system

Abstract

An object of a system according to an embodiment is to efficiently collect a raw voice of a user based on information collected from an SNS.SOLUTION: A system according to an embodiment includes an information collection unit, an emotion determination unit, and a review unit. The information collection unit collects information from the SNS. The emotion determination unit determines positive and negative emotions from the information collected by the information collection unit. The examination unit examines the information determined by the emotion determination 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 problem that it is only possible to obtain information through prepared surveys and other methods, making it impossible to gather information in a straightforward manner.

[0005] The system according to the embodiment aims to efficiently collect users' honest opinions based on information collected from SNS. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an emotion determination unit, and a scrutiny unit. The information collection unit collects information from social networking sites. The emotion determination unit determines positive or negative emotions from the information collected by the information collection unit. The scrutiny unit scrutinizes the information determined by the emotion determination unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect users' honest opinions based on information collected from SNS. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An information collection system according to an embodiment of the present invention uses a generative AI to collect information from virtual environments such as social media, improving user work efficiency in marketing and IT operations in general. In this information collection system, the generative AI independently determines positive and negative emotions from social media and collects users' actual opinions. Furthermore, by closely examining content with similar meanings, the system reduces the user's work time. This allows the information collection system to improve user work efficiency by collecting information from social media, determining emotions, and closely examining content with similar meanings.

[0029] An information collection system according to an embodiment includes an information collection unit, an emotion determination unit, and a scrutiny unit. The information collection unit collects information from social media platforms. For example, the generation AI collects posts from social media platforms such as Twitter and Instagram and analyzes their content. The generation AI uses the social media platform's API to obtain posts related to specific hashtags or keywords in real time and analyzes trends. The emotion determination unit determines positive or negative emotions from the information collected by the information collection unit. For example, the generation AI analyzes social media post data and determines a post such as "This product is really great!" as a positive emotion and a post such as "This service is completely unusable" as a negative emotion. The generation AI uses natural language processing technology to calculate positive, negative, and neutral emotion scores. The scrutiny unit scrutinizes the information determined by the emotion determination unit. For example, the generation AI analyzes social media post data and scrutinizes a post such as "This product is really great!" and a post such as "This product is the best!" as having similar meanings. Generative AI automatically recognizes synonyms and similar words and analyzes semantic similarities with high accuracy. This allows the information collection system to improve user work efficiency by collecting information from social media, determining emotions, and scrutinizing content with similar meanings. For example, generative AI can analyze social media posting data and determine positive or negative emotions to collect users' true opinions. In addition, scrutinizing content with similar meanings can reduce the user's work time.

[0030] The information gathering unit collects social media posting data in real time, allowing it to instantly reflect the latest trends. For example, the information gathering unit uses a generation AI to collect social media posting data in real time, allowing it to instantly reflect the latest trends. For example, it uses Twitter's API to obtain posts related to specific hashtags or keywords in real time and analyzes trends. This allows it to collect social media posting data in real time, allowing it to instantly reflect the latest trends.

[0031] The information collection unit can analyze the poster's profile information and collect information based on specific attributes. For example, the information collection unit uses a generation AI to analyze the poster's profile information and collect information based on specific attributes. For example, it uses Twitter's API to filter posts based on age, gender, and region, and collect opinions from specific target demographics. This makes it possible to analyze the poster's profile information and collect information based on specific attributes.

[0032] The information gathering unit can also gather information from virtual environments other than social media. For example, the generating AI gathers information from online forums. For example, it uses Reddit's API to gather content posted on specific subreddits and analyzes them by topic. This allows it to gather information from virtual environments other than social media.

[0033] The emotion determination unit can automatically translate posts in different languages ​​and analyze international emotional trends. For example, the generative AI can automatically translate posts in different languages ​​and analyze international emotional trends. For example, it can translate Twitter posts from English to Japanese and calculate an emotion score. This makes it possible to automatically translate posts in different languages ​​and analyze international emotional trends.

[0034] The refining unit can automatically recognize synonyms and similar words and analyze the similarity of meaning with high accuracy. For example, the refining unit uses a generation AI to automatically recognize synonyms and similar words and analyze the similarity of meaning with high accuracy. For example, it analyzes the content of Twitter posts, automatically recognizes synonyms and similar words, and analyzes the similarity of meaning with high accuracy. This makes it possible to automatically recognize synonyms and similar words and analyze the similarity of meaning with high accuracy.

