system

Abstract

The system according to the embodiment aims to enable users to efficiently communicate what they want to say over the phone. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a transmission unit, a recognition unit, and a notification unit. The reception unit receives input from a user. The generation unit generates a conversation based on the information received by the reception unit. The transmission unit makes a call based on the conversation generated by the generation unit. The recognition unit recognizes a response from the other party to the call made by the transmission unit. The notification unit notifies the user of the response recognized by the recognition 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 techniques have had the problem that it is difficult for users to efficiently communicate what they want to say over the phone.

[0005] The system according to the embodiment aims to enable users to efficiently communicate what they want to say over the phone. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a transmission unit, a recognition unit, and a notification unit. The reception unit receives input from a user. The generation unit generates a conversation based on the information received by the reception unit. The transmission unit makes a call based on the conversation generated by the generation unit. The recognition unit recognizes a response from the other party to the call made by the transmission unit. The notification unit notifies the user of the response recognized by the recognition unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to efficiently communicate what he or she wants to say over the phone. [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 AI ​​phone app of an embodiment of the present invention is a system that uses generation AI and AI voice recognition technology. This system allows a user to input the content they want to communicate over the phone into the generation AI within the app in advance, which then calls the specified number and uses voice recognition technology to converse with the caller and perform tasks such as making a reservation. For example, if a user wants to make a restaurant or hospital reservation, they input the content they want to communicate within the app. This information is input into the generation AI, which analyzes it and then calls the specified number. The generation AI generates an appropriate conversation based on the content entered by the user, such as "Please make a reservation at XX time on XX day." After the generation AI makes the call, it uses AI voice recognition technology to recognize the caller's response, such as "I'm free at XX time on XX day," and notifies the user. This mechanism allows users to perform tasks such as making reservations using the generation AI and AI voice recognition technology without having to make the call themselves. This eliminates the need for phone calls and enables tasks to be performed efficiently. This allows the AI ​​phone app to eliminate the need for users to make phone calls and perform tasks efficiently.

[0029] The AI ​​phone app according to the embodiment includes a reception unit, a generation unit, a transmission unit, a recognition unit, and a notification unit. The reception unit receives input from a user. The user input includes, but is not limited to, voice input, text input, and gesture input. The reception unit receives, for example, voice input to convey what the user wants to communicate. The reception unit can also receive text input to convey what the user wants to communicate. The reception unit can also receive gesture input to convey what the user wants to communicate. The generation unit generates a conversation based on the information received by the reception unit using a generation AI. The generation unit generates a conversation using, for example, natural language processing technology. The generation unit can also generate a conversation using template-based generation technology. The generation unit can also generate an appropriate conversation based on the content input by the user using the generation AI. The transmission unit makes a call based on the conversation generated by the generation unit. The transmission unit makes a call using, for example, an Internet phone. The transmission unit can also make a call using a mobile phone. The transmission unit can also make a call using a landline phone. The recognition unit recognizes a response from the other party of the call made by the calling unit. The recognition unit recognizes the response using, for example, voice recognition technology. The recognition unit can also recognize the response using text analysis technology. Furthermore, the recognition unit can also recognize the response from the other party of the call using AI voice recognition technology. The notification unit notifies the user of the response recognized by the recognition unit. The notification unit notifies the user of the response using, for example, a pop-up notification. The notification unit can also notify the user of the response using a voice notification. Furthermore, the notification unit can notify the user of the response using an email notification. As a result, the AI ​​phone app according to the embodiment can efficiently perform tasks by automatically making a call based on user input, recognizing the response, and notifying the user.

[0030] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically display content that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest content that will be used in a specific time period based on the user's past input history. In this way, by analyzing the past input history, the optimal input method can be suggested to the user. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input data into a generation AI and have the generation AI suggest the optimal input method.

[0031] When acquiring the input content, the reception unit can perform filtering based on the user's current situation and areas of interest. For example, the reception unit can prioritize displaying related appointments and tasks based on the user's current situation. The reception unit can also filter related information and simplify the input content based on the user's areas of interest. The reception unit can also exclude unnecessary information and optimize the input content based on the user's current situation and areas of interest. This makes it possible to provide more appropriate information by filtering the input content based on the user's situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's current situation data to a generation AI and have the generation AI perform filtering.

