System

Abstract

An object of a system according to an embodiment is to efficiently generate an optimum advertisement for a target user.SOLUTION: A system according to an embodiment includes an image recognition unit, a moving image generation unit, a narration generation unit, and a music generation unit. The image recognition unit recognizes characteristics of the surrounding environment and people. The moving image generation unit generates an advertisement image based on the information recognized by the image recognition unit. The narration generation section generates narration in accordance with the advertisement image generated by the moving image generation section. The music generation unit generates music in accordance with the advertisement image generated by the moving image generation unit.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 techniques have had the problem that the process for generating advertisements optimal for target users is complicated and difficult to carry out efficiently.

[0005] The system according to the embodiment aims to efficiently generate advertisements that are optimal for target users. [Means for solving the problem]

[0006] The system according to the embodiment includes an image recognition unit, a video generation unit, a narration generation unit, and a music generation unit. The image recognition unit recognizes the surrounding environment and characteristics of people. The video generation unit generates an advertising video based on information recognized by the image recognition unit. The narration generation unit generates narration to match the advertising video generated by the video generation unit. The music generation unit generates music to match the advertising video generated by the video generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently generate advertisements that are optimal for target users. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The advertisement generation system according to the embodiment of the present invention recognizes the characteristics of the surrounding environment and people, and generates advertisements using a generation AI. This allows the advertisement generation system to effectively provide advertisements targeted to target users.

[0029] An advertisement generation system according to an embodiment includes an image recognition unit, a video generation unit, a narration generation unit, and a music generation unit. The image recognition unit recognizes the surrounding environment and characteristics of people. For example, the image recognition unit uses a camera to recognize the age, gender, clothing, etc. of passersby and collects that information. The image recognition unit can also use an in-store camera to recognize products lined up on shelves and collect that information. The video generation unit generates an advertisement video based on the information recognized by the image recognition unit. For example, the video generation unit generates a video introducing appropriate products or services based on the age and gender of passersby. The video generation unit can also generate advertisement videos that change in real time based on the characteristics of passersby. The narration generation unit generates narration to match the advertisement video generated by the video generation unit. For example, the narration generation unit generates audio explaining the features and benefits of a product. The narration generation unit can also convey a personalized message by reflecting the user's name and personal information. The music generation unit generates music to match the advertisement video generated by the video generation unit. For example, the music generation unit generates background music that matches the image of the product. The music generation unit can also select a music genre or style that matches the user's emotions, allowing the advertisement generation system to effectively provide advertisements tailored to the target user.

[0030] The image recognition unit may include an information collection unit that uses a camera to recognize passersby's age, gender, clothing, etc. and collects that information. For example, the image recognition unit uses a camera to recognize passersby's age, gender, clothing, etc. and collects that information. For example, image recognition AI tracks passersby's movements in real time and predicts where they are likely to go next. For example, if it predicts that they are heading to a specific store in a shopping mall, it prepares product advertisements for that store in advance. Furthermore, image recognition AI learns passersby's past behavior patterns and predicts the area they are likely to visit next. For example, it displays advertisements for nearby cafes to passersby who have visited a cafe before. Furthermore, image recognition AI analyzes passersby's current behavior and predicts their next action. For example, if a passerby is looking at a smartphone, it displays advertisements for nearby electronics stores. This makes it possible to generate advertisements based on passersby's characteristics.

[0031] The image recognition unit may include an information collection unit that uses in-store cameras to recognize products lined up on shelves and collects that information. The image recognition unit, for example, uses in-store cameras to recognize products lined up on shelves and collects that information. For example, image recognition AI analyzes the facial expression and posture of passersby to detect signs of fatigue or stress. For example, if it determines that a passerby is tired, it displays an advertisement for a relaxation massage. In addition, image recognition AI analyzes the facial expression of passersby to estimate their mood. For example, if a passerby does not smile much, it displays an advertisement for a cafe where you can refresh yourself. In addition, image recognition AI analyzes the movements of passersby to estimate their health condition. For example, if a passerby's gait is unsteady, it displays an advertisement for a health support product. This makes it possible to generate advertisements based on in-store product information.

[0032] The video generation unit may include a video generation unit that generates videos introducing appropriate products and services according to the age and gender of passersby. The video generation unit generates videos introducing appropriate products and services according to, for example, the age and gender of passersby. For example, an image recognition AI analyzes the facial expressions of passersby in real time and estimates their emotions. For example, if a passerby is not smiling much, a humorous advertisement is displayed to elicit a smile. The image recognition AI also analyzes the emotions of passersby and generates an advertisement to elicit positive emotions. For example, if a passerby is feeling stressed, an advertisement for a relaxation product is displayed. The image recognition AI also analyzes the emotions of passersby in real time and generates an advertisement that combines images and music to elicit positive emotions. For example, an advertisement that combines moving images with soothing music is displayed. This makes it possible to generate appropriate advertising videos based on the characteristics of passersby.

[0033] The narration generation unit may include a voice generation unit that generates voice that explains the features and benefits of a product. The narration generation unit generates voice that explains the features and benefits of a product, for example. For example, an image recognition AI analyzes the clothing and belongings of passersby to infer their hobbies and preferences. For example, if the passerby is wearing sportswear, an advertisement for a sporting event is displayed. Alternatively, the image recognition AI analyzes the behavioral patterns of passersby to infer their hobbies and preferences. For example, an advertisement for the opening of a new cafe is displayed to a passerby who frequently visits a cafe. Alternatively, the image recognition AI analyzes the appearance and behavior of passersby to infer their hobbies and preferences. For example, an advertisement for a photography exhibition is displayed to a passerby who is carrying a camera. This allows the features and benefits of a product to be effectively communicated.

