System

Abstract

An object of a system according to an embodiment is to analyze the emotion, values, and lifestyle of a consumer in real time and improve products, services, and marketing strategies based on the analysis.SOLUTION: A system includes a data collection part, an analysis part, an improvement part, and a strategy part. The data collection unit collects the emotion, values, and lifestyle of the person. The analysis unit analyzes, in real time, the emotion, values, and lifestyle of the person collected by the data collection unit. The improvement unit improves the product or the service based on the insight obtained by the analysis unit. The strategy component refines the marketing strategy based on the insights obtained by the analysis component.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not being able to adequately grasp consumers' emotions, values, and lifestyles in real time and use that information to improve products, services, and marketing strategies.

[0005] The system according to the embodiment aims to analyze the emotions, values, and lifestyles of consumers in real time and to improve products, services, and marketing strategies based on the results. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, an improvement unit, and a strategy unit. The data collection unit collects consumer emotions, values, and lifestyles. The analysis unit analyzes the consumer emotions, values, and lifestyles collected by the data collection unit in real time. The improvement unit improves products and services based on the insights obtained by the analysis unit. The strategy unit refines marketing strategies based on the insights obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the emotions, values, and lifestyles of consumers in real time, and improve products, services, and marketing strategies based on the results. [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 emotional resonance platform according to an embodiment of the present invention is a system that enables companies to empathize deeply with their customers as consumers and build more personalized engagement. This system uses multimodal AI analysis technology to capture consumers' emotions, values, and lifestyles in real time, and uses this insight to improve the products and services offered by companies and refine their marketing strategies. This allows the emotional resonance platform to enable companies to empathize deeply with their customers as consumers and build more personalized engagement.

[0029] An emotional resonance platform according to an embodiment includes a data collection unit, an analysis unit, an improvement unit, and a strategy unit. The data collection unit collects the emotions, values, and lifestyles of consumers. For example, the data collection unit collects social media posts. The data collection unit can also collect voice messages. The data collection unit can also collect image and video content. The analysis unit analyzes the emotions, values, and lifestyles of consumers collected by the data collection unit in real time. For example, the analysis unit analyzes the collected social media posts to understand the emotions of consumers. The analysis unit can also analyze the collected voice messages to understand the values ​​of consumers. The analysis unit can also analyze the content of collected images and videos to understand the lifestyles of consumers. The improvement unit improves products and services based on the insights obtained by the analysis unit. For example, the improvement unit improves the design and functionality of products based on the emotions and values ​​of consumers. The improvement unit can also review how services are provided based on the lifestyles of consumers. The improvement unit can also enhance product personalization functions based on the emotions and values ​​of consumers. The strategy department refines the marketing strategy based on the insights obtained by the analysis department. For example, the strategy department reviews the content and target of advertising based on the emotions and values ​​of consumers. The strategy department can also adjust the method and timing of promotions based on the lifestyles of consumers. The strategy department can also subdivide the target audience segmentation based on the emotions and values ​​of consumers. This allows the emotional resonance platform according to the embodiment to enable companies to deeply empathize with their customers as consumers and build more personalized engagement. For example, companies can improve customer satisfaction by improving their products and services based on the emotions and values ​​of consumers. Companies can also conduct effective promotions by refining their marketing strategies based on the lifestyles of consumers.

[0030] The data collection unit can analyze the content of social media posts, voice messages, images, or videos. For example, the data collection unit uses generation AI to analyze consumers' voice data and capture changes in tone and pitch. This makes it possible to grasp subtle changes in consumers' emotions. The data collection unit also uses generation AI to analyze consumers' facial expression data and capture subtle changes in facial expressions. For example, it analyzes changes in facial expressions such as smiling and furrowing the brow to grasp emotional fluctuations. The data collection unit also uses generation AI to analyze voice and facial expression data in an integrated manner to more accurately estimate consumers' emotions. For example, it analyzes changes in voice tone and facial expressions in combination. This makes it possible to collect multifaceted information about consumers and grasp their emotions and values.

[0031] The analysis unit can also analyze consumers' purchasing history and health data from wearable devices. For example, the analysis unit uses generation AI to analyze the emotions consumers have toward specific products or services. For example, it analyzes text data from product reviews and feedback. The analysis unit also uses generation AI to build a system that tracks changes in consumers' emotions. For example, it periodically collects emotion data and analyzes emotional fluctuations over time. The analysis unit also uses an emotion estimation function to monitor consumers' emotions in real time when using products or services, and analyzes changes in emotion based on that data. This allows for a comprehensive understanding of consumers' lifestyles.

[0032] The analysis unit analyzes the company's internal data and can grasp the emotions and values ​​of the entire company. For example, the analysis unit uses generative AI to analyze the emotions and values ​​of consumers in different cultural spheres. For example, it analyzes social media posts in different languages ​​to grasp the differences in emotions between cultures. The analysis unit also uses generative AI to analyze the emotions and values ​​of consumers in different regions. For example, it analyzes purchasing history data for each region to grasp the values ​​unique to that region. The analysis unit also uses generative AI to comprehensively analyze data from different cultural spheres and regions to gain insights from a global perspective. For example, it analyzes the differences in emotions and values ​​between cultures and regions. This makes it possible to grasp the emotions and values ​​of the entire company.

[0033] The analysis unit analyzes the emotions or values ​​of consumers in different cultural spheres or regions, enabling insights from a global perspective. For example, the analysis unit uses generative AI to analyze in real time the emotions consumers feel toward content shared on social media. For example, it analyzes the text data of the posts and calculates an emotion score. The analysis unit also uses an emotion estimation function to monitor in real time the emotions consumers feel toward content on social media, and adjusts marketing strategies based on that data. The analysis unit also uses generative AI to analyze the emotion data on social media and reflect it in a company's marketing strategy. For example, it designs an advertising campaign based on content with a high percentage of positive emotions. This enables insights from a global perspective to be obtained.

[0034] The analysis unit can also analyze the influence of friends or family in the consumer's social network. The analysis unit, for example, uses a generation AI to analyze the consumer's social network data and understand the influence of friends and family. For example, it analyzes friendship data on social media. The analysis unit also uses a generation AI to comprehensively analyze the consumer's social network data and emotion data, and performs emotion analysis that takes into account the influence of friends and family. The analysis unit also uses a generation AI to analyze the consumer's social network data and analyze values ​​that take into account the influence of friends and family. This makes it possible to perform emotion analysis that takes into account the influence of friends and family.

[0035] The analysis unit can also integrate and analyze data from different devices. For example, the analysis unit uses generation AI to analyze data collected from smartphones to understand consumers' emotions and values. For example, it analyzes smartphone usage history. The analysis unit also uses generation AI to analyze data collected from tablets to understand consumers' emotions and values. For example, it analyzes tablet app usage history. The analysis unit also uses generation AI to comprehensively analyze data collected from different devices such as smartphones, tablets, and PCs to comprehensively understand consumers' emotions and values. This enables integrated analysis of data from different devices.

