System

Abstract

An object of a system according to an embodiment is to eliminate talent risk and automatically generate a commercial that meets the needs of a customer.SOLUTION: A system includes a prompt reception unit, a generation unit, and an output unit. The prompt receiving unit receives a prompt from a user. The generation unit generates a commercial on the basis of the prompt received by the prompt reception unit. The generation unit provides a CM that meets the needs of the customer by eliminating talent risk and creating a plurality of patterns. The output unit outputs the CM generated by the 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 technology has not sufficiently addressed the damage to a company's image caused by scandals involving celebrities, nor has it done enough to reduce the costs of producing commercials, so there is room for improvement.

[0005] The system according to the embodiment aims to eliminate talent risk and automatically generate commercials that meet customer needs. [Means for solving the problem]

[0006] The system according to the embodiment includes a prompt receiving unit, a generation unit, and an output unit. The prompt receiving unit receives prompts from a user. The generation unit generates a commercial based on the prompt received by the prompt receiving unit. The generation unit eliminates talent risk and provides a commercial that meets customer needs by creating multiple patterns. The output unit outputs the commercial generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can eliminate talent risk and automatically generate commercials that meet customer needs. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 automatic commercial generation system according to an embodiment of the present invention is a system in which a generation AI automatically generates commercials based on prompts input by a user, eliminates talent risk, and creates multiple patterns to provide commercials that meet customer needs. This enables the automatic commercial generation system to reduce generation costs, eliminate talent risk, and provide commercials that meet customer needs.

[0029] An automatic commercial generation system according to an embodiment includes a prompt receiving unit, a generation unit, and an output unit. The prompt receiving unit receives a prompt from a user. For example, a user may input a prompt such as, "Please create a commercial to introduce a new product." The prompt receiving unit can receive the prompt in the form of text input, voice input, image input, or the like. The generation unit generates a commercial based on the prompt received by the prompt receiving unit. For example, the generation AI generates a commercial script, video, and audio using technologies such as deep learning and GAN (generative artificial network). The generation unit also eliminates talent risk and provides commercials tailored to customer needs by creating multiple patterns. For example, the generation AI creates commercials using virtual characters and animations to eliminate the risk of talent scandals. The generation AI can also generate commercials tailored to different target demographics. The output unit outputs the commercial generated by the generation unit. For example, the generated commercial can be uploaded to a website or social media. The output unit can also convert the generated commercial into a format suitable for television broadcast. This allows the automatic commercial generation system to reduce production costs, eliminate talent risk, and provide commercials tailored to customer needs.

[0030] The generation unit can analyze a user's past purchase history or browsing history and generate individually optimized commercials. For example, the generation unit uses a generation AI to analyze a user's past purchase history and generate individually optimized commercials based on that data. For example, it creates commercials that include content related to products and services the user has previously purchased. The generation unit also analyzes the user's browsing history, identifies products and services that the user may be interested in, and generates commercials based on that. For example, it creates commercials that reflect the content of websites the user frequently visits. The generation unit also integrates purchase history and browsing history to generate optimal commercials after gaining a detailed understanding of the user's preferences. For example, it creates a commercial for a new product related to a product the user has previously purchased. This makes it possible to generate individually optimized commercials based on the user's past purchase history and browsing history.

[0031] The generation unit can analyze market trends in real time and generate the latest commercials that reflect the results. For example, the generation AI in the generation unit analyzes market trends in real time and generates the latest commercials based on that data. For example, it creates commercials that reflect current trends and popular products. The generation unit also monitors fluctuations in market trends in real time and immediately reflects the results in commercials. For example, it generates commercials that feature newly popular products or services. The generation unit also generates commercials that match seasons and events based on trend data. For example, it creates special commercials for the Christmas season. This makes it possible to generate the latest commercials that reflect market trends in real time.

[0032] The generation unit can automatically generate commercials that correspond to different languages ​​or cultures. For example, the generation AI in the generation unit automatically generates commercials that correspond to different languages. For example, a commercial with the same content is created in multiple languages, such as English, French, and Chinese. The generation unit also generates commercials that correspond to different cultures. For example, a commercial is created that includes content that matches the culture and customs of each country. The generation unit also generates commercials that reflect the characteristics of each region for the international market. For example, a commercial is created that features products or services that are popular in a particular region. This makes it possible to automatically generate commercials that correspond to different languages ​​and cultures.