[0035] The screening unit can group posts that have the same meaning even though they are expressed differently based on the context. For example, the screening unit uses a generation AI to group posts that have the same meaning even though they are expressed differently, taking context into account. For example, the content of Twitter posts is analyzed and posts that have the same meaning are grouped together, taking context into account. This makes it possible to group posts that have the same meaning even though they are expressed differently, taking context into account.

[0036] The refining unit analyzes the similarity of meaning between different platforms and can integrate information across platforms. For example, the refining unit analyzes the similarity of meaning between different platforms using a generation AI and can integrate information across platforms. For example, it analyzes the content of posts on Twitter and Instagram and integrates information based on the similarity of meaning. This allows the similarity of meaning between different platforms to be analyzed and information to be integrated across platforms.

[0037] The scrutiny unit can analyze the content of images and videos and determine semantic similarity from visual information. For example, the generative AI analyzes the content of images and determines semantic similarity from visual information. For example, it analyzes images posted on Instagram and determines semantic similarity from objects and scenes within the images. This makes it possible to analyze the content of images and videos and determine semantic similarity from visual information.

[0038] The reconciliation unit can automatically generate reports and provide immediate feedback to users. For example, the reconciliation unit uses a generation AI to automatically generate reports and provide immediate feedback to users. For example, the AI ​​analyzes the content of Twitter posts and automatically generates reports summarizing sentiment scores and trends. This allows reports to be generated automatically and feedback to be provided to users immediately.

[0039] The review unit can automatically set task priorities and propose efficient work flows. For example, the generation AI automatically sets task priorities and proposes efficient work flows. For example, the content of Twitter posts is analyzed and task priorities are set based on importance and urgency. This makes it possible to automatically set task priorities and propose efficient work flows.

[0040] The reconciliation part can link with other business tools to automate a series of tasks from information collection to analysis. For example, the generative AI can link with other business tools to automate a series of tasks from information collection to analysis. For example, it can collect Twitter posts and automatically enter the data into Excel or Google Sheets. This allows it to link with other business tools to automate a series of tasks from information collection to analysis.

[0041] The reconciliation unit can work in conjunction with a voice assistant to collect and analyze information using voice commands. For example, the generation AI can work in conjunction with a voice assistant to collect and analyze information using voice commands. For example, using Amazon Alexa or Google Assistant, it can collect Twitter post content using voice commands and provide the analysis results via voice. This allows the reconciliation unit to work in conjunction with a voice assistant to collect and analyze information using voice commands.

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

[0043] The information collection unit can also analyze a user's browsing history and collect information based on their interests. For example, it can analyze the websites and search keywords frequently visited by the user and collect related social media posts. This allows for efficient collection of information based on the user's interests. It can also analyze the user's past purchasing history and collect related product reviews and ratings. It can also analyze the activity history of online communities in which the user participates to collect trends and opinions within the communities.

[0044] The information collection unit can also collect data from IoT devices and analyze it in real time. For example, it can collect data from smart home devices and analyze trends and usage within the home. This allows for understanding consumer behavior and usage patterns within the home. It can also collect health data from wearable devices and analyze health conditions and fitness trends. It can also collect sensor data from smart cities and analyze city trends and environmental data in real time.

[0045] The information collection unit can also analyze the user's voice commands and collect information based on the voice input. For example, it can analyze questions and instructions given by the user to the voice assistant and collect related social media posts. This makes it possible to collect information based on the voice input. It can also analyze the user's voice memos and collect information related to the memo contents. Furthermore, it can analyze the audio data of online conferences in which the user participates and collect information related to the conference contents.

[0046] The information collection unit can also analyze the user's location information and collect information based on the region. For example, it can analyze the user's current location and past movement history to collect information on local trends and events. This makes it possible to collect information based on the region. It can also collect reviews and ratings of stores and facilities visited by the user. It can also collect SNS posts about local events that the user has attended and analyze the reactions and ratings of the events.

[0047] The information collection unit can also analyze the user's purchasing history and collect related product reviews and ratings. For example, it can collect reviews and ratings of products the user has previously purchased and provide information based on purchasing behavior. This makes it possible to collect information based on the user's purchasing history. It can also collect trends and ratings of products in which the user is interested. Furthermore, it can analyze the activity history of online shopping communities in which the user participates and collect trends and opinions within the communities.