[0036] The analysis unit can analyze data including data from different media. For example, the analysis unit uses a generation AI to analyze data collected from news sites to understand consumers' emotions and values. For example, it analyzes comments on news articles. The analysis unit also uses a generation AI to analyze data collected from blogs to understand consumers' emotions and values. For example, it analyzes the content of blog articles. The analysis unit also uses a generation AI to comprehensively analyze data collected from different media such as news, blogs, and forums to comprehensively understand consumers' emotions and values. This enables integrated analysis of data from different media.

[0037] The improvement department can also consider the usability of the user interface when improving the product design based on consumer emotions and values. For example, the improvement department uses generative AI to analyze consumer emotions and values ​​and improve the product design. For example, the color and layout of the user interface are adjusted based on the emotion data. The improvement department also uses generative AI to analyze consumer feedback data and improve the usability of the user interface. For example, it identifies and improves parts that are difficult to use. The improvement department also uses generative AI to improve the product design based on consumer emotions and values, and designs the user interface with usability in mind. This makes it possible to improve product design by taking the usability of the user interface into account.

[0038] Based on the insights analyzed by the generation AI, the improvement department can enhance the product's personalization functions and provide optimal settings for individual consumers. For example, the improvement department uses the generation AI to analyze consumers' emotions and values ​​and enhance the product's personalization functions. For example, the department automatically adjusts settings according to the user's preferences. The improvement department also uses the generation AI to analyze consumers' usage data and build a system that provides optimal settings for individual consumers. For example, the system provides recommended settings based on usage history. The improvement department also uses the generation AI to enhance the product's personalization functions based on consumers' emotions and values ​​and provide an optimal usage experience for individual consumers. This makes it possible to improve the product usage experience by providing optimal settings for individual consumers.

[0039] The improvement department can integrate online and offline services when reviewing how services are provided based on consumer emotions and values. For example, the improvement department uses generative AI to analyze consumer emotions and values ​​and review how services are provided. For example, integrating online and offline services. The improvement department also uses generative AI to analyze consumer feedback data and identify areas for improvement to integrate online and offline services. The improvement department also uses generative AI to review how services are provided based on consumer emotions and values ​​and integrate online and offline services. For example, linking online reservations with offline experiences. In this way, integrating online and offline services makes it possible to provide more convenient services for consumers.

[0040] The improvement department can introduce a product subscription model based on consumer emotions and values, thereby promoting continuous engagement. For example, the improvement department uses generative AI to analyze consumer emotions and values ​​and introduce a product subscription model. For example, by providing regular updates and additional functions. The improvement department also uses generative AI to analyze consumer usage data and build a system that proposes the optimal subscription model plan. For example, by providing plans based on frequency of use. The improvement department also uses generative AI to introduce a product subscription model based on consumer emotions and values, thereby promoting continuous engagement. In this way, by introducing a subscription model, continuous engagement can be promoted.

[0041] When reviewing the content of an advertisement based on consumers' emotions and values, the strategy department can also optimize the visual elements of the advertisement. For example, the strategy department uses generative AI to analyze consumers' emotions and values ​​and optimize the visual elements of the advertisement. For example, the color and design of the advertisement are adjusted based on the emotional data. The strategy department also uses generative AI to analyze consumer feedback data and improve the visual elements of the advertisement. For example, visually appealing elements are enhanced. The strategy department also uses generative AI to review the content of the advertisement based on consumers' emotions and values ​​and optimize the visual elements. In this way, by optimizing the visual elements of the advertisement, it is possible to provide a visually appealing advertisement.

[0042] Based on the insights analyzed by the generative AI, the strategy department can refine the target audience segmentation and perform more precise targeting. For example, the strategy department uses generative AI to analyze consumer emotions and values ​​and refine the target audience segmentation. For example, they classify target groups based on emotional data. The strategy department also uses generative AI to analyze consumer usage data and build a system to refine the target audience segmentation. For example, they create segments based on frequency of use and purchase history. The strategy department also uses generative AI to refine the target audience segmentation based on consumer emotions and values ​​and perform more precise targeting. This enables them to refine the target audience segmentation and perform more precise targeting.

[0043] The strategy department can utilize influencer marketing when reviewing promotion methods based on consumer emotions and values. For example, the strategy department uses generative AI to analyze consumer emotions and values ​​and review promotion methods using influencer marketing. For example, the strategy department selects appropriate influencers based on emotional data. The strategy department also uses generative AI to analyze consumer feedback data and identify areas for improvement to maximize the effectiveness of influencer marketing. The strategy department also uses generative AI to review promotion methods based on consumer emotions and values ​​and utilize influencer marketing. In this way, more effective promotions can be achieved by utilizing influencer marketing.

[0044] The strategy department can optimize the timing of marketing campaigns based on consumer emotions and values, and conduct promotions that are timed to coincide with seasons and events. For example, the strategy department uses generative AI to analyze consumer emotions and values ​​and optimize the timing of marketing campaigns. For example, it plans promotions that coincide with seasons and events based on emotion data. The strategy department also uses generative AI to analyze consumer usage data and build a system that suggests the optimal timing for marketing campaigns. For example, it adjusts the timing of campaigns based on frequency of use and purchase history. The strategy department also uses generative AI to optimize the timing of marketing campaigns based on consumer emotions and values, and conduct promotions that coincide with seasons and events. This makes it possible to optimize the timing of marketing campaigns and conduct promotions that coincide with seasons and events.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The data collection unit collects information on consumers' emotions, values, and lifestyles. For example, the data collection unit collects social media posts. The data collection unit can also collect voice messages. The data collection unit can also collect image and video content. The analysis unit analyzes the emotions, values, and lifestyles of consumers collected by the data collection unit in real time. For example, the analysis unit analyzes the collected social media posts to understand consumers' emotions. The analysis unit can also analyze the collected voice messages to understand consumers' values. The analysis unit can also analyze the collected image and video content to understand consumers' lifestyles. The improvement unit improves products and services based on the insights obtained by the analysis unit. For example, the improvement unit improves product design and functionality based on consumers' emotions and values. The improvement unit can also review service delivery methods based on consumers' lifestyles. The improvement unit can also enhance product personalization functions based on consumers' emotions and values. The strategy unit refines marketing strategies based on the insights obtained by the analysis unit. For example, the strategy department may review the content and target of advertising based on the emotions and values ​​of consumers. The strategy department may also adjust the method and timing of promotions based on the lifestyles of consumers. The strategy department may also subdivide the target audience segmentation based on the emotions and values ​​of consumers. This allows the emotional resonance platform according to the embodiment to enable companies to deeply empathize with their customers as consumers and build more personalized engagement. For example, companies can improve customer satisfaction by improving their products and services based on the emotions and values ​​of consumers. Companies can also conduct effective promotions by refining their marketing strategies based on the lifestyles of consumers.

[0047] The data collection unit can analyze the content of social media posts, voice messages, images, or videos. For example, the data collection unit uses the generation AI to analyze consumers' voice data and capture changes in tone and pitch. This makes it possible to grasp subtle changes in consumers' emotions. The data collection unit also uses the generation AI to analyze consumers' facial expression data and capture subtle changes in facial expressions. For example, it analyzes changes in facial expressions such as smiling and furrowing the brow to grasp emotional fluctuations. The data collection unit also uses the generation AI to analyze voice and facial expression data in an integrated manner to more accurately estimate consumers' emotions. For example, it analyzes changes in voice tone and facial expressions in combination. This makes it possible to collect multifaceted information about consumers and grasp their emotions and values.

