System

Abstract

An object of a system according to an embodiment is to enable real-time interaction with a user in an advertisement and to grasp user's troubles and needs.SOLUTION: A system includes a generation AI, an interaction unit, an analysis unit, and a report unit. The generated AI responds in real time to queries and questions from the user. The interaction unit responds to an inquiry or a question from the user in real time. The analysis unit analyzes the user's trouble or needs acquired by the interaction unit. The report unit reports the user's trouble or needs analyzed by the analysis unit to the advertiser.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 the problem that users cannot directly communicate or inquire through advertisements, making it difficult to provide solutions that meet users' concerns and needs.

[0005] The system according to the embodiment aims to enable real-time dialogue with users within advertisements and to understand the concerns and needs of users. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, a dialogue unit, an analysis unit, and a reporting unit. The generation AI responds in real time to inquiries and questions from users. The dialogue unit responds in real time to inquiries and questions from users. The analysis unit analyzes the user's concerns and needs acquired by the dialogue unit. The reporting unit reports the user's concerns and needs analyzed by the analysis unit to the advertiser. [Effects of the Invention]

[0007] The system according to the embodiment enables real-time dialogue with users within advertisements, and can grasp the user's concerns and 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) The participatory advertising system according to an embodiment of the present invention utilizes generative AI to enable real-time dialogue with users within advertisements and provide solutions tailored to the user's concerns and needs. This allows the participatory advertising system to grasp the user's concerns and needs in real time and report them to advertisers, thereby maximizing the effectiveness of advertising.

[0029] A participatory advertising system according to an embodiment includes a generation AI, a dialogue unit, an analysis unit, and a reporting unit. The generation AI includes a dialogue unit that responds to user inquiries and questions in real time. For example, if a user asks, "Please tell me how to use this product" in an advertisement, the dialogue unit analyzes the question and generates an appropriate answer. Furthermore, if the user provides feedback such as, "I feel the price of this product is high," the dialogue unit analyzes the feedback and reports the user's needs and concerns to the advertiser. The analysis unit analyzes the user's concerns and needs acquired by the dialogue unit. For example, the analysis unit may analyze the user's feedback using text analysis technology to identify the user's concerns and needs. The analysis unit may also analyze the user's emotions using emotion analysis technology to identify the user's emotional needs. The reporting unit reports the user's concerns and needs analyzed by the analysis unit to the advertiser. For example, the reporting unit may provide the advertiser with analysis results in the form of a report, providing the advertiser with information to consider future measures. The reporting unit may also notify the advertiser of the analysis results in real time. As a result, the participatory advertising system according to the embodiment is able to grasp the concerns and needs of users in real time and report them to advertisers, thereby maximizing advertising effectiveness.

[0030] The dialogue unit can refer to a user's past dialogue history and generate answers optimized for individual users. For example, the dialogue unit uses a generation AI to analyze a user's past dialogue history and generate optimal answers based on the user's preferences and past questions. For example, a user who previously asked a question about a specific product can be provided with new information related to that product. The dialogue unit also refers to a user's past dialogue history and generates follow-up answers to problems or questions that were previously unresolved. For example, additional information or solutions can be provided for problems that could not be resolved in previous inquiries. The dialogue unit also understands a user's interests and concerns based on the past dialogue history and generates personalized answers accordingly. For example, a user who is interested in products in a specific category can be provided with information about new products and campaigns related to that category. This improves user satisfaction by generating optimal answers based on the user's past dialogue history.

[0031] The dialogue unit can automatically suggest related products and services based on the content of the user's dialogue. For example, the dialogue unit uses a generation AI to analyze the content of the user's dialogue and automatically suggest related products and services that the user may be interested in. For example, if the user is talking about a specific brand of cosmetics, the dialogue unit will provide information on new products and campaigns from that brand. The dialogue unit also suggests related services based on the content of the user's dialogue. For example, if the user is talking about travel, the dialogue unit will provide information on travel insurance and local tours. The dialogue unit also analyzes the content of the dialogue and develops an algorithm to suggest products and services that the user may be interested in. For example, if the user is talking about a specific hobby, the dialogue unit will suggest products and services related to that hobby. In this way, by suggesting related products and services based on the content of the user's dialogue, the user's desire to purchase is increased.