[0033] The generation unit can generate commercials that incorporate elements unique to a region based on the user's geographical location information. For example, the generation unit uses a generation AI to analyze the user's geographical location information and generate commercials that incorporate elements unique to that region. For example, it creates commercials that introduce local attractions and local specialties. The generation unit also generates commercials that match local events and seasons based on the geographical location information. For example, it creates commercials related to local festivals and seasonal events. The generation unit also generates commercials that meet local needs based on the user's location information. For example, it creates commercials that reflect local consumption trends and market needs. This makes it possible to generate commercials that incorporate elements unique to a region.

[0034] The generation unit can automatically generate the personality or backstory of the virtual character, providing a character that is familiar to viewers. For example, the generation unit uses a generation AI to automatically generate the personality of a virtual character and have that character appear in a commercial. For example, it creates a character with a familiar personality or characteristics. The generation unit also uses a generation AI to automatically generate a backstory for the virtual character, allowing viewers to empathize with the character. For example, it creates a commercial that includes the character's past episodes and growth story. The generation unit also uses a generation AI to create a virtual character that is familiar to viewers, and develops a commercial centered around that character. For example, it creates a scenario in which the character introduces a product or service. This makes it possible to provide a virtual character that is familiar to viewers.

[0035] The generation unit can learn from past talent risk cases and generate commercials that eliminate high-risk elements. For example, the generation unit's generation AI learns from past talent risk cases and generates commercials that eliminate high-risk elements. For example, it creates a scenario that does not include elements that have been problematic in the past. The generation unit also builds a system in which the generation AI automatically detects and eliminates high-risk elements to avoid talent risks. For example, it creates commercials that avoid specific words, actions, and expressions. The generation unit also uses past risk cases to generate commercials in which the generation AI eliminates high-risk elements. For example, it avoids themes and character settings that are likely to be problematic. This makes it possible to generate commercials that eliminate high-risk elements.

[0036] The generation unit can generate commercials that combine virtual characters from different industries or fields. For example, the generation AI generates commercials that combine virtual characters from different industries. For example, it creates a commercial that combines a technical character with an entertainment character. The generation unit also provides new marketing methods by combining virtual characters from different fields. For example, it creates a commercial that combines the characters of an athlete and a scientist. The generation AI also generates commercials that combine characters from different industries or fields, giving viewers a fresh impression. For example, it creates a commercial that combines the characters of a fashion model and an engineer. This makes it possible to generate commercials that combine virtual characters from different industries and fields.

[0037] The generation unit can continuously improve the design or personality of the virtual character based on user feedback. For example, the generation unit builds a system in which the generation AI continuously improves the design of the virtual character based on user feedback. For example, the generation unit adjusts the character's appearance to reflect the user's opinions. The generation unit also develops a system to improve the personality of the virtual character based on user feedback. For example, the generation unit creates a character that incorporates the user's preferred personality and characteristics. The generation unit also builds a system in which the generation AI collects user feedback in real time and dynamically improves the design and personality of the virtual character. For example, the character is adjusted according to the user's emotional response. This allows the design and personality of the virtual character to be continuously improved.

[0038] The generation unit can generate optimal commercial patterns for different target segments. The generation unit, for example, builds a system in which the generation AI generates optimal commercial patterns for different target segments. For example, commercials are created to suit target segments such as young people, the elderly, and families. The generation unit also uses the generation AI to generate individually customized commercials based on demographic data for each target segment. For example, commercials are created based on age, gender, and interests. The generation unit also uses the generation AI to analyze the needs and preferences of each target segment and generate optimal commercial patterns based on that. For example, commercials are created to suit specific lifestyles and hobbies. This makes it possible to generate optimal commercial patterns for different target segments.

[0039] The generation unit can evaluate the effectiveness of commercial patterns based on user feedback and select the most effective pattern. For example, the generation unit builds a system in which the generation AI evaluates the effectiveness of commercial patterns based on user feedback. For example, it analyzes viewer reactions and comments to identify effective commercial patterns. The generation unit also evaluates the effectiveness of commercial patterns based on user feedback data and selects the most effective pattern. For example, it prioritizes displaying commercials with a lot of positive feedback. The generation unit also develops a system in which the generation AI collects user feedback in real time and dynamically evaluates the effectiveness of commercial patterns. For example, it evaluates based on the viewer's emotional response and viewing time. This makes it possible to evaluate the effectiveness of commercial patterns and select the most effective pattern.