[0048] The analysis unit can also analyze consumers' purchasing history and health data from wearable devices. For example, the analysis unit uses generation AI to analyze the emotions consumers have toward specific products or services. For example, it analyzes text data such as product reviews and feedback. The analysis unit also uses generation AI to build a system that tracks changes in consumers' emotions. For example, it periodically collects emotion data and analyzes emotional fluctuations over time. The analysis unit also uses emotion estimation functionality to monitor consumers' emotions in real time when using products or services, and analyzes changes in emotion based on that data. This allows for a comprehensive understanding of consumers' lifestyles.

[0049] The analysis unit analyzes the company's internal data and can grasp the emotions and values ​​of the entire company. For example, the analysis unit uses generative AI to analyze the emotions and values ​​of consumers in different cultural spheres. For example, it analyzes social media posts in different languages ​​to grasp the differences in emotions between cultures. The analysis unit also uses generative AI to analyze the emotions and values ​​of consumers in different regions. For example, it analyzes purchasing history data for each region to grasp the values ​​unique to that region. The analysis unit also uses generative AI to comprehensively analyze data from different cultural spheres and regions to gain insights from a global perspective. For example, it analyzes the differences in emotions and values ​​between cultures and regions. This makes it possible to grasp the emotions and values ​​of the entire company.

[0050] The analysis unit analyzes the emotions or values ​​of consumers in different cultural spheres or regions, enabling insights from a global perspective. For example, the analysis unit uses generative AI to analyze in real time the emotions consumers feel toward content shared on social media. For example, it analyzes the text data of the posts and calculates an emotion score. The analysis unit also uses emotion estimation functionality to monitor in real time the emotions consumers feel toward content on social media, and adjusts marketing strategies based on that data. The analysis unit also uses generative AI to analyze emotional data on social media and reflect it in a company's marketing strategy. For example, it designs an advertising campaign based on content with a high percentage of positive emotions. This allows insights from a global perspective to be gained.

[0051] The analysis unit can also analyze the influence of friends or family in the consumer's social network. For example, the analysis unit uses the generation AI to analyze the consumer's social network data and understand the influence of friends and family. For example, the analysis unit analyzes friendship data on social media. The analysis unit also uses the generation AI to comprehensively analyze the consumer's social network data and emotion data, and performs emotion analysis that takes into account the influence of friends and family. The analysis unit also uses the generation AI to analyze the consumer's social network data and analyze values ​​that take into account the influence of friends and family. This makes it possible to perform emotion analysis that takes into account the influence of friends and family.

[0052] The analysis unit can also integrate and analyze data from different devices. For example, the analysis unit uses generation AI to analyze data collected from smartphones to understand consumers' emotions and values. For example, it analyzes smartphone usage history. The analysis unit also uses generation AI to analyze data collected from tablets to understand consumers' emotions and values. For example, it analyzes tablet app usage history. The analysis unit also uses generation AI to comprehensively analyze data collected from different devices such as smartphones, tablets, and PCs to comprehensively understand consumers' emotions and values. This enables integrated analysis of data from different devices.

[0053] The analysis unit can analyze data including data from different media. For example, the analysis unit uses the generation AI to analyze data collected from news sites to understand consumers' emotions and values. For example, it analyzes comments on news articles. The analysis unit also uses the generation AI to analyze data collected from blogs to understand consumers' emotions and values. For example, it analyzes the content of blog articles. The analysis unit also uses the generation AI to comprehensively analyze data collected from different media such as news, blogs, and forums to comprehensively understand consumers' emotions and values. This enables integrated analysis of data from different media.

[0054] When improving product design based on consumer emotions and values, the improvement department can also consider the usability of the user interface. For example, the improvement department uses generative AI to analyze consumer emotions and values ​​and improve product design. For example, the color and layout of the user interface are adjusted based on emotional data. The improvement department also uses generative AI to analyze consumer feedback data and improve the usability of the user interface. For example, parts that are difficult to use are identified and improved. The improvement department also uses generative AI to improve product design based on consumer emotions and values, and designs with usability of the user interface in mind. This makes it possible to improve product design by taking usability of the user interface into account.

[0055] Based on the insights analyzed by the generation AI, the improvement department can enhance the product's personalization functions and provide optimal settings for each individual consumer. For example, the improvement department uses the generation AI to analyze consumers' emotions and values ​​and enhance the product's personalization functions. For example, the department automatically adjusts settings according to the user's preferences. The improvement department also uses the generation AI to analyze consumer usage data and build a system that provides optimal settings for each individual consumer. For example, the system provides recommended settings based on usage history. The improvement department also uses the generation AI to enhance the product's personalization functions based on consumers' emotions and values ​​and provide an optimal usage experience for each individual consumer. This makes it possible to improve the product usage experience by providing optimal settings for each individual consumer.

[0056] The Improvement Department can integrate online and offline services when reviewing how services are provided based on consumer emotions and values. For example, the Improvement Department uses generative AI to analyze consumer emotions and values ​​and review how services are provided. For example, by integrating online and offline services. The Improvement Department also uses generative AI to analyze consumer feedback data and identify areas for improvement to integrate online and offline services. The Improvement Department also uses generative AI to review how services are provided based on consumer emotions and values ​​and integrate online and offline services. For example, by linking online reservations with offline experiences. In this way, by integrating online and offline services, it becomes possible to provide more convenient services for consumers.

[0057] The Improvement Department can introduce a product subscription model based on consumer emotions and values ​​to promote continuous engagement. For example, the Improvement Department can use Generative AI to analyze consumer emotions and values ​​and introduce a product subscription model. For example, by providing regular updates and additional features. The Improvement Department can also use Generative AI to analyze consumer usage data and build a system that proposes the optimal subscription model plan. For example, by providing plans based on frequency of use. The Improvement Department can also use Generative AI to introduce a product subscription model based on consumer emotions and values ​​to promote continuous engagement. In this way, introducing a subscription model can promote continuous engagement.

[0058] When reviewing the content of an advertisement based on consumers' emotions and values, the strategy department can also optimize the visual elements of the advertisement. For example, the strategy department uses generative AI to analyze consumers' emotions and values ​​and optimize the visual elements of the advertisement. For example, the color and design of the advertisement can be adjusted based on the emotional data. The strategy department also uses generative AI to analyze consumer feedback data and improve the visual elements of the advertisement. For example, visually appealing elements can be enhanced. The strategy department also uses generative AI to review the content of the advertisement based on consumers' emotions and values ​​and optimize the visual elements. In this way, optimizing the visual elements of the advertisement makes it possible to provide a visually appealing advertisement.

[0059] Based on the insights analyzed by the generative AI, the strategy department can refine the target audience segmentation and perform more precise targeting. For example, the strategy department uses generative AI to analyze consumer emotions and values ​​and refine the target audience segmentation. For example, they could classify target groups based on emotional data. The strategy department also uses generative AI to analyze consumer usage data and build a system to refine the target audience segmentation. For example, they could create segments based on frequency of use and purchase history. The strategy department also uses generative AI to refine the target audience segmentation based on consumer emotions and values ​​and perform more precise targeting. This allows for more refined targeting by refining the target audience segmentation.

