system

Abstract

The system according to the embodiment aims to enable a user to quickly reach a desired menu. [Solution] A system according to an embodiment includes an analysis unit, a notification unit, and a connection unit. The analysis unit analyzes a user's verbal declaration. The notification unit notifies the user of a corresponding menu based on the analysis result by the analysis unit. The connection unit automatically connects to the menu notified by the notification unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that it takes time to reach the desired menu in the guidance provided by the inquiry desk, which can be frustrating for users.

[0005] The system according to the embodiment aims to enable a user to quickly reach a desired menu. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a notification unit, and a connection unit. The analysis unit analyzes the user's verbal declaration. The notification unit notifies the user of a corresponding menu based on the analysis result by the analysis unit. The connection unit automatically connects to the menu notified by the notification unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable the user to quickly reach the desired menu. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) The inquiry guidance system according to an embodiment of the present invention involves a user verbally reporting their inquiry. The AI ​​then analyzes the report, determines the appropriate menu, notifies the user, and connects the user to the appropriate menu. The system begins with the user verbally reporting their inquiry. The AI ​​then analyzes the report and determines the appropriate menu. The AI ​​includes an "analysis unit" that analyzes the user's verbal response, a "notification unit" that notifies the user of the determined menu, and a "connection unit" that automatically connects the user to the appropriate menu. This allows the user to quickly navigate to the desired menu and reduce stress. For example, if a user verbally reports, "I would like to check my invoice," the AI ​​analyzes the report and determines the appropriate menu. The AI ​​then notifies the user, "Connecting to the invoice confirmation menu," and automatically connects the user to the appropriate menu. This allows the user to quickly navigate to the desired menu and reduce stress. Similar effects are expected at other companies' service centers, making the system widely used. The inquiry guidance system thus reduces user stress and allows the user to quickly navigate to the desired menu.

[0029] The inquiry desk guidance system according to the embodiment includes an analysis unit, a notification unit, and a connection unit. The analysis unit analyzes a user's verbal statement. The user's verbal statement includes, but is not limited to, voice commands and natural language questions. The analysis unit converts the user's verbal statement into text data using, for example, speech recognition technology. The analysis unit can also analyze the text data and determine a corresponding menu item using natural language processing technology. For example, the analysis unit converts the user's verbal statement into text data in real time using speech recognition technology. The analysis unit can also extract keywords from the text data using natural language processing technology to determine a corresponding menu item. The analysis unit can also analyze the user's verbal statement and determine a corresponding menu item using a generation AI. For example, the generation AI performs analysis using a model that inputs the user's verbal statement and outputs a corresponding menu item. The notification unit notifies the user of a corresponding menu item based on the analysis results obtained by the analysis unit. The notification unit notifies the user of a corresponding menu item by voice using, for example, speech synthesis technology. The notification unit can also notify the user of the corresponding menu using a text message. For example, the notification unit can notify the user by using voice synthesis technology, saying, "Connecting to the bill confirmation menu." The notification unit can also notify the user by using a text message, saying, "Connecting to the bill confirmation menu." The notification unit can also notify the user by using a generation AI, saying, "Connecting to the bill confirmation menu." For example, the generation AI uses the analysis results by the analysis unit as input and performs notification using a model that notifies the user of the corresponding menu. The connection unit automatically connects to the menu notified by the notification unit. The connection unit can, for example, connect the user to the corresponding menu using a telephone line. The connection unit can also connect the user to the corresponding menu using an internet line. For example, the connection unit can connect the user to the "bill confirmation menu" using a telephone line. The connection unit can also connect the user to the "bill confirmation menu" using an internet line. The connection unit can also connect the user to the corresponding menu using the generation AI.For example, the generation AI receives the menu notified by the notification unit as input and connects using a model that connects to the corresponding menu. As a result, the inquiry desk guidance system according to the embodiment analyzes the content of the user's inquiry, notifies the user of the corresponding menu, and connects, allowing the user to quickly reach the desired menu and reducing stress.

[0030] The analysis unit can analyze the user's past inquiry history and select an analysis method. For example, the analysis unit prioritizes analysis of menu items that the user has frequently inquired about in the past. The analysis unit can also extract specific patterns from the user's past inquiry history and select an optimal analysis method. Furthermore, the analysis unit can quickly analyze similar inquiry content based on the user's past inquiry history. This improves the accuracy of analysis by selecting an optimal analysis method based on the past inquiry history. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past inquiry history data into the generation AI and have the generation AI select an optimal analysis method.

