System

Abstract

An object of a system according to an embodiment is to customize an advertisement by utilizing geographical location information of a user and a trend of a region.SOLUTION: A system includes a position information acquisition part, a trend data collection part, a data analysis part, an advertisement generation part, and an advertisement provision part. The position information acquisition unit acquires geographical position information of a user. The trend data collection part collects trend data of an area. The data analyzer analyzes the data acquired by the position information acquirer and the trend data collector. The advertisement generation unit generates an advertisement based on the data analyzed by the data analysis unit. The advertisement providing unit provides the advertisement generated by the advertisement generation unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately customize advertisements by effectively utilizing users' geographic location information and local trends, and there is room for improvement.

[0005] The system according to the embodiment aims to customize advertisements by utilizing the geographic location information of the user and local trends. [Means for solving the problem]

[0006] The system according to the embodiment includes a location information acquisition unit, a trend data collection unit, a data analysis unit, an advertisement generation unit, and an advertisement provision unit. The location information acquisition unit acquires geographical location information of a user. The trend data collection unit collects regional trend data. The data analysis unit analyzes the data acquired by the location information acquisition unit and the trend data collection unit. The advertisement generation unit generates an advertisement based on the data analyzed by the data analysis unit. The advertisement provision unit provides the advertisement generated by the advertisement generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can utilize the geographic location information of the user and local trends to customize advertisements. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An advertisement customization system according to an embodiment of the present invention customizes and provides advertisements in real time based on a user's geographic location information and regional trend data. The advertisement customization system acquires the user's geographic location information, collects regional trend data, and uses a generation AI to analyze the data to generate advertisements and provide them to the user. For example, the advertisement customization system acquires location information from the user's smartphone or GPS device. The advertisement customization system then collects trend data from social media sites, news sites, local event information, and the like. The generation AI analyzes the collected geographic location information and trend data to generate advertisements based on the user's current location and regional trends. The generated advertisements are provided to the user in real time. This allows businesses to deliver advertisements tailored to local characteristics, contributing to the development of local economies and the improvement of customer satisfaction. The advertisement customization system can customize and provide advertisements in real time based on the user's geographic location information and regional trend data. For example, if a user is in a specific region, advertisements for products and services popular in that region are displayed. This mechanism allows businesses to deliver advertisements tailored to local characteristics, contributing to the development of local economies and the improvement of customer satisfaction.

[0029] An advertisement customization system according to an embodiment includes a location information acquisition unit, a trend data collection unit, a data analysis unit, an advertisement generation unit, and an advertisement provision unit. The location information acquisition unit acquires geographical location information of a user. For example, the location information acquisition unit can acquire location information from a user's smartphone or GPS device. The location information acquisition unit can also acquire location information using Wi-Fi location information or cell tower data. The trend data collection unit collects local trend data. For example, the trend data collection unit can collect trend data from social media sites, news sites, local event information, etc. The trend data collection unit can also collect data from local bulletin boards and online forums. The data analysis unit analyzes the collected geographical location information and trend data using a generation AI. For example, the data analysis unit can analyze data using a deep learning model or natural language processing technology. The data analysis unit can also analyze data patterns using a machine learning algorithm. The advertisement generation unit generates an advertisement based on the data analyzed by the data analysis unit. For example, the advertisement generation unit can generate an advertisement based on the user's current location and local trends using a generation AI. The advertisement generation unit may also generate advertisements in the form of banner advertisements, text advertisements, video advertisements, etc. The advertisement provision unit provides the generated advertisements to users in real time. For example, the advertisement provision unit may display the generated advertisements on a smartphone screen. The advertisement provision unit may also provide advertisements using push notification or real-time delivery. As a result, the advertisement customization system according to the embodiment may customize and provide advertisements in real time based on the user's geographical location information and regional trend data.

[0030] The location information acquisition unit can acquire location information from a user's smartphone or GPS device. The location information acquisition unit acquires location information from, for example, the user's smartphone. For example, the location information acquisition unit can acquire location information using the GPS function of the smartphone. The location information acquisition unit can also acquire location information from the user's GPS device. For example, the location information acquisition unit can acquire location information from a dedicated GPS tracker or an in-vehicle GPS system. In this way, accurate geographical location information can be obtained by acquiring location information from the user's smartphone or GPS device.

[0031] The trend data collection unit can collect trend data from social media platforms, news sites, and local event information. The trend data collection unit collects trend data from, for example, social media platforms. For example, the trend data collection unit can collect data from social media platforms such as Twitter (registered trademark), Facebookr (registered trademark), and Instagramr (registered trademark). The trend data collection unit can also collect trend data from news sites. For example, the trend data collection unit can collect data from online news portals and local news sites. The trend data collection unit can also collect trend data from local event information. For example, the trend data collection unit can collect data from event calendars and local bulletin boards. In this way, by collecting trend data from social media platforms, news sites, local event information, etc., it is possible to grasp the latest local trends.

[0032] The data analysis unit can analyze the collected geographical location information and trend data using the generative AI. For example, the data analysis unit analyzes the collected geographical location information and trend data using the generative AI. For example, the data analysis unit can analyze the data using a deep learning model. The data analysis unit can also analyze the data using natural language processing technology. For example, the data analysis unit can analyze the collected text data and extract trends. The data analysis unit can also analyze data patterns using a machine learning algorithm. For example, the data analysis unit can cluster the collected data and identify trends by region. As a result, the use of the generative AI improves the accuracy of the analysis of the geographical location information and trend data.

[0033] The advertisement generation unit can generate advertisements based on the user's current location and regional trends using a generation AI. The advertisement generation unit generates advertisements based on, for example, the user's current location and regional trends using a generation AI. For example, the advertisement generation unit can generate advertisements using a deep learning model. The advertisement generation unit can also generate advertisements using natural language processing technology. For example, the advertisement generation unit can analyze collected text data and generate advertisement content. The advertisement generation unit can also generate advertisements in the form of banner advertisements, text advertisements, video advertisements, etc. For example, the advertisement generation unit can generate advertisements related to local specialties and event information based on the user's current location. This makes it possible to effectively generate advertisements based on the user's current location and regional trends by using a generation AI.

[0034] The advertisement providing unit can instantly provide the generated advertisement to the user. The advertisement providing unit can, for example, instantly provide the generated advertisement to the user. For example, the advertisement providing unit can display the generated advertisement on a smartphone screen. The advertisement providing unit can also provide the advertisement using a push notification. For example, the advertisement providing unit can deliver the generated advertisement in real time and instantly provide it to the user. The advertisement providing unit can also provide the advertisement through a web browser or a mobile app. For example, the advertisement providing unit can display the generated advertisement while the user is browsing a website. In this way, the generated advertisement can be provided in real time, thereby delivering a timely advertisement to the user.

[0035] The location information acquisition unit can analyze the user's past movement history and select an appropriate location information acquisition method. The location information acquisition unit, for example, analyzes the user's past movement history and selects an appropriate location information acquisition method. For example, the location information acquisition unit can adjust the frequency of location information acquisition based on places the user has frequently visited in the past. The location information acquisition unit can also analyze the user's movement pattern and acquire location information at the optimal timing. For example, the location information acquisition unit can focus on acquiring location information during a specific time period based on the user's past movement history. This makes it possible to select the optimal location information acquisition method and efficiently acquire location information by analyzing the user's past movement history.

[0036] The location information acquisition unit can perform filtering based on the user's current activity status and areas of interest when acquiring location information. For example, the location information acquisition unit can perform filtering based on the user's current activity status and areas of interest when acquiring location information. For example, when the user is shopping, the location information acquisition unit can prioritize acquiring location information of shopping areas. Furthermore, when the user is sightseeing, the location information acquisition unit can also prioritize acquiring location information of tourist spots. For example, when the user is commuting, the location information acquisition unit can prioritize acquiring location information of the commuting route. In this way, by filtering location information based on the user's current activity status and areas of interest, more relevant location information can be acquired.

[0037] The location information acquisition unit can select an appropriate acquisition means depending on the user's input method when acquiring location information. For example, the location information acquisition unit can select an appropriate acquisition means depending on the user's input method when acquiring location information. For example, if the user is using voice input, the location information acquisition unit can acquire location information using voice recognition technology. Also, if the user is using text input, the location information acquisition unit can acquire location information using text analysis technology. For example, if the user is using image input, the location information acquisition unit can acquire location information using image recognition technology. In this way, by selecting the optimal acquisition means depending on the user's input method, location information can be acquired efficiently.

