on what activities the resident is engaged in. For example, the collection unit collects data on whether the resident is exercising, eating, shopping, etc. The collection unit can also collect location information of the resident. For example, the collection unit uses GPS data or Wi-Fi location information to determine where the resident is located. This allows for more accurate determination of lifestyle scenes by collecting detailed data on the resident's activities and locations. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the resident's activity details and location information into AI, which then performs analysis.

[0062] The analysis unit can analyze the collected data and determine the life scene. For example, the analysis unit determines what life scene the consumer is currently in based on the collected data. For example, the analysis unit can analyze the data using statistical analysis and determine the life scene. The analysis unit can also analyze the data using a machine learning algorithm and determine the life scene. For example, the analysis unit inputs the collected data into a machine learning algorithm, and the algorithm determines the life scene. In this way, the life scene can be accurately determined by analyzing the collected data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into AI, and the AI ​​can determine the life scene.

[0063] The generation unit can generate advertisements suitable for the consumer based on the determined life scene. The generation unit generates advertisements that are most suitable for the consumer based on, for example, the determined life scene. For example, the generation unit generates advertisements for commuting-related products and services for a consumer commuting to work. The generation unit can also generate advertisements for relaxation-related products and services for a consumer relaxing at home. This improves the relevance of advertisements by generating optimal advertisements based on life scenes. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI (generation AI or LLM), or may be performed without using a generation AI. For example, the generation unit can input the determined life scene into the generation AI, which then generates the advertisement.

[0064] The distribution unit can distribute the generated advertisement based on the consumer's current lifestyle scene. The distribution unit, for example, distributes the generated advertisement in accordance with the consumer's current lifestyle scene. For example, if the consumer is commuting, the distribution unit can display an advertisement suitable for the commute on a smartphone. Furthermore, if the consumer is relaxing at home, the distribution unit can also display an advertisement suitable for relaxation on a computer or television. In this way, by distributing advertisements in accordance with lifestyle scenes, the engagement rate improves. Some or all of the above-described processing in the distribution unit may be performed using, or without, AI, for example. For example, the distribution unit can input the generated advertisement into AI, which then distributes the advertisement.

[0065] The collection unit can estimate the emotions of the resident and adjust the timing of data collection based on the estimated emotions of the resident. For example, if the resident is stressed, the collection unit refrains from collecting data and collects data when the resident is relaxed. Furthermore, if the resident is excited, the collection unit can immediately collect data and perform real-time analysis. Furthermore, if the resident is tired, the collection unit can delay data collection and resume collection after the resident has rested. This allows for more appropriate data collection by adjusting the timing of data collection based on the resident's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or without an AI. For example, the collection unit can input the resident's emotion data into an AI, which can then adjust the timing of data collection.

[0066] The collection unit can analyze the consumer's past behavioral history and select an appropriate data collection method. For example, the collection unit prioritizes data collection at places the consumer has frequently visited in the past. The collection unit can also select a relevant data collection method based on specific activities the consumer has performed in the past. Furthermore, the collection unit can analyze the consumer's past behavioral patterns and suggest the most efficient data collection method. This allows the optimal data collection method to be selected by analyzing the past behavioral history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the consumer's past behavioral history into AI, which can select the optimal data collection method.

[0067] The collection unit can filter data based on the consumer's current health condition and mood when collecting data. For example, if the consumer is in good health, the collection unit collects detailed data. Furthermore, if the consumer is in poor health, the collection unit can refrain from collecting data and attempt to collect again at a later date. Furthermore, if the consumer is in good mood, the collection unit can prioritize collecting positive data. This enables more appropriate data collection by filtering data based on the consumer's health condition and mood. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the consumer's health condition and mood data into AI, which can then filter the data.

[0068] The collection unit can estimate the emotions of the resident and determine the priority of data to be collected based on the estimated emotions of the resident. For example, if the resident is relaxed, the collection unit can prioritize collecting detailed data. Furthermore, if the resident is stressed, the collection unit can also prioritize collecting only basic data. Furthermore, if the resident is excited, the collection unit can prioritize real-time data collection. Thus, by determining the priority of data based on emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the resident's emotion data into AI, which can then determine the priority of the data.