[0060] The Strategy Department can utilize influencer marketing when reviewing promotion methods based on consumer emotions and values. For example, the Strategy Department can use generative AI to analyze consumer emotions and values ​​and review promotion methods using influencer marketing. For example, the Strategy Department can select appropriate influencers based on emotional data. The Strategy Department can also use generative AI to analyze consumer feedback data and identify areas for improvement to maximize the effectiveness of influencer marketing. The Strategy Department can also use generative AI to review promotion methods based on consumer emotions and values ​​and utilize influencer marketing. This makes it possible to utilize influencer marketing for more effective promotions.

[0061] The Strategy Department can optimize the timing of marketing campaigns based on consumer emotions and values, and conduct promotions that are timed to coincide with seasons and events. For example, the Strategy Department uses generative AI to analyze consumer emotions and values ​​and optimize the timing of marketing campaigns. For example, it plans promotions that coincide with seasons and events based on emotion data. The Strategy Department also uses generative AI to analyze consumer usage data and build a system that suggests the optimal timing for marketing campaigns. For example, it adjusts the timing of campaigns based on frequency of use and purchase history. The Strategy Department also uses generative AI to optimize the timing of marketing campaigns based on consumer emotions and values, and conduct promotions that coincide with seasons and events. This makes it possible to optimize the timing of marketing campaigns and conduct promotions that coincide with seasons and events.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The data collection unit collects information on consumers' emotions, values, and lifestyles. For example, the data collection unit collects information on social media posts, voice messages, images, and video content. Step 2: The analysis unit analyzes the consumer's emotions, values, and lifestyles collected by the data collection unit in real time. For example, it analyzes the collected social media posts, voice messages, images, and video content to understand the consumer's emotions, values, and lifestyles. Step 3: The Improvement Department uses the insights gained by the Analysis Department to improve products and services. For example, they improve product design and functionality based on consumers' emotions and values, and they review how services are provided based on consumers' lifestyles. Step 4: The Strategy Department refines the marketing strategy based on the insights gained by the Analytics Department. For example, they revise the content and targeting of advertising based on consumers' emotions and values, and adjust the method and timing of promotions based on consumers' lifestyles.

[0064] (Example 2) The emotional resonance platform according to an embodiment of the present invention is a system that enables companies to empathize deeply with their customers as consumers and build more personalized engagement. This system uses multimodal AI analysis technology to capture consumers' emotions, values, and lifestyles in real time, and uses this insight to improve the products and services offered by companies and refine their marketing strategies. This allows the emotional resonance platform to enable companies to empathize deeply with their customers as consumers and build more personalized engagement.

[0065] An emotional resonance platform according to an embodiment includes a data collection unit, an analysis unit, an improvement unit, and a strategy unit. The data collection unit collects the emotions, values, and lifestyles of consumers. For example, the data collection unit collects social media posts. The data collection unit can also collect voice messages. The data collection unit can also collect image and video content. The analysis unit analyzes the emotions, values, and lifestyles of consumers collected by the data collection unit in real time. For example, the analysis unit analyzes the collected social media posts to understand the emotions of consumers. The analysis unit can also analyze the collected voice messages to understand the values ​​of consumers. The analysis unit can also analyze the content of collected images and videos to understand the lifestyles of consumers. The improvement unit improves products and services based on the insights obtained by the analysis unit. For example, the improvement unit improves the design and functionality of products based on the emotions and values ​​of consumers. The improvement unit can also review how services are provided based on the lifestyles of consumers. The improvement unit can also enhance product personalization functions based on the emotions and values ​​of consumers. The strategy department refines the marketing strategy based on the insights obtained by the analysis department. For example, the strategy department reviews the content and target of advertising based on the emotions and values ​​of consumers. The strategy department can also adjust the method and timing of promotions based on the lifestyles of consumers. The strategy department can also subdivide the target audience segmentation based on the emotions and values ​​of consumers. This allows the emotional resonance platform according to the embodiment to enable companies to deeply empathize with their customers as consumers and build more personalized engagement. For example, companies can improve customer satisfaction by improving their products and services based on the emotions and values ​​of consumers. Companies can also conduct effective promotions by refining their marketing strategies based on the lifestyles of consumers.

[0066] The data collection unit can analyze the content of social media posts, voice messages, images, or videos. For example, the data collection unit uses generation AI to analyze consumers' voice data and capture changes in tone and pitch. This makes it possible to grasp subtle changes in consumers' emotions. The data collection unit also uses generation AI to analyze consumers' facial expression data and capture subtle changes in facial expressions. For example, it analyzes changes in facial expressions such as smiling and furrowing the brow to grasp emotional fluctuations. The data collection unit also uses generation AI to analyze voice and facial expression data in an integrated manner to more accurately estimate consumers' emotions. For example, it analyzes changes in voice tone and facial expressions in combination. This makes it possible to collect multifaceted information about consumers and grasp their emotions and values.

[0067] The analysis unit can also take into account subtle changes in voice tone or facial expressions when analyzing consumers' emotions and values. The analysis unit, for example, uses generation AI to analyze consumers' purchase history data to understand patterns of consumer behavior. For example, it analyzes purchase frequency and types of purchased products. The analysis unit also uses generation AI to analyze health data collected from wearable devices to understand consumers' health conditions and activity levels. For example, it analyzes heart rate and step count data. The analysis unit also uses generation AI to comprehensively analyze purchase history data and health data to comprehensively understand consumers' lifestyles. For example, it analyzes the relationship between health conditions and consumption behavior. This makes it possible to understand subtle changes in consumers' emotions.

[0068] The analysis unit can also analyze consumers' purchasing history and health data from wearable devices. For example, the analysis unit uses generation AI to analyze the emotions consumers have toward specific products or services. For example, it analyzes text data from product reviews and feedback. The analysis unit also uses generation AI to build a system that tracks changes in consumers' emotions. For example, it periodically collects emotion data and analyzes emotional fluctuations over time. The analysis unit also uses an emotion estimation function to monitor consumers' emotions in real time when using products or services, and analyzes changes in emotion based on that data. This allows for a comprehensive understanding of consumers' lifestyles.

[0069] The analysis unit can perform a detailed analysis of the emotions consumers have toward specific products or services and track changes in those emotions. For example, the analysis unit uses generation AI to analyze employee feedback data to understand the emotions and values ​​of the entire company. For example, it analyzes the results of an employee satisfaction survey. The analysis unit also uses generation AI to analyze work report data to understand the company's business processes and organizational culture. For example, it analyzes text data of work reports. The analysis unit also uses generation AI to comprehensively analyze employee feedback data and work report data to comprehensively understand the emotions and values ​​of the entire company. This makes it possible to grasp changes in consumers' emotions in detail.

[0070] The analysis unit analyzes the company's internal data and can grasp the emotions and values ​​of the entire company. For example, the analysis unit uses generative AI to analyze the emotions and values ​​of consumers in different cultural spheres. For example, it analyzes social media posts in different languages ​​to grasp the differences in emotions between cultures. The analysis unit also uses generative AI to analyze the emotions and values ​​of consumers in different regions. For example, it analyzes purchasing history data for each region to grasp the values ​​unique to that region. The analysis unit also uses generative AI to comprehensively analyze data from different cultural spheres and regions to gain insights from a global perspective. For example, it analyzes the differences in emotions and values ​​between cultures and regions. This makes it possible to grasp the emotions and values ​​of the entire company.