[0038] When acquiring location information, the location information acquisition unit can prioritize acquiring highly relevant location information by taking into account the user's geographical location information. For example, when acquiring location information, the location information acquisition unit prioritizes acquiring highly relevant location information by taking into account the user's geographical location information. For example, when the user is in an urban area, the location information acquisition unit can prioritize acquiring location information of commercial facilities and restaurants. Furthermore, when the user is in the suburbs, the location information acquisition unit can also prioritize acquiring location information of natural parks and tourist spots. For example, when the user is in a specific area, the location information acquisition unit can prioritize acquiring location information of local specialties and event information. In this way, by taking into account the user's geographical location information, highly relevant location information can be prioritized.

[0039] The location information acquisition unit can analyze the user's social media activity when acquiring location information and acquire related location information. For example, the location information acquisition unit can acquire location information of places where the user has checked in on social media. The location information acquisition unit can also analyze the content of the user's posts on social media and acquire related location information. For example, the location information acquisition unit can acquire related location information by referring to the activities of the user's friends on social media. In this way, related location information can be efficiently acquired by analyzing the user's social media activity.

[0040] The location information acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring location information. For example, the location information acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring location information. For example, the location information acquisition unit can preferentially acquire location information of places that the user has previously rated highly. The location information acquisition unit can also avoid acquiring location information of places that the user has previously rated poorly. For example, the location information acquisition unit can adjust the frequency and method of acquiring location information based on the user's past feedback. In this way, the acquisition method can be customized by reflecting the user's past feedback, and more appropriate location information can be acquired.

[0041] The trend data collection unit can select the type of data to collect based on the characteristics of the region when collecting trend data. For example, the trend data collection unit can select the type of data to collect based on the characteristics of the region when collecting trend data. For example, in urban areas, the trend data collection unit can prioritize collecting trend data on commercial facilities and restaurants. In suburban areas, the trend data collection unit can also prioritize collecting trend data on natural parks and tourist spots. For example, in a specific region, the trend data collection unit can prioritize collecting trend data on local specialties and event information. In this way, by selecting the type of data to collect based on the characteristics of the region, more relevant trend data can be collected.

[0042] The trend data collection unit can evaluate the reliability of the data to be collected when collecting trend data and prioritize collecting highly reliable data. For example, the trend data collection unit can collect trend data from highly reliable news sites or official social media accounts when collecting trend data. The trend data collection unit can also select highly reliable data sources based on user feedback. For example, the trend data collection unit can prioritize collecting highly reliable data using an algorithm that evaluates the reliability of data. In this way, by evaluating the reliability of the data to be collected, highly reliable trend data can be prioritized.

[0043] The trend data collection unit can adjust the update frequency of the collected data when collecting trend data. The trend data collection unit, for example, adjusts the update frequency of the collected data when collecting trend data. For example, the trend data collection unit can collect the latest trend data in real time. The trend data collection unit can also periodically collect past trend data. For example, the trend data collection unit can adjust the update frequency of the data according to the user's interests. In this way, by adjusting the update frequency of the collected data, the latest trend data can be collected efficiently.

[0044] The trend data collection unit can select data to collect by taking into consideration local event information when collecting trend data. For example, the trend data collection unit can collect related trend data based on local event information. The trend data collection unit can also select the type of data to collect by referring to a local event calendar. For example, the trend data collection unit can collect local event information in real time and reflect it in the trend data. In this way, by taking into consideration local event information, highly relevant trend data can be collected efficiently.

[0045] The trend data collection unit can analyze activity on local news sites and social media sites when collecting trend data and collect related data. For example, the trend data collection unit can analyze activity on local news sites and social media sites when collecting trend data and collect related data. For example, the trend data collection unit can collect the latest trend data from local news sites. The trend data collection unit can also analyze the content posted on local social media accounts and collect related trend data. For example, the trend data collection unit can monitor activity on local news sites and social media sites in real time and reflect the results in trend data. This makes it possible to efficiently collect related trend data by analyzing activity on local news sites and social media sites.

[0046] The trend data collection unit can customize the collection method by reflecting feedback from local residents when collecting trend data. The trend data collection unit, for example, customizes the collection method by reflecting feedback from local residents when collecting trend data. For example, the trend data collection unit can select the type of trend data to collect based on feedback from local residents. The trend data collection unit can also adjust the collection method by reflecting the opinions of local residents. For example, the trend data collection unit can collect feedback from local residents in real time and reflect it in the trend data. In this way, the collection method can be customized by reflecting feedback from local residents, and more appropriate trend data can be collected.

[0047] The data analysis unit can select an analysis algorithm based on the characteristics of the region when analyzing data. For example, the data analysis unit can select an analysis algorithm based on the characteristics of the region when analyzing data. For example, in urban areas, the data analysis unit can select an algorithm that analyzes data on commercial facilities and restaurants. In suburban areas, the data analysis unit can also select an algorithm that analyzes data on natural parks and tourist spots. For example, in a specific region, the data analysis unit can select an algorithm that analyzes data on local specialties and event information. In this way, by selecting an analysis algorithm based on the characteristics of the region, more relevant data analysis can be performed.

[0048] The data analysis unit can evaluate the reliability of the data to be analyzed during data analysis and prioritize the analysis of highly reliable data. For example, the data analysis unit can prioritize the analysis of highly reliable data during data analysis. For example, the data analysis unit can prioritize the analysis of data collected from highly reliable news sites or official social media accounts. The data analysis unit can also select highly reliable data sources based on user feedback. For example, the data analysis unit can prioritize the analysis of highly reliable data using an algorithm that evaluates the reliability of data. In this way, by evaluating the reliability of the data to be analyzed, highly reliable data can be prioritized for analysis.

[0049] The data analysis unit can adjust the update frequency of the data to be analyzed during data analysis. The data analysis unit, for example, adjusts the update frequency of the data to be analyzed during data analysis. For example, the data analysis unit can analyze the latest data in real time. The data analysis unit can also periodically analyze data including past data. For example, the data analysis unit can adjust the update frequency of the data according to the user's interests. In this way, by adjusting the update frequency of the data to be analyzed, the latest data can be analyzed efficiently.

[0050] The data analysis unit can select data to analyze by taking into consideration local event information when analyzing data. For example, the data analysis unit can select data to analyze by taking into consideration local event information when analyzing data. For example, the data analysis unit can prioritize analysis of related data based on local event information. The data analysis unit can also refer to a local event calendar to select the type of data to analyze. For example, the data analysis unit can analyze local event information in real time and reflect the results in trend data. This allows highly relevant data to be analyzed efficiently by taking into consideration local event information.

[0051] The data analysis unit can analyze activity on local news sites and social media during data analysis and analyze related data. For example, the data analysis unit can analyze activity on local news sites and social media during data analysis and analyze related data. For example, the data analysis unit can prioritize analysis of data collected from local news sites. The data analysis unit can also analyze content posted on local social media accounts and analyze related data. For example, the data analysis unit can analyze activity on local news sites and social media in real time and reflect the results in trend data. This makes it possible to efficiently analyze related data by analyzing activity on local news sites and social media.

[0052] The data analysis unit can customize the analysis method by reflecting feedback from local residents when analyzing data. For example, the data analysis unit can customize the analysis method by reflecting feedback from local residents when analyzing data. For example, the data analysis unit can select the type of data to analyze based on feedback from local residents. The data analysis unit can also adjust the analysis method by reflecting the opinions of local residents. For example, the data analysis unit can collect feedback from local residents in real time and reflect it in the data analysis. In this way, the analysis method can be customized by reflecting feedback from local residents, allowing for more appropriate data analysis.

[0053] The advertisement generation unit can select advertisement content based on regional characteristics when generating an advertisement. The advertisement generation unit, for example, selects advertisement content based on regional characteristics when generating an advertisement. For example, the advertisement generation unit can preferentially generate advertisements for commercial facilities and restaurants in urban areas. The advertisement generation unit can also preferentially generate advertisements for natural parks and tourist spots in suburban areas. For example, the advertisement generation unit can preferentially generate advertisements for local specialties and event information in a specific region. In this way, by selecting advertisement content based on regional characteristics, more relevant advertisements can be generated.