[0040] The generation unit can generate commercial patterns optimized for different media platforms. The generation unit, for example, builds a system in which a generation AI generates commercial patterns optimized for different media platforms. For example, commercials are created for television, the Internet, and mobile. The generation unit also takes into account the characteristics of each media platform and generates the optimal commercial pattern using the generation AI. For example, long commercials are created for television and short commercials for mobile. The generation unit also generates the optimal commercial pattern using the generation AI based on viewer data for each media platform. For example, a commercial including interactive elements is created for the Internet. This makes it possible to generate commercial patterns optimized for different media platforms.

[0041] The generation unit can automatically generate commercial patterns according to seasons or events. For example, the generation unit builds a system in which the generation AI automatically generates commercial patterns according to seasons and events. For example, commercials are created to match seasonal events such as Christmas and Halloween. The generation unit also generates commercial patterns using the generation AI based on seasonal and event data to enable timely marketing. For example, commercials are created to introduce products and services related to a specific season. The generation unit also develops a system in which the generation AI collects seasonal and event information in real time and automatically generates commercial patterns based on that information. For example, commercials are updated in accordance with seasonal changes and events. This makes it possible to automatically generate commercial patterns according to seasons and events.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The generation unit can generate commercials containing health-related information based on the user's health data. For example, it can analyze the user's fitness data and create commercials related to exercise and health foods. The generation unit can also generate commercials customized according to the user's health condition. For example, if the user is feeling stressed, it can provide a commercial related to relaxation. The generation unit can also generate commercials containing information on preventive medicine and health management based on the user's health data. For example, it can create a commercial emphasizing the importance of regular health checks. In this way, commercials containing health-related information can be generated based on the user's health data.

[0044] The generation unit can analyze the user's social media activity and generate commercials that include topics that are likely to interest them. For example, it can create commercials that introduce related products and services based on content that the user frequently shares. The generation unit can also analyze the activities of the user's followers and friends and generate commercials that match trends. For example, it can create commercials that reflect topics that the user's friends are interested in. The generation unit can also generate optimal commercials based on the user's social media engagement data. For example, it can create commercials related to posts that the user receives many likes and comments. This makes it possible to generate commercials that include topics that are likely to interest the user based on the user's social media activity.

[0045] The generation unit can generate commercials including related content based on the user's hobbies and interests. For example, if the user is interested in sports, the generation unit can create commercials related to sports equipment and events. The generation unit can also generate commercials customized to the user's hobbies. For example, if the user is interested in cooking, the generation unit can provide commercials related to cooking recipes and kitchenware. The generation unit can also generate commercials introducing related new products and services based on the user's interest data. For example, if the user is interested in travel, the generation unit can create commercials related to travel destinations and accommodations. In this way, commercials including related content can be generated based on the user's hobbies and interests.

[0046] The generation unit can generate commercials that include limited offers and special offers to increase the user's purchasing motivation. For example, a commercial introducing limited-time discounts and special offers is created. The generation unit can also generate commercials that include offers that are individually customized based on the user's purchasing history. For example, a commercial is created that provides special offers related to products that have been purchased in the past. The generation unit can also generate commercials that include information about limited-edition products to increase the user's purchasing motivation. For example, a commercial introducing new products or limited-edition products is created. In this way, commercials that include limited offers and special offers can be generated to increase the user's purchasing motivation.