[0071] The analysis unit analyzes the emotions or values ​​of consumers in different cultural spheres or regions, enabling insights from a global perspective. For example, the analysis unit uses generative AI to analyze in real time the emotions consumers feel toward content shared on social media. For example, it analyzes the text data of the posts and calculates an emotion score. The analysis unit also uses an emotion estimation function to monitor in real time the emotions consumers feel toward content on social media, and adjusts marketing strategies based on that data. The analysis unit also uses generative AI to analyze the emotion data on social media and reflect it in a company's marketing strategy. For example, it designs an advertising campaign based on content with a high percentage of positive emotions. This enables insights from a global perspective to be obtained.

[0072] The analysis unit analyzes in real time the emotions consumers feel toward content shared on social media, and can reflect this in a company's marketing strategy. The analysis unit, for example, uses a generative AI to analyze in real time the emotions consumers feel toward content shared on social media. For example, it analyzes the text data of the posted content and calculates an emotion score. The analysis unit also uses an emotion estimation function to monitor in real time the emotions consumers feel toward content on social media, and adjusts marketing strategies based on this data. The analysis unit also uses a generative AI to analyze the emotion data on social media, and reflects this in a company's marketing strategy. For example, it designs an advertising campaign based on content with a high percentage of positive emotions. This makes it possible to adjust marketing strategies based on the emotion data on social media.

[0073] When analyzing consumers' emotions and values, the analysis unit can also take into account emotional fluctuations due to seasons or time of day. The analysis unit, for example, uses generation AI to analyze consumers' emotional data and understand seasonal emotional fluctuations. For example, it analyzes the content of social media posts by season. The analysis unit also uses generation AI to analyze consumers' emotional data and understand emotional fluctuations by time of day. For example, it analyzes the difference between emotions in the morning and evening. The analysis unit also uses generation AI to perform analysis that takes into account emotional fluctuations due to seasons and time of day, and understands patterns of emotional fluctuations due to consumers. This makes it possible to understand emotional fluctuations due to seasons and time of day.

[0074] The analysis unit can also analyze the influence of friends or family in the consumer's social network. The analysis unit, for example, uses a generation AI to analyze the consumer's social network data and understand the influence of friends and family. For example, it analyzes friendship data on social media. The analysis unit also uses a generation AI to comprehensively analyze the consumer's social network data and emotion data, and performs emotion analysis that takes into account the influence of friends and family. The analysis unit also uses a generation AI to analyze the consumer's social network data and analyze values ​​that take into account the influence of friends and family. This makes it possible to perform emotion analysis that takes into account the influence of friends and family.

[0075] The analysis unit can also integrate and analyze data from different devices. For example, the analysis unit uses generation AI to analyze data collected from smartphones to understand consumers' emotions and values. For example, it analyzes smartphone usage history. The analysis unit also uses generation AI to analyze data collected from tablets to understand consumers' emotions and values. For example, it analyzes tablet app usage history. The analysis unit also uses generation AI to comprehensively analyze data collected from different devices such as smartphones, tablets, and PCs to comprehensively understand consumers' emotions and values. This enables integrated analysis of data from different devices.

[0076] The analysis unit can analyze data including data from different media. For example, the analysis unit uses a generation AI to analyze data collected from news sites to understand consumers' emotions and values. For example, it analyzes comments on news articles. The analysis unit also uses a generation AI to analyze data collected from blogs to understand consumers' emotions and values. For example, it analyzes the content of blog articles. The analysis unit also uses a generation AI to comprehensively analyze data collected from different media such as news, blogs, and forums to comprehensively understand consumers' emotions and values. This enables integrated analysis of data from different media.

[0077] The analysis unit can analyze the emotions consumers have toward specific brands and products in real time and reflect this in brand strategies. For example, the analysis unit uses generative AI to analyze the emotions consumers have toward specific brands in real time. For example, it analyzes social media posts related to the brand. The analysis unit also uses emotion estimation functions to monitor the emotions consumers have toward specific products in real time and adjust brand strategies based on this data. The analysis unit also uses generative AI to analyze emotional data toward brands and products and reflect this in the company's brand strategy. For example, it can strengthen brand messages that have a high proportion of positive emotions. This makes it possible to adjust brand strategies based on emotional data toward brands and products.

[0078] The improvement department can also consider the usability of the user interface when improving the product design based on consumer emotions and values. For example, the improvement department uses generative AI to analyze consumer emotions and values ​​and improve the product design. For example, the color and layout of the user interface are adjusted based on the emotion data. The improvement department also uses generative AI to analyze consumer feedback data and improve the usability of the user interface. For example, it identifies and improves parts that are difficult to use. The improvement department also uses generative AI to improve the product design based on consumer emotions and values, and designs the user interface with usability in mind. This makes it possible to improve product design by taking the usability of the user interface into account.

[0079] Based on the insights analyzed by the generation AI, the improvement department can enhance the product's personalization functions and provide optimal settings for individual consumers. For example, the improvement department uses the generation AI to analyze consumers' emotions and values ​​and enhance the product's personalization functions. For example, the department automatically adjusts settings according to the user's preferences. The improvement department also uses the generation AI to analyze consumers' usage data and build a system that provides optimal settings for individual consumers. For example, the system provides recommended settings based on usage history. The improvement department also uses the generation AI to enhance the product's personalization functions based on consumers' emotions and values ​​and provide an optimal usage experience for individual consumers. This makes it possible to improve the product usage experience by providing optimal settings for individual consumers.

[0080] The improvement department can use the emotion estimation function to monitor the emotions of consumers when they use the product in real time and make improvements to improve the user experience. For example, the improvement department uses the emotion estimation function to build a system that monitors the emotions of consumers when they use the product in real time. For example, it analyzes facial expressions and voices during use. The improvement department also uses generative AI to analyze consumer emotional data and make improvements to improve the user experience. For example, if negative emotions are detected, it identifies areas for improvement. The improvement department also uses the emotion estimation function to collect consumer emotional data in real time and make improvements to improve the product user experience based on that data. This makes it possible to monitor consumer emotions in real time and make improvements to improve the user experience.

[0081] The improvement department can integrate online and offline services when reviewing how services are provided based on consumer emotions and values. For example, the improvement department uses generative AI to analyze consumer emotions and values ​​and review how services are provided. For example, integrating online and offline services. The improvement department also uses generative AI to analyze consumer feedback data and identify areas for improvement to integrate online and offline services. The improvement department also uses generative AI to review how services are provided based on consumer emotions and values ​​and integrate online and offline services. For example, linking online reservations with offline experiences. In this way, integrating online and offline services makes it possible to provide more convenient services for consumers.

[0082] The improvement department can introduce a product subscription model based on consumer emotions and values, thereby promoting continuous engagement. For example, the improvement department uses generative AI to analyze consumer emotions and values ​​and introduce a product subscription model. For example, by providing regular updates and additional functions. The improvement department also uses generative AI to analyze consumer usage data and build a system that proposes the optimal subscription model plan. For example, by providing plans based on frequency of use. The improvement department also uses generative AI to introduce a product subscription model based on consumer emotions and values, thereby promoting continuous engagement. In this way, by introducing a subscription model, continuous engagement can be promoted.