[0054] The advertisement generation unit can evaluate the reliability of the advertisement to be generated when generating an advertisement, and prioritize generating highly reliable advertisements. For example, the advertisement generation unit can evaluate the reliability of the advertisement to be generated when generating an advertisement, and prioritize generating highly reliable advertisements. For example, the advertisement generation unit can generate an advertisement based on data collected from highly reliable news sites or official social media accounts. The advertisement generation unit can also select a highly reliable data source based on user feedback. For example, the advertisement generation unit can prioritize generating highly reliable advertisements using an algorithm that evaluates the reliability of data. In this way, by evaluating the reliability of the advertisement to be generated, highly reliable advertisements can be prioritized.

[0055] The advertisement generation unit can adjust the update frequency of the advertisement to be generated when generating the advertisement. The advertisement generation unit, for example, adjusts the update frequency of the advertisement to be generated when generating the advertisement. For example, the advertisement generation unit can generate the latest advertisement in real time. The advertisement generation unit can also periodically update advertisements, including past advertisements. For example, the advertisement generation unit can adjust the update frequency of the advertisement according to the user's interests. In this way, by adjusting the update frequency of the advertisement to be generated, the latest advertisements can be efficiently generated.

[0056] The advertisement generation unit can select advertisement content taking into consideration local event information when generating an advertisement. For example, the advertisement generation unit can select advertisement content taking into consideration local event information when generating an advertisement. For example, the advertisement generation unit can preferentially generate relevant advertisements based on local event information. The advertisement generation unit can also select advertisement content by referring to a local event calendar. For example, the advertisement generation unit can generate advertisements that reflect local event information in real time. This allows for efficient generation of highly relevant advertisements by taking into consideration local event information.

[0057] The advertisement generation unit can analyze activity on local news sites and SNS when generating an advertisement and generate a relevant advertisement. For example, the advertisement generation unit can analyze activity on local news sites and SNS when generating an advertisement and generate a relevant advertisement. For example, the advertisement generation unit can generate an advertisement based on data collected from local news sites. The advertisement generation unit can also analyze the content posted on local SNS accounts and generate a relevant advertisement. For example, the advertisement generation unit can generate an advertisement that reflects activity on local news sites and SNS in real time. This makes it possible to efficiently generate relevant advertisements by analyzing activity on local news sites and SNS.

[0058] The advertisement generation unit can customize the content of the advertisement by reflecting feedback from local residents when generating the advertisement. For example, the advertisement generation unit can customize the content of the advertisement by reflecting feedback from local residents when generating the advertisement. For example, the advertisement generation unit can select the content of the advertisement based on feedback from local residents. The advertisement generation unit can also adjust the content of the advertisement by reflecting the opinions of local residents. For example, the advertisement generation unit can collect feedback from local residents in real time and reflect the feedback in the content of the advertisement. In this way, the content of the advertisement can be customized by reflecting the feedback from local residents, and more appropriate advertisements can be generated.

[0059] The advertisement providing unit can select the type of advertisement to be provided based on the characteristics of the region when providing an advertisement. For example, the advertisement providing unit selects the type of advertisement to be provided based on the characteristics of the region when providing an advertisement. For example, the advertisement providing unit can preferentially provide advertisements for commercial facilities and restaurants in urban areas. Furthermore, the advertisement providing unit can preferentially provide advertisements for natural parks and tourist spots in suburban areas. For example, the advertisement providing unit can preferentially provide advertisements for local specialties and event information in a specific region. In this way, by selecting the type of advertisement to be provided based on the characteristics of the region, it is possible to provide more relevant advertisements.

[0060] The advertisement providing unit can evaluate the reliability of the advertisement to be provided when providing an advertisement, and provide highly reliable advertisements with priority. For example, the advertisement providing unit can provide advertisements based on data collected from highly reliable news sites or official social media accounts when providing an advertisement. The advertisement providing unit can also select highly reliable data sources based on user feedback. For example, the advertisement providing unit can use an algorithm that evaluates the reliability of data to provide highly reliable advertisements with priority. In this way, by evaluating the reliability of the advertisements to be provided, highly reliable advertisements can be provided with priority.

[0061] The advertisement providing unit can adjust the update frequency of the advertisement to be provided when providing the advertisement. The advertisement providing unit, for example, adjusts the update frequency of the advertisement to be provided when providing the advertisement. For example, the advertisement providing unit can provide the latest advertisements in real time. The advertisement providing unit can also periodically update advertisements, including past advertisements. For example, the advertisement providing unit can adjust the update frequency of advertisements according to the user's interests. In this way, by adjusting the update frequency of the advertisements to be provided, the latest advertisements can be provided efficiently.

[0062] The advertisement provision unit can select an advertisement to be provided by taking into consideration local event information when providing an advertisement. For example, the advertisement provision unit can select an advertisement to be provided by taking into consideration local event information when providing an advertisement. For example, the advertisement provision unit can preferentially provide relevant advertisements based on local event information. The advertisement provision unit can also select the type of advertisement to be provided by referring to a local event calendar. For example, the advertisement provision unit can provide advertisements that reflect local event information in real time. This makes it possible to efficiently provide highly relevant advertisements by taking into consideration local event information.

[0063] The advertisement providing unit can analyze activity on local news sites and SNS when providing an advertisement and provide a relevant advertisement. For example, the advertisement providing unit can analyze activity on local news sites and SNS when providing an advertisement and provide a relevant advertisement. For example, the advertisement providing unit can provide an advertisement based on data collected from local news sites. The advertisement providing unit can also analyze the content posted on local SNS accounts and provide a relevant advertisement. For example, the advertisement providing unit can provide an advertisement that reflects activity on local news sites and SNS in real time. This makes it possible to efficiently provide relevant advertisements by analyzing activity on local news sites and SNS.

[0064] The advertisement providing unit can customize the advertisement providing method by reflecting feedback from local residents when providing an advertisement. The advertisement providing unit, for example, customizes the advertisement providing method by reflecting feedback from local residents when providing an advertisement. For example, the advertisement providing unit can select the type of advertisement to provide based on feedback from local residents. The advertisement providing unit can also adjust the advertisement providing method by reflecting the opinions of local residents. For example, the advertisement providing unit can collect feedback from local residents in real time and reflect the feedback in the advertisement providing method. In this way, the advertisement providing unit can customize the advertisement providing method by reflecting the feedback from local residents, and provide more appropriate advertisements.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The advertisement customization system may further include a purchase history analysis unit that analyzes a user's purchase history. The purchase history analysis unit analyzes the user's past purchase history to understand the user's purchasing trends. For example, the purchase history analysis unit may collect data on products and services purchased by the user in the past and identify the user's preferences and interests. The purchase history analysis unit may also analyze the user's purchase frequency and purchase timing to determine the optimal timing for providing advertisements. Furthermore, the purchase history analysis unit may generate advertisements for related products and services based on the user's purchase history. This makes it possible to provide more personalized advertisements by utilizing the user's purchase history.

[0067] The advertisement customization system may further include a device usage monitoring unit that monitors the user's device usage. The device usage monitoring unit monitors the user's device usage patterns and determines the optimal timing for providing advertisements. For example, the device usage monitoring unit may identify the time periods during which the user frequently uses their smartphone and provide advertisements during those time periods. The device usage monitoring unit may also provide advertisements that avoid the time periods during which the user is not using the device. Furthermore, the device usage monitoring unit may provide relevant advertisements when the user is using a specific application. This allows for effective advertisement provision by taking into account the user's device usage.

[0068] The ad customization system may further include a social media analysis unit that analyzes a user's social media activity. The social media analysis unit analyzes the user's social media activity and customizes the content of the ad. For example, the social media analysis unit may analyze the accounts the user follows and the content of their posts to identify the user's interests. The social media analysis unit may also analyze the groups and events the user participates in and provide relevant ads. Furthermore, the social media analysis unit may analyze the content of the user's posts and comments to provide ads that reflect the user's emotions and opinions. This makes it possible to provide more personalized ads by utilizing the user's social media activity.

[0069] The advertisement customization system may further include a health data analysis unit that analyzes the user's health data. The health data analysis unit analyzes the user's health status and customizes the advertisement content. For example, the health data analysis unit may analyze data collected from the user's fitness tracker or smartwatch to understand the user's exercise habits and health status. The health data analysis unit may also analyze the user's food records and sleep data to provide health-related advertisements. Furthermore, the health data analysis unit may analyze the user's health goals and progress to provide advertisements that motivate the user. In this way, by utilizing the user's health data, more relevant advertisements can be provided.

