system

Abstract

An object of the system according to the embodiment is to grasp a trend of a competitor and feedback of a customer in real time and comprehensively analyze the trend and the feedback.SOLUTION: A system according to an embodiment includes an information collection unit, a feedback collection unit, and an integration analysis unit. The information collector automatically collects information from competitors' websites, SNS, press releases, brochures, etc. The feedback collection unit automatically collects feedback from SNS posts, comments on review sites, interviews, and the like of customers. The integrated analysis unit integrally analyzes the information collected by the information collection unit and the feedback collection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making it difficult to grasp competitor trends and customer feedback in real time and perform comprehensive analysis.

[0005] The system according to the embodiment aims to grasp the trends of competitors and customer feedback in real time and to perform comprehensive analysis. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, a feedback collection unit, and an integrated analysis unit. The information collection unit automatically collects information from competitors' websites, social media, press releases, brochures, etc. The feedback collection unit automatically collects feedback from customers' social media posts, comments on review sites, interviews, etc. The integrated analysis unit performs an integrated analysis of the information collected by the information collection unit and the feedback collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the trends of competitors and customer feedback in real time and perform comprehensive analysis. [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 pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The information collection and analysis system according to an embodiment of the present invention automatically collects and comprehensively analyzes a variety of information from competitors and customers. This system collects information from competitors' websites, social media, press releases, brochures, and other sources, as well as customer feedback such as social media posts, comments on review sites, and interviews, and comprehensively analyzes this information. This allows the information collection and analysis system to grasp competitor trends, new feature release information, and customer feedback and needs in real time, thereby strengthening competitiveness and improving services.

[0029] An information collection and analysis system according to an embodiment includes an information collection unit, a feedback collection unit, and an integrated analysis unit. The information collection unit automatically collects information from competitors' websites, social media, press releases, brochures, etc. For example, it obtains information about new products from competitors' websites and collects user reactions and comments from social media. It also obtains official announcements from press releases and brochures. The feedback collection unit automatically collects feedback from customers' social media posts, comments on review sites, interviews, etc. For example, it collects product usage experiences posted by customers on social media and reviews written on review sites, and also obtains interview content as text data. The integrated analysis unit comprehensively analyzes the information collected by the information collection unit and the feedback collection unit. For example, it compares information about new feature releases by competitors with customer needs and identifies areas for improvement in the company's own products. It also analyzes competitors' trends and customer reactions to revise marketing strategies and propose new services. As a result, the information collection and analysis system according to an embodiment automatically collects and comprehensively analyzes a variety of information from competitors and customers, thereby strengthening competitiveness and improving services.

[0030] The information gathering unit not only collects information from competitors' websites or social media, but also analyzes audio data from video content and podcasts, enabling an integrated analysis of visual and audio information. For example, the information gathering unit collects text information from competitors' websites and social media, as well as videos introducing competitor products from video platforms such as YouTube and Vimeo, and converts the audio data into text using speech recognition technology. This allows for an integrated analysis of visual and audio information, enabling a detailed understanding of the features of competitor products and user reactions. This allows for an integrated analysis of visual and audio information, enabling a detailed understanding of the features of competitor products and user reactions.

[0031] The information gathering unit automatically collects competitors' product manuals or technical documents and analyzes the detailed technical information to understand the technical advantages of the competitors' products. The information gathering unit automatically collects product manuals and technical documents from competitors' websites and technical forums, for example, and analyzes the detailed technical information using natural language processing technology. For example, it extracts product specifications and technical features to evaluate the technical advantages of the competitors' products. This makes it possible to evaluate the technical advantages of the competitors' products.

[0032] The feedback collection unit collects not only customer posts on social media or comments on review sites, but also the contents of customer support chat logs and emails, allowing for a detailed understanding of customer problems and requests. The feedback collection unit automatically collects, for example, customer posts on social media or comments on review sites, as well as the contents of customer support chat logs and emails, allowing for a detailed understanding of customer problems and requests. For example, it collects product usage experiences posted by customers on social media and reviews written on review sites, and also obtains the contents of customer support chat logs and emails as text data. This allows for a detailed understanding of customer problems and requests.

[0033] When collecting customer feedback, the feedback collection unit can use speech recognition technology to acquire the contents of telephone interviews as text data and use it for analysis. When collecting customer feedback, the feedback collection unit can use speech recognition technology to acquire the contents of telephone interviews as text data and use it for analysis. For example, when collecting customer feedback, the feedback collection unit can convert the feedback provided by the customer over the phone into text using speech recognition technology and analyze it using natural language processing technology. In this way, the contents of the telephone interviews can be acquired as text data and used for analysis.