[0031] The analysis unit can perform filtering based on the user's current situation or areas of interest. For example, if the user declares their current situation, the analysis unit performs analysis based on that situation. The analysis unit can also perform analysis based on the user's areas of interest, which are registered in advance. Furthermore, if the user is in a specific situation, the analysis unit can prioritize analysis of menus related to that situation. This makes it possible to provide a more relevant menu by filtering based on the user's current situation or areas of interest. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's current situation data into a generation AI and have the generation AI perform filtering.

[0032] The analysis unit can prioritize analyzing highly relevant menus based on the user's geographical location information. For example, if the user is in a specific area, the analysis unit prioritizes analyzing menus related to that area. The analysis unit can also prioritize analyzing menus that provide the closest services based on the user's current location. Furthermore, if the user is traveling, the analysis unit can also prioritize analyzing menus related to the user's travel destination. In this way, by taking the user's geographical location information into consideration, it is possible to provide menus related to the area preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information data into the generation AI and cause the generation AI to analyze highly relevant menus.

[0033] The analysis unit can analyze the user's social media activity and analyze related menus. For example, the analysis unit can analyze the content of the user's social media posts and present related menus. The analysis unit can also prioritize analysis of related menus based on the user's social media interests. Furthermore, the analysis unit can analyze the optimal menu based on the user's social media activity history. In this way, by analyzing social media activity, it is possible to provide a menu based on the user's interests. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's social media activity data into the generation AI and cause the generation AI to analyze related menus.

[0034] The notification unit can adjust the level of detail of the notification based on the importance of the menu. For example, in the case of an important menu, the notification unit provides a notification including detailed information. In addition, in the case of a general menu, the notification unit can also provide a concise notification. Furthermore, in the case of an urgent menu, the notification unit can also provide a quick notification. In this way, by adjusting the level of detail of the notification according to the importance of the menu, important information can be appropriately conveyed. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input menu importance data to the generation AI and cause the generation AI to adjust the level of detail of the notification.

[0035] The notification unit can apply different notification algorithms depending on the menu category. For example, in the case of a support-related menu, the notification unit provides a notification including detailed procedures. In addition, in the case of an information-related menu, the notification unit can provide a concise notification. Furthermore, in the case of a menu that requires an emergency response, the notification unit can provide a quick notification. Thus, by applying a notification algorithm according to the menu category, more appropriate notifications can be provided. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input menu category data into the generation AI and cause the generation AI to apply the notification algorithm.

[0036] The notification unit can determine the priority of notifications based on the time of menu submission. For example, the notification unit gives top priority to notifications for urgent menu items. The notification unit can also give priority to notifications for menu items with an upcoming submission deadline. Furthermore, the notification unit can also give regular notifications for menu items with a distant submission deadline. In this way, by determining the priority of notifications based on the time of menu submission, menu items with high urgency can be given priority. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input menu submission time data into the generation AI and have the generation AI determine the priority of notifications.

[0037] The notification unit can adjust the order of notifications based on the relevance of the menus. For example, the notification unit prioritizes notifying the user of a menu that is most relevant to the user's current situation. The notification unit can also prioritize notifying the user of a highly relevant menu based on the user's past inquiry history. Furthermore, the notification unit can also prioritize notifying the user of a highly relevant menu based on the user's areas of interest. In this way, by adjusting the order of notifications based on the relevance of the menus, the most relevant menu to the user can be prioritized. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input menu relevance data to a generation AI and cause the generation AI to adjust the order of notifications.

[0038] The connection unit can select a connection method by analyzing the user's past connection history. For example, the connection unit can prioritize connecting menus that the user has frequently connected to in the past. The connection unit can also extract specific patterns from the user's past connection history and select an optimal connection method. Furthermore, the connection unit can quickly connect similar connection content based on the user's past connection history. This improves connection accuracy by selecting an optimal connection method based on the past connection history. Some or all of the above-described processing in the connection unit can be performed using, for example, AI, or can be performed without using AI. For example, the connection unit can input the user's past connection history data into a generation AI and cause the generation AI to select an optimal connection method.