[0070] The advertisement customization system may further include a voice data analysis unit that analyzes the user's voice data. The voice data analysis unit analyzes the user's voice data and customizes the content of the advertisement. For example, the voice data analysis unit may analyze the user's voice commands and conversation content to identify the user's interests and concerns. The voice data analysis unit may also analyze the user's tone of voice and emotions to provide advertisements that correspond to the user's emotions. Furthermore, the voice data analysis unit may analyze the user's voice data in real time and provide relevant advertisements instantly. In this way, by utilizing the user's voice data, more personalized advertisements can be provided.

[0071] The advertisement customization system may further include a purchasing intent analysis unit that estimates a user's purchasing intent and adjusts the content of advertisements based on the estimated purchasing intent. The purchasing intent analysis unit estimates a user's purchasing intent in real time and adjusts the content of advertisements to match the user's purchasing intent. For example, if a user shows a high purchasing intent, the purchasing intent analysis unit may provide advertisements that include special offers or discount information. Furthermore, if a user shows a low purchasing intent, the purchasing intent analysis unit may provide advertisements that emphasize the appeal of products. Furthermore, the purchasing intent analysis unit may adjust the frequency of advertisement display based on the user's purchasing intent. This maximizes the effectiveness of advertisements by providing advertisements that match the user's purchasing intent.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The location information acquisition unit acquires the user's geographic location information. For example, the location information acquisition unit can acquire the location information from the user's smartphone or GPS device. The location information acquisition unit can also acquire the location information using Wi-Fi location information or cell tower data. Step 2: The trend data collection unit collects local trend data. For example, the trend data collection unit can collect trend data from social media, news sites, local event information, etc. It can also collect data from local bulletin boards and online forums. Step 3: The data analysis unit analyzes the collected geographic location information and trend data using generative AI. For example, the data analysis unit can analyze the data using deep learning models or natural language processing techniques. It can also use machine learning algorithms to analyze patterns in the data. Step 4: The ad generation unit generates ads based on the data analyzed by the data analysis unit. For example, the ad generation unit can use AI to generate ads based on the user's current location and regional trends. It can also generate ads in the form of banner ads, text ads, video ads, etc. Step 5: The advertisement provision unit provides the generated advertisement to the user in real time. For example, the advertisement provision unit can display the generated advertisement on a smartphone screen. The advertisement can also be provided using push notifications or real-time distribution.

[0074] (Example 2) An advertisement customization system according to an embodiment of the present invention customizes and provides advertisements in real time based on a user's geographic location information and regional trend data. The advertisement customization system acquires the user's geographic location information, collects regional trend data, and uses a generation AI to analyze the data to generate advertisements and provide them to the user. For example, the advertisement customization system acquires location information from the user's smartphone or GPS device. The advertisement customization system then collects trend data from social media sites, news sites, local event information, and the like. The generation AI analyzes the collected geographic location information and trend data to generate advertisements based on the user's current location and regional trends. The generated advertisements are provided to the user in real time. This allows businesses to deliver advertisements tailored to local characteristics, contributing to the development of local economies and the improvement of customer satisfaction. The advertisement customization system can customize and provide advertisements in real time based on the user's geographic location information and regional trend data. For example, if a user is in a specific region, advertisements for products and services popular in that region are displayed. This mechanism allows businesses to deliver advertisements tailored to local characteristics, contributing to the development of local economies and the improvement of customer satisfaction.

[0075] An advertisement customization system according to an embodiment includes a location information acquisition unit, a trend data collection unit, a data analysis unit, an advertisement generation unit, and an advertisement provision unit. The location information acquisition unit acquires geographical location information of a user. For example, the location information acquisition unit can acquire location information from a user's smartphone or GPS device. The location information acquisition unit can also acquire location information using Wi-Fi location information or cell tower data. The trend data collection unit collects local trend data. For example, the trend data collection unit can collect trend data from social media sites, news sites, local event information, etc. The trend data collection unit can also collect data from local bulletin boards and online forums. The data analysis unit analyzes the collected geographical location information and trend data using a generation AI. For example, the data analysis unit can analyze data using a deep learning model or natural language processing technology. The data analysis unit can also analyze data patterns using a machine learning algorithm. The advertisement generation unit generates an advertisement based on the data analyzed by the data analysis unit. For example, the advertisement generation unit can generate an advertisement based on the user's current location and local trends using a generation AI. The advertisement generation unit may also generate advertisements in the form of banner advertisements, text advertisements, video advertisements, etc. The advertisement provision unit provides the generated advertisements to users in real time. For example, the advertisement provision unit may display the generated advertisements on a smartphone screen. The advertisement provision unit may also provide advertisements using push notification or real-time delivery. As a result, the advertisement customization system according to the embodiment may customize and provide advertisements in real time based on the user's geographical location information and regional trend data.

[0076] The location information acquisition unit can acquire location information from a user's smartphone or GPS device. The location information acquisition unit acquires location information from, for example, the user's smartphone. For example, the location information acquisition unit can acquire location information using the GPS function of the smartphone. The location information acquisition unit can also acquire location information from the user's GPS device. For example, the location information acquisition unit can acquire location information from a dedicated GPS tracker or an in-vehicle GPS system. In this way, accurate geographical location information can be obtained by acquiring location information from the user's smartphone or GPS device.

[0077] The trend data collection unit can collect trend data from social media, news sites, and local event information. The trend data collection unit, for example, collects trend data from social media. For example, the trend data collection unit can collect data from social media platforms such as Twitter, Facebook, and Instagram. The trend data collection unit can also collect trend data from news sites. For example, the trend data collection unit can collect data from online news portals and local news sites. The trend data collection unit can also collect trend data from local event information. For example, the trend data collection unit can collect data from event calendars and local bulletin boards. In this way, by collecting trend data from social media, news sites, local event information, etc., it is possible to grasp the latest local trends.

[0078] The data analysis unit can analyze the collected geographical location information and trend data using the generative AI. For example, the data analysis unit analyzes the collected geographical location information and trend data using the generative AI. For example, the data analysis unit can analyze the data using a deep learning model. The data analysis unit can also analyze the data using natural language processing technology. For example, the data analysis unit can analyze the collected text data and extract trends. The data analysis unit can also analyze data patterns using a machine learning algorithm. For example, the data analysis unit can cluster the collected data and identify trends by region. As a result, the use of the generative AI improves the accuracy of the analysis of the geographical location information and trend data.

[0079] The advertisement generation unit can generate advertisements based on the user's current location and regional trends using a generation AI. The advertisement generation unit generates advertisements based on, for example, the user's current location and regional trends using a generation AI. For example, the advertisement generation unit can generate advertisements using a deep learning model. The advertisement generation unit can also generate advertisements using natural language processing technology. For example, the advertisement generation unit can analyze collected text data and generate advertisement content. The advertisement generation unit can also generate advertisements in the form of banner advertisements, text advertisements, video advertisements, etc. For example, the advertisement generation unit can generate advertisements related to local specialties and event information based on the user's current location. This makes it possible to effectively generate advertisements based on the user's current location and regional trends by using a generation AI.

[0080] The advertisement providing unit can instantly provide the generated advertisement to the user. The advertisement providing unit can, for example, instantly provide the generated advertisement to the user. For example, the advertisement providing unit can display the generated advertisement on a smartphone screen. The advertisement providing unit can also provide the advertisement using a push notification. For example, the advertisement providing unit can deliver the generated advertisement in real time and instantly provide it to the user. The advertisement providing unit can also provide the advertisement through a web browser or a mobile app. For example, the advertisement providing unit can display the generated advertisement while the user is browsing a website. In this way, the generated advertisement can be provided in real time, thereby delivering a timely advertisement to the user.