[0047] The generation unit can generate commercials including information related to the user's lifestyle based on the user's lifestyle data. For example, if the user has an active lifestyle, it creates commercials related to fitness and the outdoors. The generation unit can also generate commercials customized to the user's lifestyle. For example, if the user is health-conscious, it provides commercials related to health foods and supplements. The generation unit can also generate commercials introducing related new products and services based on the user's lifestyle data. For example, if the user has an eco-friendly lifestyle, it creates commercials introducing environmentally friendly products and services. In this way, commercials including information related to the user's lifestyle can be generated based on the user's lifestyle data.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The prompt receiving unit receives a prompt from a user. For example, the user can input a prompt such as "Please create a commercial to introduce a new product." The prompt receiving unit can also receive prompts in the form of text input, voice input, image input, etc. Step 2: The generation unit generates a commercial based on the prompts received by the prompt reception unit. For example, the generation AI uses technologies such as deep learning and GAN (generative artificial network) to generate the commercial's script, video, and audio. The generation unit also eliminates talent risk and provides commercials that meet customer needs by creating multiple patterns. For example, the generation AI creates commercials using virtual characters and animations, eliminating the risk of talent scandals. The generation AI can also generate commercials tailored to different target demographics. Step 3: The output unit outputs the commercial generated by the generation unit. For example, the generated commercial can be uploaded to a website or social media. The output unit can also convert the generated commercial into a format suitable for television broadcasting.

[0050] (Example 2) The automatic commercial generation system according to an embodiment of the present invention is a system in which a generation AI automatically generates commercials based on prompts input by a user, eliminates talent risk, and creates multiple patterns to provide commercials that meet customer needs. This enables the automatic commercial generation system to reduce generation costs, eliminate talent risk, and provide commercials that meet customer needs.

[0051] An automatic commercial generation system according to an embodiment includes a prompt receiving unit, a generation unit, and an output unit. The prompt receiving unit receives a prompt from a user. For example, a user may input a prompt such as, "Please create a commercial to introduce a new product." The prompt receiving unit can receive the prompt in the form of text input, voice input, image input, or the like. The generation unit generates a commercial based on the prompt received by the prompt receiving unit. For example, the generation AI generates a commercial script, video, and audio using technologies such as deep learning and GAN (generative artificial network). The generation unit also eliminates talent risk and provides commercials tailored to customer needs by creating multiple patterns. For example, the generation AI creates commercials using virtual characters and animations to eliminate the risk of talent scandals. The generation AI can also generate commercials tailored to different target demographics. The output unit outputs the commercial generated by the generation unit. For example, the generated commercial can be uploaded to a website or social media. The output unit can also convert the generated commercial into a format suitable for television broadcast. This allows the automatic commercial generation system to reduce production costs, eliminate talent risk, and provide commercials tailored to customer needs.

[0052] The generation unit can analyze a user's past purchase history or browsing history and generate individually optimized commercials. For example, the generation unit uses a generation AI to analyze a user's past purchase history and generate individually optimized commercials based on that data. For example, it creates commercials that include content related to products and services the user has previously purchased. The generation unit also analyzes the user's browsing history, identifies products and services that the user may be interested in, and generates commercials based on that. For example, it creates commercials that reflect the content of websites the user frequently visits. The generation unit also integrates purchase history and browsing history to generate optimal commercials after gaining a detailed understanding of the user's preferences. For example, it creates a commercial for a new product related to a product the user has previously purchased. This makes it possible to generate individually optimized commercials based on the user's past purchase history and browsing history.

[0053] The generation unit can analyze market trends in real time and generate the latest commercials that reflect the results. For example, the generation AI in the generation unit analyzes market trends in real time and generates the latest commercials based on that data. For example, it creates commercials that reflect current trends and popular products. The generation unit also monitors fluctuations in market trends in real time and immediately reflects the results in commercials. For example, it generates commercials that feature newly popular products or services. The generation unit also generates commercials that match seasons and events based on trend data. For example, it creates special commercials for the Christmas season. This makes it possible to generate the latest commercials that reflect market trends in real time.

[0054] The generation unit can use the emotion estimation function to generate commercials that correspond to the user's emotional state. For example, the generation unit uses the emotion estimation function to analyze the user's emotional state in real time and generate commercials based on the results. For example, when the user is feeling positive, the generation unit creates a commercial with fun content. The generation unit also generates commercials that include emotional elements based on the user's emotional data. For example, it creates commercials that incorporate moving stories and heartwarming scenes. The generation unit also uses the emotion estimation function to dynamically adjust the content of commercials according to changes in the user's emotions. For example, it provides a commercial with content that will help the user relax when they are feeling stressed. This makes it possible to generate commercials that correspond to the user's emotional state.

