system

Abstract

An object of a system according to an embodiment is to appropriately filter an input from a user interface while preventing contamination of generated AI.SOLUTION: A system in accordance with an embodiment comprises a generative AI filter AI. The production AI filter AI receives input from the user interface (UI) and filters the input before sending it to the backyard production AI.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 techniques do not adequately provide effective filtering systems to prevent contamination of generated AI, and there is room for improvement.

[0005] The system according to the embodiment aims to appropriately filter input from the user interface while preventing contamination of the generation AI. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI filter AI that receives input from a user interface (UI) and filters the input before sending it to the back-end generation AI. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately filter input from the user interface while preventing contamination of the generation AI. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The generative AI system according to an embodiment of the present invention is a system that prevents contamination of a generative AI by using another generative AI when providing the generative AI using its own large-scale language model (LLM). This generative AI system communicates with the back-end generative AI through a generative AI filter AI via a user interface (UI). This allows the generative AI system to prevent contamination of the LLM and provide safe and appropriate information to users.

[0029] A generative AI system according to an embodiment includes a generative AI filter AI, a user interface (UI), and a back-end generative AI. The generative AI filter AI receives input from the user interface (UI) and filters it before sending it to the back-end generative AI. For example, the generative AI filter AI detects and filters specific keywords or phrases. The generative AI filter AI also filters feedback from the generative AI. For example, if the text generated by the generative AI contains inappropriate content, it detects and corrects it. Furthermore, the generative AI filter AI can be customized for each company. For example, filtering rules specific to a specific industry or field can be set. This allows the generative AI system to prevent contamination of LLMs and provide users with safe and appropriate information. For example, using a filter AI customized for each company enables filtering specific to a specific industry or field, making the use of generative AI safer and more effective.

[0030] Generative AI filter AI can detect and filter specific keywords and phrases when they are included. Generative AI filter AI detects and filters specific keywords and phrases when they are included. For example, it detects and filters inappropriate words and spam words. Generative AI filter AI can also learn keywords and phrases specialized for specific industries or fields and filter them based on that. For example, it detects and filters technical terms in the medical industry and specific phrases in the financial industry. This makes it possible to prevent the inflow of undesirable data by detecting and filtering specific keywords and phrases.

[0031] The generative AI filter AI can filter the feedback content from the generative AI. The generative AI filter AI filters the feedback content from the generative AI. For example, if the text generated by the generative AI contains inappropriate content, it detects and corrects it. The generative AI filter AI also learns the user's past input history and performs filtering optimized for individual users. For example, it filters based on keywords and phrases frequently used by a specific user. In this way, by filtering the feedback content from the generative AI, it is possible to provide appropriate information to the user.

[0032] The Generative AI Filter AI can be customized for each company. For example, it is possible to set filtering rules specialized for a specific industry or field. The Generative AI Filter AI can also change filtering rules to suit the needs of the company. For example, filtering rules can be set based on the company's security policy to prevent information leaks. This makes it possible to customize filtering for each company, specializing in specific industries and fields.

[0033] Generative AI filter AI can learn a user's past input history and perform filtering optimized for each individual user. Generative AI filter AI, for example, learns a user's past input history and generates filtering rules optimized for each individual user. For example, it performs filtering based on keywords and phrases frequently used by a specific user. Generative AI filter AI can also handle voice input and filter voice data. For example, it uses voice recognition technology to convert voice data into text and performs filtering. This allows it to learn a user's past input history and perform filtering optimized for each individual user, making it possible to provide more personalized information.

[0034] The generative AI filter AI can monitor user input in real time and issue an alert if it detects a specific pattern or anomaly. For example, the generative AI filter AI can monitor user input in real time and issue an alert if it detects a specific pattern or anomaly. For example, it can notify an administrator if spam or inappropriate content is detected. The generative AI filter AI is also equipped with an emotion estimation function, which analyzes the emotion of the user's voice input in real time and makes suggestions to elicit positive emotions. For example, it can analyze the tone and pitch of the voice and make positive suggestions if negative emotions are detected. This enables rapid response by monitoring user input in real time and issuing an alert if a specific pattern or anomaly is detected.