[0032] When acquiring input content, the reception unit can prioritize acquiring highly relevant content based on the user's geographical location information. For example, the reception unit can prioritize displaying reservations for nearby restaurants or hospitals based on the user's current location. The reception unit can also filter relevant information and simplify the input content by taking the user's geographical location information into consideration. The reception unit can also exclude unnecessary information and optimize the input content based on the user's current location. This allows for more appropriate information to be provided by prioritizing acquisition of highly relevant content based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location data to the generation AI and cause the generation AI to acquire highly relevant content.

[0033] The reception unit can analyze the user's social media activity and acquire related content when acquiring the input content. For example, the reception unit can analyze the user's social media activity and prioritize displaying related appointments and tasks. The reception unit can also filter related information and simplify the input content based on the user's social media activity. The reception unit can also exclude unnecessary information and optimize the input content based on the user's social media activity. This allows for acquiring related content based on the user's social media activity, thereby providing more appropriate information. Some or all of the above-described processing in the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to acquire related content.

[0034] When generating a conversation, the generation unit can adjust the level of detail of the conversation based on the importance of the input content. For example, the generation unit generates a detailed conversation in the case of an important reservation or task. The generation unit can also generate a concise conversation in the case of a simple inquiry. The generation unit can also adjust the level of detail of the conversation based on the importance of the user's input content to generate an optimal conversation. In this way, by adjusting the level of detail of the conversation based on the importance of the input content, a more appropriate conversation can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's input data to the generation AI and cause the generation AI to adjust the level of detail of the conversation.

[0035] When generating a conversation, the generation unit can apply different generation algorithms depending on the category of the input content. For example, in the case of a restaurant reservation, the generation unit can apply a generation algorithm specialized for reservations. In addition, in the case of a hospital reservation, the generation unit can also apply a generation algorithm specialized for medical care. In addition, in the case of other tasks, the generation unit can apply the optimal generation algorithm depending on the category. In this way, by applying different generation algorithms depending on the category of the input content, more appropriate conversation can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's input data to the generation AI and cause the generation AI to apply a generation algorithm depending on the category.

[0036] When generating a conversation, the generation unit can determine the priority of the conversation based on the time when the input content was submitted. For example, in the case of an urgent reservation or task, the generation unit can prioritize the generation of the conversation. Furthermore, if the submission time is late, the generation unit can also postpone the generation of the conversation. Furthermore, the generation unit can determine the priority of the conversation based on the time when the input content was submitted and generate an optimal conversation. In this way, by determining the priority of the conversation based on the time when the input content was submitted, more appropriate conversations can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's input data to the generation AI and have the generation AI determine the priority of the conversations.

[0037] When generating a conversation, the generation unit can adjust the order of the conversation based on the relevance of the input content. For example, the generation unit can prioritize placing important content at the beginning of the conversation. The generation unit can also generate a conversation by placing less relevant content later. The generation unit can also adjust the order of the conversation based on the relevance of the input content to generate an optimal conversation. In this way, by adjusting the order of the conversation based on the relevance of the input content, a more appropriate conversation can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user input data to the generation AI and cause the generation AI to adjust the order of the conversation.

[0038] When making a call, the calling unit can select a calling method by referring to past calling history. The calling unit, for example, selects the optimal calling method based on calling methods used by the user in the past. The calling unit can also select the most efficient calling method from the user's past calling history. The calling unit can also analyze the user's past calling history and suggest the optimal calling method. In this way, the optimal calling method can be selected by referring to the past calling history. Some or all of the above-mentioned processing in the calling unit may be performed using, for example, AI, or may be performed without using AI. For example, the calling unit can input the user's past calling data into a generation AI and have the generation AI select a calling method.

[0039] When making a call, the calling unit can customize the call content based on the attribute information of the call recipient. For example, if the call recipient is a restaurant, the calling unit customizes the call content specialized for reservations. Furthermore, if the call recipient is a hospital, the calling unit can also customize the call content specialized for medical care. Furthermore, the calling unit can also customize the optimal call content based on the attribute information of the call recipient. This enables more appropriate calls to be made by customizing the call content based on the attribute information of the call recipient. Some or all of the above-described processing in the calling unit may be performed using, for example, AI, or may be performed without using AI. For example, the calling unit can input the attribute data of the call recipient into a generation AI and have the generation AI customize the call content.