[0039] The connection unit can customize the connection means based on the user's current situation. For example, when the user declares their current situation, the connection unit establishes a connection based on that situation. The connection unit can also grasp the user's current situation in real time and select the optimal connection means. Furthermore, when the user is in a specific situation, the connection unit can preferentially select a connection means related to that situation. This allows for a more appropriate connection by customizing the connection means based on the user's current situation. Some or all of the above-described processing in the connection unit may be performed using, or without, AI. For example, the connection unit can input the user's current situation data into a generation AI and have the generation AI customize the connection means.

[0040] The connection unit can select a connection method based on the user's geographical location information. For example, if the user is in a specific area, the connection unit can preferentially connect to a menu related to that area. The connection unit can also connect to a menu providing the nearest service based on the user's current location. Furthermore, if the user is traveling, the connection unit can also connect to a menu related to the user's travel destination. In this way, by taking the user's geographical location information into consideration, it is possible to preferentially connect to a menu related to the area. Some or all of the above-described processing in the connection unit may be performed using, for example, AI, or may be performed without using AI. For example, the connection unit can input the user's geographical location information data into the generation AI and cause the generation AI to select the optimal connection method.

[0041] The connection unit can analyze the user's social media activity and suggest connection means. For example, the connection unit can analyze the content of the user's social media posts and connect to a related menu. The connection unit can also connect to a related menu based on the user's social media interests. Furthermore, the connection unit can suggest the optimal connection means based on the user's social media activity history. In this way, by analyzing social media activity, it is possible to provide connection means based on the user's interests. Some or all of the above-mentioned processing in the connection unit may be performed using, for example, AI, or may be performed without using AI. For example, the connection unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest connection means.

[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 analysis unit can learn a user's past behavioral patterns and build a prediction model. For example, if a user frequently uses a specific menu item on a specific day of the week or during a specific time period, the analysis unit can learn that pattern and build a prediction model. It can also predict the menu item the user is likely to use next based on the user's past behavioral patterns and prioritize analysis. Furthermore, the analysis unit can monitor changes in the user's behavioral patterns in real time and update the prediction model. This allows the analysis unit to build a prediction model based on the user's past behavioral patterns, enabling faster and more accurate analysis.

[0044] The analysis unit can adjust the analysis method based on the user's current health condition. For example, if the user is tired, it can provide a concise and easy-to-understand analysis result. If the user is in good health, it can provide a detailed analysis result. Furthermore, if the user is ill, it can provide an analysis result that corresponds to that condition. In this way, by adjusting the analysis method based on the user's health condition, it is possible to provide more appropriate analysis results.

[0045] The analysis unit can customize the analysis results based on the user's hobbies and preferences. For example, if the user has a particular hobby, it will prioritize analyzing menus related to that hobby. It can also prioritize analyzing highly relevant menus based on the user's preferences. It can also monitor changes in the user's hobbies and preferences in real time and customize the analysis results. This allows customizing the analysis results based on the user's hobbies and preferences, making it possible to provide more relevant menus.

[0046] The notification unit can analyze the user's past notification history and select the optimal notification method. For example, it can prioritize the notification method that the user has preferred in the past. It can also extract specific patterns from the user's past notification history and select the optimal notification method. Furthermore, it can quickly notify users of similar notification content based on the user's past notification history. This improves the accuracy of notifications by selecting the optimal notification method based on the user's past notification history.

[0047] The notification unit can customize the content of the notification based on the user's current activity status. For example, when the user is at work, a simple and to-the-point notification can be sent. When the user is on a break, a notification containing detailed information can be sent. Furthermore, when the user is on the move, a quick and concise notification can be sent. This allows the notification content to be customized based on the user's current activity status, resulting in more appropriate notifications.

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

[0049] Step 1: The analysis unit analyzes the user's verbal input. The user's verbal input includes voice commands and natural language questions. The analysis unit converts the user's verbal input into text data using speech recognition technology, analyzes the text data using natural language processing technology, and determines the appropriate menu. It can also use generation AI to analyze the user's verbal input and determine the appropriate menu. Step 2: The notification unit notifies the user of the corresponding menu based on the results of the analysis by the analysis unit. The notification unit can notify the user by voice using voice synthesis technology or by using a text message. Furthermore, the notification unit can also notify the user of the corresponding menu based on the results of the analysis by the analysis unit using a generation AI. Step 3: The connection unit automatically connects to the menu notified by the notification unit. The connection unit can connect the user to the corresponding menu using a telephone line or an internet line. Furthermore, the connection unit can also use a generation AI to connect to the corresponding menu based on the menu notified by the notification unit.