[0035] The generative AI filter AI can support different languages ​​and accommodate international users. The generative AI filter AI can support different languages ​​and accommodate international users. For example, filtering rules can be set to accommodate multiple languages ​​such as English, French, and Chinese. The generative AI filter AI can also be equipped with an automatic translation function to accommodate different languages. For example, the generative AI filter AI can automatically translate feedback content into different languages, making it possible to accommodate international users. This allows it to support different languages ​​and accommodate international users.

[0036] The generative AI filter AI also supports voice input and can filter voice data. For example, the generative AI filter AI can use voice recognition technology to convert voice data into text and then filter it. The generative AI filter AI is also equipped with an emotion estimation function, which analyzes the user's emotions in response to voice input in real time and makes suggestions that elicit positive emotions. For example, it analyzes the tone and pitch of the voice and makes positive suggestions if negative emotions are detected. This allows it to support voice input and filter voice data, making it possible to accommodate a wider variety of input formats.

[0037] The generative AI filter AI can automatically summarize feedback content and extract and provide only the information that is important to the user. For example, the generative AI filter AI can automatically summarize feedback content and extract and provide only the information that is important to the user. For example, it can summarize long feedback in a short form and extract the main points. The generative AI filter AI also learns the user's past input history and performs filtering that is optimized for each individual user. For example, it filters based on keywords and phrases that a specific user frequently uses. This improves user convenience by automatically summarizing feedback content and extracting and providing only the information that is important to the user.

[0038] The generative AI filter AI can evaluate feedback content from multiple perspectives and select and provide the most appropriate feedback. The generative AI filter AI can, for example, evaluate feedback content from multiple perspectives and select and provide the most appropriate feedback. For example, it can evaluate from a technical perspective, a user perspective, and a business perspective. The generative AI filter AI can also learn the user's past input history and perform filtering optimized for individual users. For example, it can perform filtering based on keywords and phrases frequently used by a specific user. This allows it to evaluate feedback content from multiple perspectives and select and provide the most appropriate feedback, thereby providing more useful information to users.

[0039] The generative AI filter AI can automatically translate feedback content into different languages, making it possible to accommodate international users. The generative AI filter AI can, for example, automatically translate feedback content into different languages, making it possible to accommodate international users. For example, it can translate into multiple languages ​​such as English, French, and Chinese. The generative AI filter AI can also be equipped with an automatic translation function to accommodate different languages. For example, the generative AI filter AI can automatically translate feedback content into different languages, making it possible to accommodate international users. This allows the generative AI filter AI to automatically translate feedback content into different languages, making it possible to accommodate international users.

[0040] The generative AI filter AI can visualize the feedback content, allowing the user to intuitively understand it. The generative AI filter AI can, for example, visualize the feedback content, allowing the user to intuitively understand it. For example, it can display the main points of the feedback in graphs and charts. The generative AI filter AI can also learn the user's past input history and perform filtering that is optimized for each individual user. For example, it can perform filtering based on keywords and phrases frequently used by a specific user. This makes it easier for the user to grasp the information by visualizing the feedback content and allowing them to intuitively understand it.

[0041] Generative AI filter AI can automatically generate filtering rules based on a company's business processes, improving business efficiency. Generative AI filter AI can automatically generate filtering rules based on a company's business processes, improving business efficiency. For example, it can set filtering rules that align with business flows. Generative AI filter AI can also set filtering rules based on a company's security policies to prevent information leaks. For example, it can filter text that contains confidential information. This allows filtering rules to be automatically generated based on a company's business processes, improving business efficiency and increasing corporate productivity.

[0042] Generative AI filter AI can set filtering rules based on a company's security policy to prevent information leaks. Generative AI filter AI sets filtering rules based on a company's security policy to prevent information leaks. For example, it filters out text that contains confidential information. Generative AI filter AI also automatically generates filtering rules based on a company's business processes to improve business efficiency. For example, it sets filtering rules that align with business flows. This strengthens a company's information security by setting filtering rules based on the company's security policy and preventing information leaks.

[0043] Generative AI filter AI can provide filtering rules specialized for different industries and fields, meeting the needs of each industry. Generative AI filter AI can provide filtering rules specialized for different industries and fields, meeting the needs of each industry. For example, it can set filtering rules specialized for the medical or financial industry. Generative AI filter AI can also adjust filtering rules according to the skill level of a company's employees, enhancing the effectiveness of education. For example, it can set filtering rules for beginners and filtering rules for advanced users. This makes it possible to provide more effective information by providing filtering rules specialized for different industries and fields and meeting the needs of each industry.