[0040] When making a call, the calling unit can select a calling method based on the geographical location information of the call recipient. For example, if the call recipient is nearby, the calling unit makes the call quickly. Furthermore, if the call recipient is far away, the calling unit can also make the call at an appropriate time. Furthermore, the calling unit can select the optimal calling method based on the geographical location information of the call recipient. As a result, by selecting the optimal calling method based on the geographical location information of the call recipient, more appropriate calling becomes possible. Some or all of the above-mentioned processing in the calling unit may be performed using, for example, AI, or may be performed without using AI. For example, the calling unit can input the geographical location data of the call recipient into the generation AI and have the generation AI select the calling method.

[0041] When making a call, the sending unit can analyze the social media activity of the call recipient and adjust the content of the call. For example, the sending unit analyzes the social media activity of the call recipient and adjusts the relevant content of the call. The sending unit can also suggest optimal content of the call based on the social media activity of the call recipient. The sending unit can also optimize the content of the call taking the social media activity of the call recipient into consideration. This enables more appropriate calls to be made by adjusting the content of the call based on the social media activity of the call recipient. Some or all of the above-mentioned processing in the sending unit may be performed using AI, for example, or may be performed without using AI. For example, the sending unit can input the social media data of the call recipient into a generation AI and have the generation AI adjust the content of the call.

[0042] The recognition unit can improve the recognition algorithm by referring to past response data when recognizing a response. For example, the recognition unit selects an optimal recognition algorithm based on past response data. The recognition unit can also apply the most efficient recognition algorithm from the past response data. The recognition unit can also analyze past response data and optimize the recognition algorithm. In this way, the recognition algorithm can be optimized by referring to the past response data. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input past response data to a generation AI and cause the generation AI to improve the recognition algorithm.

[0043] The recognition unit can improve the recognition accuracy based on the attribute information of the callee when recognizing a response. For example, if the callee is a restaurant, the recognition unit applies a recognition algorithm specialized for reservations. Furthermore, if the callee is a hospital, the recognition unit can also apply a recognition algorithm specialized for medical care. Furthermore, the recognition unit can also apply an optimal recognition algorithm based on the attribute information of the callee. This enables more appropriate recognition by improving the recognition accuracy based on the attribute information of the callee. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input the attribute data of the callee into the generation AI and have the generation AI improve the recognition accuracy.

[0044] The recognition unit can improve the recognition accuracy based on the geographic distribution of the call recipients when recognizing a response. For example, if the call recipients are concentrated in a specific region, the recognition unit can improve the recognition accuracy by taking into account the dialects and accents of that region. Furthermore, if the call recipients are spread over a wide area, the recognition unit can also improve the recognition accuracy by taking into account the characteristics of each region. Furthermore, the recognition unit can apply an optimal recognition algorithm based on the geographic distribution of the call recipients. This enables more appropriate recognition by improving the recognition accuracy based on the geographic distribution of the call recipients. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input geographic distribution data of the call recipients to the generation AI and have the generation AI improve the recognition accuracy.

[0045] The recognition unit can improve recognition accuracy by referring to literature related to the callee when recognizing a response. For example, if the callee is a restaurant, the recognition unit can improve recognition accuracy by referring to literature related to menus and reservation systems. Furthermore, if the callee is a hospital, the recognition unit can improve recognition accuracy by referring to literature related to medical terminology and medical treatment systems. Furthermore, the recognition unit can improve recognition accuracy by referring to literature related to the callee's industry and characteristics. This enables more appropriate recognition by improving recognition accuracy based on the callee's literature related to the callee. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input data on literature related to the callee into the generation AI and have the generation AI improve recognition accuracy.

[0046] The notification unit can select a notification method by referring to past notification history when sending a notification. For example, the notification unit selects the optimal notification method based on notification methods used by the user in the past. The notification unit can also select the most efficient notification method from the user's past notification history. The notification unit can also analyze the user's past notification history and suggest the optimal notification method. In this way, the optimal notification method can be selected by referring to the past notification history. 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 the user's past notification data into a generation AI and have the generation AI select a notification method.