[0050] (Example 2) The inquiry guidance system according to an embodiment of the present invention involves a user verbally reporting their inquiry. The AI ​​then analyzes the report, determines the appropriate menu, notifies the user, and connects the user to the appropriate menu. The system begins with the user verbally reporting their inquiry. The AI ​​then analyzes the report and determines the appropriate menu. The AI ​​includes an "analysis unit" that analyzes the user's verbal response, a "notification unit" that notifies the user of the determined menu, and a "connection unit" that automatically connects the user to the appropriate menu. This allows the user to quickly navigate to the desired menu and reduce stress. For example, if a user verbally reports, "I would like to check my invoice," the AI ​​analyzes the report and determines the appropriate menu. The AI ​​then notifies the user, "Connecting to the invoice confirmation menu," and automatically connects the user to the appropriate menu. This allows the user to quickly navigate to the desired menu and reduce stress. Similar effects are expected at other companies' service centers, making the system widely used. The inquiry guidance system thus reduces user stress and allows the user to quickly navigate to the desired menu.

[0051] The inquiry desk guidance system according to the embodiment includes an analysis unit, a notification unit, and a connection unit. The analysis unit analyzes a user's verbal statement. The user's verbal statement includes, but is not limited to, voice commands and natural language questions. The analysis unit converts the user's verbal statement into text data using, for example, speech recognition technology. The analysis unit can also analyze the text data and determine a corresponding menu item using natural language processing technology. For example, the analysis unit converts the user's verbal statement into text data in real time using speech recognition technology. The analysis unit can also extract keywords from the text data using natural language processing technology to determine a corresponding menu item. The analysis unit can also analyze the user's verbal statement and determine a corresponding menu item using a generation AI. For example, the generation AI performs analysis using a model that inputs the user's verbal statement and outputs a corresponding menu item. The notification unit notifies the user of a corresponding menu item based on the analysis results obtained by the analysis unit. The notification unit notifies the user of a corresponding menu item by voice using, for example, speech synthesis technology. The notification unit can also notify the user of the corresponding menu using a text message. For example, the notification unit can notify the user by using voice synthesis technology, saying, "Connecting to the bill confirmation menu." The notification unit can also notify the user by using a text message, saying, "Connecting to the bill confirmation menu." The notification unit can also notify the user by using a generation AI, saying, "Connecting to the bill confirmation menu." For example, the generation AI uses the analysis results by the analysis unit as input and performs notification using a model that notifies the user of the corresponding menu. The connection unit automatically connects to the menu notified by the notification unit. The connection unit can, for example, connect the user to the corresponding menu using a telephone line. The connection unit can also connect the user to the corresponding menu using an internet line. For example, the connection unit can connect the user to the "bill confirmation menu" using a telephone line. The connection unit can also connect the user to the "bill confirmation menu" using an internet line. The connection unit can also connect the user to the corresponding menu using the generation AI.For example, the generation AI receives the menu notified by the notification unit as input and connects using a model that connects to the corresponding menu. As a result, the inquiry desk guidance system according to the embodiment analyzes the content of the user's inquiry, notifies the user of the corresponding menu, and connects, allowing the user to quickly reach the desired menu and reducing stress.

[0052] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit causes the generation AI to increase the accuracy of the analysis and quickly determine the appropriate menu. Furthermore, if the user is relaxed, the analysis unit can cause the generation AI to maintain normal analysis accuracy and perform a detailed analysis. Furthermore, if the user is in a hurry, the analysis unit can cause the generation AI to increase the accuracy of the analysis and quickly determine the appropriate menu. This allows for adjusting the analysis accuracy according to the user's emotions, thereby quickly determining a more appropriate menu. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI. For example, the analysis unit can input the user's voice data into the generation AI and have the generation AI perform emotion estimation.

[0053] The analysis unit can analyze the user's past inquiry history and select an analysis method. For example, the analysis unit prioritizes analysis of menu items that the user has frequently inquired about in the past. The analysis unit can also extract specific patterns from the user's past inquiry history and select an optimal analysis method. Furthermore, the analysis unit can quickly analyze similar inquiry content based on the user's past inquiry history. This improves the accuracy of analysis by selecting an optimal analysis method based on the past inquiry history. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past inquiry history data into the generation AI and have the generation AI select an optimal analysis method.