[0034] When collecting customer feedback, the feedback collection unit automatically translates feedback in different languages, making it possible to understand customer needs from an international perspective. For example, when collecting customer feedback, the feedback collection unit automatically translates feedback in different languages, making it possible to understand customer needs from an international perspective. For example, feedback posted in multiple languages, such as English, French, and Chinese, is automatically translated and analyzed in an integrated manner. This makes it possible to automatically translate feedback in different languages ​​and understand customer needs from an international perspective.

[0035] The feedback collection unit can classify customer feedback by product category or by usage scenario and identify specific areas for improvement. For example, the feedback collection unit classifies customer feedback by product category and identifies specific areas for improvement for each category. For example, the feedback collection unit classifies feedback by product category, such as smartphones, tablets, and laptops, and analyzes areas for improvement for each category. This makes it possible to classify customer feedback by product category or by usage scenario and identify specific areas for improvement.

[0036] When the integrated analysis unit performs an integrated analysis of competitor information and customer feedback, it is possible to track changes in trends using time-series data and grasp long-term market trends. For example, the integrated analysis unit collects competitor information and customer feedback as time-series data and tracks changes in trends. For example, it analyzes competitors' new product release information and customer feedback from the past few years in time series to grasp long-term market trends. This makes it possible to track changes in trends using time-series data and grasp long-term market trends.

[0037] The integrated analysis unit clusters the collected information and groups and analyzes highly related information, thereby obtaining specific insights. For example, the integrated analysis unit clusters the collected information on competitors and customer feedback, and groups and analyzes highly related information. For example, information related to the same product category or market segment is clustered to obtain specific insights. In this way, by clustering the collected information and grouping and analyzing highly related information, specific insights can be obtained.

[0038] The integrated analysis department classifies the collected information by region and country, making it possible to grasp regional market trends. For example, the integrated analysis department classifies the collected information on competitors and customer feedback by region and country, making it possible to grasp regional market trends. For example, the information is classified by region, such as America, Europe, and Asia, and the market trends for each region are analyzed. This makes it possible to grasp regional market trends.

[0039] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0040] In addition to collecting information from competitors' websites and social media, the Information Collection Department also collects technical information from patent databases and academic paper databases, allowing it to grasp technological trends. For example, it collects information on competitors' new patent applications from patent databases and obtains the latest research results from academic paper databases. This allows it to grasp in detail competitors' technological trends and the development status of new technologies.

[0041] The information gathering unit collects competitors' product review videos and unboxing videos, and can perform an integrated analysis of visual and audio information. For example, it collects product review videos from platforms such as YouTube and TikTok, and converts the audio data into text using speech recognition technology. This allows for an integrated analysis of visual and audio information, enabling a detailed understanding of the features of competitors' products and user reactions.

[0042] The feedback collection unit collects customer social media posts, comments on review sites, and online survey results, allowing for a detailed understanding of customer opinions. For example, it automatically collects responses from customers to online surveys and acquires them as text data. This allows for a detailed understanding of customer opinions and requests, which can be used to improve products and services.

[0043] When collecting customer feedback, the feedback collection department can use image recognition technology to analyze product usage and understand specific usage scenarios. For example, it can collect images of the product being used that customers have posted on social media and analyze them using image recognition technology. This allows it to understand specific usage scenarios of the product and identify areas for improvement.

[0044] When collecting customer feedback, the feedback collection unit automatically translates feedback in different languages, enabling the system to grasp customer needs from an international perspective. For example, it automatically translates feedback posted in multiple languages, such as English, French, and Chinese, and analyzes them comprehensively. This allows the system to automatically translate feedback in different languages ​​and grasp customer needs from an international perspective.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The information gathering department automatically collects information from competitors' websites, social media, press releases, brochures, etc. For example, it obtains information about new products from competitors' websites, collects user reactions and comments from social media, and obtains official announcements from press releases and brochures. Step 2: The feedback collection unit automatically collects feedback from customers' social media posts, comments on review sites, interviews, etc. For example, it collects product usage experiences posted by customers on social media and evaluations written on review sites, and also obtains interview content as text data. Step 3: The Integrated Analysis Department comprehensively analyzes the information collected by the Information Collection Department and the Feedback Collection Department. For example, they compare information about new feature releases by competitors with customer needs to identify areas for improvement in their own products. They also analyze competitors' trends and customer reactions to revise marketing strategies and propose new services.