[0069] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the resident. For example, when the resident is in a specific location, the collection unit prioritizes collecting data related to that location. Furthermore, when the resident is on the move, the collection unit can also collect data related to the resident's travel route. Furthermore, when the resident is at home, the collection unit can also collect data related to domestic activities. In this way, highly relevant data can be prioritized by taking the geographical location information into account. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the resident into AI, which can then prioritize collecting highly relevant data.

[0070] The collection unit can analyze the social media activities of consumers and collect related data when collecting data. For example, the collection unit collects related data based on information shared by consumers on social media. The collection unit can also analyze the activities of accounts followed by consumers on social media and collect related data. Furthermore, the collection unit can collect related data based on events in which consumers participate on social media. This allows for efficient collection of related data by analyzing social media activities. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the social media activity data of consumers into AI, which then collects related data.

[0071] The analysis unit can estimate the consumer's emotions and adjust the presentation method of the analysis based on the estimated consumer's emotions. For example, if the consumer is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the consumer is stressed, the analysis unit can provide concise analysis results. Furthermore, if the consumer is excited, the analysis unit can provide visually stimulating analysis results. By adjusting the presentation method of the analysis based on emotions, it is possible to provide analysis results that are easy for consumers to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input consumer emotion data into AI, which can then adjust the presentation method of the analysis.

[0072] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data of high importance. The analysis unit can also perform a concise analysis on data of low importance. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail on data of medium importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to AI, which can then adjust the level of detail of the analysis.

[0073] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a behavior analysis algorithm to behavioral data of a resident. The analysis unit can also apply a location analysis algorithm to location data of a resident. Furthermore, the analysis unit can also apply a health analysis algorithm to health data of a resident. This improves the accuracy of analysis by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category into AI, which then applies an appropriate analysis algorithm.

[0074] The analysis unit can estimate the consumer's emotions and adjust the length of the analysis based on the estimated consumer's emotions. For example, the analysis unit can perform a detailed analysis when the consumer is relaxed. The analysis unit can also perform a brief analysis when the consumer is stressed. Furthermore, the analysis unit can perform a visually stimulating analysis when the consumer is excited. By adjusting the length of the analysis based on the consumer's emotions, it is possible to provide an appropriate analysis result for the consumer. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the consumer's emotion data into an AI, which can then adjust the length of the analysis.

[0075] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit, for example, prioritizes analysis of the most recent data. The analysis unit can also analyze current data while referring to past data. Furthermore, the analysis unit can prioritize analysis of data collected during a specific period. In this way, by determining the analysis priority based on the time when the data was collected, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into AI, and the AI ​​can determine the analysis priority.

[0076] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI, which can then adjust the order of analysis.

[0077] The generation unit can estimate the consumer's emotions and adjust the advertisement generation method based on the estimated consumer's emotions. For example, if the consumer is relaxed, the generation unit can generate an advertisement with a calm tone. Furthermore, if the consumer is stressed, the generation unit can generate a simple, visually less daunting advertisement. Furthermore, if the consumer is excited, the generation unit can generate a visually stimulating advertisement. By adjusting the advertisement generation method based on emotions, an advertisement appropriate for the consumer can be generated. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input consumer emotion data into AI, which can then adjust the advertisement generation method.

[0078] The generation unit can adjust the level of detail of the advertisement based on the importance of the life scene when generating the advertisement. For example, the generation unit generates a detailed advertisement for a life scene with high importance. The generation unit can also generate a concise advertisement for a life scene with low importance. Furthermore, the generation unit can generate an advertisement with an appropriate level of detail for a life scene with medium importance. In this way, by adjusting the level of detail of the advertisement based on the importance of the life scene, an appropriate advertisement can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the importance of the life scene into AI, and the AI ​​can adjust the level of detail of the advertisement.

[0079] When generating an advertisement, the generation unit can apply different generation algorithms depending on the category of the life scene. For example, the generation unit can apply a commuting-related advertisement generation algorithm to a life scene during commuting. The generation unit can also apply a relaxation-related advertisement generation algorithm to a life scene during relaxation at home. Furthermore, the generation unit can also apply an outing-related advertisement generation algorithm to a life scene during outing. This makes it possible to generate an appropriate advertisement depending on the category of the life scene. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the category of the life scene into AI, which then applies an appropriate generation algorithm.