[0055] The generation unit can automatically generate commercials that correspond to different languages ​​or cultures. For example, the generation AI in the generation unit automatically generates commercials that correspond to different languages. For example, a commercial with the same content is created in multiple languages, such as English, French, and Chinese. The generation unit also generates commercials that correspond to different cultures. For example, a commercial is created that includes content that matches the culture and customs of each country. The generation unit also generates commercials that reflect the characteristics of each region for the international market. For example, a commercial is created that features products or services that are popular in a particular region. This makes it possible to automatically generate commercials that correspond to different languages ​​and cultures.

[0056] The generation unit can generate commercials that incorporate elements unique to a region based on the user's geographical location information. For example, the generation unit uses a generation AI to analyze the user's geographical location information and generate commercials that incorporate elements unique to that region. For example, it creates commercials that introduce local attractions and local specialties. The generation unit also generates commercials that match local events and seasons based on the geographical location information. For example, it creates commercials related to local festivals and seasonal events. The generation unit also generates commercials that meet local needs based on the user's location information. For example, it creates commercials that reflect local consumption trends and market needs. This makes it possible to generate commercials that incorporate elements unique to a region.

[0057] The generation unit uses the emotion estimation function to monitor the emotional reactions of users while they are watching commercials in real time, and is able to continuously generate optimal commercials. For example, the generation unit uses the emotion estimation function to build a system that monitors the emotional reactions of users while they are watching commercials in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The generation unit also develops a system that continuously generates optimal commercials based on the user's emotional reaction data. For example, it prioritizes displaying commercials that have a high number of positive emotional reactions. The generation unit also collects emotion estimation data in real time and builds a system that dynamically adjusts the content of commercials. For example, it changes the commercial scenario and images according to changes in the user's emotions. This makes it possible to monitor the user's emotional reactions in real time and continuously generate optimal commercials.

[0058] The generation unit can automatically generate the personality or backstory of the virtual character, providing a character that is familiar to viewers. For example, the generation unit uses a generation AI to automatically generate the personality of a virtual character and have that character appear in a commercial. For example, it creates a character with a familiar personality or characteristics. The generation unit also uses a generation AI to automatically generate a backstory for the virtual character, allowing viewers to empathize with the character. For example, it creates a commercial that includes the character's past episodes and growth story. The generation unit also uses a generation AI to create a virtual character that is familiar to viewers, and develops a commercial centered around that character. For example, it creates a scenario in which the character introduces a product or service. This makes it possible to provide a virtual character that is familiar to viewers.

[0059] The generation unit can learn from past talent risk cases and generate commercials that eliminate high-risk elements. For example, the generation unit's generation AI learns from past talent risk cases and generates commercials that eliminate high-risk elements. For example, it creates a scenario that does not include elements that have been problematic in the past. The generation unit also builds a system in which the generation AI automatically detects and eliminates high-risk elements to avoid talent risks. For example, it creates commercials that avoid specific words, actions, and expressions. The generation unit also uses past risk cases to generate commercials in which the generation AI eliminates high-risk elements. For example, it avoids themes and character settings that are likely to be problematic. This makes it possible to generate commercials that eliminate high-risk elements.

[0060] The generation unit can use the emotion estimation function to analyze the emotions viewers have toward virtual characters and select the most likable character. The generation unit, for example, uses the emotion estimation function to build a system that analyzes the emotions viewers have toward virtual characters in real time. For example, it analyzes the viewer's facial expressions and voice and calculates an emotion score. The generation unit also develops a system that selects the most likable virtual character based on the viewer's emotion data. For example, it prioritizes the use of characters with many positive emotional responses. The generation unit also builds a system that collects emotion estimation data in real time and uses it to select virtual characters. For example, it dynamically adjusts the character according to changes in the viewer's emotions. This makes it possible to select the virtual character that is most likable to the viewer.

[0061] The generation unit can generate commercials that combine virtual characters from different industries or fields. For example, the generation AI generates commercials that combine virtual characters from different industries. For example, it creates a commercial that combines a technical character with an entertainment character. The generation unit also provides new marketing methods by combining virtual characters from different fields. For example, it creates a commercial that combines the characters of an athlete and a scientist. The generation AI also generates commercials that combine characters from different industries or fields, giving viewers a fresh impression. For example, it creates a commercial that combines the characters of a fashion model and an engineer. This makes it possible to generate commercials that combine virtual characters from different industries and fields.