[0047] (Example 2) The information collection and analysis system according to an embodiment of the present invention automatically collects and comprehensively analyzes a variety of information from competitors and customers. This system collects information from competitors' websites, social media, press releases, brochures, and other sources, as well as customer feedback such as social media posts, comments on review sites, and interviews, and comprehensively analyzes this information. This allows the information collection and analysis system to grasp competitor trends, new feature release information, and customer feedback and needs in real time, thereby strengthening competitiveness and improving services.

[0048] An information collection and analysis system according to an embodiment includes an information collection unit, a feedback collection unit, and an integrated analysis unit. The information collection unit automatically collects information from competitors' websites, social media, press releases, brochures, etc. For example, it obtains information about new products from competitors' websites and collects user reactions and comments from social media. It also obtains official announcements from press releases and brochures. The feedback collection unit automatically collects feedback from customers' social media posts, comments on review sites, interviews, etc. For example, it collects product usage experiences posted by customers on social media and reviews written on review sites, and also obtains interview content as text data. The integrated analysis unit comprehensively analyzes the information collected by the information collection unit and the feedback collection unit. For example, it compares information about new feature releases by competitors with customer needs and identifies areas for improvement in the company's own products. It also analyzes competitors' trends and customer reactions to revise marketing strategies and propose new services. As a result, the information collection and analysis system according to an embodiment automatically collects and comprehensively analyzes a variety of information from competitors and customers, thereby strengthening competitiveness and improving services.

[0049] The information gathering unit not only collects information from competitors' websites or social media, but also analyzes audio data from video content and podcasts, enabling an integrated analysis of visual and audio information. For example, the information gathering unit collects text information from competitors' websites and social media, as well as videos introducing competitor products from video platforms such as YouTube and Vimeo, and converts the audio data into text using speech recognition technology. This allows for an integrated analysis of visual and audio information, enabling a detailed understanding of the features of competitor products and user reactions. This allows for an integrated analysis of visual and audio information, enabling a detailed understanding of the features of competitor products and user reactions.

[0050] The information gathering unit automatically collects competitors' product manuals or technical documents and analyzes the detailed technical information to understand the technical advantages of the competitors' products. The information gathering unit automatically collects product manuals and technical documents from competitors' websites and technical forums, for example, and analyzes the detailed technical information using natural language processing technology. For example, it extracts product specifications and technical features to evaluate the technical advantages of the competitors' products. This makes it possible to evaluate the technical advantages of the competitors' products.

[0051] The information gathering unit can use the emotion estimation function to analyze the emotional tone of competitors' press releases and social media posts and estimate the intention of the competitors' marketing strategies. For example, the information gathering unit can use the emotion estimation function to analyze the emotional tone of competitors' press releases and social media posts and identify posts with strong positive emotions. For example, the information gathering unit can analyze the emotional tone of press releases announcing new products and estimate the intention of the competitors' marketing strategies. This makes it possible to estimate the intention of the competitors' marketing strategies.

[0052] The feedback collection unit collects not only customer posts on social media or comments on review sites, but also the contents of customer support chat logs and emails, allowing for a detailed understanding of customer problems and requests. The feedback collection unit automatically collects, for example, customer posts on social media or comments on review sites, as well as the contents of customer support chat logs and emails, allowing for a detailed understanding of customer problems and requests. For example, it collects product usage experiences posted by customers on social media and reviews written on review sites, and also obtains the contents of customer support chat logs and emails as text data. This allows for a detailed understanding of customer problems and requests.

[0053] When collecting customer feedback, the feedback collection unit can use speech recognition technology to acquire the contents of telephone interviews as text data and use it for analysis. When collecting customer feedback, the feedback collection unit can use speech recognition technology to acquire the contents of telephone interviews as text data and use it for analysis. For example, when collecting customer feedback, the feedback collection unit can convert the feedback provided by the customer over the phone into text using speech recognition technology and analyze it using natural language processing technology. In this way, the contents of the telephone interviews can be acquired as text data and used for analysis.

[0054] The feedback collection unit can use the emotion estimation function to analyze the emotional tone of customer feedback and classify it into positive feedback and negative feedback. For example, the feedback collection unit can use the emotion estimation function to analyze the emotional tone of customer feedback and classify it into positive feedback and negative feedback. For example, the emotion estimation function can be used to analyze customer social media posts and comments on review sites and classify them into positive feedback and negative feedback. This makes it possible to classify customer feedback by emotional tone and distinguish between positive feedback and negative feedback.