[0054] The analysis unit can perform filtering based on the user's current situation or areas of interest. For example, if the user declares their current situation, the analysis unit performs analysis based on that situation. The analysis unit can also perform analysis based on the user's areas of interest, which are registered in advance. Furthermore, if the user is in a specific situation, the analysis unit can prioritize analysis of menus related to that situation. This makes it possible to provide a more relevant menu by filtering based on the user's current situation or areas of interest. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's current situation data into a generation AI and have the generation AI perform filtering.

[0055] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit can prioritize important menu items as analysis results. Furthermore, if the user is relaxed, the analysis unit can also present detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can prioritize the most relevant menu items as analysis results. This allows important menu items to be provided quickly by prioritizing the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's voice data into the generation AI and have the generation AI perform emotion estimation.

[0056] The analysis unit can prioritize analyzing highly relevant menus based on the user's geographical location information. For example, if the user is in a specific area, the analysis unit prioritizes analyzing menus related to that area. The analysis unit can also prioritize analyzing menus that provide the closest services based on the user's current location. Furthermore, if the user is traveling, the analysis unit can also prioritize analyzing menus related to the user's travel destination. In this way, by taking the user's geographical location information into consideration, it is possible to provide menus related to the area preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information data into the generation AI and cause the generation AI to analyze highly relevant menus.

[0057] The analysis unit can analyze the user's social media activity and analyze related menus. For example, the analysis unit can analyze the content of the user's social media posts and present related menus. The analysis unit can also prioritize analysis of related menus based on the user's social media interests. Furthermore, the analysis unit can analyze the optimal menu based on the user's social media activity history. In this way, by analyzing social media activity, it is possible to provide a menu based on the user's interests. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's social media activity data into the generation AI and cause the generation AI to analyze related menus.

[0058] The notification unit can estimate the user's emotions and adjust the notification expression method based on the estimated emotions. For example, if the user is feeling stressed, the notification unit can provide a notification in a concise and easy-to-understand manner. Furthermore, if the user is relaxed, the notification unit can also provide a notification with detailed information. Furthermore, if the user is in a hurry, the notification unit can also provide a quick notification. This allows for more appropriate notification by adjusting the notification expression method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's voice data into the generation AI and have the generation AI perform emotion estimation.

[0059] The notification unit can adjust the level of detail of the notification based on the importance of the menu. For example, in the case of an important menu, the notification unit provides a notification including detailed information. In addition, in the case of a general menu, the notification unit can also provide a concise notification. Furthermore, in the case of an urgent menu, the notification unit can also provide a quick notification. In this way, by adjusting the level of detail of the notification according to the importance of the menu, important information can be appropriately conveyed. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input menu importance data to the generation AI and cause the generation AI to adjust the level of detail of the notification.

[0060] The notification unit can apply different notification algorithms depending on the menu category. For example, in the case of a support-related menu, the notification unit provides a notification including detailed procedures. In addition, in the case of an information-related menu, the notification unit can provide a concise notification. Furthermore, in the case of a menu that requires an emergency response, the notification unit can provide a quick notification. Thus, by applying a notification algorithm according to the menu category, more appropriate notifications can be provided. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input menu category data into the generation AI and cause the generation AI to apply the notification algorithm.

[0061] The notification unit can estimate the user's emotions and adjust the length of the notification based on the estimated emotions. For example, if the user is stressed, the notification unit can provide a short, to-the-point notification. Furthermore, if the user is relaxed, the notification unit can provide a longer notification with detailed information. Furthermore, if the user is in a hurry, the notification unit can provide a quick, concise notification. This allows for more appropriate notification by adjusting the length of the notification according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit can be performed using, for example, an AI, or without an AI. For example, the notification unit can input the user's voice data into the generation AI and have the generation AI perform emotion estimation.

[0062] The notification unit can determine the priority of notifications based on the time of menu submission. For example, the notification unit gives top priority to notifications for urgent menu items. The notification unit can also give priority to notifications for menu items with an upcoming submission deadline. Furthermore, the notification unit can also give regular notifications for menu items with a distant submission deadline. In this way, by determining the priority of notifications based on the time of menu submission, menu items with high urgency can be given priority. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input menu submission time data into the generation AI and have the generation AI determine the priority of notifications.