[0032] The dialogue unit can provide customized coupons and special offers based on the content of the user's dialogue. For example, the dialogue unit uses a generation AI to analyze the content of the user's dialogue and provide customized coupons for products and services that the user may be interested in. For example, if the user is talking about a specific restaurant, a discount coupon for that restaurant may be provided. The dialogue unit also provides special offers for specific services based on the content of the user's dialogue. For example, if the user is talking about fitness, a free trial ticket to a fitness club may be provided. The dialogue unit also analyzes the content of the dialogue and develops an algorithm for providing customized special offers for products and services that the user may be interested in. For example, if the user is talking about a specific brand of clothing, a special offer for new products from that brand may be provided. In this way, providing customized coupons and special offers based on the content of the user's dialogue increases the user's desire to purchase.

[0033] The analysis unit can analyze the content of user dialogue and cluster and report common concerns and needs. For example, the analysis unit uses a generation AI to analyze the content of user dialogue, extract common keywords and phrases, and cluster them. For example, users who are concerned about prices may be grouped into one cluster. The analysis unit also develops an algorithm for clustering common concerns and needs based on the content of user dialogue. For example, it may cluster users who are dissatisfied with a particular product and report that information to the advertiser. The analysis unit also builds a system that visualizes common user concerns and needs based on the clustering results and reports them to the advertiser. For example, it may make it possible to identify at a glance users who have concerns about products in a particular category. This allows advertisers to take effective measures by clustering and reporting common user concerns and needs.

[0034] The analysis unit can predict potential needs and problems based on the content of user dialogue and make suggestions to advertisers. For example, the analysis unit develops an algorithm for the generation AI to analyze the content of user dialogue and predict potential needs and problems. For example, it identifies needs that have not yet been clearly expressed from the content of users' frequent questions. The analysis unit also builds a system that predicts potential needs and problems based on the content of user dialogue and makes suggestions to advertisers. For example, if a user repeatedly asks about a specific product, it reports the potential need for that product. The analysis unit also analyzes the content of user dialogue to predict potential needs and problems and makes specific suggestions to advertisers. For example, if a user frequently asks about a specific function, it reports the need for that function. This allows the generation AI to predict users' potential needs and problems and make suggestions to advertisers, which is expected to improve advertising effectiveness.

[0035] The analysis unit can compare the needs of different regions and cultural spheres based on the content of user dialogue and provide insights from a global perspective. For example, the analysis unit develops an algorithm for the generation AI to analyze the content of user dialogue and compare the needs of different regions and cultural spheres. For example, it compares the reactions of users in different regions to the same product. The analysis unit also builds a system for comparing the needs of different cultural spheres based on the content of user dialogue and providing insights from a global perspective. For example, it identifies products and services that are popular in specific cultural spheres. The analysis unit also analyzes the content of user dialogue to compare the needs of different regions and cultural spheres and makes specific proposals to advertisers. For example, it proposes marketing strategies for specific regions. This allows advertisers to develop effective marketing strategies by comparing the needs of different regions and cultural spheres and providing insights from a global perspective.

[0036] The analysis unit can identify needs according to seasons and events based on the content of user dialogue and make suggestions to advertisers. For example, the analysis unit develops an algorithm for the generation AI to analyze the content of user dialogue and identify needs according to seasons and events. For example, it identifies needs for products and services related to Christmas and Valentine's Day. The analysis unit also builds a system that identifies needs according to seasons and events based on the content of user dialogue and makes suggestions to advertisers. For example, it identifies needs for travel and leisure related to summer vacation. The analysis unit also analyzes the content of user dialogue to identify needs according to seasons and events and makes specific suggestions to advertisers. For example, it proposes marketing strategies tailored to specific events. In this way, by identifying needs according to seasons and events and making suggestions to advertisers, it is expected that advertising effectiveness will be improved.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The dialogue unit can estimate the user's hobbies and interests based on the content of the user's dialogue and provide content that matches them. For example, if the user is talking about movies, the latest movie information and recommended movies can be provided. If the user is interested in sports, the latest sports news and game results can be provided. Furthermore, if the user is interested in a particular music genre, information on new songs and artists in that genre can be provided. This increases user satisfaction by providing content that matches the user's hobbies and interests.