[0044] Generative AI filter AI can adjust filtering rules according to the skill level of a company's employees, enhancing the effectiveness of training. Generative AI filter AI can adjust filtering rules according to the skill level of a company's employees, enhancing the effectiveness of training. For example, it can set filtering rules for beginners and filtering rules for advanced users. Generative AI filter AI can also automatically generate filtering rules based on a company's business processes, improving business efficiency. For example, it can set filtering rules that align with business flows. This allows filtering rules to be adjusted according to the skill level of a company's employees, enhancing the effectiveness of training and promoting the improvement of employees' skills.

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

[0046] The generative AI system can further include a health management unit that monitors the user's health status. For example, it can measure the user's heart rate and blood pressure in real time and issue an alert if an abnormality is detected. The health management unit can also learn from the user's past health data and provide health advice optimized for each individual user. For example, if a specific user has a history of high blood pressure, it can provide appropriate exercise and dietary advice to that user. In this way, the generative AI system can support the user's health management by monitoring the user's health status and providing appropriate advice.

[0047] The generative AI system can further include a hobby estimation unit that learns the user's hobbies and interests. For example, the hobby estimation unit can estimate the user's hobbies and interests based on the user's past search keywords and the history of websites visited. The hobby estimation unit can also provide customized content based on the user's hobbies. For example, if a particular user is interested in music, the system can provide the user with the latest music news and recommended playlists. In this way, the generative AI system can learn the user's hobbies and interests and provide customized content, improving the user experience.

[0048] The generative AI system can further include a behavior analysis unit that analyzes the user's behavioral patterns. For example, the behavioral patterns during a specific time period are analyzed based on the user's past behavior history. The behavior analysis unit can also make appropriate suggestions based on the user's behavioral patterns. For example, if a particular user has the habit of jogging every morning, the system can provide the user with weather forecasts and jogging course suggestions. In this way, the generative AI system can analyze the user's behavioral patterns and make appropriate suggestions to support the user's daily life.

[0049] The generative AI system can further include a learning management unit that manages the user's learning progress. For example, if a user is taking an online course, the learning management unit can monitor the user's progress in real time. The learning management unit can also provide appropriate learning advice based on the user's learning progress. For example, if a particular user is struggling with a particular task, the learning management unit can provide additional learning resources or hints to that user. In this way, the generative AI system can improve the user's learning effectiveness by managing the user's learning progress and providing appropriate advice.

[0050] The generative AI system can further include a purchasing analysis unit that analyzes a user's purchasing history. For example, the purchasing trend of a user can be analyzed based on the user's history of past purchases of products and services. The purchasing analysis unit can also provide customized product and service suggestions based on the user's purchasing trend. For example, if a particular user frequently purchases sporting goods, the purchasing analysis unit can provide the user with information about new products and sales. This allows the generative AI system to analyze the user's purchasing history and provide customized suggestions, thereby improving the user's purchasing experience.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The Generative AI Filter AI receives input from the user interface (UI). For example, text or commands entered by the user are sent to the Generative AI Filter AI through the UI. Step 2: The Generative AI Filter AI filters the input it receives before sending it to the back-end Generative AI. For example, if it contains specific keywords or phrases, it will detect and filter them. The Generative AI Filter AI also filters the feedback from the Generative AI, detecting and correcting any inappropriate content. Step 3: Generative AI Filter AI can be customized for each company. For example, filtering rules can be set for specific industries or fields. This allows the generative AI system to prevent contamination of LLM and provide users with safe and appropriate information.

[0053] (Example 2) The generative AI system according to an embodiment of the present invention is a system that prevents contamination of a generative AI by using another generative AI when providing the generative AI using its own large-scale language model (LLM). This generative AI system communicates with the back-end generative AI through a generative AI filter AI via a user interface (UI). This allows the generative AI system to prevent contamination of the LLM and provide safe and appropriate information to users.

[0054] A generative AI system according to an embodiment includes a generative AI filter AI, a user interface (UI), and a back-end generative AI. The generative AI filter AI receives input from the user interface (UI) and filters it before sending it to the back-end generative AI. For example, the generative AI filter AI detects and filters specific keywords or phrases. The generative AI filter AI also filters feedback from the generative AI. For example, if the text generated by the generative AI contains inappropriate content, it detects and corrects it. Furthermore, the generative AI filter AI can be customized for each company. For example, filtering rules specific to a specific industry or field can be set. This allows the generative AI system to prevent contamination of LLMs and provide users with safe and appropriate information. For example, using a filter AI customized for each company enables filtering specific to a specific industry or field, making the use of generative AI safer and more effective.