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

[0049] Step 1: The information gathering unit collects information from social media. For example, the generation AI collects posts from social media sites such as Twitter and Instagram and analyzes their content. Using the social media API, the generation AI retrieves posts related to specific hashtags and keywords in real time and analyzes trends. Step 2: The emotion determination unit determines positive or negative emotions from the information collected by the information collection unit. For example, the generation AI analyzes social media posting data and determines a post such as "This product is truly amazing!" as a positive emotion, and a post such as "This service is completely unusable" as a negative emotion. The generation AI uses natural language processing technology to calculate a positive, negative, or neutral emotion score. Step 3: The refining unit refining the information judged by the emotion judgment unit. For example, the generation AI analyzes social media posting data and refining posts such as "This product is truly amazing!" and "This product is the best!" as having similar meanings. The generation AI automatically recognizes synonyms and similar words and analyzes semantic similarities with high accuracy.

[0050] (Example 2) An information collection system according to an embodiment of the present invention uses a generative AI to collect information from virtual environments such as social media, improving user work efficiency in marketing and IT operations in general. In this information collection system, the generative AI independently determines positive and negative emotions from social media and collects users' actual opinions. Furthermore, by closely examining content with similar meanings, the system reduces the user's work time. This allows the information collection system to improve user work efficiency by collecting information from social media, determining emotions, and closely examining content with similar meanings.

[0051] An information collection system according to an embodiment includes an information collection unit, an emotion determination unit, and a scrutiny unit. The information collection unit collects information from social media platforms. For example, the generation AI collects posts from social media platforms such as Twitter and Instagram and analyzes their content. The generation AI uses the social media platform's API to obtain posts related to specific hashtags or keywords in real time and analyzes trends. The emotion determination unit determines positive or negative emotions from the information collected by the information collection unit. For example, the generation AI analyzes social media post data and determines a post such as "This product is really great!" as a positive emotion and a post such as "This service is completely unusable" as a negative emotion. The generation AI uses natural language processing technology to calculate positive, negative, and neutral emotion scores. The scrutiny unit scrutinizes the information determined by the emotion determination unit. For example, the generation AI analyzes social media post data and scrutinizes a post such as "This product is really great!" and a post such as "This product is the best!" as having similar meanings. Generative AI automatically recognizes synonyms and similar words and analyzes semantic similarities with high accuracy. This allows the information collection system to improve user work efficiency by collecting information from social media, determining emotions, and scrutinizing content with similar meanings. For example, generative AI can analyze social media posting data and determine positive or negative emotions to collect users' true opinions. In addition, scrutinizing content with similar meanings can reduce the user's work time.

[0052] The information gathering unit collects social media posting data in real time, allowing it to instantly reflect the latest trends. For example, the information gathering unit uses a generation AI to collect social media posting data in real time, allowing it to instantly reflect the latest trends. For example, it uses Twitter's API to obtain posts related to specific hashtags or keywords in real time and analyzes trends. This allows it to collect social media posting data in real time, allowing it to instantly reflect the latest trends.

[0053] The information collection unit can analyze the poster's profile information and collect information based on specific attributes. For example, the information collection unit uses a generation AI to analyze the poster's profile information and collect information based on specific attributes. For example, it uses Twitter's API to filter posts based on age, gender, and region, and collect opinions from specific target demographics. This makes it possible to analyze the poster's profile information and collect information based on specific attributes.

[0054] The emotion determination unit can use the emotion estimation function to analyze the poster's emotional state in real time and track emotional fluctuations. The emotion determination unit can, for example, use the emotion estimation function to analyze the poster's emotional state in real time and track emotional fluctuations. For example, it can analyze the content of a Twitter post, calculate an emotion score of positive, negative, or neutral, and graph the emotional fluctuations over time. This makes it possible to analyze the poster's emotional state in real time and track emotional fluctuations.

[0055] The information gathering unit can also gather information from virtual environments other than social media. For example, the generating AI gathers information from online forums. For example, it uses Reddit's API to gather content posted on specific subreddits and analyzes them by topic. This allows it to gather information from virtual environments other than social media.

[0056] The emotion determination unit can analyze the content of images and videos and determine emotions from visual information. For example, the emotion determination unit uses a generative AI to analyze the content of an image and determine emotions from visual information. For example, it can analyze images posted on Instagram and calculate a positive, negative, or neutral emotion score from the facial expressions and colors in the image. This makes it possible to analyze the content of images and videos and determine emotions from visual information.