[0039] The dialogue unit can infer the user's learning needs based on the content of the user's dialogue and provide appropriate learning resources. For example, if the user has a question about a specific subject, it can provide learning materials and online courses related to that subject. If the user wants to learn a new skill, it can provide tutorials and practice questions related to that skill. Furthermore, if the user is preparing for an exam, it can provide reference books and mock exams that will be useful for the exam. In this way, providing resources that meet the user's learning needs improves user satisfaction.

[0040] The dialogue unit can support the user's travel plans based on the content of the user's dialogue. For example, if the user is talking about a travel destination, it can provide tourist spots and recommended restaurants at that destination. Also, if the user asks about travel preparations, it can provide a list of necessary items to pack and travel tips. Furthermore, if the user encounters any problems during their trip, it can provide local information and emergency contact information. This support for the user's travel plans improves user satisfaction.

[0041] The dialogue unit can provide career advice to the user based on the content of the user's dialogue. For example, if the user is considering a career change, it can provide information on suitable occupations and industries. If the user is aiming to improve their skills, it can provide information on related training programs and qualification acquisition. Furthermore, if the user is seeking advice about a problem at work, it can also provide advice and resources for resolving the problem. In this way, by providing advice on the user's career, user satisfaction can be improved.

[0042] The dialogue unit can provide event information tailored to the user's hobbies and interests based on the content of the user's dialogue. For example, if the user is interested in music, information on nearby concerts and live events can be provided. If the user is interested in sports, information on nearby sporting events can be provided. Furthermore, if the user is interested in art, information on nearby art exhibitions and workshops can be provided. This improves user satisfaction by providing event information tailored to the user's hobbies and interests.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The dialogue unit responds to user inquiries and questions in real time. For example, if a user asks "How do I use this product?" in an advertisement, the dialogue unit analyzes the question and generates an appropriate answer. Also, if the user provides feedback such as "I think the price of this product is high," the dialogue unit analyzes the feedback and reports the user's needs and concerns to the advertiser. Step 2: The analysis unit analyzes the user's concerns and needs acquired by the dialogue unit. For example, the analysis unit may analyze the user's feedback using text analysis technology to identify the user's concerns and needs. It may also analyze the user's emotions using emotion analysis technology to identify the user's emotional needs. Step 3: The reporting unit reports the user's concerns and needs analyzed by the analysis unit to the advertiser. For example, the reporting unit may provide the analysis results to the advertiser in the form of a report, providing information for the advertiser to consider their next steps. The reporting unit may also notify the advertiser of the analysis results in real time.

[0045] (Example 2) The participatory advertising system according to an embodiment of the present invention utilizes generative AI to enable real-time dialogue with users within advertisements and provide solutions tailored to the user's concerns and needs. This allows the participatory advertising system to grasp the user's concerns and needs in real time and report them to advertisers, thereby maximizing the effectiveness of advertising.

[0046] A participatory advertising system according to an embodiment includes a generation AI, a dialogue unit, an analysis unit, and a reporting unit. The generation AI includes a dialogue unit that responds to user inquiries and questions in real time. For example, if a user asks, "Please tell me how to use this product" in an advertisement, the dialogue unit analyzes the question and generates an appropriate answer. Furthermore, if the user provides feedback such as, "I feel the price of this product is high," the dialogue unit analyzes the feedback and reports the user's needs and concerns to the advertiser. The analysis unit analyzes the user's concerns and needs acquired by the dialogue unit. For example, the analysis unit may analyze the user's feedback using text analysis technology to identify the user's concerns and needs. The analysis unit may also analyze the user's emotions using emotion analysis technology to identify the user's emotional needs. The reporting unit reports the user's concerns and needs analyzed by the analysis unit to the advertiser. For example, the reporting unit may provide the advertiser with analysis results in the form of a report, providing the advertiser with information to consider future measures. The reporting unit may also notify the advertiser of the analysis results in real time. As a result, the participatory advertising system according to the embodiment is able to grasp the concerns and needs of users in real time and report them to advertisers, thereby maximizing advertising effectiveness.