[0055] When collecting customer feedback, the feedback collection unit automatically translates feedback in different languages, making it possible to understand customer needs from an international perspective. For example, when collecting customer feedback, the feedback collection unit automatically translates feedback in different languages, making it possible to understand customer needs from an international perspective. For example, feedback posted in multiple languages, such as English, French, and Chinese, is automatically translated and analyzed in an integrated manner. This makes it possible to automatically translate feedback in different languages ​​and understand customer needs from an international perspective.

[0056] The feedback collection unit can classify customer feedback by product category or by usage scenario and identify specific areas for improvement. For example, the feedback collection unit classifies customer feedback by product category and identifies specific areas for improvement for each category. For example, the feedback collection unit classifies feedback by product category, such as smartphones, tablets, and laptops, and analyzes areas for improvement for each category. This makes it possible to classify customer feedback by product category or by usage scenario and identify specific areas for improvement.

[0057] The feedback collection unit can use the emotion estimation function to collect the emotional reactions of other customers to the customer's feedback and identify feedback that is likely to be empathized with. The feedback collection unit, for example, uses the emotion estimation function to collect the emotional reactions of other customers to the customer's feedback and identify feedback that is likely to be empathized with. For example, feedback with a high number of positive emotional reactions is identified and classified as feedback that is likely to be empathized with. This makes it possible to collect the emotional reactions of other customers to the customer's feedback and identify feedback that is likely to be empathized with.

[0058] When the integrated analysis unit performs an integrated analysis of competitor information and customer feedback, it is possible to track changes in trends using time-series data and grasp long-term market trends. For example, the integrated analysis unit collects competitor information and customer feedback as time-series data and tracks changes in trends. For example, it analyzes competitors' new product release information and customer feedback from the past few years in time series to grasp long-term market trends. This makes it possible to track changes in trends using time-series data and grasp long-term market trends.

[0059] The integrated analysis unit clusters the collected information and groups and analyzes highly related information, thereby obtaining specific insights. For example, the integrated analysis unit clusters the collected information on competitors and customer feedback, and groups and analyzes highly related information. For example, information related to the same product category or market segment is clustered to obtain specific insights. In this way, by clustering the collected information and grouping and analyzing highly related information, specific insights can be obtained.

[0060] The integrated analysis unit can use the emotion estimation function to compare new feature release information of competitors with customer emotional reactions and identify the features that customers are most interested in. For example, the integrated analysis unit can use the emotion estimation function to compare new feature release information of competitors with customer emotional reactions and identify the features that customers are most interested in. For example, it can identify features that have many positive customer emotional reactions to the new feature release. This makes it possible to identify the features that customers are most interested in.

[0061] The integrated analysis department classifies the collected information by region and country, making it possible to grasp regional market trends. For example, the integrated analysis department classifies the collected information on competitors and customer feedback by region and country, making it possible to grasp regional market trends. For example, the information is classified by region, such as America, Europe, and Asia, and the market trends for each region are analyzed. This makes it possible to grasp regional market trends.

[0062] The integrated analysis unit uses the emotion estimation function to monitor users' emotional reactions to the integratedly analyzed information in real time, and can formulate an optimal marketing strategy. The integrated analysis unit, for example, uses the emotion estimation function to monitor users' emotional reactions to the integratedly analyzed information in real time, and can formulate an optimal marketing strategy. For example, the integrated analysis unit formulates a marketing strategy based on information that has a high proportion of positive emotional reactions from users. This makes it possible to monitor users' emotional reactions in real time and formulate an optimal marketing strategy.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] In addition to collecting information from competitors' websites and social media, the Information Collection Department also collects technical information from patent databases and academic paper databases, allowing it to grasp technological trends. For example, it collects information on competitors' new patent applications from patent databases and obtains the latest research results from academic paper databases. This allows it to grasp in detail competitors' technological trends and the development status of new technologies.

[0065] The Information Collection Department can use the emotion estimation function to analyze the emotional tone of competitors' customer support forums and FAQ pages to identify the problems and dissatisfaction customers are experiencing. For example, the emotion estimation function can be used to analyze posts on customer support forums and identify posts with strong negative sentiment. This allows the department to understand customer dissatisfaction and problems with competitors' products and services, which can be used to improve the company's own products.

[0066] The information gathering unit collects competitors' product review videos and unboxing videos, and can perform an integrated analysis of visual and audio information. For example, it collects product review videos from platforms such as YouTube and TikTok, and converts the audio data into text using speech recognition technology. This allows for an integrated analysis of visual and audio information, enabling a detailed understanding of the features of competitors' products and user reactions.