[0047] The notification unit can customize the notification content based on the user's current situation at the time of notification. For example, when the user is in a meeting, the notification unit can provide a quiet notification method. Furthermore, when the user is on the move, the notification unit can also provide a visually easy-to-understand notification method. Furthermore, the notification unit can customize optimal notification content based on the user's current situation. This enables more appropriate notifications by customizing the notification content based on the user's current situation. 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 the user's current situation data into a generation AI and have the generation AI customize the notification content.

[0048] The notification unit can select a notification method based on the user's geographical location information when notifying the user. For example, when the user is in a specific location, the notification unit provides a notification method appropriate for that location. Furthermore, when the user is moving, the notification unit can also provide a visually easy-to-understand notification method. Furthermore, the notification unit can select the optimal notification method based on the user's geographical location information. This enables more appropriate notification by selecting the optimal notification method based on the user's geographical location information. 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 the user's geographical location data to a generation AI and have the generation AI select a notification method.

[0049] The notification unit can analyze the user's social media activity and adjust the notification content at the time of notification. For example, the notification unit analyzes the user's social media activity and adjusts the relevant notification content. The notification unit can also suggest optimal notification content based on the user's social media activity. The notification unit can also optimize the notification content taking the user's social media activity into consideration. This enables more appropriate notifications by adjusting the notification content based on the user's social media activity. 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 the user's social media data into a generation AI and have the generation AI adjust the notification content.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The reception unit can also monitor the user's health condition and adjust the priority of input content based on the health condition. For example, if the user has data indicating poor health, it can prioritize input of emergency medical appointments. Alternatively, if the user is healthy, it can prioritize input of regular tasks. Furthermore, it can also suggest an appropriate input method based on the user's health condition. This allows for more appropriate input by adjusting the priority of input content according to the user's health condition.

[0052] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display content that the user has frequently input in the past as candidates. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest content that will be used in a specific time period based on the user's past input history. In this way, it is possible to suggest the optimal input method for the user by analyzing the past input history.

[0053] The reception unit can perform filtering based on the user's current situation and areas of interest. For example, based on the user's current situation, related appointments and tasks can be preferentially displayed. In addition, based on the user's areas of interest, related information can be filtered to simplify the input content. Furthermore, based on the user's current situation and areas of interest, unnecessary information can be excluded and the input content can be optimized. In this way, by filtering the input content based on the user's situation and areas of interest, more appropriate information can be provided.

[0054] When acquiring input content, the reception unit can prioritize acquiring highly relevant content based on the user's geographical location information. For example, reservations for nearby restaurants or hospitals can be displayed with priority based on the user's current location. The reception unit can also filter relevant information and simplify the input content by taking the user's geographical location information into consideration. Furthermore, the reception unit can also optimize the input content by excluding unnecessary information based on the user's current location. This allows the reception unit to provide more appropriate information by prioritizing acquisition of highly relevant content based on the user's geographical location information.

[0055] When acquiring input content, the reception unit can analyze the user's social media activity and acquire related content. For example, the reception unit can analyze the user's social media activity and prioritize displaying related reservations and tasks. The reception unit can also filter related information based on the user's social media activity and simplify the input content. Furthermore, the reception unit can exclude unnecessary information and optimize the input content based on the user's social media activity. This allows the reception unit to provide more appropriate information by acquiring related content based on the user's social media activity.

[0056] When generating a conversation, the generation unit can adjust the level of detail of the conversation based on the importance of the input content. For example, a detailed conversation can be generated for an important reservation or task. A concise conversation can also be generated for a simple inquiry. Furthermore, the generation unit can adjust the level of detail of the conversation based on the importance of the user's input content to generate an optimal conversation. In this way, by adjusting the level of detail of the conversation based on the importance of the input content, a more appropriate conversation can be generated.