[0083] The improvement department uses the emotion estimation function to monitor the emotions of consumers when they use the service in real time, and can flexibly adjust how the service is provided. For example, the improvement department uses the emotion estimation function to build a system that monitors the emotions of consumers when they use the service in real time. For example, it analyzes facial expressions and voices while using the service. The improvement department also uses generative AI to analyze consumers' emotional data and flexibly adjust how the service is provided. For example, it takes countermeasures if negative emotions are detected. The improvement department also uses the emotion estimation function to collect consumers' emotional data in real time and flexibly adjust how the service is provided based on that data. This makes it possible to monitor consumers' emotions in real time and flexibly adjust how the service is provided.

[0084] When reviewing the content of an advertisement based on consumers' emotions and values, the strategy department can also optimize the visual elements of the advertisement. For example, the strategy department uses generative AI to analyze consumers' emotions and values ​​and optimize the visual elements of the advertisement. For example, the color and design of the advertisement are adjusted based on the emotional data. The strategy department also uses generative AI to analyze consumer feedback data and improve the visual elements of the advertisement. For example, visually appealing elements are enhanced. The strategy department also uses generative AI to review the content of the advertisement based on consumers' emotions and values ​​and optimize the visual elements. In this way, by optimizing the visual elements of the advertisement, it is possible to provide a visually appealing advertisement.

[0085] Based on the insights analyzed by the generative AI, the strategy department can refine the target audience segmentation and perform more precise targeting. For example, the strategy department uses generative AI to analyze consumer emotions and values ​​and refine the target audience segmentation. For example, they classify target groups based on emotional data. The strategy department also uses generative AI to analyze consumer usage data and build a system to refine the target audience segmentation. For example, they create segments based on frequency of use and purchase history. The strategy department also uses generative AI to refine the target audience segmentation based on consumer emotions and values ​​and perform more precise targeting. This enables them to refine the target audience segmentation and perform more precise targeting.

[0086] The strategy department can utilize influencer marketing when reviewing promotion methods based on consumer emotions and values. For example, the strategy department uses generative AI to analyze consumer emotions and values ​​and review promotion methods using influencer marketing. For example, the strategy department selects appropriate influencers based on emotional data. The strategy department also uses generative AI to analyze consumer feedback data and identify areas for improvement to maximize the effectiveness of influencer marketing. The strategy department also uses generative AI to review promotion methods based on consumer emotions and values ​​and utilize influencer marketing. In this way, more effective promotions can be achieved by utilizing influencer marketing.

[0087] The strategy department can optimize the timing of marketing campaigns based on consumer emotions and values, and conduct promotions that are timed to coincide with seasons and events. For example, the strategy department uses generative AI to analyze consumer emotions and values ​​and optimize the timing of marketing campaigns. For example, it plans promotions that coincide with seasons and events based on emotion data. The strategy department also uses generative AI to analyze consumer usage data and build a system that suggests the optimal timing for marketing campaigns. For example, it adjusts the timing of campaigns based on frequency of use and purchase history. The strategy department also uses generative AI to optimize the timing of marketing campaigns based on consumer emotions and values, and conduct promotions that coincide with seasons and events. This makes it possible to optimize the timing of marketing campaigns and conduct promotions that coincide with seasons and events.

[0088] The strategy department uses the emotion estimation function to monitor consumers' emotions toward promotions in real time, allowing it to flexibly adjust the content of the promotions. For example, the strategy department uses the emotion estimation function to build a system that monitors consumers' emotions toward promotions in real time. For example, it analyzes facial expressions and voices during promotions. The strategy department also uses generative AI to analyze consumers' emotional data and flexibly adjust the content of promotions. For example, it changes the content of promotions if negative emotions are detected. The strategy department also uses the emotion estimation function to collect consumers' emotional data in real time and flexibly adjust the content of promotions based on that data. This makes it possible to flexibly adjust the content of promotions to be effective in line with consumers' emotions.

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

[0090] The data collection unit collects information on consumers' emotions, values, and lifestyles. For example, the data collection unit collects social media posts. The data collection unit can also collect voice messages. The data collection unit can also collect image and video content. The analysis unit analyzes the emotions, values, and lifestyles of consumers collected by the data collection unit in real time. For example, the analysis unit analyzes the collected social media posts to understand consumers' emotions. The analysis unit can also analyze the collected voice messages to understand consumers' values. The analysis unit can also analyze the collected image and video content to understand consumers' lifestyles. The improvement unit improves products and services based on the insights obtained by the analysis unit. For example, the improvement unit improves product design and functionality based on consumers' emotions and values. The improvement unit can also review service delivery methods based on consumers' lifestyles. The improvement unit can also enhance product personalization functions based on consumers' emotions and values. The strategy unit refines marketing strategies based on the insights obtained by the analysis unit. For example, the strategy department may review the content and target of advertising based on the emotions and values ​​of consumers. The strategy department may also adjust the method and timing of promotions based on the lifestyles of consumers. The strategy department may also subdivide the target audience segmentation based on the emotions and values ​​of consumers. This allows the emotional resonance platform according to the embodiment to enable companies to deeply empathize with their customers as consumers and build more personalized engagement. For example, companies can improve customer satisfaction by improving their products and services based on the emotions and values ​​of consumers. Companies can also conduct effective promotions by refining their marketing strategies based on the lifestyles of consumers.

[0091] The data collection unit can analyze the content of social media posts, voice messages, images, or videos. For example, the data collection unit uses the generation AI to analyze consumers' voice data and capture changes in tone and pitch. This makes it possible to grasp subtle changes in consumers' emotions. The data collection unit also uses the generation AI to analyze consumers' facial expression data and capture subtle changes in facial expressions. For example, it analyzes changes in facial expressions such as smiling and furrowing the brow to grasp emotional fluctuations. The data collection unit also uses the generation AI to analyze voice and facial expression data in an integrated manner to more accurately estimate consumers' emotions. For example, it analyzes changes in voice tone and facial expressions in combination. This makes it possible to collect multifaceted information about consumers and grasp their emotions and values.

[0092] The analysis unit can also take into account subtle changes in voice tone or facial expressions when analyzing consumers' emotions and values. For example, the analysis unit uses the generation AI to analyze consumers' purchase history data to understand patterns of consumer behavior. For example, it analyzes purchase frequency and types of products purchased. The analysis unit also uses the generation AI to analyze health data collected from wearable devices to understand consumers' health conditions and activity levels. For example, it analyzes heart rate and step count data. The analysis unit also uses the generation AI to comprehensively analyze purchase history data and health data to understand consumers' lifestyles comprehensively. For example, it analyzes the relationship between health conditions and consumption behavior. This makes it possible to understand subtle changes in consumers' emotions.

[0093] The analysis unit can also analyze consumers' purchasing history and health data from wearable devices. For example, the analysis unit uses generation AI to analyze the emotions consumers have toward specific products or services. For example, it analyzes text data such as product reviews and feedback. The analysis unit also uses generation AI to build a system that tracks changes in consumers' emotions. For example, it periodically collects emotion data and analyzes emotional fluctuations over time. The analysis unit also uses emotion estimation functionality to monitor consumers' emotions in real time when using products or services, and analyzes changes in emotion based on that data. This allows for a comprehensive understanding of consumers' lifestyles.