[0063] The notification unit can adjust the order of notifications based on the relevance of the menus. For example, the notification unit prioritizes notifying the user of a menu that is most relevant to the user's current situation. The notification unit can also prioritize notifying the user of a highly relevant menu based on the user's past inquiry history. Furthermore, the notification unit can also prioritize notifying the user of a highly relevant menu based on the user's areas of interest. In this way, by adjusting the order of notifications based on the relevance of the menus, the most relevant menu to the user can be prioritized. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input menu relevance data to a generation AI and cause the generation AI to adjust the order of notifications.

[0064] The connection unit can estimate the user's emotion and adjust the connection method based on the estimated emotion. For example, if the user is stressed, the connection unit can quickly connect. Furthermore, if the user is relaxed, the connection unit can also apply a normal connection method. Furthermore, if the user is in a hurry, the connection unit can also perform connection in the shortest time. By adjusting the connection method according to the user's emotion, more appropriate connection can be performed. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the connection unit can be performed using, for example, an AI, or without an AI. For example, the connection unit can input the user's voice data into the generation AI and have the generation AI perform emotion estimation.

[0065] The connection unit can select a connection method by analyzing the user's past connection history. For example, the connection unit can prioritize connecting menus that the user has frequently connected to in the past. The connection unit can also extract specific patterns from the user's past connection history and select an optimal connection method. Furthermore, the connection unit can quickly connect similar connection content based on the user's past connection history. This improves connection accuracy by selecting an optimal connection method based on the past connection history. Some or all of the above-described processing in the connection unit can be performed using, for example, AI, or can be performed without using AI. For example, the connection unit can input the user's past connection history data into a generation AI and cause the generation AI to select an optimal connection method.

[0066] The connection unit can customize the connection means based on the user's current situation. For example, when the user declares their current situation, the connection unit establishes a connection based on that situation. The connection unit can also grasp the user's current situation in real time and select the optimal connection means. Furthermore, when the user is in a specific situation, the connection unit can preferentially select a connection means related to that situation. This allows for a more appropriate connection by customizing the connection means based on the user's current situation. Some or all of the above-described processing in the connection unit may be performed using, or without, AI. For example, the connection unit can input the user's current situation data into a generation AI and have the generation AI customize the connection means.

[0067] The connection unit can estimate the user's emotions and determine connection priorities based on the estimated emotions. For example, if the user is feeling stressed, the connection unit can prioritize connections. The connection unit can also apply a normal connection order when the user is relaxed. Furthermore, the connection unit can quickly establish connections when the user is in a hurry. This allows important connections to be established quickly by determining connection priorities according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the connection unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the connection unit can input the user's voice data into the generation AI and have the generation AI perform emotion estimation.

[0068] The connection unit can select a connection method based on the user's geographical location information. For example, if the user is in a specific area, the connection unit can preferentially connect to a menu related to that area. The connection unit can also connect to a menu providing the nearest service based on the user's current location. Furthermore, if the user is traveling, the connection unit can also connect to a menu related to the user's travel destination. In this way, by taking the user's geographical location information into consideration, it is possible to preferentially connect to a menu related to the area. Some or all of the above-described processing in the connection unit may be performed using, for example, AI, or may be performed without using AI. For example, the connection unit can input the user's geographical location information data into the generation AI and cause the generation AI to select the optimal connection method.