[0080] The generation unit can estimate the consumer's emotions and adjust the length of the advertisement based on the estimated consumer's emotions. For example, if the consumer is relaxed, the generation unit can generate a longer advertisement. Furthermore, if the consumer is stressed, the generation unit can generate a short, to-the-point advertisement. Furthermore, if the consumer is excited, the generation unit can generate a visually stimulating advertisement. By adjusting the length of the advertisement based on the consumer's emotions, an appropriate advertisement can be generated. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input consumer emotion data into AI, which can then adjust the length of the advertisement.

[0081] When generating advertisements, the generation unit can determine the priority of advertisements based on the time when the life scenes were collected. The generation unit, for example, generates advertisements based on the most recent life scenes. The generation unit can also generate advertisements based on current life scenes while referring to past life scenes. Furthermore, the generation unit can also generate advertisements based on life scenes collected during a specific period. In this way, by determining the priority of advertisements based on the time when they were collected, advertisements based on the most recent life scenes can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the time when the life scenes were collected into AI, which can then determine the priority of advertisements.

[0082] The generation unit can adjust the order of advertisements based on the relevance of life scenes when generating advertisements. The generation unit, for example, generates advertisements based on highly relevant life scenes. The generation unit can also generate advertisements based on less relevant life scenes. Furthermore, the generation unit can dynamically adjust the order of advertisements according to the relevance of life scenes. This allows the order of advertisements to be adjusted based on relevance, thereby generating advertisements that are optimal for consumers. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the relevance of life scenes into AI, which can then adjust the order of advertisements.

[0083] The distribution unit can estimate the consumer's emotions and adjust the timing of advertisement delivery based on the estimated consumer's emotions. For example, if the consumer is relaxed, the distribution unit can immediately deliver an advertisement. Furthermore, if the consumer is feeling stressed, the distribution unit can refrain from delivering an advertisement and deliver it at a time when the consumer is relaxed. Furthermore, if the consumer is excited, the distribution unit can deliver an advertisement in real time. This allows the advertisement to be delivered at the optimal timing for the consumer by adjusting the advertisement delivery timing based on the consumer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the distribution unit may be performed using an AI, for example, or without an AI. For example, the distribution unit can input consumer emotion data into an AI, which can then adjust the advertisement delivery timing.

[0084] When delivering an ad, the delivery unit can select the optimal delivery method by analyzing the consumer's past ad response history. For example, the delivery unit prioritizes delivery methods for ads to which the consumer has responded favorably in the past. The delivery unit can also avoid delivery methods for ads that the consumer has ignored in the past. Furthermore, the delivery unit can analyze the consumer's past ad response history and suggest the most effective delivery method. In this way, the optimal delivery method can be selected by analyzing the past ad response history. Some or all of the above-mentioned processing in the delivery unit may be performed using, for example, AI, or may be performed without using AI. For example, the delivery unit can input the consumer's past ad response history into AI, which can select the optimal delivery method.

[0085] The distribution unit can customize the distribution means based on the consumer's current activity status when distributing advertisements. For example, if the consumer is on the move, the distribution unit distributes advertisements to a smartphone. If the consumer is at home, the distribution unit can also distribute advertisements to a television or computer. Furthermore, if the consumer is out, the distribution unit can also distribute advertisements to digital signage. In this way, by customizing the distribution means based on the consumer's current activity status, advertisements can be delivered in a manner optimal for the consumer. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input the consumer's current activity status into AI, which then customizes the distribution means.

[0086] The distribution unit can estimate the consumer's emotions and determine the priority of advertisement delivery based on the estimated consumer's emotions. For example, if the consumer is relaxed, the distribution unit can prioritize delivery of highly relevant advertisements. Furthermore, if the consumer is stressed, the distribution unit can prioritize delivery of less visually invasive advertisements. Furthermore, if the consumer is excited, the distribution unit can prioritize delivery of visually stimulating advertisements. Thus, by determining the priority of advertisement delivery based on emotions, advertisements optimal for consumers can be delivered preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the distribution unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the distribution unit can input consumer emotion data into an AI, which then determines the priority of advertisement delivery.