[0081] The location information acquisition unit can estimate the user's emotion and adjust the timing of acquiring location information based on the estimated user emotion. The location information acquisition unit, for example, estimates the user's emotion and adjusts the timing of acquiring location information based on the estimated user emotion. For example, if the user is stressed, the location information acquisition unit can reduce the frequency of acquiring location information to reduce battery consumption. Furthermore, if the user is relaxed, the location information acquisition unit can increase the frequency of acquiring location information to collect more detailed location data. For example, if the user is in a hurry, the location information acquisition unit can acquire location information in real time and provide advertisements quickly. This allows the timing of acquiring location information to be adjusted according to the user's emotion, thereby reducing battery consumption and acquiring location information at an appropriate time. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0082] The location information acquisition unit can analyze the user's past movement history and select an appropriate location information acquisition method. The location information acquisition unit, for example, analyzes the user's past movement history and selects an appropriate location information acquisition method. For example, the location information acquisition unit can adjust the frequency of location information acquisition based on places the user has frequently visited in the past. The location information acquisition unit can also analyze the user's movement pattern and acquire location information at the optimal timing. For example, the location information acquisition unit can focus on acquiring location information during a specific time period based on the user's past movement history. This makes it possible to select the optimal location information acquisition method and efficiently acquire location information by analyzing the user's past movement history.

[0083] The location information acquisition unit can perform filtering based on the user's current activity status and areas of interest when acquiring location information. For example, the location information acquisition unit can perform filtering based on the user's current activity status and areas of interest when acquiring location information. For example, when the user is shopping, the location information acquisition unit can prioritize acquiring location information of shopping areas. Furthermore, when the user is sightseeing, the location information acquisition unit can also prioritize acquiring location information of tourist spots. For example, when the user is commuting, the location information acquisition unit can prioritize acquiring location information of the commuting route. In this way, by filtering location information based on the user's current activity status and areas of interest, more relevant location information can be acquired.

[0084] The location information acquisition unit can select an appropriate acquisition means depending on the user's input method when acquiring location information. For example, the location information acquisition unit can select an appropriate acquisition means depending on the user's input method when acquiring location information. For example, if the user is using voice input, the location information acquisition unit can acquire location information using voice recognition technology. Also, if the user is using text input, the location information acquisition unit can acquire location information using text analysis technology. For example, if the user is using image input, the location information acquisition unit can acquire location information using image recognition technology. In this way, by selecting the optimal acquisition means depending on the user's input method, location information can be acquired efficiently.

[0085] The location information acquisition unit can estimate the user's emotion and determine the priority of location information to be acquired based on the estimated user's emotion. The location information acquisition unit can, for example, estimate the user's emotion and determine the priority of location information to be acquired based on the estimated user's emotion. For example, if the user is excited, the location information acquisition unit can prioritize acquiring location information of tourist spots and event venues. Furthermore, if the user is tired, the location information acquisition unit can prioritize acquiring location information of rest areas and cafes. For example, if the user is relaxing, the location information acquisition unit can prioritize acquiring location information of natural parks and walking paths. In this way, by determining the priority of location information based on the user's emotion, it is possible to prioritize acquiring location information that is most relevant to the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] When acquiring location information, the location information acquisition unit can prioritize acquiring highly relevant location information by taking into account the user's geographical location information. For example, when acquiring location information, the location information acquisition unit prioritizes acquiring highly relevant location information by taking into account the user's geographical location information. For example, when the user is in an urban area, the location information acquisition unit can prioritize acquiring location information of commercial facilities and restaurants. Furthermore, when the user is in the suburbs, the location information acquisition unit can also prioritize acquiring location information of natural parks and tourist spots. For example, when the user is in a specific area, the location information acquisition unit can prioritize acquiring location information of local specialties and event information. In this way, by taking into account the user's geographical location information, highly relevant location information can be prioritized.

[0087] The location information acquisition unit can analyze the user's social media activity when acquiring location information and acquire related location information. For example, the location information acquisition unit can acquire location information of places where the user has checked in on social media. The location information acquisition unit can also analyze the content of the user's posts on social media and acquire related location information. For example, the location information acquisition unit can acquire related location information by referring to the activities of the user's friends on social media. In this way, related location information can be efficiently acquired by analyzing the user's social media activity.

[0088] The location information acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring location information. For example, the location information acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring location information. For example, the location information acquisition unit can preferentially acquire location information of places that the user has previously rated highly. The location information acquisition unit can also avoid acquiring location information of places that the user has previously rated poorly. For example, the location information acquisition unit can adjust the frequency and method of acquiring location information based on the user's past feedback. In this way, the acquisition method can be customized by reflecting the user's past feedback, and more appropriate location information can be acquired.

[0089] The trend data collection unit can estimate a user's emotions and adjust the trend data collection method based on the estimated user emotions. The trend data collection unit, for example, estimates a user's emotions and adjusts the trend data collection method based on the estimated user emotions. For example, when the user is excited, the trend data collection unit can prioritize collecting the most recent trend data. Furthermore, when the user is relaxed, the trend data collection unit can also collect past trend data. For example, when the user is stressed, the trend data collection unit can reduce the amount of data collected and save battery consumption. This allows trend data to be collected efficiently by adjusting the trend data collection method based on the user'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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0090] The trend data collection unit can select the type of data to collect based on the characteristics of the region when collecting trend data. For example, the trend data collection unit can select the type of data to collect based on the characteristics of the region when collecting trend data. For example, in urban areas, the trend data collection unit can prioritize collecting trend data on commercial facilities and restaurants. In suburban areas, the trend data collection unit can also prioritize collecting trend data on natural parks and tourist spots. For example, in a specific region, the trend data collection unit can prioritize collecting trend data on local specialties and event information. In this way, by selecting the type of data to collect based on the characteristics of the region, more relevant trend data can be collected.

[0091] The trend data collection unit can evaluate the reliability of the data to be collected when collecting trend data and prioritize collecting highly reliable data. For example, the trend data collection unit can collect trend data from highly reliable news sites or official social media accounts when collecting trend data. The trend data collection unit can also select highly reliable data sources based on user feedback. For example, the trend data collection unit can prioritize collecting highly reliable data using an algorithm that evaluates the reliability of data. In this way, by evaluating the reliability of the data to be collected, highly reliable trend data can be prioritized.

[0092] The trend data collection unit can adjust the update frequency of the collected data when collecting trend data. The trend data collection unit, for example, adjusts the update frequency of the collected data when collecting trend data. For example, the trend data collection unit can collect the latest trend data in real time. The trend data collection unit can also periodically collect past trend data. For example, the trend data collection unit can adjust the update frequency of the data according to the user's interests. In this way, by adjusting the update frequency of the collected data, the latest trend data can be collected efficiently.

[0093] The trend data collection unit can estimate a user's emotions and determine the priority of trend data to be collected based on the estimated user emotions. The trend data collection unit can, for example, estimate a user's emotions and determine the priority of trend data to be collected based on the estimated user emotions. For example, when a user is excited, the trend data collection unit can prioritize collecting the most recent trend data. Furthermore, when a user is relaxed, the trend data collection unit can also collect past trend data. For example, when a user is stressed, the trend data collection unit can reduce the amount of data to be collected and reduce battery consumption. In this way, by determining the priority of trend data to be collected based on the user's emotions, it is possible to prioritize collecting trend data that is most relevant to the user. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0094] The trend data collection unit can select data to collect by taking into consideration local event information when collecting trend data. For example, the trend data collection unit can collect related trend data based on local event information. The trend data collection unit can also select the type of data to collect by referring to a local event calendar. For example, the trend data collection unit can collect local event information in real time and reflect it in the trend data. In this way, by taking into consideration local event information, highly relevant trend data can be collected efficiently.

[0095] The trend data collection unit can analyze activity on local news sites and social media sites when collecting trend data and collect related data. For example, the trend data collection unit can analyze activity on local news sites and social media sites when collecting trend data and collect related data. For example, the trend data collection unit can collect the latest trend data from local news sites. The trend data collection unit can also analyze the content posted on local social media accounts and collect related trend data. For example, the trend data collection unit can monitor activity on local news sites and social media sites in real time and reflect the results in trend data. This makes it possible to efficiently collect related trend data by analyzing activity on local news sites and social media sites.

[0096] The trend data collection unit can customize the collection method by reflecting feedback from local residents when collecting trend data. The trend data collection unit, for example, customizes the collection method by reflecting feedback from local residents when collecting trend data. For example, the trend data collection unit can select the type of trend data to collect based on feedback from local residents. The trend data collection unit can also adjust the collection method by reflecting the opinions of local residents. For example, the trend data collection unit can collect feedback from local residents in real time and reflect it in the trend data. In this way, the collection method can be customized by reflecting feedback from local residents, and more appropriate trend data can be collected.