[0055] Generative AI filter AI can detect and filter specific keywords and phrases when they are included. Generative AI filter AI detects and filters specific keywords and phrases when they are included. For example, it detects and filters inappropriate words and spam words. Generative AI filter AI can also learn keywords and phrases specialized for specific industries or fields and filter them based on that. For example, it detects and filters technical terms in the medical industry and specific phrases in the financial industry. This makes it possible to prevent the inflow of undesirable data by detecting and filtering specific keywords and phrases.

[0056] The generative AI filter AI can filter the feedback content from the generative AI. The generative AI filter AI filters the feedback content from the generative AI. For example, if the text generated by the generative AI contains inappropriate content, it detects and corrects it. The generative AI filter AI also learns the user's past input history and performs filtering optimized for individual users. For example, it filters based on keywords and phrases frequently used by a specific user. In this way, by filtering the feedback content from the generative AI, it is possible to provide appropriate information to the user.

[0057] The Generative AI Filter AI can be customized for each company. For example, it is possible to set filtering rules specialized for a specific industry or field. The Generative AI Filter AI can also change filtering rules to suit the needs of the company. For example, filtering rules can be set based on the company's security policy to prevent information leaks. This makes it possible to customize filtering for each company, specializing in specific industries and fields.

[0058] The generative AI filter AI can add an emotion estimation function to analyze the emotions expressed by users' input and filter out inputs containing negative emotions. The generative AI filter AI can add an emotion estimation function to analyze the emotions expressed by users' input. For example, if the input text contains negative emotions such as anger or sadness, the text can be filtered so that it is not sent to the back-end generative AI. The generative AI filter AI can also support different languages ​​to accommodate international users. For example, filtering rules can be set to support multiple languages, including English, French, and Chinese. This allows for safer information provision by analyzing the emotions expressed by users' input and filtering out inputs containing negative emotions.

[0059] Generative AI filter AI can learn a user's past input history and perform filtering optimized for each individual user. Generative AI filter AI, for example, learns a user's past input history and generates filtering rules optimized for each individual user. For example, it performs filtering based on keywords and phrases frequently used by a specific user. Generative AI filter AI can also handle voice input and filter voice data. For example, it uses voice recognition technology to convert voice data into text and performs filtering. This allows it to learn a user's past input history and perform filtering optimized for each individual user, making it possible to provide more personalized information.

[0060] The generative AI filter AI can monitor user input in real time and issue an alert if it detects a specific pattern or anomaly. For example, the generative AI filter AI can monitor user input in real time and issue an alert if it detects a specific pattern or anomaly. For example, it can notify an administrator if spam or inappropriate content is detected. The generative AI filter AI is also equipped with an emotion estimation function, which analyzes the emotion of the user's voice input in real time and makes suggestions to elicit positive emotions. For example, it can analyze the tone and pitch of the voice and make positive suggestions if negative emotions are detected. This enables rapid response by monitoring user input in real time and issuing an alert if a specific pattern or anomaly is detected.

[0061] The generative AI filter AI can support different languages ​​and accommodate international users. The generative AI filter AI can support different languages ​​and accommodate international users. For example, filtering rules can be set to accommodate multiple languages ​​such as English, French, and Chinese. The generative AI filter AI can also be equipped with an automatic translation function to accommodate different languages. For example, the generative AI filter AI can automatically translate feedback content into different languages, making it possible to accommodate international users. This allows it to support different languages ​​and accommodate international users.

[0062] The generative AI filter AI also supports voice input and can filter voice data. For example, the generative AI filter AI can use voice recognition technology to convert voice data into text and then filter it. The generative AI filter AI is also equipped with an emotion estimation function, which analyzes the user's emotions in response to voice input in real time and makes suggestions that elicit positive emotions. For example, it analyzes the tone and pitch of the voice and makes positive suggestions if negative emotions are detected. This allows it to support voice input and filter voice data, making it possible to accommodate a wider variety of input formats.