[0057] The emotion determination unit uses the emotion estimation function to automatically generate advertisements and content based on the emotions of the poster, and can perform targeted advertising. The emotion determination unit, for example, uses the emotion estimation function to automatically generate advertisements based on the emotions of the poster. For example, advertisements for related products are displayed to posters who have positive emotions. This allows for automatic generation of advertisements and content based on the emotions of the poster, and can perform targeted advertising.

[0058] The emotion determination unit understands the context of the post and can finely classify the intensity and nuances of the emotion. For example, the generative AI can analyze the content of a Twitter post, calculate an emotion score of positive, negative, or neutral, and quantify the intensity of the emotion. This allows the context of the post to be understood and the intensity and nuances of the emotion to be finely classified.

[0059] The emotion determination unit can determine multiple emotions simultaneously and analyze complex emotional states. For example, the emotion determination unit uses a generation AI to simultaneously determine multiple emotions and analyze complex emotional states. For example, it analyzes the content of Twitter posts, simultaneously calculating positive, negative, and neutral emotion scores, and analyzing complex emotional states. This makes it possible to simultaneously determine multiple emotions and analyze complex emotional states.

[0060] The emotion determination unit can use the emotion estimation function to track changes in the poster's emotions over time and analyze the trend of emotions. The emotion determination unit can, for example, use the emotion estimation function to track changes in the poster's emotions over time and analyze the trend of emotions. For example, the emotion determination unit can analyze the content of Twitter posts and graph the change in emotions over time. This makes it possible to track changes in the poster's emotions over time and analyze the trend of emotions.

[0061] The emotion determination unit can automatically translate posts in different languages ​​and analyze international emotional trends. For example, the generative AI can automatically translate posts in different languages ​​and analyze international emotional trends. For example, it can translate Twitter posts from English to Japanese and calculate an emotion score. This makes it possible to automatically translate posts in different languages ​​and analyze international emotional trends.

[0062] The emotion determination unit can analyze audio data and determine emotions from the tone and intonation of the voice. For example, the generative AI analyzes audio data and determines emotions from the tone and intonation of the voice. For example, it analyzes YouTube video audio and calculates an emotion score of positive, negative, or neutral. This makes it possible to analyze audio data and determine emotions from the tone and intonation of the voice.

[0063] The emotion determination unit uses the emotion estimation function to automatically generate feedback for the user based on the emotion determination result, thereby improving the user experience. The emotion determination unit, for example, uses the emotion estimation function to automatically generate feedback for the user based on the emotion determination result. For example, a message of gratitude is automatically sent to a user who has positive emotions. In this way, feedback for the user is automatically generated based on the emotion determination result, thereby improving the user experience.

[0064] The refining unit can automatically recognize synonyms and similar words and analyze the similarity of meaning with high accuracy. For example, the refining unit uses a generation AI to automatically recognize synonyms and similar words and analyze the similarity of meaning with high accuracy. For example, it analyzes the content of Twitter posts, automatically recognizes synonyms and similar words, and analyzes the similarity of meaning with high accuracy. This makes it possible to automatically recognize synonyms and similar words and analyze the similarity of meaning with high accuracy.

[0065] The screening unit can group posts that have the same meaning even though they are expressed differently based on the context. For example, the screening unit uses a generation AI to group posts that have the same meaning even though they are expressed differently, taking context into account. For example, the content of Twitter posts is analyzed and posts that have the same meaning are grouped together, taking context into account. This makes it possible to group posts that have the same meaning even though they are expressed differently, taking context into account.

[0066] The inspection unit can use the emotion estimation function to check the consistency of emotions for posts with similar meanings and detect emotional bias. The inspection unit, for example, uses the emotion estimation function to check the consistency of emotions for posts with similar meanings and detect emotional bias. For example, the inspection unit analyzes the content of Twitter posts, calculates emotion scores for posts with similar meanings, and checks the consistency of emotions. This makes it possible to check the consistency of emotions for posts with similar meanings and detect emotional bias.

[0067] The refining unit analyzes the similarity of meaning between different platforms and can integrate information across platforms. For example, the refining unit analyzes the similarity of meaning between different platforms using a generation AI and can integrate information across platforms. For example, it analyzes the content of posts on Twitter and Instagram and integrates information based on the similarity of meaning. This allows the similarity of meaning between different platforms to be analyzed and information to be integrated across platforms.