[0062] The generation unit can continuously improve the design or personality of the virtual character based on user feedback. For example, the generation unit builds a system in which the generation AI continuously improves the design of the virtual character based on user feedback. For example, the generation unit adjusts the character's appearance to reflect the user's opinions. The generation unit also develops a system to improve the personality of the virtual character based on user feedback. For example, the generation unit creates a character that incorporates the user's preferred personality and characteristics. The generation unit also builds a system in which the generation AI collects user feedback in real time and dynamically improves the design and personality of the virtual character. For example, the character is adjusted according to the user's emotional response. This allows the design and personality of the virtual character to be continuously improved.

[0063] The generation unit uses the emotion estimation function to monitor the emotions that viewers have toward virtual characters in real time and continuously generate optimal characters. The generation unit, for example, uses the emotion estimation function to build a system that monitors the emotions that viewers have toward virtual characters in real time. For example, it analyzes the viewer's facial expressions and voice and calculates an emotion score. The generation unit also develops a system that continuously generates optimal virtual characters based on viewer emotional response data. For example, it prioritizes the use of characters with a high number of positive emotional responses. The generation unit also builds a system that collects emotion estimation data in real time and uses it to generate virtual characters. For example, it dynamically adjusts the character according to changes in the viewer's emotions. This makes it possible to monitor the viewer's emotions in real time and continuously generate optimal characters.

[0064] The generation unit can generate optimal commercial patterns for different target segments. The generation unit, for example, builds a system in which the generation AI generates optimal commercial patterns for different target segments. For example, commercials are created to suit target segments such as young people, the elderly, and families. The generation unit also uses the generation AI to generate individually customized commercials based on demographic data for each target segment. For example, commercials are created based on age, gender, and interests. The generation unit also uses the generation AI to analyze the needs and preferences of each target segment and generate optimal commercial patterns based on that. For example, commercials are created to suit specific lifestyles and hobbies. This makes it possible to generate optimal commercial patterns for different target segments.

[0065] The generation unit can evaluate the effectiveness of commercial patterns based on user feedback and select the most effective pattern. For example, the generation unit builds a system in which the generation AI evaluates the effectiveness of commercial patterns based on user feedback. For example, it analyzes viewer reactions and comments to identify effective commercial patterns. The generation unit also evaluates the effectiveness of commercial patterns based on user feedback data and selects the most effective pattern. For example, it prioritizes displaying commercials with a lot of positive feedback. The generation unit also develops a system in which the generation AI collects user feedback in real time and dynamically evaluates the effectiveness of commercial patterns. For example, it evaluates based on the viewer's emotional response and viewing time. This makes it possible to evaluate the effectiveness of commercial patterns and select the most effective pattern.

[0066] The generation unit can use the emotion estimation function to analyze viewers' emotional responses and generate commercial patterns that will elicit the most positive responses. For example, the generation unit uses the emotion estimation function to analyze viewers' emotional responses in real time and generate commercial patterns that will elicit the most positive responses based on that data. For example, it analyzes viewers' facial expressions and voices and calculates an emotion score. The generation unit also develops a system that uses the generation AI to identify commercial patterns that will elicit the most positive responses based on viewers' emotional data. For example, it uses scenarios and images that frequently elicit positive emotional responses. The generation unit also builds a system that collects emotion estimation data in real time and uses it to generate commercial patterns. For example, it dynamically adjusts the content of commercials according to changes in viewers' emotions. This makes it possible to analyze viewers' emotional responses and generate commercial patterns that will elicit the most positive responses.

[0067] The generation unit can generate commercial patterns optimized for different media platforms. The generation unit, for example, builds a system in which a generation AI generates commercial patterns optimized for different media platforms. For example, commercials are created for television, the Internet, and mobile. The generation unit also takes into account the characteristics of each media platform and generates the optimal commercial pattern using the generation AI. For example, long commercials are created for television and short commercials for mobile. The generation unit also generates the optimal commercial pattern using the generation AI based on viewer data for each media platform. For example, a commercial including interactive elements is created for the Internet. This makes it possible to generate commercial patterns optimized for different media platforms.