[0047] The dialogue unit can refer to a user's past dialogue history and generate answers optimized for individual users. For example, the dialogue unit uses a generation AI to analyze a user's past dialogue history and generate optimal answers based on the user's preferences and past questions. For example, a user who previously asked a question about a specific product can be provided with new information related to that product. The dialogue unit also refers to a user's past dialogue history and generates follow-up answers to problems or questions that were previously unresolved. For example, additional information or solutions can be provided for problems that could not be resolved in previous inquiries. The dialogue unit also understands a user's interests and concerns based on the past dialogue history and generates personalized answers accordingly. For example, a user who is interested in products in a specific category can be provided with information about new products and campaigns related to that category. This improves user satisfaction by generating optimal answers based on the user's past dialogue history.

[0048] The dialogue unit can analyze the user's tone of voice and facial expressions and generate answers that correspond to their emotions. For example, the generation AI analyzes the user's tone of voice and generates a calm answer if the user is excited, or a friendly answer if the user is calm. For example, if the user is excited, it provides steps to calmly solve the problem. The dialogue unit also analyzes the user's facial expressions and generates answers that correspond to their emotions. For example, if the user looks confused, it provides a more detailed explanation or additional information. The dialogue unit also analyzes both the tone of voice and facial expressions and generates answers that best suit the user's emotional state. For example, if the user is tired, it provides a concise and easy-to-understand answer, reducing the user's burden. This increases user satisfaction by generating answers that correspond to the user's emotions.

[0049] The dialogue unit can use the emotion estimation function to estimate the user's emotions in real time and generate answers that elicit positive emotions. For example, using the emotion estimation function, if the user has negative emotions, the dialogue unit generates answers that include words of encouragement or gratitude to elicit positive emotions. For example, if the user is dissatisfied, the dialogue unit makes specific suggestions to resolve the dissatisfaction. The dialogue unit also estimates the user's emotions in real time and generates answers that include humor or light jokes to elicit positive emotions. For example, if the user is feeling a little down, the dialogue unit advances the conversation by incorporating light jokes. The dialogue unit also uses the emotion estimation function to generate answers that include compliments or success stories to further reinforce the user's positive emotions. For example, if the user shares a successful experience, the dialogue unit provides words praising the success. In this way, the dialogue unit estimates the user's emotions in real time and elicits positive emotions, thereby improving user satisfaction.

[0050] The dialogue unit can automatically suggest related products and services based on the content of the user's dialogue. For example, the dialogue unit uses a generation AI to analyze the content of the user's dialogue and automatically suggest related products and services that the user may be interested in. For example, if the user is talking about a specific brand of cosmetics, the dialogue unit will provide information on new products and campaigns from that brand. The dialogue unit also suggests related services based on the content of the user's dialogue. For example, if the user is talking about travel, the dialogue unit will provide information on travel insurance and local tours. The dialogue unit also analyzes the content of the dialogue and develops an algorithm to suggest products and services that the user may be interested in. For example, if the user is talking about a specific hobby, the dialogue unit will suggest products and services related to that hobby. In this way, by suggesting related products and services based on the content of the user's dialogue, the user's desire to purchase is increased.

[0051] The dialogue unit can provide customized coupons and special offers based on the content of the user's dialogue. For example, the dialogue unit uses a generation AI to analyze the content of the user's dialogue and provide customized coupons for products and services that the user may be interested in. For example, if the user is talking about a specific restaurant, a discount coupon for that restaurant may be provided. The dialogue unit also provides special offers for specific services based on the content of the user's dialogue. For example, if the user is talking about fitness, a free trial ticket to a fitness club may be provided. The dialogue unit also analyzes the content of the dialogue and develops an algorithm for providing customized special offers for products and services that the user may be interested in. For example, if the user is talking about a specific brand of clothing, a special offer for new products from that brand may be provided. In this way, providing customized coupons and special offers based on the content of the user's dialogue increases the user's desire to purchase.