[0069] The connection unit can analyze the user's social media activity and suggest connection means. For example, the connection unit can analyze the content of the user's social media posts and connect to a related menu. The connection unit can also connect to a related menu based on the user's social media interests. Furthermore, the connection unit can suggest the optimal connection means based on the user's social media activity history. In this way, by analyzing social media activity, it is possible to provide connection means based on the user's interests. Some or all of the above-mentioned processing in the connection unit may be performed using, for example, AI, or may be performed without using AI. For example, the connection unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest connection means. === Hard Collateral 1-1 === Each of the multiple elements, including the analysis unit, notification unit, and connection unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit may be realized by the processor 46 of the smart device 14 and convert the user's verbal input into text data using voice recognition technology. The analysis unit may also be realized by the specific processing unit 290 of the data processing device 12 and analyze the text data using natural language processing technology to determine the appropriate menu. The notification unit may be realized, for example, by the control unit 46A of the smart device 14 and notify the user of the appropriate menu by voice using voice synthesis technology. The notification unit may also be realized by the specific processing unit 290 of the data processing device 12 and notify the user of the appropriate menu using a text message. The connection unit may be realized, for example, by the control unit 46A of the smart device 14 and connect the user to the appropriate menu using a telephone line. The connection unit may also be realized by the specific processing unit 290 of the data processing device 12 and connect the user to the appropriate menu using an Internet line. === Hard Collateral 1-2 === Each of the multiple elements, including the analysis unit, notification unit, and connection unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit may be realized by the processor 46 of the smart glasses 214 and convert the user's verbal input into text data using voice recognition technology. The analysis unit may also be realized by the specific processing unit 290 of the data processing device 12 and analyze the text data using natural language processing technology to determine the appropriate menu. The notification unit may be realized, for example, by the control unit 46A of the smart glasses 214 and notify the user of the appropriate menu by voice using voice synthesis technology. The notification unit may also be realized by the specific processing unit 290 of the data processing device 12 and notify the user of the appropriate menu using a text message. The connection unit may be realized, for example, by the control unit 46A of the smart glasses 214 and connect the user to the appropriate menu using a telephone line. The connection unit may also be realized by the specific processing unit 290 of the data processing device 12 and connect the user to the appropriate menu using an Internet line. === Hard Collateral 1-3 === Each of the multiple elements, including the analysis unit, notification unit, and connection unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the headset-type terminal 314 and converts the user's verbal response into text data using voice recognition technology. The analysis unit is also realized by the specific processing unit 290 of the data processing device 12 and can analyze the text data using natural language processing technology to determine the appropriate menu. The notification unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and notifies the user of the appropriate menu by voice using voice synthesis technology. The notification unit is also realized by the specific processing unit 290 of the data processing device 12 and can notify the user of the appropriate menu by text message. The connection unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and connects the user to the appropriate menu using a telephone line. The connection unit is also realized by the specific processing unit 290 of the data processing device 12 and can connect the user to the appropriate menu using an Internet line. === Hard Collateral 1-4 === Each of the multiple elements, including the analysis unit, notification unit, and connection unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit may be realized by the processor 46 of the robot 414 and convert the user's verbal response into text data using voice recognition technology. The analysis unit may also be realized by the specific processing unit 290 of the data processing device 12 and analyze the text data using natural language processing technology to determine the appropriate menu. The notification unit may be realized, for example, by the control unit 46A of the robot 414 and notify the user of the appropriate menu by voice using voice synthesis technology. The notification unit may also be realized by the specific processing unit 290 of the data processing device 12 and notify the user of the appropriate menu using a text message. The connection unit may be realized, for example, by the control unit 46A of the robot 414 and connect the user to the appropriate menu using a telephone line. The connection unit may also be realized by the specific processing unit 290 of the data processing device 12 and connect the user to the appropriate menu using an Internet line.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The analysis unit can analyze the user's voice tone and speaking rate to estimate the user's level of urgency. For example, if the user's voice is high and speaking rate is fast, it can determine that the level of urgency is high, and prioritize analysis of a menu that can respond quickly. Alternatively, if the user's voice is low and speaking rate is slow, it can determine that the level of urgency is low, and apply a normal analysis procedure. Furthermore, the analysis unit can monitor changes in the user's voice tone and speaking rate in real time and adjust the analysis method according to changes in the level of urgency. This allows the level of urgency to be estimated based on the user's voice tone and speaking rate, and appropriate responses to be taken.

[0072] The analysis unit can learn a user's past behavioral patterns and build a prediction model. For example, if a user frequently uses a specific menu item on a specific day of the week or during a specific time period, the analysis unit can learn that pattern and build a prediction model. It can also predict the menu item the user is likely to use next based on the user's past behavioral patterns and prioritize analysis. Furthermore, the analysis unit can monitor changes in the user's behavioral patterns in real time and update the prediction model. This allows the analysis unit to build a prediction model based on the user's past behavioral patterns, enabling faster and more accurate analysis.

[0073] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated emotions. For example, if the user is feeling anxious, important menu items can be presented as the analysis results with priority. If the user is feeling relieved, detailed analysis results can be presented. Furthermore, if the user is feeling anxious, the most relevant menu items can be presented as the analysis results with priority. In this way, by adjusting the analysis priority according to the user's emotions, important menu items can be presented quickly.

[0074] The analysis unit can adjust the analysis method based on the user's current health condition. For example, if the user is tired, it can provide a concise and easy-to-understand analysis result. If the user is in good health, it can provide a detailed analysis result. Furthermore, if the user is ill, it can provide an analysis result that corresponds to that condition. In this way, by adjusting the analysis method based on the user's health condition, it is possible to provide more appropriate analysis results.