[0097] The data analysis unit can estimate the user's emotion and adjust the data analysis method based on the estimated user's emotion. For example, the data analysis unit can estimate the user's emotion and adjust the data analysis method based on the estimated user's emotion. For example, the data analysis unit can perform detailed data analysis when the user is relaxed. Furthermore, the data analysis unit can perform quick data analysis when the user is in a hurry. For example, the data analysis unit can provide visually stimulating data analysis results when the user is excited. This allows for efficient data analysis by adjusting the data analysis method based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0098] The data analysis unit can select an analysis algorithm based on the characteristics of the region when analyzing data. For example, the data analysis unit can select an analysis algorithm based on the characteristics of the region when analyzing data. For example, in urban areas, the data analysis unit can select an algorithm that analyzes data on commercial facilities and restaurants. In suburban areas, the data analysis unit can also select an algorithm that analyzes data on natural parks and tourist spots. For example, in a specific region, the data analysis unit can select an algorithm that analyzes data on local specialties and event information. In this way, by selecting an analysis algorithm based on the characteristics of the region, more relevant data analysis can be performed.

[0099] The data analysis unit can evaluate the reliability of the data to be analyzed during data analysis and prioritize the analysis of highly reliable data. For example, the data analysis unit can prioritize the analysis of highly reliable data during data analysis. For example, the data analysis unit can prioritize the analysis of data collected from highly reliable news sites or official social media accounts. The data analysis unit can also select highly reliable data sources based on user feedback. For example, the data analysis unit can prioritize the analysis of highly reliable data using an algorithm that evaluates the reliability of data. In this way, by evaluating the reliability of the data to be analyzed, highly reliable data can be prioritized for analysis.

[0100] The data analysis unit can adjust the update frequency of the data to be analyzed during data analysis. The data analysis unit, for example, adjusts the update frequency of the data to be analyzed during data analysis. For example, the data analysis unit can analyze the latest data in real time. The data analysis unit can also periodically analyze data including past data. For example, the data analysis unit can adjust the update frequency of the data according to the user's interests. In this way, by adjusting the update frequency of the data to be analyzed, the latest data can be analyzed efficiently.

[0101] The data analysis unit can estimate the user's emotions and determine the priority of data to be analyzed based on the estimated user emotions. The data analysis unit, for example, estimates the user's emotions and determines the priority of data to be analyzed based on the estimated user emotions. For example, if the user is excited, the data analysis unit can prioritize analyzing the most recent data. Furthermore, if the user is relaxed, the data analysis unit can also analyze past data. For example, if the user is feeling stressed, the data analysis unit can reduce the amount of data to be analyzed and perform analysis quickly. This allows the data most relevant to the user to be prioritized for analysis by determining the priority of data to be analyzed based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0102] The data analysis unit can select data to analyze by taking into consideration local event information when analyzing data. For example, the data analysis unit can select data to analyze by taking into consideration local event information when analyzing data. For example, the data analysis unit can prioritize analysis of related data based on local event information. The data analysis unit can also refer to a local event calendar to select the type of data to analyze. For example, the data analysis unit can analyze local event information in real time and reflect the results in trend data. This allows highly relevant data to be analyzed efficiently by taking into consideration local event information.

[0103] The data analysis unit can analyze activity on local news sites and social media during data analysis and analyze related data. For example, the data analysis unit can analyze activity on local news sites and social media during data analysis and analyze related data. For example, the data analysis unit can prioritize analysis of data collected from local news sites. The data analysis unit can also analyze content posted on local social media accounts and analyze related data. For example, the data analysis unit can analyze activity on local news sites and social media in real time and reflect the results in trend data. This makes it possible to efficiently analyze related data by analyzing activity on local news sites and social media.

[0104] The data analysis unit can customize the analysis method by reflecting feedback from local residents when analyzing data. For example, the data analysis unit can customize the analysis method by reflecting feedback from local residents when analyzing data. For example, the data analysis unit can select the type of data to analyze based on feedback from local residents. The data analysis unit can also adjust the analysis method by reflecting the opinions of local residents. For example, the data analysis unit can collect feedback from local residents in real time and reflect it in the data analysis. In this way, the analysis method can be customized by reflecting feedback from local residents, allowing for more appropriate data analysis.

[0105] The advertisement generation unit can estimate a user's emotions and adjust the advertisement presentation method based on the estimated user emotions. The advertisement generation unit, for example, estimates a user's emotions and adjusts the advertisement presentation method based on the estimated user emotions. For example, the advertisement generation unit can generate an advertisement with a calm tone when the user is relaxed. The advertisement generation unit can also generate a visually stimulating advertisement when the user is excited. For example, the advertisement generation unit can generate a simple, highly visible advertisement when the user is stressed. This allows the advertisement presentation method to be adjusted based on the user's emotions, thereby generating the most effective advertisement for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0106] The advertisement generation unit can select advertisement content based on regional characteristics when generating an advertisement. The advertisement generation unit, for example, selects advertisement content based on regional characteristics when generating an advertisement. For example, the advertisement generation unit can preferentially generate advertisements for commercial facilities and restaurants in urban areas. The advertisement generation unit can also preferentially generate advertisements for natural parks and tourist spots in suburban areas. For example, the advertisement generation unit can preferentially generate advertisements for local specialties and event information in a specific region. In this way, by selecting advertisement content based on regional characteristics, more relevant advertisements can be generated.

[0107] The advertisement generation unit can evaluate the reliability of the advertisement to be generated when generating an advertisement, and prioritize generating highly reliable advertisements. For example, the advertisement generation unit can evaluate the reliability of the advertisement to be generated when generating an advertisement, and prioritize generating highly reliable advertisements. For example, the advertisement generation unit can generate an advertisement based on data collected from highly reliable news sites or official social media accounts. The advertisement generation unit can also select a highly reliable data source based on user feedback. For example, the advertisement generation unit can prioritize generating highly reliable advertisements using an algorithm that evaluates the reliability of data. In this way, by evaluating the reliability of the advertisement to be generated, highly reliable advertisements can be prioritized.

[0108] The advertisement generation unit can adjust the update frequency of the advertisement to be generated when generating the advertisement. The advertisement generation unit, for example, adjusts the update frequency of the advertisement to be generated when generating the advertisement. For example, the advertisement generation unit can generate the latest advertisement in real time. The advertisement generation unit can also periodically update advertisements, including past advertisements. For example, the advertisement generation unit can adjust the update frequency of the advertisement according to the user's interests. In this way, by adjusting the update frequency of the advertisement to be generated, the latest advertisements can be efficiently generated.

[0109] The advertisement generation unit can estimate a user's emotion and determine the priority of advertisements to be generated based on the estimated user's emotion. The advertisement generation unit can, for example, estimate a user's emotion and determine the priority of advertisements to be generated based on the estimated user's emotion. For example, if the user is excited, the advertisement generation unit can prioritize generating visually stimulating advertisements. Also, if the user is relaxed, the advertisement generation unit can prioritize generating advertisements with a calm tone. For example, if the user is stressed, the advertisement generation unit can prioritize generating simple, highly visible advertisements. In this way, by prioritizing advertisements to be generated based on the user's emotion, it is possible to prioritize generating advertisements that are most effective for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0110] The advertisement generation unit can select advertisement content taking into consideration local event information when generating an advertisement. For example, the advertisement generation unit can select advertisement content taking into consideration local event information when generating an advertisement. For example, the advertisement generation unit can preferentially generate relevant advertisements based on local event information. The advertisement generation unit can also select advertisement content by referring to a local event calendar. For example, the advertisement generation unit can generate advertisements that reflect local event information in real time. This allows for efficient generation of highly relevant advertisements by taking into consideration local event information.

[0111] The advertisement generation unit can analyze activity on local news sites and SNS when generating an advertisement and generate a relevant advertisement. For example, the advertisement generation unit can analyze activity on local news sites and SNS when generating an advertisement and generate a relevant advertisement. For example, the advertisement generation unit can generate an advertisement based on data collected from local news sites. The advertisement generation unit can also analyze the content posted on local SNS accounts and generate a relevant advertisement. For example, the advertisement generation unit can generate an advertisement that reflects activity on local news sites and SNS in real time. This makes it possible to efficiently generate relevant advertisements by analyzing activity on local news sites and SNS.