[0034] The music generation unit may include a background music generation unit that generates background music that matches the image of the product. The music generation unit generates background music that matches the image of the product, for example. For example, an image recognition AI analyzes the behavior of passersby and estimates whether they are with friends or family. For example, if multiple people are traveling, restaurant advertisements for groups are displayed. The image recognition AI also analyzes the characteristics of passersby and estimates their social relationships. For example, if a parent and child are traveling, an event advertisement for families is displayed. The image recognition AI also analyzes the behavior patterns of passersby and estimates their social relationships. For example, if friends are shopping together, an advertisement for a group discount is displayed. This makes it possible to increase the effectiveness of advertising by providing music that matches the image of the product.

[0035] The video generation unit may include an advertising video generation unit that generates advertising videos that change in real time according to the characteristics of passersby. The video generation unit generates advertising videos that change in real time according to the characteristics of passersby, for example. For example, an image recognition AI analyzes the emotions of passersby in real time and generates an advertisement that suggests music that matches those emotions. For example, if a passerby feels like relaxing, an advertisement with relaxing music is displayed. Furthermore, the image recognition AI analyzes the emotions of passersby and generates an advertisement that suggests video content that matches those emotions. For example, if a passerby feels like cheering up, an advertisement with energetic video is displayed. Furthermore, the image recognition AI analyzes the emotions of passersby in real time and generates an advertisement that combines music and video content that matches those emotions. For example, an advertisement that combines inspiring video with soothing music is displayed. This makes it possible to provide advertisements that change in real time according to the characteristics of passersby.

[0036] The video generation unit may include a story generation unit that reflects the user's past behavioral data and creates a personalized story. The video generation unit, for example, reflects the user's past behavioral data and creates a personalized story. For example, the video generation AI analyzes the user's past purchasing history and generates a personalized advertising video based on that data. For example, a story related to products purchased in the past is created. The video generation AI also analyzes the user's past browsing history and generates a personalized advertising video based on that data. For example, a story related to products or services viewed in the past is created. The video generation AI also analyzes the user's past behavioral data and generates a personalized advertising video based on that data. For example, a story related to places visited or events attended in the past is created. In this way, personalized advertising videos can be generated based on the user's past behavioral data.

[0037] The video generation unit may include an introduction unit that reflects the user's current location information and introduces nearby stores and services. The video generation unit, for example, reflects the user's current location information and introduces nearby stores and services. For example, the video generation AI acquires the user's current location information and generates an advertising video introducing nearby stores and services based on that data. For example, it displays an advertisement for the cafe or restaurant closest to the current location. The video generation AI also acquires the user's current location information and generates an advertising video introducing nearby events and activities based on that data. For example, it displays an advertisement for the concert or exhibition closest to the current location. The video generation AI also acquires the user's current location information and generates an advertising video introducing nearby sales and campaigns based on that data. For example, it displays sales information for the store closest to the current location. This makes it possible to generate advertising videos introducing nearby stores and services based on the user's current location information.

[0038] The narration generation unit may include a message generation unit that reflects the user's name and personal information and delivers a personalized message. The narration generation unit, for example, reflects the user's name and personal information and delivers a personalized message. For example, the narration AI incorporates the user's name into the voice to generate a personalized message. For example, it generates a message such as, "Hello, Yamada-san. We have a special offer for you today." The narration AI also incorporates the user's past purchase history into the voice to generate a personalized message. For example, it generates a message such as, "We'd like to introduce new products related to your last purchase." The narration AI also incorporates the user's personal information into the voice to generate a personalized message. For example, it generates a message such as, "Happy birthday. We've prepared a special gift for you." This makes it possible to deliver a personalized message based on the user's name and personal information.

[0039] The narration generation unit may include a message generation unit that reflects the user's past purchase history and conveys a message encouraging repeat purchases. The narration generation unit, for example, reflects the user's past purchase history and conveys a message encouraging repeat purchases. For example, a narration AI incorporates the user's past purchase history into voice to generate a message encouraging repeat purchases. For example, it generates a message such as, "Would you like to purchase the shampoo you purchased last time again?" The narration AI also incorporates the user's past purchase history into voice to generate a message encouraging repeat purchases. For example, it generates a message such as, "We recommend that you repeat the spa service you used last time." The narration AI also incorporates the user's past purchase history into voice to generate a message encouraging repeat purchases. For example, it generates a message such as, "Would you like to purchase the wine you purchased last time again?" This makes it possible to convey a message encouraging repeat purchases based on the user's past purchase history.

[0040] The music generation unit may include a background music generation unit that reflects the user's past music preferences and provides preferred background music. The music generation unit, for example, reflects the user's past music preferences and provides preferred background music. For example, a music generation AI analyzes the user's past music preferences and generates preferred background music based on the data. For example, background music that reflects the style of music by artists the user has listened to in the past is provided. The music generation AI also analyzes the user's past music preferences and generates preferred background music based on the data. For example, background music that reflects music genres that the user has listened to in the past is provided. The music generation AI also analyzes the user's past music preferences and generates preferred background music based on the data. For example, background music that reflects the characteristics of songs that the user has added to a playlist in the past is provided. In this way, preferred background music can be provided based on the user's past music preferences.

[0041] The advertising video generation unit may include a distribution unit and a display unit that distribute and display the advertising video through digital signage or an online platform. The advertising video generation unit distributes and displays the advertising video through, for example, digital signage or an online platform. For example, advertising videos displayed on digital signage change in real time according to the characteristics of passersby. For example, advertisements based on age and gender may be displayed. Furthermore, online platforms may display personalized advertisements based on a user's browsing history and interests. For example, advertisements related to products previously viewed may be displayed. Furthermore, advertising videos may be distributed at optimal times based on user behavior data. For example, specific advertisements may be displayed at specific times. This allows the advertising video to be distributed and displayed through digital signage or an online platform.