[0052] The dialogue unit uses the emotion estimation function to change advertisement content in real time according to the user's emotions, thereby providing more effective advertisements. For example, the dialogue unit uses the emotion estimation function to provide advertisement content in real time to further reinforce a user's positive emotions when the user has those emotions. For example, if the user is happy, the dialogue unit displays advertisements including success stories and positive messages. The dialogue unit also analyzes the user's emotions in real time, and if the user has negative emotions, the dialogue unit provides advertisement content to alleviate those emotions. For example, if the user is dissatisfied, the dialogue unit displays advertisements including specific suggestions for resolving the dissatisfaction. The dialogue unit also uses the emotion estimation function to develop a system for changing advertisement content in real time according to the user's emotions. For example, if the user is excited, the dialogue unit displays advertisements including steps for calmly resolving the problem. In this way, the advertisement content can be changed in real time according to the user's emotions, thereby improving the effectiveness of the advertisement.

[0053] The analysis unit can analyze the content of user dialogue and cluster and report common concerns and needs. For example, the analysis unit uses a generation AI to analyze the content of user dialogue, extract common keywords and phrases, and cluster them. For example, users who are concerned about prices may be grouped into one cluster. The analysis unit also develops an algorithm for clustering common concerns and needs based on the content of user dialogue. For example, it may cluster users who are dissatisfied with a particular product and report that information to the advertiser. The analysis unit also builds a system that visualizes common user concerns and needs based on the clustering results and reports them to the advertiser. For example, it may make it possible to identify at a glance users who have concerns about products in a particular category. This allows advertisers to take effective measures by clustering and reporting common user concerns and needs.

[0054] The analysis unit can predict potential needs and problems based on the content of user dialogue and make suggestions to advertisers. For example, the analysis unit develops an algorithm for the generation AI to analyze the content of user dialogue and predict potential needs and problems. For example, it identifies needs that have not yet been clearly expressed from the content of users' frequent questions. The analysis unit also builds a system that predicts potential needs and problems based on the content of user dialogue and makes suggestions to advertisers. For example, if a user repeatedly asks about a specific product, it reports the potential need for that product. The analysis unit also analyzes the content of user dialogue to predict potential needs and problems and makes specific suggestions to advertisers. For example, if a user frequently asks about a specific function, it reports the need for that function. This allows the generation AI to predict users' potential needs and problems and make suggestions to advertisers, which is expected to improve advertising effectiveness.

[0055] The analysis unit can use the emotion estimation function to analyze the user's emotions and identify and report their emotional needs and concerns. For example, the analysis unit uses the emotion estimation function to analyze emotions from the content of the user's dialogue and identify their emotional needs and concerns. For example, if the user is feeling anxious, the analysis unit reports specific suggestions for relieving the anxiety. The analysis unit also builds a system for analyzing the user's emotions in real time and identifying their emotional needs and concerns. For example, if the user is excited, the analysis unit reports suggestions for easing the excitement. The analysis unit also uses the emotion estimation function to analyze the user's emotions and identify and report their emotional needs and concerns to the advertiser. For example, if the user is confused, the analysis unit makes specific suggestions for relieving the confusion. In this way, by analyzing the user's emotions and identifying and reporting their emotional needs and concerns, the advertiser can take effective measures.

[0056] The analysis unit can compare the needs of different regions and cultural spheres based on the content of user dialogue and provide insights from a global perspective. For example, the analysis unit develops an algorithm for the generation AI to analyze the content of user dialogue and compare the needs of different regions and cultural spheres. For example, it compares the reactions of users in different regions to the same product. The analysis unit also builds a system for comparing the needs of different cultural spheres based on the content of user dialogue and providing insights from a global perspective. For example, it identifies products and services that are popular in specific cultural spheres. The analysis unit also analyzes the content of user dialogue to compare the needs of different regions and cultural spheres and makes specific proposals to advertisers. For example, it proposes marketing strategies for specific regions. This allows advertisers to develop effective marketing strategies by comparing the needs of different regions and cultural spheres and providing insights from a global perspective.