[0068] The scrutiny unit can analyze the content of images and videos and determine semantic similarity from visual information. For example, the generative AI analyzes the content of images and determines semantic similarity from visual information. For example, it analyzes images posted on Instagram and determines semantic similarity from objects and scenes within the images. This makes it possible to analyze the content of images and videos and determine semantic similarity from visual information.

[0069] The reconciliation unit can use the emotion estimation function to collect users' emotional reactions to posts with similar meanings and propose a marketing strategy based on the emotions. The reconciliation unit can, for example, use the emotion estimation function to collect users' emotional reactions to posts with similar meanings and propose a marketing strategy based on the emotions. For example, the reconciliation unit can analyze the content of Twitter posts and propose a marketing strategy based on the emotion score. This makes it possible to collect users' emotional reactions to posts with similar meanings and propose a marketing strategy based on the emotions.

[0070] The reconciliation unit can automatically generate reports and provide immediate feedback to users. For example, the reconciliation unit uses a generation AI to automatically generate reports and provide immediate feedback to users. For example, the AI ​​analyzes the content of Twitter posts and automatically generates reports summarizing sentiment scores and trends. This allows reports to be generated automatically and feedback to be provided to users immediately.

[0071] The review unit can automatically set task priorities and propose efficient work flows. For example, the generation AI automatically sets task priorities and proposes efficient work flows. For example, the content of Twitter posts is analyzed and task priorities are set based on importance and urgency. This makes it possible to automatically set task priorities and propose efficient work flows.

[0072] The reconciliation unit can use the emotion estimation function to analyze the user's emotional state and suggest a work environment that reduces stress. For example, the reconciliation unit can use the emotion estimation function to analyze the user's emotional state and suggest a work environment that reduces stress. For example, the reconciliation unit can analyze the content of Twitter posts, evaluate the stress level, and suggest improvements to the work environment. This makes it possible to analyze the user's emotional state and suggest a work environment that reduces stress.

[0073] The reconciliation part can link with other business tools to automate a series of tasks from information collection to analysis. For example, the generative AI can link with other business tools to automate a series of tasks from information collection to analysis. For example, it can collect Twitter posts and automatically enter the data into Excel or Google Sheets. This allows it to link with other business tools to automate a series of tasks from information collection to analysis.

[0074] The reconciliation unit can work in conjunction with a voice assistant to collect and analyze information using voice commands. For example, the generation AI can work in conjunction with a voice assistant to collect and analyze information using voice commands. For example, using Amazon Alexa or Google Assistant, it can collect Twitter post content using voice commands and provide the analysis results via voice. This allows the reconciliation unit to work in conjunction with a voice assistant to collect and analyze information using voice commands.

[0075] The reconciliation unit uses the emotion estimation function to automatically generate a work schedule based on the user's emotions, thereby maximizing work efficiency. The reconciliation unit, for example, uses the emotion estimation function to automatically generate a work schedule based on the user's emotions, thereby maximizing work efficiency. For example, the reconciliation unit analyzes the content of Twitter posts and proposes an optimal work schedule based on the emotion score. This allows the reconciliation unit to automatically generate a work schedule based on the user's emotions, thereby maximizing work efficiency.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The information collection unit can also analyze a user's browsing history and collect information based on their interests. For example, it can analyze the websites and search keywords frequently visited by the user and collect related social media posts. This allows for efficient collection of information based on the user's interests. It can also analyze the user's past purchasing history and collect related product reviews and ratings. It can also analyze the activity history of online communities in which the user participates to collect trends and opinions within the communities.

[0078] The information collection unit can also collect data from IoT devices and analyze it in real time. For example, it can collect data from smart home devices and analyze trends and usage within the home. This allows for understanding consumer behavior and usage patterns within the home. It can also collect health data from wearable devices and analyze health conditions and fitness trends. It can also collect sensor data from smart cities and analyze city trends and environmental data in real time.

[0079] The information collection unit can also analyze the user's voice commands and collect information based on the voice input. For example, it can analyze questions and instructions given by the user to the voice assistant and collect related social media posts. This makes it possible to collect information based on the voice input. It can also analyze the user's voice memos and collect information related to the memo contents. Furthermore, it can analyze the audio data of online conferences in which the user participates and collect information related to the conference contents.