[0042] The advertising video generation unit may include a distribution unit that distributes advertising videos in an optimal format depending on the user's device. The advertising video generation unit distributes advertising videos in an optimal format depending on the user's device, for example. For example, advertising videos are distributed in an optimal format depending on the user's device, such as a smartphone, tablet, or PC. For example, a vertical advertising video is generated for a smartphone. Furthermore, advertising videos are distributed at an optimal resolution depending on the screen size of the user's device. For example, high-quality advertising videos are distributed to a high-resolution display. Furthermore, advertising videos are distributed at an optimal bitrate depending on the connection speed of the user's device. For example, low-bitrate advertising videos are distributed to a slow connection environment. This makes it possible to distribute advertising videos in an optimal format depending on the user's device.

[0043] The advertising video generation unit may include a distribution unit that distributes advertising videos at optimal timing based on user behavior data. The advertising video generation unit distributes advertising videos at optimal timing based on, for example, user behavior data. For example, advertising videos are distributed at optimal timing by analyzing the user's past behavior data. For example, a specific advertisement is displayed during a specific time period. Furthermore, advertising videos are distributed at optimal timing by analyzing the user's current behavior in real time. For example, a relevant advertisement is displayed immediately after the user takes a specific action. Furthermore, advertising videos learn the user's behavior patterns and are distributed at optimal timing. For example, a relevant advertisement is displayed when the user is in a specific location. This makes it possible to distribute advertising videos at optimal timing based on the user's behavior data.

[0044] The advertising video generation unit may include a generation unit that is personalized based on the user's interests and concerns. The advertising video generation unit is personalized, for example, based on the user's interests and concerns. For example, the advertising video is personalized based on the data obtained by analyzing the user's past browsing history. For example, advertisements related to products previously viewed are displayed. The advertising video is also personalized based on the data obtained by analyzing the user's past purchasing history. For example, advertisements related to products previously purchased are displayed. The advertising video is also personalized based on the data obtained by analyzing the user's interests and concerns. For example, advertisements related to specific hobbies or interests are displayed. This makes it possible to generate advertising videos that are personalized based on the user's interests and concerns.

[0045] The advertising video generation unit may include a distribution unit that distributes advertising videos at optimal timing based on user behavior data. The advertising video generation unit distributes advertising videos at optimal timing based on, for example, user behavior data. For example, advertising videos are distributed at optimal timing by analyzing the user's past behavior data. For example, a specific advertisement is displayed during a specific time period. Furthermore, advertising videos are distributed at optimal timing by analyzing the user's current behavior in real time. For example, a relevant advertisement is displayed immediately after the user takes a specific action. Furthermore, advertising videos learn the user's behavior patterns and are distributed at optimal timing. For example, a relevant advertisement is displayed when the user is in a specific location. This makes it possible to distribute advertising videos at optimal timing based on the user's behavior data.

[0046] The advertising video generation unit may include a generation unit that is personalized based on the user's interests and concerns. The advertising video generation unit is personalized, for example, based on the user's interests and concerns. For example, the advertising video is personalized based on the data obtained by analyzing the user's past browsing history. For example, advertisements related to products previously viewed are displayed. The advertising video is also personalized based on the data obtained by analyzing the user's past purchasing history. For example, advertisements related to products previously purchased are displayed. The advertising video is also personalized based on the data obtained by analyzing the user's interests and concerns. For example, advertisements related to specific hobbies or interests are displayed. This makes it possible to generate advertising videos that are personalized based on the user's interests and concerns.

[0047] The advertising video generation unit may include a distribution unit that distributes advertising videos at optimal timing based on user behavior data. The advertising video generation unit distributes advertising videos at optimal timing based on, for example, user behavior data. For example, advertising videos are distributed at optimal timing by analyzing the user's past behavior data. For example, a specific advertisement is displayed during a specific time period. Furthermore, advertising videos are distributed at optimal timing by analyzing the user's current behavior in real time. For example, a relevant advertisement is displayed immediately after the user takes a specific action. Furthermore, advertising videos learn the user's behavior patterns and are distributed at optimal timing. For example, a relevant advertisement is displayed when the user is in a specific location. This makes it possible to distribute advertising videos at optimal timing based on the user's behavior data.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The advertisement generation system may further include a health monitoring unit that monitors the user's health condition. For example, the health monitoring unit may measure the user's heart rate and number of steps in real time and evaluate the user's health condition based on that data. For example, if the user's heart rate is high, advertisements for relaxation products may be displayed. Alternatively, if the user's number of steps is low, advertisements for fitness-related products may be displayed. Furthermore, the health monitoring unit may analyze the user's sleep patterns and evaluate the quality of the sleep. For example, if the user is determined to be sleep deprived, advertisements for sleep aids may be displayed. This makes it possible to provide appropriate advertisements based on the user's health condition.

[0050] The advertisement generation system may further include a hobby and preference learning unit that learns the user's hobbies and preferences. For example, the hobby and preference learning unit analyzes the user's past behavioral data to learn the user's hobbies and preferences. For example, the hobby and preference learning unit estimates the user's hobbies based on content viewed in the past and events attended in the past. The hobby and preference learning unit also analyzes the user's social media posts to learn the user's hobbies and preferences. For example, if there are many posts about a particular genre of music or movies, advertisements related to that genre can be displayed. Furthermore, the hobby and preference learning unit analyzes the user's purchase history to learn the user's hobbies and preferences. For example, if the user frequently purchases products from a particular brand, advertisements related to that brand can be displayed. This makes it possible to provide advertisements personalized based on the user's hobbies and preferences.

[0051] The advertisement generation system may further include a location information optimization unit that optimizes advertisements using the user's location information. For example, the location information optimization unit acquires the user's current location information and generates advertisements introducing nearby stores and services based on that data. For example, advertisements for cafes and restaurants closest to the current location may be displayed. The location information optimization unit may also analyze the user's past location information and learn behavioral patterns. For example, advertisements related to a specific area may be displayed to a user who frequently visits that area. Furthermore, the location information optimization unit may adjust the timing of advertisements based on the user's location information. For example, relevant advertisements may be displayed immediately after the user arrives at a specific location. This makes it possible to provide effective advertisements based on the user's location information.