[0094] The analysis unit can perform a detailed analysis of the emotions consumers have toward specific products or services and track changes in those emotions. For example, the analysis unit uses the generation AI to analyze employee feedback data to understand the emotions and values ​​of the entire company. For example, it can analyze the results of an employee satisfaction survey. The analysis unit also uses the generation AI to analyze work report data to understand the company's business processes and organizational culture. For example, it can analyze text data from work reports. The analysis unit also uses the generation AI to comprehensively analyze employee feedback data and work report data to comprehensively understand the emotions and values ​​of the entire company. This makes it possible to grasp changes in consumers' emotions in detail.

[0095] The analysis unit analyzes the company's internal data and can grasp the emotions and values ​​of the entire company. For example, the analysis unit uses generative AI to analyze the emotions and values ​​of consumers in different cultural spheres. For example, it analyzes social media posts in different languages ​​to grasp the differences in emotions between cultures. The analysis unit also uses generative AI to analyze the emotions and values ​​of consumers in different regions. For example, it analyzes purchasing history data for each region to grasp the values ​​unique to that region. The analysis unit also uses generative AI to comprehensively analyze data from different cultural spheres and regions to gain insights from a global perspective. For example, it analyzes the differences in emotions and values ​​between cultures and regions. This makes it possible to grasp the emotions and values ​​of the entire company.

[0096] The analysis unit analyzes the emotions or values ​​of consumers in different cultural spheres or regions, enabling insights from a global perspective. For example, the analysis unit uses generative AI to analyze in real time the emotions consumers feel toward content shared on social media. For example, it analyzes the text data of the posts and calculates an emotion score. The analysis unit also uses emotion estimation functionality to monitor in real time the emotions consumers feel toward content on social media, and adjusts marketing strategies based on that data. The analysis unit also uses generative AI to analyze emotional data on social media and reflect it in a company's marketing strategy. For example, it designs an advertising campaign based on content with a high percentage of positive emotions. This allows insights from a global perspective to be gained.

[0097] The analysis unit can analyze the emotions consumers feel toward content shared on social media in real time and reflect this in a company's marketing strategy. For example, the analysis unit uses a generation AI to analyze the emotions consumers feel toward content shared on social media in real time. For example, it analyzes the text data of the posted content and calculates an emotion score. The analysis unit also uses an emotion estimation function to monitor consumers' emotions toward content on social media in real time and adjust marketing strategies based on this data. The analysis unit also uses a generation AI to analyze the emotion data on social media and reflect this in a company's marketing strategy. For example, it can design an advertising campaign based on content with a high proportion of positive emotions. This makes it possible to adjust marketing strategies based on the emotion data on social media.

[0098] When analyzing consumers' emotions and values, the analysis unit can also take into account emotional fluctuations due to seasons or time of day. For example, the analysis unit uses the generation AI to analyze consumers' emotional data and understand seasonal emotional fluctuations. For example, it analyzes the content of social media posts by season. The analysis unit also uses the generation AI to analyze consumers' emotional data and understand emotional fluctuations by time of day. For example, it analyzes the difference between emotions in the morning and evening. The analysis unit also uses the generation AI to perform analysis that takes into account emotional fluctuations due to seasons and time of day, and understands patterns of emotional fluctuations due to consumers. This makes it possible to understand emotional fluctuations due to seasons and time of day.

[0099] The analysis unit can also analyze the influence of friends or family in the consumer's social network. For example, the analysis unit uses the generation AI to analyze the consumer's social network data and understand the influence of friends and family. For example, the analysis unit analyzes friendship data on social media. The analysis unit also uses the generation AI to comprehensively analyze the consumer's social network data and emotion data, and performs emotion analysis that takes into account the influence of friends and family. The analysis unit also uses the generation AI to analyze the consumer's social network data and analyze values ​​that take into account the influence of friends and family. This makes it possible to perform emotion analysis that takes into account the influence of friends and family.

[0100] The analysis unit can also integrate and analyze data from different devices. For example, the analysis unit uses generation AI to analyze data collected from smartphones to understand consumers' emotions and values. For example, it analyzes smartphone usage history. The analysis unit also uses generation AI to analyze data collected from tablets to understand consumers' emotions and values. For example, it analyzes tablet app usage history. The analysis unit also uses generation AI to comprehensively analyze data collected from different devices such as smartphones, tablets, and PCs to comprehensively understand consumers' emotions and values. This enables integrated analysis of data from different devices.

[0101] The analysis unit can analyze data including data from different media. For example, the analysis unit uses the generation AI to analyze data collected from news sites to understand consumers' emotions and values. For example, it analyzes comments on news articles. The analysis unit also uses the generation AI to analyze data collected from blogs to understand consumers' emotions and values. For example, it analyzes the content of blog articles. The analysis unit also uses the generation AI to comprehensively analyze data collected from different media such as news, blogs, and forums to comprehensively understand consumers' emotions and values. This enables integrated analysis of data from different media.

[0102] The analysis unit can analyze the emotions consumers have toward specific brands and products in real time and reflect this in brand strategies. For example, the analysis unit uses generation AI to analyze the emotions consumers have toward specific brands in real time. For example, it analyzes social media posts related to the brand. The analysis unit also uses emotion estimation functions to monitor the emotions consumers have toward specific products in real time and adjust brand strategies based on this data. The analysis unit also uses generation AI to analyze emotional data toward brands and products and reflect this in the company's brand strategy. For example, it can strengthen brand messages that have a high proportion of positive emotions. This makes it possible to adjust brand strategies based on emotional data toward brands and products.

[0103] When improving product design based on consumer emotions and values, the improvement department can also consider the usability of the user interface. For example, the improvement department uses generative AI to analyze consumer emotions and values ​​and improve product design. For example, the color and layout of the user interface are adjusted based on emotional data. The improvement department also uses generative AI to analyze consumer feedback data and improve the usability of the user interface. For example, parts that are difficult to use are identified and improved. The improvement department also uses generative AI to improve product design based on consumer emotions and values, and designs with usability of the user interface in mind. This makes it possible to improve product design by taking usability of the user interface into account.

[0104] Based on the insights analyzed by the generation AI, the improvement department can enhance the product's personalization functions and provide optimal settings for each individual consumer. For example, the improvement department uses the generation AI to analyze consumers' emotions and values ​​and enhance the product's personalization functions. For example, the department automatically adjusts settings according to the user's preferences. The improvement department also uses the generation AI to analyze consumer usage data and build a system that provides optimal settings for each individual consumer. For example, the system provides recommended settings based on usage history. The improvement department also uses the generation AI to enhance the product's personalization functions based on consumers' emotions and values ​​and provide an optimal usage experience for each individual consumer. This makes it possible to improve the product usage experience by providing optimal settings for each individual consumer.