[0063] Generative AI filter AI is equipped with an emotion estimation function and can analyze the user's emotions in response to voice input in real time and make suggestions that elicit positive emotions. Generative AI filter AI, for example, is equipped with an emotion estimation function and can analyze the user's emotions in response to voice input in real time. For example, it can analyze the tone and pitch of the voice and make positive suggestions if negative emotions are detected. Generative AI filter AI also learns the user's past input history and performs filtering optimized for individual users. For example, it performs filtering based on keywords and phrases frequently used by a specific user. This improves the user experience by analyzing the user's emotions in response to voice input in real time and making suggestions that elicit positive emotions.

[0064] The generative AI filter AI can perform sentiment analysis on the feedback content from the generative AI and correct feedback with negative sentiment. For example, the generative AI filter AI can perform sentiment analysis on the feedback content from the generative AI and correct feedback with negative sentiment. For example, it can convert negative expressions into positive expressions. The generative AI filter AI also learns the user's past input history and performs filtering optimized for individual users. For example, it filters based on keywords and phrases frequently used by a particular user. This makes it possible to provide more appropriate feedback to users by performing sentiment analysis on the feedback content from the generative AI and correcting feedback with negative sentiment.

[0065] The generative AI filter AI can automatically summarize feedback content and extract and provide only the information that is important to the user. For example, the generative AI filter AI can automatically summarize feedback content and extract and provide only the information that is important to the user. For example, it can summarize long feedback in a short form and extract the main points. The generative AI filter AI also learns the user's past input history and performs filtering that is optimized for each individual user. For example, it filters based on keywords and phrases that a specific user frequently uses. This improves user convenience by automatically summarizing feedback content and extracting and providing only the information that is important to the user.

[0066] The generative AI filter AI can evaluate feedback content from multiple perspectives and select and provide the most appropriate feedback. The generative AI filter AI can, for example, evaluate feedback content from multiple perspectives and select and provide the most appropriate feedback. For example, it can evaluate from a technical perspective, a user perspective, and a business perspective. The generative AI filter AI can also learn the user's past input history and perform filtering optimized for individual users. For example, it can perform filtering based on keywords and phrases frequently used by a specific user. This allows it to evaluate feedback content from multiple perspectives and select and provide the most appropriate feedback, thereby providing more useful information to users.

[0067] The generative AI filter AI can automatically translate feedback content into different languages, making it possible to accommodate international users. The generative AI filter AI can, for example, automatically translate feedback content into different languages, making it possible to accommodate international users. For example, it can translate into multiple languages ​​such as English, French, and Chinese. The generative AI filter AI can also be equipped with an automatic translation function to accommodate different languages. For example, the generative AI filter AI can automatically translate feedback content into different languages, making it possible to accommodate international users. This allows the generative AI filter AI to automatically translate feedback content into different languages, making it possible to accommodate international users.

[0068] The generative AI filter AI can visualize the feedback content, allowing the user to intuitively understand it. The generative AI filter AI can, for example, visualize the feedback content, allowing the user to intuitively understand it. For example, it can display the main points of the feedback in graphs and charts. The generative AI filter AI can also learn the user's past input history and perform filtering that is optimized for each individual user. For example, it can perform filtering based on keywords and phrases frequently used by a specific user. This makes it easier for the user to grasp the information by visualizing the feedback content and allowing them to intuitively understand it.

[0069] The generative AI filter AI is equipped with an emotion estimation function and can monitor the user's emotional response to feedback content in real time and provide optimal feedback. The generative AI filter AI, for example, is equipped with an emotion estimation function and can monitor the user's emotional response to feedback content in real time. For example, it can analyze the user's facial expressions and voice and calculate an emotion score. The generative AI filter AI also learns the user's past input history and performs filtering optimized for individual users. For example, it filters based on keywords and phrases frequently used by a specific user. This allows the user's emotional response to feedback content to be monitored in real time and optimal feedback to be provided, improving the user experience.

[0070] When customized for each company, the generative AI filter AI can use its emotion estimation function to analyze employee emotions and set filtering rules that elicit positive emotions. When customized for each company, the generative AI filter AI can, for example, use its emotion estimation function to analyze employee emotions and set filtering rules that elicit positive emotions. For example, it can provide feedback to reduce employee stress. The generative AI filter AI can also automatically generate filtering rules based on a company's business processes to improve work efficiency. For example, it can set filtering rules that align with the business flow. This allows the generative AI filter AI to analyze employee emotions and set filtering rules that elicit positive emotions when customized for each company, thereby improving employee satisfaction.