[0052] The advertisement generation system can further include a recommendation unit that makes recommendations based on the user's purchase history. For example, the recommendation unit analyzes the user's past purchase history and displays advertisements for related products. For example, it displays advertisements for new products related to products purchased in the past. The recommendation unit also generates personalized advertisements based on the user's purchase history. For example, if the user frequently purchases products from a particular brand, it can also display advertisements for new products from that brand. Furthermore, the recommendation unit displays advertisements for related services based on the user's purchase history. For example, it can also display advertisements for new services related to services used in the past. This makes it possible to provide effective advertisements based on the user's purchase history.

[0053] The advertisement generation system may further include a device analysis unit that analyzes the user's device usage. For example, the device analysis unit may analyze the user's device usage in real time and generate advertisements based on that data. For example, if the user frequently uses a smartphone, mobile advertisements may be displayed. The device analysis unit may also analyze the user's device settings and app usage and display relevant advertisements. For example, if the user frequently uses a particular app, advertisements related to that app may be displayed. Furthermore, the device analysis unit may analyze the remaining battery level and connection status of the user's device and adjust the content of the advertisements. For example, if the battery is low, an effective advertisement may be displayed in a short time. This makes it possible to provide effective advertisements based on the user's device usage.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The image recognition unit recognizes the characteristics of the surrounding environment and people. For example, it uses a camera to recognize the age, gender, clothing, etc. of passersby and collects that information. It can also use cameras in stores to recognize products lined up on shelves and collect that information. Step 2: The video generation unit generates advertising videos based on the information recognized by the image recognition unit. For example, it generates videos introducing appropriate products and services based on the age and gender of passersby. It can also generate advertising videos that change in real time based on the characteristics of passersby. Step 3: The narration generator generates a narration to match the advertising video generated by the video generator. For example, it generates a voice that explains the features and benefits of a product. It can also reflect the user's name and personal information to convey a personalized message. Step 4: The music generation unit generates music to match the advertising video generated by the video generation unit. For example, it generates background music that matches the image of the product. It can also select a music genre or style that matches the user's emotions.

[0056] (Example 2) The advertisement generation system according to the embodiment of the present invention recognizes the characteristics of the surrounding environment and people, and generates advertisements using a generation AI. This allows the advertisement generation system to effectively provide advertisements targeted to target users.

[0057] An advertisement generation system according to an embodiment includes an image recognition unit, a video generation unit, a narration generation unit, and a music generation unit. The image recognition unit recognizes the surrounding environment and characteristics of people. For example, the image recognition unit uses a camera to recognize the age, gender, clothing, etc. of passersby and collects that information. The image recognition unit can also use an in-store camera to recognize products lined up on shelves and collect that information. The video generation unit generates an advertisement video based on the information recognized by the image recognition unit. For example, the video generation unit generates a video introducing appropriate products or services based on the age and gender of passersby. The video generation unit can also generate advertisement videos that change in real time based on the characteristics of passersby. The narration generation unit generates narration to match the advertisement video generated by the video generation unit. For example, the narration generation unit generates audio explaining the features and benefits of a product. The narration generation unit can also convey a personalized message by reflecting the user's name and personal information. The music generation unit generates music to match the advertisement video generated by the video generation unit. For example, the music generation unit generates background music that matches the image of the product. The music generation unit can also select a music genre or style that matches the user's emotions, allowing the advertisement generation system to effectively provide advertisements tailored to the target user.

[0058] The image recognition unit may include an information collection unit that uses a camera to recognize passersby's age, gender, clothing, etc. and collects that information. For example, the image recognition unit uses a camera to recognize passersby's age, gender, clothing, etc. and collects that information. For example, image recognition AI tracks passersby's movements in real time and predicts where they are likely to go next. For example, if it predicts that they are heading to a specific store in a shopping mall, it prepares product advertisements for that store in advance. Furthermore, image recognition AI learns passersby's past behavior patterns and predicts the area they are likely to visit next. For example, it displays advertisements for nearby cafes to passersby who have visited a cafe before. Furthermore, image recognition AI analyzes passersby's current behavior and predicts their next action. For example, if a passerby is looking at a smartphone, it displays advertisements for nearby electronics stores. This makes it possible to generate advertisements based on passersby's characteristics.

[0059] The image recognition unit may include an information collection unit that uses in-store cameras to recognize products lined up on shelves and collects that information. The image recognition unit, for example, uses in-store cameras to recognize products lined up on shelves and collects that information. For example, image recognition AI analyzes the facial expression and posture of passersby to detect signs of fatigue or stress. For example, if it determines that a passerby is tired, it displays an advertisement for a relaxation massage. In addition, image recognition AI analyzes the facial expression of passersby to estimate their mood. For example, if a passerby does not smile much, it displays an advertisement for a cafe where you can refresh yourself. In addition, image recognition AI analyzes the movements of passersby to estimate their health condition. For example, if a passerby's gait is unsteady, it displays an advertisement for a health support product. This makes it possible to generate advertisements based on in-store product information.

[0060] The video generation unit may include a video generation unit that generates videos introducing appropriate products and services according to the age and gender of passersby. The video generation unit generates videos introducing appropriate products and services according to, for example, the age and gender of passersby. For example, an image recognition AI analyzes the facial expressions of passersby in real time and estimates their emotions. For example, if a passerby is not smiling much, a humorous advertisement is displayed to elicit a smile. The image recognition AI also analyzes the emotions of passersby and generates an advertisement to elicit positive emotions. For example, if a passerby is feeling stressed, an advertisement for a relaxation product is displayed. The image recognition AI also analyzes the emotions of passersby in real time and generates an advertisement that combines images and music to elicit positive emotions. For example, an advertisement that combines moving images with soothing music is displayed. This makes it possible to generate appropriate advertising videos based on the characteristics of passersby.