[0112] The advertisement generation unit can customize the content of the advertisement by reflecting feedback from local residents when generating the advertisement. For example, the advertisement generation unit can customize the content of the advertisement by reflecting feedback from local residents when generating the advertisement. For example, the advertisement generation unit can select the content of the advertisement based on feedback from local residents. The advertisement generation unit can also adjust the content of the advertisement by reflecting the opinions of local residents. For example, the advertisement generation unit can collect feedback from local residents in real time and reflect the feedback in the content of the advertisement. In this way, the content of the advertisement can be customized by reflecting the feedback from local residents, and more appropriate advertisements can be generated.

[0113] The advertisement serving unit can estimate a user's emotions and adjust the advertisement serving method based on the estimated user emotions. The advertisement serving unit, for example, estimates a user's emotions and adjusts the advertisement serving method based on the estimated user emotions. For example, if the user is relaxed, the advertisement serving unit can serve an advertisement in a calm tone. Furthermore, if the user is excited, the advertisement serving unit can serve a visually stimulating advertisement. For example, if the user is feeling stressed, the advertisement serving unit can serve a simple, highly visible advertisement. This allows the advertisement serving method to be adjusted based on the user's emotions, thereby providing the most effective advertisement for the user. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0114] The advertisement providing unit can select the type of advertisement to be provided based on the characteristics of the region when providing an advertisement. For example, the advertisement providing unit selects the type of advertisement to be provided based on the characteristics of the region when providing an advertisement. For example, the advertisement providing unit can preferentially provide advertisements for commercial facilities and restaurants in urban areas. Furthermore, the advertisement providing unit can preferentially provide advertisements for natural parks and tourist spots in suburban areas. For example, the advertisement providing unit can preferentially provide advertisements for local specialties and event information in a specific region. In this way, by selecting the type of advertisement to be provided based on the characteristics of the region, it is possible to provide more relevant advertisements.

[0115] The advertisement providing unit can evaluate the reliability of the advertisement to be provided when providing an advertisement, and provide highly reliable advertisements with priority. For example, the advertisement providing unit can provide advertisements based on data collected from highly reliable news sites or official social media accounts when providing an advertisement. The advertisement providing unit can also select highly reliable data sources based on user feedback. For example, the advertisement providing unit can use an algorithm that evaluates the reliability of data to provide highly reliable advertisements with priority. In this way, by evaluating the reliability of the advertisements to be provided, highly reliable advertisements can be provided with priority.

[0116] The advertisement providing unit can adjust the update frequency of the advertisement to be provided when providing the advertisement. The advertisement providing unit, for example, adjusts the update frequency of the advertisement to be provided when providing the advertisement. For example, the advertisement providing unit can provide the latest advertisements in real time. The advertisement providing unit can also periodically update advertisements, including past advertisements. For example, the advertisement providing unit can adjust the update frequency of advertisements according to the user's interests. In this way, by adjusting the update frequency of the advertisements to be provided, the latest advertisements can be provided efficiently.

[0117] The advertisement serving unit can estimate a user's emotions and determine the priority of advertisements to be provided based on the estimated user emotions. The advertisement serving unit, for example, estimates a user's emotions and determines the priority of advertisements to be provided based on the estimated user emotions. For example, if the user is excited, the advertisement serving unit can prioritize providing visually stimulating advertisements. Furthermore, if the user is relaxed, the advertisement serving unit can prioritize providing advertisements with a calm tone. For example, if the user is stressed, the advertisement serving unit can prioritize providing simple, highly visible advertisements. In this way, by determining the priority of advertisements to be provided based on the user's emotions, it is possible to prioritize providing the most effective advertisements for the user. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0118] The advertisement provision unit can select an advertisement to be provided by taking into consideration local event information when providing an advertisement. For example, the advertisement provision unit can select an advertisement to be provided by taking into consideration local event information when providing an advertisement. For example, the advertisement provision unit can preferentially provide relevant advertisements based on local event information. The advertisement provision unit can also select the type of advertisement to be provided by referring to a local event calendar. For example, the advertisement provision unit can provide advertisements that reflect local event information in real time. This makes it possible to efficiently provide highly relevant advertisements by taking into consideration local event information.

[0119] The advertisement providing unit can analyze activity on local news sites and SNS when providing an advertisement and provide a relevant advertisement. For example, the advertisement providing unit can analyze activity on local news sites and SNS when providing an advertisement and provide a relevant advertisement. For example, the advertisement providing unit can provide an advertisement based on data collected from local news sites. The advertisement providing unit can also analyze the content posted on local SNS accounts and provide a relevant advertisement. For example, the advertisement providing unit can provide an advertisement that reflects activity on local news sites and SNS in real time. This makes it possible to efficiently provide relevant advertisements by analyzing activity on local news sites and SNS.

[0120] The advertisement providing unit can customize the advertisement providing method by reflecting feedback from local residents when providing an advertisement. The advertisement providing unit, for example, customizes the advertisement providing method by reflecting feedback from local residents when providing an advertisement. For example, the advertisement providing unit can select the type of advertisement to provide based on feedback from local residents. The advertisement providing unit can also adjust the advertisement providing method by reflecting the opinions of local residents. For example, the advertisement providing unit can collect feedback from local residents in real time and reflect the feedback in the advertisement providing method. In this way, the advertisement providing unit can customize the advertisement providing method by reflecting the feedback from local residents, and provide more appropriate advertisements. === Hard Collateral 1-1 === Each of the multiple elements, including the location information acquisition unit, trend data collection unit, data analysis unit, advertisement generation unit, and advertisement provision 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 location information acquisition unit can acquire the user's geographical location information using the camera 42 or microphone 38B of the smart device 14. For example, the trend data collection unit can collect trend data from social networking sites or news sites using the specific processing unit 290 of the data processing device 12. For example, the data analysis unit can analyze the geographical location information and trend data collected by the specific processing unit 290 of the data processing device 12. For example, the advertisement generation unit can generate an advertisement based on the data analyzed by the specific processing unit 290 of the data processing device 12. For example, the advertisement provision unit can provide an advertisement generated by the control unit 46A of the smart device 14 to the user in real time. === Hard Collateral 1-2 === Each of the multiple elements, including the location information acquisition unit, trend data collection unit, data analysis unit, advertisement generation unit, and advertisement provision 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 location information acquisition unit can acquire the user's geographical location information using the camera 42 or microphone 238 of the smart glasses 214. For example, the trend data collection unit can collect trend data from social networking sites or news sites by the specific processing unit 290 of the data processing device 12. For example, the data analysis unit can analyze the geographical location information and trend data collected by the specific processing unit 290 of the data processing device 12. For example, the advertisement generation unit can generate an advertisement based on the data analyzed by the specific processing unit 290 of the data processing device 12. For example, the advertisement provision unit can provide the advertisement generated by the control unit 46A of the smart glasses 214 to the user in real time. === Hard Collateral 1-3 === Each of the multiple elements including the above-described location information acquisition unit, trend data collection unit, data analysis unit, advertisement generation unit, and advertisement provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the location information acquisition unit can acquire geographical location information of a user using the camera 42 or microphone 238 of the headset type terminal 314. For example, the trend data collection unit can collect trend data from social networking sites or news sites by the specific processing unit 290 of the data processing device 12. For example, the data analysis unit can analyze the geographical location information and trend data collected by the specific processing unit 290 of the data processing device 12. For example, the advertisement generation unit can generate an advertisement based on the data analyzed by the specific processing unit 290 of the data processing device 12. For example, the advertisement provision unit can provide an advertisement generated by the control unit 46A of the headset type terminal 314 to a user in real time. === Hard Collateral 1-4 === Each of the multiple elements including the location information acquisition unit, trend data collection unit, data analysis unit, advertisement generation unit, and advertisement provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the location information acquisition unit can acquire geographical location information of a user using the camera 42 or microphone 238 of the robot 414. For example, the trend data collection unit can collect trend data from social networking sites or news sites by the specific processing unit 290 of the data processing device 12. For example, the data analysis unit can analyze the geographical location information and trend data collected by the specific processing unit 290 of the data processing device 12. For example, the advertisement generation unit can generate an advertisement based on the data analyzed by the specific processing unit 290 of the data processing device 12. For example, the advertisement provision unit can provide an advertisement generated by the control unit 46A of the robot 414 to a user in real time.

[0121] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0122] The advertisement customization system may further include a purchase history analysis unit that analyzes a user's purchase history. The purchase history analysis unit analyzes the user's past purchase history to understand the user's purchasing trends. For example, the purchase history analysis unit may collect data on products and services purchased by the user in the past and identify the user's preferences and interests. The purchase history analysis unit may also analyze the user's purchase frequency and purchase timing to determine the optimal timing for providing advertisements. Furthermore, the purchase history analysis unit may generate advertisements for related products and services based on the user's purchase history. This makes it possible to provide more personalized advertisements by utilizing the user's purchase history.