[0087] When delivering advertisements, the delivery unit can select the optimal delivery method by taking into account the geographical location information of the consumer. For example, if the consumer is in a specific location, the delivery unit delivers an advertisement related to that location. Furthermore, if the consumer is on the move, the delivery unit can also deliver an advertisement related to the consumer's route. Furthermore, if the consumer is at home, the delivery unit can also deliver an advertisement related to domestic activities. In this way, the optimal delivery method can be selected by taking into account the geographical location information. Some or all of the above-mentioned processing in the delivery unit may be performed using, for example, AI, or may be performed without using AI. For example, the delivery unit can input the consumer's geographical location information into AI, which can then select the optimal delivery method.

[0088] When delivering advertisements, the distribution unit can analyze consumers' social media activities and suggest distribution methods. For example, the distribution unit distributes relevant advertisements based on information shared by consumers on social media. The distribution unit can also analyze the activities of accounts followed by consumers on social media and distribute relevant advertisements. Furthermore, the distribution unit can distribute relevant advertisements based on events in which consumers are participating on social media. In this way, by analyzing social media activities, the optimal distribution method can be proposed. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input consumers' social media activity data into AI, which then proposes the optimal distribution method. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and distribution unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects activity and location information of a resident using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to determine a lifestyle scene. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an advertisement based on the determined lifestyle scene. The distribution unit is realized, for example, by the control unit 46A of the smart device 14, and distributes the generated advertisement tailored to the resident's current lifestyle scene. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and distribution unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects activity and location information of a resident using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to determine a lifestyle scene. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an advertisement based on the determined lifestyle scene. The distribution unit is realized, for example, by the control unit 46A of the smart glasses 214, and distributes the generated advertisement tailored to the resident's current lifestyle scene. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and distribution unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects activity and location information of a resident using the camera 42 and microphone 238 of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to determine a life scene. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an advertisement based on the determined life scene. The distribution unit is realized, for example, by the control unit 46A of the headset type terminal 314, and distributes the generated advertisement tailored to the resident's current life scene. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and distribution unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects activity and location information of a resident using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to determine a lifestyle scene. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an advertisement based on the determined lifestyle scene. The distribution unit is realized, for example, by the control unit 46A of the robot 414, and distributes the generated advertisement tailored to the resident's current lifestyle scene.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The advertisement targeting system may further include a purchase history analysis unit that analyzes a user's purchase history. The purchase history analysis unit collects data on products and services purchased by the user in the past and analyzes the user's purchasing trends based on this data. For example, it can identify product categories that the user has frequently purchased in the past and generate advertisements related to those categories. The purchase history analysis unit can also identify products that the user tends to purchase in connection with specific seasons or events and generate advertisements tailored to those timings. Furthermore, the purchase history analysis unit can generate advertisements that recommend products that are compatible with products the user has purchased in the past. This makes it possible to provide more personalized advertisements based on the user's purchase history.

[0091] The collection unit can analyze the user's social media activity to understand the user's interests. For example, the collection unit can analyze posts that the user has "liked" or "shared" on social media to identify the user's interests. The collection unit can also analyze the accounts the user follows and groups the user joins to understand the user's areas of interest. Furthermore, the collection unit can analyze hashtags the user uses on social media to identify topics of interest to the user. This makes it possible to generate more relevant advertisements based on the user's social media activity.

[0092] The analysis unit can estimate the user's emotions and adjust the content of advertisements based on the estimated emotions. For example, if the user is feeling stressed, an advertisement for a product or service with a relaxing effect can be generated. If the user is excited, an advertisement with a high level of entertainment can be generated. Furthermore, if the user is sad, an advertisement that brightens the mood can be generated. This makes it possible to increase the effectiveness of advertisements by providing advertisements that correspond to the user's emotions.

[0093] The generation unit can analyze a user's past ad click history and extract characteristics of ads with high click rates. For example, the generation unit can analyze the design and message of ads that the user has clicked on in the past and generate a new ad based on this. The generation unit can also identify ads that users tend to click during specific time periods and generate ads tailored to those time periods. Furthermore, the generation unit can identify ads that users tend to click on using specific devices and generate ads optimized for those devices. This makes it possible to provide more effective ads based on the user's ad click history.

[0094] The distribution unit can estimate the user's emotions and adjust the timing of advertisement delivery based on the estimated emotions. For example, if the user is relaxed, the advertisement can be delivered immediately. Also, if the user is feeling stressed, the advertisement delivery can be refrained from and delivered at a time when the user is relaxed. Furthermore, if the user is excited, the advertisement can be delivered in real time. In this way, by adjusting the advertisement delivery timing based on emotions, the advertisement can be delivered at the optimal timing for the user.