[0061] The narration generation unit may include a voice generation unit that generates voice that explains the features and benefits of a product. The narration generation unit generates voice that explains the features and benefits of a product, for example. For example, an image recognition AI analyzes the clothing and belongings of passersby to infer their hobbies and preferences. For example, if the passerby is wearing sportswear, an advertisement for a sporting event is displayed. Alternatively, the image recognition AI analyzes the behavioral patterns of passersby to infer their hobbies and preferences. For example, an advertisement for the opening of a new cafe is displayed to a passerby who frequently visits a cafe. Alternatively, the image recognition AI analyzes the appearance and behavior of passersby to infer their hobbies and preferences. For example, an advertisement for a photography exhibition is displayed to a passerby who is carrying a camera. This allows the features and benefits of a product to be effectively communicated.

[0062] The music generation unit may include a background music generation unit that generates background music that matches the image of the product. The music generation unit generates background music that matches the image of the product, for example. For example, an image recognition AI analyzes the behavior of passersby and estimates whether they are with friends or family. For example, if multiple people are traveling, restaurant advertisements for groups are displayed. The image recognition AI also analyzes the characteristics of passersby and estimates their social relationships. For example, if a parent and child are traveling, an event advertisement for families is displayed. The image recognition AI also analyzes the behavior patterns of passersby and estimates their social relationships. For example, if friends are shopping together, an advertisement for a group discount is displayed. This makes it possible to increase the effectiveness of advertising by providing music that matches the image of the product.

[0063] The video generation unit may include an advertising video generation unit that generates advertising videos that change in real time according to the characteristics of passersby. The video generation unit generates advertising videos that change in real time according to the characteristics of passersby, for example. For example, an image recognition AI analyzes the emotions of passersby in real time and generates an advertisement that suggests music that matches those emotions. For example, if a passerby feels like relaxing, an advertisement with relaxing music is displayed. Furthermore, the image recognition AI analyzes the emotions of passersby and generates an advertisement that suggests video content that matches those emotions. For example, if a passerby feels like cheering up, an advertisement with energetic video is displayed. Furthermore, the image recognition AI analyzes the emotions of passersby in real time and generates an advertisement that combines music and video content that matches those emotions. For example, an advertisement that combines inspiring video with soothing music is displayed. This makes it possible to provide advertisements that change in real time according to the characteristics of passersby.

[0064] The video generation unit may include a story generation unit that reflects the user's past behavioral data and creates a personalized story. The video generation unit, for example, reflects the user's past behavioral data and creates a personalized story. For example, the video generation AI analyzes the user's past purchasing history and generates a personalized advertising video based on that data. For example, a story related to products purchased in the past is created. The video generation AI also analyzes the user's past browsing history and generates a personalized advertising video based on that data. For example, a story related to products or services viewed in the past is created. The video generation AI also analyzes the user's past behavioral data and generates a personalized advertising video based on that data. For example, a story related to places visited or events attended in the past is created. In this way, personalized advertising videos can be generated based on the user's past behavioral data.

[0065] The video generation unit may include an introduction unit that reflects the user's current location information and introduces nearby stores and services. The video generation unit, for example, reflects the user's current location information and introduces nearby stores and services. For example, the video generation AI acquires the user's current location information and generates an advertising video introducing nearby stores and services based on that data. For example, it displays an advertisement for the cafe or restaurant closest to the current location. The video generation AI also acquires the user's current location information and generates an advertising video introducing nearby events and activities based on that data. For example, it displays an advertisement for the concert or exhibition closest to the current location. The video generation AI also acquires the user's current location information and generates an advertising video introducing nearby sales and campaigns based on that data. For example, it displays sales information for the store closest to the current location. This makes it possible to generate advertising videos introducing nearby stores and services based on the user's current location information.

[0066] The video generation unit can include an adjustment unit that adjusts the visual effects in real time according to the user's emotions. The video generation unit, for example, adjusts the visual effects in real time according to the user's emotions. For example, the video generation AI analyzes the user's emotions in real time and adjusts the visual effects in accordance with the emotions. For example, bright colors and up-tempo music are used for a user who has positive emotions. The video generation AI also analyzes the user's emotions in real time and adjusts the visual effects in accordance with the emotions. For example, calm colors and relaxation music are used for a user who wants to relax. The video generation AI also analyzes the user's emotions in real time and adjusts the visual effects in accordance with the emotions. For example, vivid colors and energetic music are used for a user who is feeling energetic. In this way, the visual effects can be adjusted in real time according to the user's emotions.

[0067] The narration generation unit may include a message generation unit that reflects the user's name and personal information and delivers a personalized message. The narration generation unit, for example, reflects the user's name and personal information and delivers a personalized message. For example, the narration AI incorporates the user's name into the voice to generate a personalized message. For example, it generates a message such as, "Hello, Yamada-san. We have a special offer for you today." The narration AI also incorporates the user's past purchase history into the voice to generate a personalized message. For example, it generates a message such as, "We'd like to introduce new products related to your last purchase." The narration AI also incorporates the user's personal information into the voice to generate a personalized message. For example, it generates a message such as, "Happy birthday. We've prepared a special gift for you." This makes it possible to deliver a personalized message based on the user's name and personal information.

[0068] The narration generation unit may include a message generation unit that reflects the user's past purchase history and conveys a message encouraging repeat purchases. The narration generation unit, for example, reflects the user's past purchase history and conveys a message encouraging repeat purchases. For example, a narration AI incorporates the user's past purchase history into voice to generate a message encouraging repeat purchases. For example, it generates a message such as, "Would you like to purchase the shampoo you purchased last time again?" The narration AI also incorporates the user's past purchase history into voice to generate a message encouraging repeat purchases. For example, it generates a message such as, "We recommend that you repeat the spa service you used last time." The narration AI also incorporates the user's past purchase history into voice to generate a message encouraging repeat purchases. For example, it generates a message such as, "Would you like to purchase the wine you purchased last time again?" This makes it possible to convey a message encouraging repeat purchases based on the user's past purchase history.