[0080] The information collection unit can also analyze the user's location information and collect information based on the region. For example, it can analyze the user's current location and past movement history to collect information on local trends and events. This makes it possible to collect information based on the region. It can also collect reviews and ratings of stores and facilities visited by the user. It can also collect SNS posts about local events that the user has attended and analyze the reactions and ratings of the events.

[0081] The information collection unit can also analyze the user's purchasing history and collect related product reviews and ratings. For example, it can collect reviews and ratings of products the user has previously purchased and provide information based on purchasing behavior. This makes it possible to collect information based on the user's purchasing history. It can also collect trends and ratings of products in which the user is interested. Furthermore, it can analyze the activity history of online shopping communities in which the user participates and collect trends and opinions within the communities.

[0082] The determination unit can estimate the user's emotions and provide appropriate feedback based on the estimated user emotions. For example, if the user has positive emotions, it can provide messages of encouragement or gratitude. This makes it possible to provide feedback based on the user's emotions. Also, if the user has negative emotions, it can provide messages of comfort or encouragement. Furthermore, it is also possible to suggest appropriate actions based on the user's emotions.

[0083] The determination unit can estimate the user's emotions and recommend appropriate content based on the estimated user emotions. For example, if the user has positive emotions, it can recommend fun videos or articles. This makes it possible to recommend content based on the user's emotions. Also, if the user has negative emotions, it can recommend relaxing music or soothing content. Furthermore, it is also possible to recommend content that is useful for learning or self-development based on the user's emotions.

[0084] The determination unit can estimate the user's emotions and display appropriate advertisements based on the estimated user emotions. For example, if the user has positive emotions, advertisements for related products are displayed. This makes it possible to display advertisements based on the user's emotions. Also, if the user has negative emotions, advertisements for products that help them relax can be displayed. Furthermore, it is also possible to display advertisements for specific services or events based on the user's emotions.

[0085] The determination unit can estimate the user's emotions and suggest appropriate actions based on the estimated user emotions. For example, if the user has positive emotions, it can suggest a new project or challenge. This makes it possible to suggest actions based on the user's emotions. Also, if the user has negative emotions, it can suggest relaxation or rest. Furthermore, it is possible to provide appropriate feedback and support based on the user's emotions.

[0086] The determination unit can estimate the user's emotions and suggest an appropriate communication method based on the estimated user's emotions. For example, if the user has positive emotions, proactive communication is suggested. This makes it possible to suggest a communication method based on the user's emotions. Also, if the user has negative emotions, careful communication can be suggested. Furthermore, it is also possible to suggest communication at an appropriate time based on the user's emotions.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The information gathering unit collects information from social media. For example, the generation AI collects posts from social media sites such as Twitter and Instagram and analyzes their content. Using the social media API, the generation AI retrieves posts related to specific hashtags and keywords in real time and analyzes trends. Step 2: The emotion determination unit determines positive or negative emotions from the information collected by the information collection unit. For example, the generation AI analyzes social media posting data and determines a post such as "This product is truly amazing!" as a positive emotion, and a post such as "This service is completely unusable" as a negative emotion. The generation AI uses natural language processing technology to calculate a positive, negative, or neutral emotion score. Step 3: The refining unit refining the information judged by the emotion judgment unit. For example, the generation AI analyzes social media posting data and refining posts such as "This product is truly amazing!" and "This product is the best!" as having similar meanings. The generation AI automatically recognizes synonyms and similar words and analyzes semantic similarities with high accuracy.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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."

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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]

[0156] 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 department that collects information from SNS, an emotion determination unit that determines positive or negative emotions from the information collected by the information collection unit; and a scrutiny unit that scrutinizes the information determined by the emotion determination unit. A system characterized by:

2. The information collecting unit Collecting post data from the aforementioned social media sites in real time and instantly reflecting the latest trends 2. The system of claim 1.

3. The emotion determination unit Analyze the poster's emotional state in real time and track emotional fluctuations 2. The system of claim 1.

4. The inspection unit Automatically recognize synonyms or similar words and analyze semantic similarities with high accuracy 2. The system of claim 1.

5. The emotion determination unit Understand the context of the post and classify the intensity and nuance of the sentiment 2. The system of claim 1.

6. The inspection unit Check the consistency of the sentiment for posts with similar meanings and detect bias in the sentiment 2. The system of claim 1.

7. The inspection unit Automatically generate reports to provide immediate feedback to users 2. The system of claim 1.

8. The inspection unit Analyzing the user's emotional state and proposing a work environment to reduce stress 2. The system of claim 1.

Images