[0075] The analysis unit can estimate the user's emotions and adjust the way in which the analysis results are presented based on the estimated emotions. For example, if the user is feeling stressed, a concise and easy-to-understand analysis result can be presented. If the user is relaxed, a detailed analysis result can be presented. Furthermore, if the user is in a hurry, the analysis result can be presented quickly. In this way, by adjusting the way in which the analysis results are presented according to the user's emotions, more appropriate analysis results can be provided.

[0076] The analysis unit can customize the analysis results based on the user's hobbies and preferences. For example, if the user has a particular hobby, it will prioritize analyzing menus related to that hobby. It can also prioritize analyzing highly relevant menus based on the user's preferences. It can also monitor changes in the user's hobbies and preferences in real time and customize the analysis results. This allows customizing the analysis results based on the user's hobbies and preferences, making it possible to provide more relevant menus.

[0077] The analysis unit can analyze a user's social media activity and infer their emotions. For example, it can infer emotions from the content of a user's posts and adjust the analysis results based on those emotions. It can also prioritize analysis of related menus based on the user's social media interests. It can also select the optimal analysis method based on the user's social media activity history. This makes it possible to provide analysis results based on the user's emotions by analyzing social media activity.

[0078] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is feeling stressed, the notification unit can send a prompt notification. If the user is relaxed, the notification unit can send a notification at a normal timing. Furthermore, if the user is in a hurry, the notification unit can send a notification in the shortest time possible. In this way, by adjusting the timing of notifications according to the user's emotions, more appropriate notifications can be sent.

[0079] The notification unit can analyze the user's past notification history and select the optimal notification method. For example, it can prioritize the notification method that the user has preferred in the past. It can also extract specific patterns from the user's past notification history and select the optimal notification method. Furthermore, it can quickly notify users of similar notification content based on the user's past notification history. This improves the accuracy of notifications by selecting the optimal notification method based on the user's past notification history.

[0080] The notification unit can customize the content of the notification based on the user's current activity status. For example, when the user is at work, a simple and to-the-point notification can be sent. When the user is on a break, a notification containing detailed information can be sent. Furthermore, when the user is on the move, a quick and concise notification can be sent. This allows the notification content to be customized based on the user's current activity status, resulting in more appropriate notifications.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The analysis unit analyzes the user's verbal input. The user's verbal input includes voice commands and natural language questions. The analysis unit converts the user's verbal input into text data using speech recognition technology, analyzes the text data using natural language processing technology, and determines the appropriate menu. It can also use generation AI to analyze the user's verbal input and determine the appropriate menu. Step 2: The notification unit notifies the user of the corresponding menu based on the results of the analysis by the analysis unit. The notification unit can notify the user by voice using voice synthesis technology or by using a text message. Furthermore, the notification unit can also notify the user of the corresponding menu based on the results of the analysis by the analysis unit using a generation AI. Step 3: The connection unit automatically connects to the menu notified by the notification unit. The connection unit can connect the user to the corresponding menu using a telephone line or an internet line. Furthermore, the connection unit can also use a generation AI to connect to the corresponding menu based on the menu notified by the notification unit.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0085] 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.

[0086] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] 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.

[0099] 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.

[0100] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0101] 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.

[0102] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0117] 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.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0120] 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.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The 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.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0131] 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.

[0132] 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.

[0133] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0134] 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.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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."

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] [Explanation of symbols]

[0155] 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 analysis unit that analyzes the user's verbal declaration; a notification unit that notifies a user of a corresponding menu based on the analysis result by the analysis unit; a connection unit that automatically connects to the menu notified by the notification unit; A system characterized by:

2. The analysis unit Estimate the user's emotions and adjust the accuracy of analysis based on the estimated emotions.

2. The system of claim 1.

3. The analysis unit Analyze the user's past inquiry history and select an analysis method 2. The system of claim 1.

4. The analysis unit Filtering based on the user's current situation or interests 2. The system of claim 1.

5. The analysis unit Estimate the user's emotions and prioritize the analysis results based on the estimated emotions.

2. The system of claim 1.

6. The analysis unit Prioritize relevant menus based on the user's geographic location 2. The system of claim 1.

7. The analysis unit Analyze your social media activity and analyze related menus 2. The system of claim 1.

8. The notification unit Inferring user emotions and adjusting notification presentation based on the inferred emotions 2. The system of claim 1.

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