[0069] The narration generation unit may include a narration generation unit that generates narration in a tone and tempo that correspond to the user's emotions. The narration generation unit generates narration in a tone and tempo that correspond to the user's emotions, for example. For example, a narration AI analyzes the user's emotions in real time and generates narration in a tone and tempo that correspond to the emotions. For example, a narration AI generates narration in a calm tone and at a slow tempo for a user who feels like relaxing. The narration AI also analyzes the user's emotions in real time and generates narration in a tone and tempo that correspond to the emotions. For example, a narration AI generates narration in a bright tone and at a fast tempo for a user who is feeling energetic. The narration AI also analyzes the user's emotions in real time and generates narration in a tone and tempo that correspond to the emotions. For example, a narration AI generates narration in an emotional tone and at a moderate tempo for a user who is feeling emotional. This makes it possible to generate narration in a tone and tempo that correspond to the user's emotions.

[0070] The music generation unit may include a background music generation unit that reflects the user's past music preferences and provides preferred background music. The music generation unit, for example, reflects the user's past music preferences and provides preferred background music. For example, a music generation AI analyzes the user's past music preferences and generates preferred background music based on the data. For example, background music that reflects the style of music by artists the user has listened to in the past is provided. The music generation AI also analyzes the user's past music preferences and generates preferred background music based on the data. For example, background music that reflects music genres that the user has listened to in the past is provided. The music generation AI also analyzes the user's past music preferences and generates preferred background music based on the data. For example, background music that reflects the characteristics of songs that the user has added to a playlist in the past is provided. In this way, preferred background music can be provided based on the user's past music preferences.

[0071] The music generation unit may include a selection unit that selects a music genre and style according to the user's emotions. The music generation unit, for example, selects a music genre and style according to the user's emotions. For example, the music generation AI analyzes the user's emotions in real time and selects a music genre and style according to those emotions. For example, classical music is provided to a user who wants to relax. The music generation AI also analyzes the user's emotions in real time and selects a music genre and style according to those emotions. For example, rock music is provided to a user who is feeling energetic. The music generation AI also analyzes the user's emotions in real time and selects a music genre and style according to those emotions. For example, ballad music is provided to a user who is feeling emotional. In this way, the effectiveness of advertising can be increased by selecting a music genre and style according to the user's emotions.

[0072] The advertising video generation unit may include a distribution unit and a display unit that distribute and display the advertising video through digital signage or an online platform. The advertising video generation unit distributes and displays the advertising video through, for example, digital signage or an online platform. For example, advertising videos displayed on digital signage change in real time according to the characteristics of passersby. For example, advertisements based on age and gender may be displayed. Furthermore, online platforms may display personalized advertisements based on a user's browsing history and interests. For example, advertisements related to products previously viewed may be displayed. Furthermore, advertising videos may be distributed at optimal times based on user behavior data. For example, specific advertisements may be displayed at specific times. This allows the advertising video to be distributed and displayed through digital signage or an online platform.

[0073] The advertising video generation unit may include a distribution unit that distributes advertising videos in an optimal format depending on the user's device. The advertising video generation unit distributes advertising videos in an optimal format depending on the user's device, for example. For example, advertising videos are distributed in an optimal format depending on the user's device, such as a smartphone, tablet, or PC. For example, a vertical advertising video is generated for a smartphone. Furthermore, advertising videos are distributed at an optimal resolution depending on the screen size of the user's device. For example, high-quality advertising videos are distributed to a high-resolution display. Furthermore, advertising videos are distributed at an optimal bitrate depending on the connection speed of the user's device. For example, low-bitrate advertising videos are distributed to a slow connection environment. This makes it possible to distribute advertising videos in an optimal format depending on the user's device.

[0074] The advertising video generation unit may include a distribution unit that distributes advertising videos at optimal timing based on user behavior data. The advertising video generation unit distributes advertising videos at optimal timing based on, for example, user behavior data. For example, advertising videos are distributed at optimal timing by analyzing the user's past behavior data. For example, a specific advertisement is displayed during a specific time period. Furthermore, advertising videos are distributed at optimal timing by analyzing the user's current behavior in real time. For example, a relevant advertisement is displayed immediately after the user takes a specific action. Furthermore, advertising videos learn the user's behavior patterns and are distributed at optimal timing. For example, a relevant advertisement is displayed when the user is in a specific location. This makes it possible to distribute advertising videos at optimal timing based on the user's behavior data.

[0075] The advertising video generation unit may include a generation unit that is personalized based on the user's interests and concerns. The advertising video generation unit is personalized, for example, based on the user's interests and concerns. For example, the advertising video is personalized based on the data obtained by analyzing the user's past browsing history. For example, advertisements related to products previously viewed are displayed. The advertising video is also personalized based on the data obtained by analyzing the user's past purchasing history. For example, advertisements related to products previously purchased are displayed. The advertising video is also personalized based on the data obtained by analyzing the user's interests and concerns. For example, advertisements related to specific hobbies or interests are displayed. This makes it possible to generate advertising videos that are personalized based on the user's interests and concerns.

[0076] The advertising video generation unit may include an adjustment unit that adjusts in real time according to the user's emotions. The advertising video generation unit is adjusted in real time according to, for example, the user's emotions. For example, the advertising video is analyzed in real time and adjusted according to the user's emotions. For example, bright colors and up-tempo music are used for a user who has positive emotions. The advertising video is also analyzed in real time and adjusted according to the user's emotions. For example, calm colors and relaxation music are used for a user who wants to relax. The advertising video is also analyzed in real time and adjusted according to the user's emotions. For example, bright colors and energetic music are used for a user who is feeling energetic. In this way, the advertising video can be adjusted in real time according to the user's emotions.