[0057] When generating a conversation, the generation unit can apply different generation algorithms depending on the category of the input content. For example, in the case of a restaurant reservation, a generation algorithm specialized for reservations can be applied. In addition, in the case of a hospital reservation, a generation algorithm specialized for medical care can be applied. Furthermore, for other tasks, the optimal generation algorithm depending on the category can be applied. In this way, by applying different generation algorithms depending on the category of the input content, more appropriate conversations can be generated.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The reception unit receives input from the user. The input from the user includes voice input, text input, gesture input, and the like. For example, the reception unit receives what the user wants to communicate using voice input. It is also possible to receive what the user wants to communicate using text input or gesture input. Step 2: The generation unit generates a conversation based on the information received by the reception unit. The generation unit generates a conversation using generative AI, natural language processing technology, and template-based generation technology. This allows an appropriate conversation to be generated based on the content entered by the user. Step 3: The calling unit makes a call based on the conversation generated by the generating unit. The calling unit can make the call using an internet phone, a mobile phone, a landline phone, or the like. Step 4: The recognition unit recognizes the response of the person on the other end of the call made by the calling unit. The recognition unit recognizes the response using voice recognition technology, text analysis technology, and AI voice recognition technology. Step 5: The notification unit notifies the user of the response recognized by the recognition unit. The notification unit can notify the user of the response using a pop-up notification, a voice notification, an email notification, or the like.

[0060] (Example 2) The AI ​​phone app of an embodiment of the present invention is a system that uses generation AI and AI voice recognition technology. This system allows a user to input the content they want to communicate over the phone into the generation AI within the app in advance, which then calls the specified number and uses voice recognition technology to converse with the caller and perform tasks such as making a reservation. For example, if a user wants to make a restaurant or hospital reservation, they input the content they want to communicate within the app. This information is input into the generation AI, which analyzes it and then calls the specified number. The generation AI generates an appropriate conversation based on the content entered by the user, such as "Please make a reservation at XX time on XX day." After the generation AI makes the call, it uses AI voice recognition technology to recognize the caller's response, such as "I'm free at XX time on XX day," and notifies the user. This mechanism allows users to perform tasks such as making reservations using the generation AI and AI voice recognition technology without having to make the call themselves. This eliminates the need for phone calls and enables tasks to be performed efficiently. This allows the AI ​​phone app to eliminate the need for users to make phone calls and perform tasks efficiently.

[0061] The AI ​​phone app according to the embodiment includes a reception unit, a generation unit, a transmission unit, a recognition unit, and a notification unit. The reception unit receives input from a user. The user input includes, but is not limited to, voice input, text input, and gesture input. The reception unit receives, for example, voice input to convey what the user wants to communicate. The reception unit can also receive text input to convey what the user wants to communicate. The reception unit can also receive gesture input to convey what the user wants to communicate. The generation unit generates a conversation based on the information received by the reception unit using a generation AI. The generation unit generates a conversation using, for example, natural language processing technology. The generation unit can also generate a conversation using template-based generation technology. The generation unit can also generate an appropriate conversation based on the content input by the user using the generation AI. The transmission unit makes a call based on the conversation generated by the generation unit. The transmission unit makes a call using, for example, an Internet phone. The transmission unit can also make a call using a mobile phone. The transmission unit can also make a call using a landline phone. The recognition unit recognizes a response from the other party of the call made by the calling unit. The recognition unit recognizes the response using, for example, voice recognition technology. The recognition unit can also recognize the response using text analysis technology. Furthermore, the recognition unit can also recognize the response from the other party of the call using AI voice recognition technology. The notification unit notifies the user of the response recognized by the recognition unit. The notification unit notifies the user of the response using, for example, a pop-up notification. The notification unit can also notify the user of the response using a voice notification. Furthermore, the notification unit can notify the user of the response using an email notification. As a result, the AI ​​phone app according to the embodiment can efficiently perform tasks by automatically making a call based on user input, recognizing the response, and notifying the user.

[0062] The reception unit can estimate the user's emotions and adjust the priority of input content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prioritize input of important appointments and urgent tasks. Furthermore, if the user is relaxed, the reception unit can provide an option to input detailed information and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to allow the user to quickly input necessary information. This allows for more appropriate input by adjusting the priority of input content 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 reception unit can be performed using, for example, an AI. For example, the reception unit can input the user's voice data into the generation AI and have the generation AI estimate the user's emotions.

[0063] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically display content that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest content that will be used in a specific time period based on the user's past input history. In this way, by analyzing the past input history, the optimal input method can be suggested to the user. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input data into a generation AI and have the generation AI suggest the optimal input method.