[0071] Generative AI filter AI can automatically generate filtering rules based on a company's business processes, improving business efficiency. Generative AI filter AI can automatically generate filtering rules based on a company's business processes, improving business efficiency. For example, it can set filtering rules that align with business flows. Generative AI filter AI can also set filtering rules based on a company's security policies to prevent information leaks. For example, it can filter text that contains confidential information. This allows filtering rules to be automatically generated based on a company's business processes, improving business efficiency and increasing corporate productivity.

[0072] Generative AI filter AI can set filtering rules based on a company's security policy to prevent information leaks. Generative AI filter AI sets filtering rules based on a company's security policy to prevent information leaks. For example, it filters out text that contains confidential information. Generative AI filter AI also automatically generates filtering rules based on a company's business processes to improve business efficiency. For example, it sets filtering rules that align with business flows. This strengthens a company's information security by setting filtering rules based on the company's security policy and preventing information leaks.

[0073] Generative AI filter AI can provide filtering rules specialized for different industries and fields, meeting the needs of each industry. Generative AI filter AI can provide filtering rules specialized for different industries and fields, meeting the needs of each industry. For example, it can set filtering rules specialized for the medical or financial industry. Generative AI filter AI can also adjust filtering rules according to the skill level of a company's employees, enhancing the effectiveness of education. For example, it can set filtering rules for beginners and filtering rules for advanced users. This makes it possible to provide more effective information by providing filtering rules specialized for different industries and fields and meeting the needs of each industry.

[0074] Generative AI filter AI can adjust filtering rules according to the skill level of a company's employees, enhancing the effectiveness of training. Generative AI filter AI can adjust filtering rules according to the skill level of a company's employees, enhancing the effectiveness of training. For example, it can set filtering rules for beginners and filtering rules for advanced users. Generative AI filter AI can also automatically generate filtering rules based on a company's business processes, improving business efficiency. For example, it can set filtering rules that align with business flows. This allows filtering rules to be adjusted according to the skill level of a company's employees, enhancing the effectiveness of training and promoting the improvement of employees' skills.

[0075] Generative AI filter AI is equipped with an emotion estimation function and can monitor the emotions of a company's employees in real time and provide filtering rules that elicit positive emotions. Generative AI filter AI, for example, is equipped with an emotion estimation function and monitors the emotions of a company's employees in real time. For example, it analyzes employees' facial expressions and voices and calculates an emotion score. Generative AI filter AI also automatically generates filtering rules based on a company's business processes to improve business efficiency. For example, it sets filtering rules that align with the business flow. This allows it to monitor the emotions of a company's employees in real time and provide filtering rules that elicit positive emotions, thereby improving employee satisfaction and productivity.

[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 generative AI system can further include a health management unit that monitors the user's health status. For example, it can measure the user's heart rate and blood pressure in real time and issue an alert if an abnormality is detected. The health management unit can also learn from the user's past health data and provide health advice optimized for each individual user. For example, if a specific user has a history of high blood pressure, it can provide appropriate exercise and dietary advice to that user. In this way, the generative AI system can support the user's health management by monitoring the user's health status and providing appropriate advice.

[0078] The generative AI system can further include a hobby estimation unit that learns the user's hobbies and interests. For example, the hobby estimation unit can estimate the user's hobbies and interests based on the user's past search keywords and the history of websites visited. The hobby estimation unit can also provide customized content based on the user's hobbies. For example, if a particular user is interested in music, the system can provide the user with the latest music news and recommended playlists. In this way, the generative AI system can learn the user's hobbies and interests and provide customized content, improving the user experience.

[0079] The generative AI system can further include a behavior analysis unit that analyzes the user's behavioral patterns. For example, the behavioral patterns during a specific time period are analyzed based on the user's past behavior history. The behavior analysis unit can also make appropriate suggestions based on the user's behavioral patterns. For example, if a particular user has the habit of jogging every morning, the system can provide the user with weather forecasts and jogging course suggestions. In this way, the generative AI system can analyze the user's behavioral patterns and make appropriate suggestions to support the user's daily life.

[0080] The generative AI system can further include a learning management unit that manages the user's learning progress. For example, if a user is taking an online course, the learning management unit can monitor the user's progress in real time. The learning management unit can also provide appropriate learning advice based on the user's learning progress. For example, if a particular user is struggling with a particular task, the learning management unit can provide additional learning resources or hints to that user. In this way, the generative AI system can improve the user's learning effectiveness by managing the user's learning progress and providing appropriate advice.