[0077] The advertising video generation unit may include a distribution unit that distributes advertising videos at optimal timing based on user behavior data. The advertising video generation unit distributes advertising videos at optimal timing based on, for example, user behavior data. For example, advertising videos are distributed at optimal timing by analyzing the user's past behavior data. For example, a specific advertisement is displayed during a specific time period. Furthermore, advertising videos are distributed at optimal timing by analyzing the user's current behavior in real time. For example, a relevant advertisement is displayed immediately after the user takes a specific action. Furthermore, advertising videos learn the user's behavior patterns and are distributed at optimal timing. For example, a relevant advertisement is displayed when the user is in a specific location. This makes it possible to distribute advertising videos at optimal timing based on the user's behavior data.

[0078] The advertising video generation unit may include a generation unit that is personalized based on the user's interests and concerns. The advertising video generation unit is personalized, for example, based on the user's interests and concerns. For example, the advertising video is personalized based on the data obtained by analyzing the user's past browsing history. For example, advertisements related to products previously viewed are displayed. The advertising video is also personalized based on the data obtained by analyzing the user's past purchasing history. For example, advertisements related to products previously purchased are displayed. The advertising video is also personalized based on the data obtained by analyzing the user's interests and concerns. For example, advertisements related to specific hobbies or interests are displayed. This makes it possible to generate advertising videos that are personalized based on the user's interests and concerns.

[0079] The advertising video generation unit may include an adjustment unit that adjusts in real time according to the user's emotions. The advertising video generation unit is adjusted in real time according to, for example, the user's emotions. For example, the advertising video is analyzed in real time and adjusted according to the user's emotions. For example, bright colors and up-tempo music are used for a user who has positive emotions. The advertising video is also analyzed in real time and adjusted according to the user's emotions. For example, calm colors and relaxation music are used for a user who wants to relax. The advertising video is also analyzed in real time and adjusted according to the user's emotions. For example, bright colors and energetic music are used for a user who is feeling energetic. In this way, the advertising video can be adjusted in real time according to the user's emotions.

[0080] The advertising video generation unit may include a distribution unit that distributes advertising videos at optimal timing based on user behavior data. The advertising video generation unit distributes advertising videos at optimal timing based on, for example, user behavior data. For example, advertising videos are distributed at optimal timing by analyzing the user's past behavior data. For example, a specific advertisement is displayed during a specific time period. Furthermore, advertising videos are distributed at optimal timing by analyzing the user's current behavior in real time. For example, a relevant advertisement is displayed immediately after the user takes a specific action. Furthermore, advertising videos learn the user's behavior patterns and are distributed at optimal timing. For example, a relevant advertisement is displayed when the user is in a specific location. This allows advertising videos to be distributed at optimal timing based on the user's behavior data.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The advertisement generation system may further include a health monitoring unit that monitors the user's health condition. For example, the health monitoring unit may measure the user's heart rate and number of steps in real time and evaluate the user's health condition based on that data. For example, if the user's heart rate is high, advertisements for relaxation products may be displayed. Alternatively, if the user's number of steps is low, advertisements for fitness-related products may be displayed. Furthermore, the health monitoring unit may analyze the user's sleep patterns and evaluate the quality of the sleep. For example, if the user is determined to be sleep deprived, advertisements for sleep aids may be displayed. This makes it possible to provide appropriate advertisements based on the user's health condition.

[0083] The advertisement generation system may further include a purchase intention estimation unit that estimates a user's purchase intention. For example, the purchase intention estimation unit analyzes the user's past purchase history and browsing history to evaluate the user's current purchase intention. For example, advertisements related to products that the user has frequently purchased in the past may be displayed. The purchase intention estimation unit may also analyze the user's current behavior in real time to estimate the user's purchase intention. For example, if the user has been looking at a particular product for a long time, an advertisement for that product may be displayed. Furthermore, the purchase intention estimation unit may analyze the user's emotions to estimate the user's purchase intention. For example, if the user has positive emotions, it may determine that the user has a high purchase intention and display advertisements for related products. This makes it possible to provide effective advertisements based on the user's purchase intention.

[0084] The advertisement generation system may further include a hobby and preference learning unit that learns the user's hobbies and preferences. For example, the hobby and preference learning unit analyzes the user's past behavioral data to learn the user's hobbies and preferences. For example, the hobby and preference learning unit estimates the user's hobbies based on content viewed in the past and events attended in the past. The hobby and preference learning unit also analyzes the user's social media posts to learn the user's hobbies and preferences. For example, if there are many posts about a particular genre of music or movies, advertisements related to that genre can be displayed. Furthermore, the hobby and preference learning unit analyzes the user's purchase history to learn the user's hobbies and preferences. For example, if the user frequently purchases products from a particular brand, advertisements related to that brand can be displayed. This makes it possible to provide advertisements personalized based on the user's hobbies and preferences.

[0085] The advertisement generation system may further include an emotion adjustment unit that estimates the user's emotion and adjusts the content of the advertisement based on the estimated emotion. For example, the emotion adjustment unit analyzes the user's facial expressions and voice to estimate the emotion. For example, if the user is smiling, an advertisement that evokes positive emotions may be displayed. Alternatively, if the user is determined to be tired, an advertisement for a relaxation product may be displayed. Furthermore, the emotion adjustment unit adjusts the tone and style of the advertisement according to the user's emotion. For example, an advertisement with a calm tone may be displayed to a user who feels like relaxing. This allows the content of the advertisement to be adjusted in real time based on the user's emotion.

[0086] The advertisement generation system may further include a location information optimization unit that optimizes advertisements using the user's location information. For example, the location information optimization unit acquires the user's current location information and generates advertisements introducing nearby stores and services based on that data. For example, advertisements for cafes and restaurants closest to the current location may be displayed. The location information optimization unit may also analyze the user's past location information and learn behavioral patterns. For example, advertisements related to a specific area may be displayed to a user who frequently visits that area. Furthermore, the location information optimization unit may adjust the timing of advertisements based on the user's location information. For example, relevant advertisements may be displayed immediately after the user arrives at a specific location. This makes it possible to provide effective advertisements based on the user's location information.