[0064] When acquiring the input content, the reception unit can perform filtering based on the user's current situation and areas of interest. For example, the reception unit can prioritize displaying related appointments and tasks based on the user's current situation. The reception unit can also filter related information and simplify the input content based on the user's areas of interest. The reception unit can also exclude unnecessary information and optimize the input content based on the user's current situation and areas of interest. This makes it possible to provide more appropriate information by filtering the input content based on the user's situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's current situation data to a generation AI and have the generation AI perform filtering.

[0065] The reception unit can estimate the user's emotions and adjust the confirmation method for the input content based on the estimated user emotions. For example, if the user is nervous, the reception unit can provide a simple, highly visible confirmation method. Furthermore, if the user is relaxed, the reception unit can provide detailed confirmation options and suggest a customizable confirmation method. Furthermore, if the user is in a hurry, the reception unit can provide a quick confirmation method and simplify the confirmation of the input content. This allows for more appropriate confirmation by adjusting the confirmation method for the input content 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 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 reception unit can be performed using, for example, an AI. For example, the reception unit can input the user's voice data into the generation AI and have the generation AI estimate the user's emotions.

[0066] When acquiring input content, the reception unit can prioritize acquiring highly relevant content based on the user's geographical location information. For example, the reception unit can prioritize displaying reservations for nearby restaurants or hospitals based on the user's current location. The reception unit can also filter relevant information and simplify the input content by taking the user's geographical location information into consideration. The reception unit can also exclude unnecessary information and optimize the input content based on the user's current location. This allows for more appropriate information to be provided by prioritizing acquisition of highly relevant content based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location data to the generation AI and cause the generation AI to acquire highly relevant content.

[0067] The reception unit can analyze the user's social media activity and acquire related content when acquiring the input content. For example, the reception unit can analyze the user's social media activity and prioritize displaying related appointments and tasks. The reception unit can also filter related information and simplify the input content based on the user's social media activity. The reception unit can also exclude unnecessary information and optimize the input content based on the user's social media activity. This allows for acquiring related content based on the user's social media activity, thereby providing more appropriate information. Some or all of the above-described processing in the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to acquire related content.

[0068] The generation unit can estimate the user's emotions and adjust the way the conversation is expressed based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can use a polite and relaxed expression. If the user is in a hurry, the generation unit can use a concise and quick expression. If the user is excited, the generation unit can use an expression that adds a visually stimulating effect. This allows the generation of more appropriate conversations by adjusting the way the conversation is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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-mentioned processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's voice data into the generation AI and cause the generation AI to adjust the way the conversation is expressed.

[0069] When generating a conversation, the generation unit can adjust the level of detail of the conversation based on the importance of the input content. For example, the generation unit generates a detailed conversation in the case of an important reservation or task. The generation unit can also generate a concise conversation in the case of a simple inquiry. The generation unit can also adjust the level of detail of the conversation based on the importance of the user's input content to generate an optimal conversation. In this way, by adjusting the level of detail of the conversation based on the importance of the input content, a more appropriate conversation can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's input data to the generation AI and cause the generation AI to adjust the level of detail of the conversation.

[0070] When generating a conversation, the generation unit can apply different generation algorithms depending on the category of the input content. For example, in the case of a restaurant reservation, the generation unit can apply a generation algorithm specialized for reservations. In addition, in the case of a hospital reservation, the generation unit can also apply a generation algorithm specialized for medical care. In addition, in the case of other tasks, the generation unit can apply the optimal generation algorithm depending on the category. In this way, by applying different generation algorithms depending on the category of the input content, more appropriate conversation can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's input data to the generation AI and cause the generation AI to apply a generation algorithm depending on the category.

[0071] The generation unit can estimate the user's emotions and adjust the length of the conversation based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point conversation. Furthermore, if the user is relaxed, the generation unit can generate a longer conversation that includes detailed explanations. Furthermore, if the user is excited, the generation unit can generate a conversation that adds visually stimulating effects. This allows for the generation of more appropriate conversations by adjusting the length of the conversation 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 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 generation unit can be performed using, for example, an AI, or without an AI. For example, the generation unit can input the user's voice data into the generation AI and have the generation AI adjust the length of the conversation.