[0057] The analysis unit can identify needs according to seasons and events based on the content of user dialogue and make suggestions to advertisers. For example, the analysis unit develops an algorithm for the generation AI to analyze the content of user dialogue and identify needs according to seasons and events. For example, it identifies needs for products and services related to Christmas and Valentine's Day. The analysis unit also builds a system that identifies needs according to seasons and events based on the content of user dialogue and makes suggestions to advertisers. For example, it identifies needs for travel and leisure related to summer vacation. The analysis unit also analyzes the content of user dialogue to identify needs according to seasons and events and makes specific suggestions to advertisers. For example, it proposes marketing strategies tailored to specific events. In this way, by identifying needs according to seasons and events and making suggestions to advertisers, it is expected that advertising effectiveness will be improved.

[0058] The analysis unit can use the emotion estimation function to identify needs based on the user's emotions and suggest advertising content that is likely to resonate with the user emotionally. The analysis unit, for example, uses the emotion estimation function to build a system for identifying needs based on the user's emotions. For example, if the user is feeling anxious, the analysis unit makes specific suggestions to alleviate the anxiety. The analysis unit also analyzes the user's emotions in real time and suggests advertising content that is likely to resonate with the user emotionally. For example, if the user is happy, the analysis unit displays an advertisement that includes a positive message to further enhance the user's joy. The analysis unit also uses the emotion estimation function to identify needs based on the user's emotions and make specific suggestions to the advertiser. For example, if the user is confused, the analysis unit makes specific suggestions to alleviate the confusion. In this way, by identifying needs based on the user's emotions and suggesting advertising content that is likely to resonate with the user emotionally, improved advertising effectiveness can be expected.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The dialogue unit can estimate the user's health condition based on the content of the user's dialogue and provide appropriate advice. For example, if the user feels tired, it can provide advice on relaxation methods and the importance of rest. If the user feels stressed, it can suggest stress relief methods and relaxation techniques. Furthermore, if the user asks a health-related question, it can also provide health information from a reliable source. This improves user satisfaction by providing advice tailored to the user's health condition.

[0061] The dialogue unit can estimate the user's hobbies and interests based on the content of the user's dialogue and provide content that matches them. For example, if the user is talking about movies, the latest movie information and recommended movies can be provided. If the user is interested in sports, the latest sports news and game results can be provided. Furthermore, if the user is interested in a particular music genre, information on new songs and artists in that genre can be provided. This increases user satisfaction by providing content that matches the user's hobbies and interests.

[0062] The dialogue unit can infer the user's learning needs based on the content of the user's dialogue and provide appropriate learning resources. For example, if the user has a question about a specific subject, it can provide learning materials and online courses related to that subject. If the user wants to learn a new skill, it can provide tutorials and practice questions related to that skill. Furthermore, if the user is preparing for an exam, it can provide reference books and mock exams that will be useful for the exam. In this way, providing resources that meet the user's learning needs improves user satisfaction.

[0063] The dialogue unit can support the user's travel plans based on the content of the user's dialogue. For example, if the user is talking about a travel destination, it can provide tourist spots and recommended restaurants at that destination. Also, if the user asks about travel preparations, it can provide a list of necessary items to pack and travel tips. Furthermore, if the user encounters any problems during their trip, it can provide local information and emergency contact information. This support for the user's travel plans improves user satisfaction.

[0064] The dialogue unit can provide career advice to the user based on the content of the user's dialogue. For example, if the user is considering a career change, it can provide information on suitable occupations and industries. If the user is aiming to improve their skills, it can provide information on related training programs and qualification acquisition. Furthermore, if the user is seeking advice about a problem at work, it can also provide advice and resources for resolving the problem. In this way, by providing advice on the user's career, user satisfaction can be improved.

[0065] The dialogue unit can estimate the user's emotions in real time using the emotion estimation function and provide music that matches the user's emotions. For example, if the user feels like relaxing, relaxing music can be provided. If the user feels like cheering up, energetic music can be provided. Furthermore, if the user feels like concentrating, music that helps improve concentration can be provided. In this way, providing music that matches the user's emotions improves user satisfaction.