[0087] The advertisement generation system may further include a social media analysis unit that analyzes a user's social media activity. For example, the social media analysis unit may analyze a user's posts and comments to infer their interests. For example, if there are many posts about a particular brand or product, advertisements related to that brand or product may be displayed. The social media analysis unit may also analyze the activities of the user's followers and friends to infer their interests. For example, advertisements related to places frequently visited by friends may be displayed. Furthermore, the social media analysis unit may analyze a user's emotions and adjust the content of advertisements. For example, advertisements with a bright tone may be displayed to users who have positive emotions. This makes it possible to provide personalized advertisements based on the user's social media activity.

[0088] The advertisement generation system may further include a voice analysis unit that analyzes a user's voice commands. For example, the voice analysis unit analyzes a user's voice commands in real time and generates an advertisement based on the content of the voice commands. For example, if a user says, "I'm looking for a nearby restaurant," an advertisement for a restaurant that meets the request is displayed. The voice analysis unit also analyzes the user's voice tone and tempo to estimate emotions. For example, if the user speaks in an excited tone, an energetic advertisement can be displayed. Furthermore, the voice analysis unit customizes the content of the advertisement based on the user's voice commands. For example, in response to a question about a specific product or service, an advertisement for that product or service can be displayed. This makes it possible to provide effective advertisements based on the user's voice commands.

[0089] The advertisement generation system can further include a recommendation unit that makes recommendations based on the user's purchase history. For example, the recommendation unit analyzes the user's past purchase history and displays advertisements for related products. For example, it displays advertisements for new products related to products purchased in the past. The recommendation unit also generates personalized advertisements based on the user's purchase history. For example, if the user frequently purchases products from a particular brand, it can also display advertisements for new products from that brand. Furthermore, the recommendation unit displays advertisements for related services based on the user's purchase history. For example, it can also display advertisements for new services related to services used in the past. This makes it possible to provide effective advertisements based on the user's purchase history.

[0090] The advertisement generation system may further include a device analysis unit that analyzes the user's device usage. For example, the device analysis unit may analyze the user's device usage in real time and generate advertisements based on that data. For example, if the user frequently uses a smartphone, mobile advertisements may be displayed. The device analysis unit may also analyze the user's device settings and app usage and display relevant advertisements. For example, if the user frequently uses a particular app, advertisements related to that app may be displayed. Furthermore, the device analysis unit may analyze the remaining battery level and connection status of the user's device and adjust the content of the advertisements. For example, if the battery is low, an effective advertisement may be displayed in a short time. This makes it possible to provide effective advertisements based on the user's device usage.

[0091] The advertisement generation system may further include a feedback collection unit that collects user feedback and evaluates the effectiveness of the advertisement. For example, the feedback collection unit collects user responses to the advertisement in real time and evaluates the effectiveness of the advertisement based on the data. For example, the feedback collection unit tracks user behavior after viewing the advertisement and evaluates whether or not the user made a purchase. The feedback collection unit also analyzes user emotions to evaluate the effectiveness of the advertisement. For example, it can analyze the user's facial expressions and voice after viewing the advertisement and evaluate whether or not there was a positive response. Furthermore, the feedback collection unit collects direct feedback from users to help improve the advertisement. For example, it can also collect user opinions and impressions through surveys and reviews. This makes it possible to evaluate and improve the effectiveness of the advertisement based on user feedback.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The image recognition unit recognizes the characteristics of the surrounding environment and people. For example, it uses a camera to recognize the age, gender, clothing, etc. of passersby and collects that information. It can also use cameras in stores to recognize products lined up on shelves and collect that information. Step 2: The video generation unit generates advertising videos based on the information recognized by the image recognition unit. For example, it generates videos introducing appropriate products and services based on the age and gender of passersby. It can also generate advertising videos that change in real time based on the characteristics of passersby. Step 3: The narration generator generates a narration to match the advertising video generated by the video generator. For example, it generates a voice that explains the features and benefits of a product. It can also reflect the user's name and personal information to convey a personalized message. Step 4: The music generation unit generates music to match the advertising video generated by the video generation unit. For example, it generates background music that matches the image of the product. It can also select a music genre or style that matches the user's emotions.

[0094] 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.

[0095] 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.

[0096] 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.

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

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

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

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

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

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

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

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

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

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

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] 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.

[0121] 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.

[0122] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0161] 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. An image recognition unit that recognizes the characteristics of the surrounding environment and people; a video generation unit that generates an advertising video based on the information recognized by the image recognition unit; a narration generation unit that generates a narration in accordance with the advertising video generated by the video generation unit; a music generation unit that generates music in accordance with the advertising video generated by the video generation unit. A system characterized by:

2. The image recognition unit The system is equipped with an information collection unit that uses a camera to recognize the age, gender, clothing, etc. of passersby and collects that information.

2. The system of claim 1.

3. The image recognition unit The system is equipped with an information collection unit that uses cameras in the store to recognize products lined up on shelves and collect that information.

2. The system of claim 1.

4. The video generation unit Equipped with a video generation unit that generates videos introducing appropriate products and services according to the age and gender of passersby 2. The system of claim 1.

5. The narration generation unit Equipped with a voice generation unit that generates voice that explains the features and benefits of the product 2. The system of claim 1.

6. The music generation unit Equipped with a background music generator that generates background music that matches the image of the product 2. The system of claim 1.

7. The video generation unit It has an advertising video generation unit that generates advertising videos that change in real time according to the characteristics of passersby.

2. The system of claim 1.

8. The video generation unit It has a story generation unit that creates personalized stories by reflecting the user's past behavioral data.

2. The system of claim 1.

Images