[0123] The advertisement customization system may further include a device usage monitoring unit that monitors the user's device usage. The device usage monitoring unit monitors the user's device usage patterns and determines the optimal timing for providing advertisements. For example, the device usage monitoring unit may identify the time periods during which the user frequently uses their smartphone and provide advertisements during those time periods. The device usage monitoring unit may also provide advertisements that avoid the time periods during which the user is not using the device. Furthermore, the device usage monitoring unit may provide relevant advertisements when the user is using a specific application. This allows for effective advertisement provision by taking into account the user's device usage.

[0124] The advertisement customization system may further include an emotion analysis unit that estimates a user's emotion and adjusts the content of the advertisement based on the estimated user's emotion. The emotion analysis unit estimates a user's emotion in real time and adjusts the content of the advertisement to match the user's emotion. For example, if the user is happy, the emotion analysis unit may provide a bright and positive advertisement. If the user is sad, the emotion analysis unit may provide an advertisement containing a comforting or encouraging message. Furthermore, if the user is excited, the emotion analysis unit may provide an energetic and stimulating advertisement. This maximizes the effectiveness of the advertisement by providing an advertisement that matches the user's emotion.

[0125] The ad customization system may further include a social media analysis unit that analyzes a user's social media activity. The social media analysis unit analyzes the user's social media activity and customizes the content of the ad. For example, the social media analysis unit may analyze the accounts the user follows and the content of their posts to identify the user's interests. The social media analysis unit may also analyze the groups and events the user participates in and provide relevant ads. Furthermore, the social media analysis unit may analyze the content of the user's posts and comments to provide ads that reflect the user's emotions and opinions. This makes it possible to provide more personalized ads by utilizing the user's social media activity.

[0126] The advertisement customization system may further include a health data analysis unit that analyzes the user's health data. The health data analysis unit analyzes the user's health status and customizes the advertisement content. For example, the health data analysis unit may analyze data collected from the user's fitness tracker or smartwatch to understand the user's exercise habits and health status. The health data analysis unit may also analyze the user's food records and sleep data to provide health-related advertisements. Furthermore, the health data analysis unit may analyze the user's health goals and progress to provide advertisements that motivate the user. In this way, by utilizing the user's health data, more relevant advertisements can be provided.

[0127] The advertisement customization system may further include a display format adjustment unit that estimates a user's emotion and adjusts the display format of the advertisement based on the estimated user's emotion. The display format adjustment unit changes the display format of the advertisement according to the user's emotion. For example, if the user is relaxed, the display format adjustment unit may display an advertisement with a quiet and calm tone. If the user is excited, the display format adjustment unit may display a visually stimulating advertisement. Furthermore, if the user is stressed, the display format adjustment unit may display a simple, highly visible advertisement. In this way, the effectiveness of the advertisement can be maximized by adjusting the display format of the advertisement based on the user's emotion.

[0128] The advertisement customization system may further include a voice data analysis unit that analyzes the user's voice data. The voice data analysis unit analyzes the user's voice data and customizes the content of the advertisement. For example, the voice data analysis unit may analyze the user's voice commands and conversation content to identify the user's interests and concerns. The voice data analysis unit may also analyze the user's tone of voice and emotions to provide advertisements that correspond to the user's emotions. Furthermore, the voice data analysis unit may analyze the user's voice data in real time and provide relevant advertisements instantly. In this way, by utilizing the user's voice data, more personalized advertisements can be provided.

[0129] The advertisement customization system may further include a delivery timing adjustment unit that estimates a user's emotions and adjusts the timing of advertisement delivery based on the estimated user emotions. The delivery timing adjustment unit changes the timing of advertisement delivery according to the user's emotions. For example, the delivery timing adjustment unit can deliver advertisements slowly when the user is relaxed. Also, the delivery timing adjustment unit can deliver advertisements quickly when the user is excited. Furthermore, the delivery timing adjustment unit can temporarily stop advertisement delivery when the user is stressed. In this way, the effectiveness of advertisements can be maximized by adjusting the timing of advertisement delivery based on the user's emotions.

[0130] The advertisement customization system may further include a purchasing intent analysis unit that estimates a user's purchasing intent and adjusts the content of advertisements based on the estimated purchasing intent. The purchasing intent analysis unit estimates a user's purchasing intent in real time and adjusts the content of advertisements to match the user's purchasing intent. For example, if a user shows a high purchasing intent, the purchasing intent analysis unit may provide advertisements that include special offers or discount information. Furthermore, if a user shows a low purchasing intent, the purchasing intent analysis unit may provide advertisements that emphasize the appeal of products. Furthermore, the purchasing intent analysis unit may adjust the frequency of advertisement display based on the user's purchasing intent. This maximizes the effectiveness of advertisements by providing advertisements that match the user's purchasing intent.

[0131] The advertisement customization system may further include a targeting adjustment unit that estimates a user's emotions and adjusts advertisement targeting based on the estimated user emotions. The targeting adjustment unit changes advertisement targeting according to the user's emotions. For example, if the user is relaxed, the targeting adjustment unit may provide advertisements for products or services that have a relaxing effect. Furthermore, if the user is excited, the targeting adjustment unit may provide advertisements related to entertainment or activities. Furthermore, if the user is stressed, the targeting adjustment unit may provide advertisements related to relaxation or stress relief. In this way, by adjusting advertisement targeting based on the user's emotions, the effectiveness of advertisements can be maximized.

[0132] The processing flow of the second embodiment will be briefly explained below.

[0133] Step 1: The location information acquisition unit acquires the user's geographic location information. For example, the location information acquisition unit can acquire the location information from the user's smartphone or GPS device. The location information acquisition unit can also acquire the location information using Wi-Fi location information or cell tower data. Step 2: The trend data collection unit collects local trend data. For example, the trend data collection unit can collect trend data from social media, news sites, local event information, etc. It can also collect data from local bulletin boards and online forums. Step 3: The data analysis unit analyzes the collected geographic location information and trend data using generative AI. For example, the data analysis unit can analyze the data using deep learning models or natural language processing techniques. It can also use machine learning algorithms to analyze patterns in the data. Step 4: The ad generation unit generates ads based on the data analyzed by the data analysis unit. For example, the ad generation unit can use AI to generate ads based on the user's current location and regional trends. It can also generate ads in the form of banner ads, text ads, video ads, etc. Step 5: The advertisement provision unit provides the generated advertisement to the user in real time. For example, the advertisement provision unit can display the generated advertisement on a smartphone screen. The advertisement can also be provided using push notifications or real-time distribution.

[0134] 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.

[0135] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

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

[0139] 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.

[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 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.

[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 (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).

[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] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0171] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0185] 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.

[0186] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0192] 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."

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] [Explanation of symbols]

[0206] 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 location information acquisition unit that acquires geographical location information of a user; a trend data collection unit that collects regional trend data; a data analysis unit that analyzes the data acquired by the location information acquisition unit and the trend data collection unit; an advertisement generation unit that generates an advertisement based on the data analyzed by the data analysis unit; an advertisement providing unit that provides the advertisement generated by the advertisement generating unit to a user; A system characterized by:

2. The location information acquisition unit Obtain location information from the user's smartphone or GPS device 2. The system of claim 1.

3. The trend data collection unit Collect trend data from social media, news sites, and local event information 2. The system of claim 1.

4. The data analysis unit Analyze collected geographic location information and trend data using AI generation.

2. The system of claim 1.

5. The advertisement generation unit Generate ads based on the user's current location and local trends using AI 2. The system of claim 1.

6. The advertisement providing unit The generated advertisement is immediately provided to the user.

2. The system of claim 1.

7. The location information acquisition unit Estimates the user's emotions and adjusts the timing of acquiring location information based on the estimated user emotions.

2. The system of claim 1.

8. The location information acquisition unit Analyze the user's past movement history and select the appropriate method for obtaining location information 2. The system of claim 1.

9. The location information acquisition unit Filter location information based on the user's current activity or interests 2. The system of claim 1.

10. The location information acquisition unit When acquiring location information, select the appropriate acquisition method depending on the user's input method.

2. The system of claim 1.

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