[0066] The dialogue unit can estimate the user's emotions in real time using the emotion estimation function and suggest exercises that correspond to the user's emotions. For example, if the user is feeling stressed, it can suggest exercises to relieve stress. If the user feels like relaxing, it can suggest relaxation exercises. Furthermore, if the user feels like being energized, it can suggest exercises to increase energy. In this way, suggesting exercises that correspond to the user's emotions improves user satisfaction.

[0067] The dialogue unit can use the emotion estimation function to estimate the user's emotions in real time and provide a reading list that corresponds to the user's emotions. For example, if the user feels like relaxing, a relaxing book can be provided. If the user feels like cheering up, an energetic book can be provided. Furthermore, if the user feels like concentrating, a book that will help improve concentration can be provided. In this way, by providing a reading list that corresponds to the user's emotions, user satisfaction can be improved.

[0068] The dialogue unit can estimate the user's emotions in real time using the emotion estimation function and suggest relaxation techniques according to the user's emotions. For example, if the user is feeling stressed, relaxation techniques such as deep breathing or meditation can be suggested. If the user feels like relaxing, relaxation techniques such as yoga or stretching can be suggested. Furthermore, if the user feels like feeling energized, relaxation techniques such as light exercise or dancing can be suggested. In this way, by suggesting relaxation techniques according to the user's emotions, user satisfaction can be improved.

[0069] The dialogue unit can provide event information tailored to the user's hobbies and interests based on the content of the user's dialogue. For example, if the user is interested in music, information on nearby concerts and live events can be provided. If the user is interested in sports, information on nearby sporting events can be provided. Furthermore, if the user is interested in art, information on nearby art exhibitions and workshops can be provided. This improves user satisfaction by providing event information tailored to the user's hobbies and interests.

[0070] The processing flow of the second embodiment will be briefly explained below.

[0071] Step 1: The dialogue unit responds to user inquiries and questions in real time. For example, if a user asks "How do I use this product?" in an advertisement, the dialogue unit analyzes the question and generates an appropriate answer. Also, if the user provides feedback such as "I think the price of this product is high," the dialogue unit analyzes the feedback and reports the user's needs and concerns to the advertiser. Step 2: The analysis unit analyzes the user's concerns and needs acquired by the dialogue unit. For example, the analysis unit may analyze the user's feedback using text analysis technology to identify the user's concerns and needs. It may also analyze the user's emotions using emotion analysis technology to identify the user's emotional needs. Step 3: The reporting unit reports the user's concerns and needs analyzed by the analysis unit to the advertiser. For example, the reporting unit may provide the analysis results to the advertiser in the form of a report, providing information for the advertiser to consider their next steps. The reporting unit may also notify the advertiser of the analysis results in real time.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0076] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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).

[0081] 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.

[0082] 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.

[0083] 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.

[0084] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0085] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0100] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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."

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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]

[0139] 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. Equipped with generative AI, The generated AI is a dialogue unit that responds in real time to inquiries and questions from users; an analysis unit that analyzes the worries and needs of the user acquired by the dialogue unit; a reporting unit that reports to an advertiser the worries and needs of the user analyzed by the analysis unit. A system characterized by:

2. The dialogue unit Refer to the user's past dialogue history and generate an answer optimized for each individual user.

2. The system of claim 1.

3. The dialogue unit Analyzing the user's tone of voice and facial expressions to generate responses according to their emotions 2. The system of claim 1.

4. The dialogue unit Estimate the user's emotions in real time and generate answers that elicit positive emotions 2. The system of claim 1.

5. The dialogue unit Based on the content of the user's dialogue, related products and services are automatically suggested.

2. The system of claim 1.

6. The dialogue unit Based on the content of the user's interaction, customized coupons and special offers are provided.

2. The system of claim 1.

7. The dialogue unit To provide more effective advertisements by changing advertisement content in real time according to the user's emotions 2. The system of claim 1.

8. The analysis unit Analyze the content of the user's dialogue, cluster common concerns and needs, and report them.

2. The system of claim 1.

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