[0105] The improvement department can use the emotion estimation function to monitor the emotions of consumers when they use the product in real time and make improvements to improve the user experience. For example, the improvement department uses the emotion estimation function to build a system that monitors the emotions of consumers when they use the product in real time. For example, it analyzes facial expressions and voices during use. The improvement department also uses generative AI to analyze consumers' emotional data and make improvements to improve the user experience. For example, if negative emotions are detected, it identifies areas for improvement. The improvement department also uses the emotion estimation function to collect consumers' emotional data in real time and make improvements to improve the product's user experience based on that data. This makes it possible to monitor consumers' emotions in real time and make improvements to improve the user experience.

[0106] The Improvement Department can integrate online and offline services when reviewing how services are provided based on consumer emotions and values. For example, the Improvement Department uses generative AI to analyze consumer emotions and values ​​and review how services are provided. For example, by integrating online and offline services. The Improvement Department also uses generative AI to analyze consumer feedback data and identify areas for improvement to integrate online and offline services. The Improvement Department also uses generative AI to review how services are provided based on consumer emotions and values ​​and integrate online and offline services. For example, by linking online reservations with offline experiences. In this way, by integrating online and offline services, it becomes possible to provide more convenient services for consumers.

[0107] The Improvement Department can introduce a product subscription model based on consumer emotions and values ​​to promote continuous engagement. For example, the Improvement Department can use Generative AI to analyze consumer emotions and values ​​and introduce a product subscription model. For example, by providing regular updates and additional features. The Improvement Department can also use Generative AI to analyze consumer usage data and build a system that proposes the optimal subscription model plan. For example, by providing plans based on frequency of use. The Improvement Department can also use Generative AI to introduce a product subscription model based on consumer emotions and values ​​to promote continuous engagement. In this way, introducing a subscription model can promote continuous engagement.

[0108] The improvement department uses the emotion estimation function to monitor the emotions of consumers when they use the service in real time, and can flexibly adjust how the service is provided. For example, the improvement department uses the emotion estimation function to build a system that monitors the emotions of consumers when they use the service in real time. For example, by analyzing facial expressions and voices while using the service. The improvement department also uses generative AI to analyze consumers' emotional data and flexibly adjust how the service is provided. For example, if negative emotions are detected, countermeasures are taken. The improvement department also uses the emotion estimation function to collect consumers' emotional data in real time and flexibly adjust how the service is provided based on that data. This makes it possible to monitor consumers' emotions in real time and flexibly adjust how the service is provided.

[0109] When reviewing the content of an advertisement based on consumers' emotions and values, the strategy department can also optimize the visual elements of the advertisement. For example, the strategy department uses generative AI to analyze consumers' emotions and values ​​and optimize the visual elements of the advertisement. For example, the color and design of the advertisement can be adjusted based on the emotional data. The strategy department also uses generative AI to analyze consumer feedback data and improve the visual elements of the advertisement. For example, visually appealing elements can be enhanced. The strategy department also uses generative AI to review the content of the advertisement based on consumers' emotions and values ​​and optimize the visual elements. In this way, optimizing the visual elements of the advertisement makes it possible to provide a visually appealing advertisement.

[0110] Based on the insights analyzed by the generative AI, the strategy department can refine the target audience segmentation and perform more precise targeting. For example, the strategy department uses generative AI to analyze consumer emotions and values ​​and refine the target audience segmentation. For example, they could classify target groups based on emotional data. The strategy department also uses generative AI to analyze consumer usage data and build a system to refine the target audience segmentation. For example, they could create segments based on frequency of use and purchase history. The strategy department also uses generative AI to refine the target audience segmentation based on consumer emotions and values ​​and perform more precise targeting. This allows for more refined targeting by refining the target audience segmentation.

[0111] The Strategy Department can utilize influencer marketing when reviewing promotion methods based on consumer emotions and values. For example, the Strategy Department can use generative AI to analyze consumer emotions and values ​​and review promotion methods using influencer marketing. For example, the Strategy Department can select appropriate influencers based on emotional data. The Strategy Department can also use generative AI to analyze consumer feedback data and identify areas for improvement to maximize the effectiveness of influencer marketing. The Strategy Department can also use generative AI to review promotion methods based on consumer emotions and values ​​and utilize influencer marketing. This makes it possible to utilize influencer marketing for more effective promotions.

[0112] The Strategy Department can optimize the timing of marketing campaigns based on consumer emotions and values, and conduct promotions that are timed to coincide with seasons and events. For example, the Strategy Department uses generative AI to analyze consumer emotions and values ​​and optimize the timing of marketing campaigns. For example, it plans promotions that coincide with seasons and events based on emotion data. The Strategy Department also uses generative AI to analyze consumer usage data and build a system that suggests the optimal timing for marketing campaigns. For example, it adjusts the timing of campaigns based on frequency of use and purchase history. The Strategy Department also uses generative AI to optimize the timing of marketing campaigns based on consumer emotions and values, and conduct promotions that coincide with seasons and events. This makes it possible to optimize the timing of marketing campaigns and conduct promotions that coincide with seasons and events.

[0113] The strategy department uses the emotion estimation function to monitor consumers' emotions toward promotions in real time, enabling it to flexibly adjust the content of the promotions. For example, the strategy department uses the emotion estimation function to build a system that monitors consumers' emotions toward promotions in real time. For example, it analyzes facial expressions and voices during promotions. The strategy department also uses generative AI to analyze consumers' emotional data and flexibly adjust the content of promotions. For example, it changes the content of promotions if negative emotions are detected. The strategy department also uses the emotion estimation function to collect consumers' emotional data in real time and flexibly adjust the content of promotions based on that data. This makes it possible to flexibly adjust the content of promotions to be effective in line with consumers' emotions.

[0114] The processing flow of the second embodiment will be briefly explained below.

[0115] Step 1: The data collection unit collects information on consumers' emotions, values, and lifestyles. For example, the data collection unit collects information on social media posts, voice messages, images, and video content. Step 2: The analysis unit analyzes the consumer's emotions, values, and lifestyles collected by the data collection unit in real time. For example, it analyzes the collected social media posts, voice messages, images, and video content to understand the consumer's emotions, values, and lifestyles. Step 3: The Improvement Department uses the insights gained by the Analysis Department to improve products and services. For example, they improve product design and functionality based on consumers' emotions and values, and they review how services are provided based on consumers' lifestyles. Step 4: The Strategy Department refines the marketing strategy based on the insights gained by the Analytics Department. For example, they revise the content and targeting of advertising based on consumers' emotions and values, and adjust the method and timing of promotions based on consumers' lifestyles.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] 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.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] 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.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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]

[0183] 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 data collection department that collects information on consumers' emotions, values, and lifestyles; an analysis unit that analyzes in real time the emotions, values, and lifestyles of the people collected by the data collection unit; an improvement unit that improves products and services based on the insights obtained by the analysis unit; a strategy unit that refines a marketing strategy based on the insight obtained by the analysis unit. A system characterized by:

2. The data collection unit Analyzing the content of posts, voice messages, images or videos on said SNS 2. The system of claim 1.

3. The analysis unit When analyzing the emotions and values ​​of the consumer, subtle changes in tone of voice or facial expressions are also taken into consideration.

2. The system of claim 1.

4. The analysis unit Analyze the consumer's purchasing history and health data from wearable devices.

2. The system of claim 1.

5. The analysis unit Analyzing in detail the emotions felt by the consumer toward a particular product or service, and tracking changes in those emotions.

2. The system of claim 1.

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