[0068] The generation unit can automatically generate commercial patterns according to seasons or events. For example, the generation unit builds a system in which the generation AI automatically generates commercial patterns according to seasons and events. For example, commercials are created to match seasonal events such as Christmas and Halloween. The generation unit also generates commercial patterns using the generation AI based on seasonal and event data to enable timely marketing. For example, commercials are created to introduce products and services related to a specific season. The generation unit also develops a system in which the generation AI collects seasonal and event information in real time and automatically generates commercial patterns based on that information. For example, commercials are updated in accordance with seasonal changes and events. This makes it possible to automatically generate commercial patterns according to seasons and events.

[0069] The generation unit uses the emotion estimation function to monitor viewers' emotional responses in real time and continuously generate optimal commercial patterns. The generation unit, for example, uses the emotion estimation function to build a system that monitors viewers' emotional responses in real time. For example, it analyzes viewers' facial expressions and voices and calculates an emotion score. The generation unit also develops a system that continuously generates optimal commercial patterns based on viewers' emotional response data. For example, it prioritizes displaying commercials that have a high number of positive emotional responses. The generation unit also collects emotion estimation data in real time and builds a system that dynamically adjusts the content of commercials. For example, it changes the commercial scenario and images according to changes in viewers' emotions. This makes it possible to monitor viewers' emotional responses in real time and continuously generate optimal commercial patterns.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The generation unit can generate commercials containing health-related information based on the user's health data. For example, it can analyze the user's fitness data and create commercials related to exercise and health foods. The generation unit can also generate commercials customized according to the user's health condition. For example, if the user is feeling stressed, it can provide a commercial related to relaxation. The generation unit can also generate commercials containing information on preventive medicine and health management based on the user's health data. For example, it can create a commercial emphasizing the importance of regular health checks. In this way, commercials containing health-related information can be generated based on the user's health data.

[0072] The generation unit can use the emotion estimation function to select music that corresponds to the user's emotional state and incorporate it into the commercial. For example, cheerful music is used when the user has positive emotions. The generation unit can also generate commercials that include emotionally appealing music based on the user's emotional data. For example, music that matches a moving scene is selected. The generation unit can also use the emotion estimation function to dynamically adjust the music according to changes in the user's emotions. For example, calm music is provided when the user is relaxed. This allows music that corresponds to the user's emotional state to be selected and incorporated into the commercial.

[0073] The generation unit can analyze the user's social media activity and generate commercials that include topics that are likely to interest them. For example, it can create commercials that introduce related products and services based on content that the user frequently shares. The generation unit can also analyze the activities of the user's followers and friends and generate commercials that match trends. For example, it can create commercials that reflect topics that the user's friends are interested in. The generation unit can also generate optimal commercials based on the user's social media engagement data. For example, it can create commercials related to posts that the user receives many likes and comments. This makes it possible to generate commercials that include topics that are likely to interest the user based on the user's social media activity.

[0074] The generation unit can use the emotion estimation function to analyze the user's emotional reactions to commercials they have viewed in the past and generate optimal commercials. For example, it can incorporate elements from commercials to which the user previously responded positively. The generation unit can also identify the most effective commercial pattern from the user's past viewing history based on the user's emotional data. For example, it can create commercials that include scenes that moved the user or made them laugh. The generation unit can also use the emotion estimation function to dynamically adjust the content of commercials based on the user's past emotional reactions. For example, it can provide commercials with content that will help the user relax when they are feeling stressed. This allows the generation of optimal commercials based on the user's past emotional reactions.

[0075] The generation unit can generate commercials including related content based on the user's hobbies and interests. For example, if the user is interested in sports, the generation unit can create commercials related to sports equipment and events. The generation unit can also generate commercials customized to the user's hobbies. For example, if the user is interested in cooking, the generation unit can provide commercials related to cooking recipes and kitchenware. The generation unit can also generate commercials introducing related new products and services based on the user's interest data. For example, if the user is interested in travel, the generation unit can create commercials related to travel destinations and accommodations. In this way, commercials including related content can be generated based on the user's hobbies and interests.