[0081] The generative AI system can further include a purchasing analysis unit that analyzes a user's purchasing history. For example, the purchasing trend of a user can be analyzed based on the user's history of past purchases of products and services. The purchasing analysis unit can also provide customized product and service suggestions based on the user's purchasing trend. For example, if a particular user frequently purchases sporting goods, the purchasing analysis unit can provide the user with information about new products and sales. This allows the generative AI system to analyze the user's purchasing history and provide customized suggestions, thereby improving the user's purchasing experience.

[0082] The generative AI system can further include an emotion feedback unit that estimates the user's emotions and provides appropriate feedback based on the estimated emotions. For example, it can analyze text or voice data entered by the user and calculate an emotion score. The emotion feedback unit can also provide positive feedback based on the user's emotions. For example, if the user is feeling stressed, it can provide the user with relaxation techniques or encouraging messages. In this way, the generative AI system can support the user's mental health by estimating the user's emotions and providing appropriate feedback.

[0083] The generative AI system may further include an emotional content unit that estimates a user's emotions and provides appropriate content based on the estimated emotions. For example, the emotional content unit may analyze text or voice data entered by the user and calculate an emotional score. The emotional content unit may also provide customized content based on the user's emotions. For example, if a user is feeling sad, the emotional content unit may provide uplifting music or videos to the user. In this way, the generative AI system can improve the user's mood by estimating the user's emotions and providing appropriate content.

[0084] The generative AI system can further include an emotion advice unit that estimates the user's emotions and provides appropriate advice based on the estimated emotions. For example, it can analyze text or voice data entered by the user and calculate an emotion score. The emotion advice unit can also provide appropriate advice based on the user's emotions. For example, if the user is feeling anxious, it can provide the user with advice on relaxation methods and stress management. In this way, the generative AI system can support the user's mental health by estimating the user's emotions and providing appropriate advice.

[0085] The generative AI system can further include an emotion notification unit that estimates the user's emotions and provides appropriate notifications based on the estimated emotions. For example, it can analyze text or voice data entered by the user and calculate an emotion score. The emotion notification unit can also provide appropriate notifications based on the user's emotions. For example, if the user is feeling angry, it can provide notifications and advice to the user to help them stay calm. In this way, the generative AI system can support the user's emotional management by estimating the user's emotions and providing appropriate notifications.

[0086] The generative AI system may further include an emotional entertainment unit that estimates the user's emotions and provides appropriate entertainment based on the estimated emotions. For example, the emotional entertainment unit may analyze text or voice data entered by the user and calculate an emotional score. The emotional entertainment unit may also provide customized entertainment based on the user's emotions. For example, if the user is tired, the emotional entertainment unit may provide the user with a relaxing movie or game. In this way, the generative AI system can improve the user's mood by estimating the user's emotions and providing appropriate entertainment.

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

[0088] Step 1: The Generative AI Filter AI receives input from the user interface (UI). For example, text or commands entered by the user are sent to the Generative AI Filter AI through the UI. Step 2: The Generative AI Filter AI filters the input it receives before sending it to the back-end Generative AI. For example, if it contains specific keywords or phrases, it will detect and filter them. The Generative AI Filter AI also filters the feedback from the Generative AI, detecting and correcting any inappropriate content. Step 3: Generative AI Filter AI can be customized for each company. For example, filtering rules can be set for specific industries or fields. This allows the generative AI system to prevent contamination of LLM and provide users with safe and appropriate information.

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

[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. Equipped with AI generation filter AI, The generated AI filter AI is It receives input from the user interface (UI), Filtering the input before sending it to the back-end generation AI A system characterized by:

2. The generated AI filter AI is Detect and filter if specific keywords or phrases are included 2. The system of claim 1.

3. The generated AI filter AI is Learns the user's past input history and performs filtering optimized for each individual user 2. The system of claim 1.

4. The generated AI filter AI is Sentiment analysis is performed on the feedback content from the generation AI, and feedback with negative sentiment is corrected.

2. The system of claim 1.

5. The generated AI filter AI is In customization for each company, the emotion estimation function is used to analyze employee emotions and set filtering rules to elicit positive emotions.

2. The system of claim 1.

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