[0067] The information gathering unit can use the emotion estimation function to analyze the emotional tone of competitors' advertising campaigns and estimate the effectiveness of the advertising. For example, it can analyze the emotional tone of competitors' television and online advertisements and identify advertisements with strong positive emotions. This allows the effectiveness of competitors' advertising campaigns to be estimated and reflected in the company's own advertising strategy.

[0068] The feedback collection unit collects customer social media posts, comments on review sites, and online survey results, allowing for a detailed understanding of customer opinions. For example, it automatically collects responses from customers to online surveys and acquires them as text data. This allows for a detailed understanding of customer opinions and requests, which can be used to improve products and services.

[0069] The feedback collection unit uses the emotion estimation function to analyze the emotional tone of customer feedback and monitor customer satisfaction in real time. For example, the emotion estimation function can analyze customer social media posts and comments on review sites, and determine that a high level of positive emotion indicates high satisfaction. This allows for real-time monitoring of customer satisfaction and rapid response.

[0070] When collecting customer feedback, the feedback collection department can use image recognition technology to analyze product usage and understand specific usage scenarios. For example, it can collect images of the product being used that customers have posted on social media and analyze them using image recognition technology. This allows it to understand specific usage scenarios of the product and identify areas for improvement.

[0071] The feedback collection unit can use the emotion estimation function to collect other customers' emotional reactions to the customer's feedback and identify feedback that is likely to be empathized with. For example, feedback with a high proportion of positive emotional reactions can be identified and classified as feedback that is likely to be empathized with. This makes it possible to collect other customers' emotional reactions to the customer's feedback and identify feedback that is likely to be empathized with.

[0072] When collecting customer feedback, the feedback collection unit automatically translates feedback in different languages, enabling the system to grasp customer needs from an international perspective. For example, it automatically translates feedback posted in multiple languages, such as English, French, and Chinese, and analyzes them comprehensively. This allows the system to automatically translate feedback in different languages ​​and grasp customer needs from an international perspective.

[0073] The integrated analysis department can use the sentiment estimation function to compare new feature release information of competitors with customer emotional reactions and identify the features that customers are most interested in. For example, it can identify features that have many positive customer reactions to new feature releases. This makes it possible to identify the features that customers are most interested in.

[0074] The processing flow of the second embodiment will be briefly explained below.

[0075] Step 1: The information gathering department automatically collects information from competitors' websites, social media, press releases, brochures, etc. For example, it obtains information about new products from competitors' websites, collects user reactions and comments from social media, and obtains official announcements from press releases and brochures. Step 2: The feedback collection unit automatically collects feedback from customers' social media posts, comments on review sites, interviews, etc. For example, it collects product usage experiences posted by customers on social media and evaluations written on review sites, and also obtains interview content as text data. Step 3: The Integrated Analysis Department comprehensively analyzes the information collected by the Information Collection Department and the Feedback Collection Department. For example, they compare information about new feature releases by competitors with customer needs to identify areas for improvement in their own products. They also analyze competitors' trends and customer reactions to revise marketing strategies and propose new services.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0080] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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).

[0085] 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.

[0086] 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.

[0087] 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.

[0088] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0089] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0090] 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.

[0091] 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.

[0092] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0093] 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.

[0094] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0095] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0104] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0105] 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.

[0106] 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.

[0107] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] 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.

[0109] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0110] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] 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.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0130] 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."

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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]

[0143] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An information gathering section that automatically collects information from competitors' websites, social media, press releases, brochures, etc. A feedback collection section that automatically collects feedback from customers' social media posts, comments on review sites, interviews, etc. an integrated analysis unit that analyzes the information collected by the information collection unit and the feedback collection unit in an integrated manner; A system characterized by:

2. The information collecting unit In addition to collecting information from the competitors' websites or social media, we also analyze audio data from video content and podcasts to comprehensively analyze visual and auditory information. The system of claim 1 .

3. The feedback collection unit: In addition to the customer's social media posts or comments on the review site, we also collect customer support chat logs and email content to understand the customer's problems and requests in detail. The system of claim 1 .

4. The integrated analysis unit When integrating competitor information and customer feedback, time series data is used to track trends and understand long-term market trends. The system of claim 1 .

5. The information collecting unit Using the emotion estimation function, the emotional tone of the competitor's press releases and social media posts is analyzed to estimate the intention of the competitor's marketing strategy. The system of claim 1 .

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