[0095] The collection unit can collect the user's health data and generate advertisements based on the user's health condition. For example, if the user is exercising, advertisements for sports equipment and health foods can be generated. If the user is in poor health, advertisements for medicines and health management services can be generated. Furthermore, if the user provides data based on the results of a health check, advertisements for products and services useful for improving health can be generated based on the results. This makes it possible to increase the relevance of advertisements by providing advertisements that are tailored to the user's health condition.

[0096] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is feeling stressed, data collection can be refrained from and collected when the user is relaxed. Also, if the user is excited, data can be collected immediately and analyzed in real time. Furthermore, if the user is tired, data collection can be delayed and resumed after the user has rested. This allows for more appropriate data collection by adjusting the timing of data collection based on the user's emotions.

[0097] The analysis unit can analyze the user's past behavioral history and customize the content of advertisements based on the user's behavioral patterns. For example, it can generate advertisements related to places the user has frequently visited in the past. It can also generate advertisements appropriate for a specific time period based on the user's activities during that time period. It can also generate advertisements tailored to the timing of the user's activities related to a specific season or event. This makes it possible to provide more personalized advertisements based on the user's behavioral history.

[0098] The generation unit can estimate the user's emotions and adjust the design of the advertisement based on the estimated emotions. For example, if the user is relaxed, an advertisement with a calm tone can be generated. Also, if the user is stressed, a simple and visually less invasive advertisement can be generated. Furthermore, if the user is excited, a visually stimulating advertisement can be generated. In this way, by adjusting the design of the advertisement based on emotions, it is possible to generate an advertisement appropriate for the user.

[0099] The distribution unit can customize the advertisement distribution method by taking into account the user's geographical location information. For example, if the user is in a specific location, advertisements related to that location can be distributed. If the user is traveling, advertisements related to the user's route can be distributed. Furthermore, if the user is at home, advertisements related to activities at home can be distributed. In this way, by taking into account the geographical location information, advertisements can be distributed in the most optimal way for the user.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The collection unit collects data related to lifestyle scenes. Data related to lifestyle scenes includes detailed data such as what activities consumers are engaged in and where they are located. For example, the collection unit collects data such as whether consumers are commuting to work, relaxing at home, exercising, eating, or shopping. Step 2: The analysis unit analyzes the data collected by the collection unit and determines the lifestyle scene. Statistical analysis and machine learning algorithms are used for the analysis. For example, the analysis unit determines the lifestyle scene the consumer is currently in based on the collected data. Step 3: The generation unit generates advertisements based on the lifestyle scenes determined by the analysis unit. A generation AI (generative AI or LLM) is used for generation. For example, the generation unit generates advertisements for commuting-related products and services for people commuting, and for people relaxing at home, generates advertisements for relaxation-related products and services. Step 4: The distribution unit distributes the advertisements generated by the generation unit according to the consumer's current lifestyle. Devices such as smartphones, PCs, and televisions are used for distribution. For example, if the consumer is commuting, an advertisement suitable for commuting is displayed on the smartphone, and if the consumer is relaxing at home, an advertisement suitable for relaxation is displayed on the PC or television.

[0102] 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.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0104] 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.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] [Explanation of symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data related to life scenes; an analysis unit that analyzes the data collected by the collection unit and determines a living scene; a generation unit that generates an advertisement based on the life scene determined by the analysis unit; a distribution unit that distributes the advertisement generated by the generation unit. A system characterized by:

2. The collecting unit Collecting consumer activity and location information The system of claim 1 .

3. The analysis unit Analyzing collected data and determining living situations The system of claim 1 .

4. The generation unit Generate advertisements that are appropriate for each individual based on the lifestyle scenarios identified The system of claim 1 .

5. The distribution unit The generated advertisements are delivered based on the consumer's current lifestyle. The system of claim 1 .

6. The collecting unit Estimate consumer sentiment and adjust data collection timing based on the estimated consumer sentiment. The system of claim 1 .

7. The collecting unit Analyze consumers' past behavioral history and select the appropriate data collection method The system of claim 1 .

8. The collecting unit When collecting data, filter it based on the consumer's current health status or mood. The system of claim 1 .

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