[0076] The generation unit can use the emotion estimation function to incorporate visual effects into commercials that correspond to the user's emotional state. For example, bright colors and dynamic effects are used when the user has positive emotions. The generation unit can also generate commercials that include visual effects that appeal to emotions based on the user's emotional data. For example, effects that match moving scenes are selected. The generation unit can also use the emotion estimation function to dynamically adjust visual effects according to changes in the user's emotions. For example, calm effects are provided when the user is relaxed. This makes it possible to incorporate visual effects into commercials that correspond to the user's emotional state.

[0077] The generation unit can generate commercials that include limited offers and special offers to increase the user's purchasing motivation. For example, a commercial introducing limited-time discounts and special offers is created. The generation unit can also generate commercials that include offers that are individually customized based on the user's purchasing history. For example, a commercial is created that provides special offers related to products that have been purchased in the past. The generation unit can also generate commercials that include information about limited-edition products to increase the user's purchasing motivation. For example, a commercial introducing new products or limited-edition products is created. In this way, commercials that include limited offers and special offers can be generated to increase the user's purchasing motivation.

[0078] The generation unit can use the emotion estimation function to select narration that corresponds to the user's emotional state and incorporate it into the commercial. For example, when the user has positive emotions, it uses narration in a bright tone. The generation unit can also generate commercials that include emotional narration based on the user's emotional data. For example, it selects narration that matches a moving scene. The generation unit can also use the emotion estimation function to dynamically adjust the narration according to changes in the user's emotions. For example, it provides narration in a calm tone when the user is relaxed. This allows narration that corresponds to the user's emotional state to be selected and incorporated into the commercial.

[0079] The generation unit can generate commercials including information related to the user's lifestyle based on the user's lifestyle data. For example, if the user has an active lifestyle, it creates commercials related to fitness and the outdoors. The generation unit can also generate commercials customized to the user's lifestyle. For example, if the user is health-conscious, it provides commercials related to health foods and supplements. The generation unit can also generate commercials introducing related new products and services based on the user's lifestyle data. For example, if the user has an eco-friendly lifestyle, it creates commercials introducing environmentally friendly products and services. In this way, commercials including information related to the user's lifestyle can be generated based on the user's lifestyle data.

[0080] The generation unit can use the emotion estimation function to incorporate interactive elements into commercials that correspond to the user's emotional state. For example, when the user has positive emotions, it can provide fun interactive games or quizzes. The generation unit can also generate commercials that include emotionally appealing interactive elements based on the user's emotional data. For example, it can select interactive elements related to a moving story. The generation unit can also use the emotion estimation function to dynamically adjust the interactive elements according to changes in the user's emotions. For example, it can provide calm interactive elements when the user is relaxed. This makes it possible to incorporate interactive elements into commercials that correspond to the user's emotional state.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The prompt receiving unit receives a prompt from a user. For example, the user can input a prompt such as "Please create a commercial to introduce a new product." The prompt receiving unit can also receive prompts in the form of text input, voice input, image input, etc. Step 2: The generation unit generates a commercial based on the prompts received by the prompt reception unit. For example, the generation AI uses technologies such as deep learning and GAN (generative artificial network) to generate the commercial's script, video, and audio. The generation unit also eliminates talent risk and provides commercials that meet customer needs by creating multiple patterns. For example, the generation AI creates commercials using virtual characters and animations, eliminating the risk of talent scandals. The generation AI can also generate commercials tailored to different target demographics. Step 3: The output unit outputs the commercial generated by the generation unit. For example, the generated commercial can be uploaded to a website or social media. The output unit can also convert the generated commercial into a format suitable for television broadcasting.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] 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 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] 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 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] 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 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system that automatically generates commercials using generation AI, a prompt receiving unit that receives a prompt from a user; a generating unit that generates a commercial based on the prompt received by the prompt receiving unit; an output unit that outputs the CM generated by the generation unit, The generation part is Eliminate talent risk and create multiple patterns to provide commercials that meet customer needs A system characterized by:

2. The generation unit Automatically generate commercials for different languages ​​or cultures 2. The system of claim 1.

3. The generation unit Automatically generate a personality or backstory for a virtual character, making them more relatable to the audience 2. The system of claim 1.

4. The generation unit Generate optimal commercial patterns for different target demographics 2. The system of claim 1.

5. The generation unit Generate commercials according to the user's emotional state 2. The system of claim 1.

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