[0072] When generating a conversation, the generation unit can determine the priority of the conversation based on the time when the input content was submitted. For example, in the case of an urgent reservation or task, the generation unit can prioritize the generation of the conversation. Furthermore, if the submission time is late, the generation unit can also postpone the generation of the conversation. Furthermore, the generation unit can determine the priority of the conversation based on the time when the input content was submitted and generate an optimal conversation. In this way, by determining the priority of the conversation based on the time when the input content was submitted, more appropriate conversations can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's input data to the generation AI and have the generation AI determine the priority of the conversations.

[0073] When generating a conversation, the generation unit can adjust the order of the conversation based on the relevance of the input content. For example, the generation unit can prioritize placing important content at the beginning of the conversation. The generation unit can also generate a conversation by placing less relevant content later. The generation unit can also adjust the order of the conversation based on the relevance of the input content to generate an optimal conversation. In this way, by adjusting the order of the conversation based on the relevance of the input content, a more appropriate conversation can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user input data to the generation AI and cause the generation AI to adjust the order of the conversation.

[0074] The calling unit can estimate the user's emotions and adjust the timing of the call based on the estimated user's emotions. For example, if the user is relaxed, the calling unit can make the call at an appropriate time. Furthermore, if the user is in a hurry, the calling unit can also make the call quickly. Furthermore, if the user is excited, the calling unit can also make the call at a more appropriate time by adjusting the timing of the call 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 calling unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the calling unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of the call.

[0075] When making a call, the calling unit can select a calling method by referring to past calling history. The calling unit, for example, selects the optimal calling method based on calling methods used by the user in the past. The calling unit can also select the most efficient calling method from the user's past calling history. The calling unit can also analyze the user's past calling history and suggest the optimal calling method. In this way, the optimal calling method can be selected by referring to the past calling history. Some or all of the above-mentioned processing in the calling unit may be performed using, for example, AI, or may be performed without using AI. For example, the calling unit can input the user's past calling data into a generation AI and have the generation AI select a calling method.

[0076] When making a call, the calling unit can customize the call content based on the attribute information of the call recipient. For example, if the call recipient is a restaurant, the calling unit customizes the call content specialized for reservations. Furthermore, if the call recipient is a hospital, the calling unit can also customize the call content specialized for medical care. Furthermore, the calling unit can also customize the optimal call content based on the attribute information of the call recipient. This enables more appropriate calls to be made by customizing the call content based on the attribute information of the call recipient. Some or all of the above-described processing in the calling unit may be performed using, for example, AI, or may be performed without using AI. For example, the calling unit can input the attribute data of the call recipient into a generation AI and have the generation AI customize the call content.

[0077] The transmission unit can estimate the user's emotions and determine the priority of the message content based on the estimated user emotions. For example, when the user is feeling stressed, the transmission unit prioritizes important message content. The transmission unit can also provide detailed message content when the user is relaxed. The transmission unit can also quickly determine message content when the user is in a hurry. This enables more appropriate message transmission by determining the priority of message content 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, 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 transmission unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the transmission unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the message content.

[0078] When making a call, the calling unit can select a calling method based on the geographical location information of the call recipient. For example, if the call recipient is nearby, the calling unit makes the call quickly. Furthermore, if the call recipient is far away, the calling unit can also make the call at an appropriate time. Furthermore, the calling unit can select the optimal calling method based on the geographical location information of the call recipient. As a result, by selecting the optimal calling method based on the geographical location information of the call recipient, more appropriate calling becomes possible. Some or all of the above-mentioned processing in the calling unit may be performed using, for example, AI, or may be performed without using AI. For example, the calling unit can input the geographical location data of the call recipient into the generation AI and have the generation AI select the calling method.

[0079] When making a call, the sending unit can analyze the social media activity of the call recipient and adjust the content of the call. For example, the sending unit analyzes the social media activity of the call recipient and adjusts the relevant content of the call. The sending unit can also suggest optimal content of the call based on the social media activity of the call recipient. The sending unit can also optimize the content of the call taking the social media activity of the call recipient into consideration. This enables more appropriate calls to be made by adjusting the content of the call based on the social media activity of the call recipient. Some or all of the above-mentioned processing in the sending unit may be performed using AI, for example, or may be performed without using AI. For example, the sending unit can input the social media data of the call recipient into a generation AI and have the generation AI adjust the content of the call.

[0080] The recognition unit can estimate the user's emotions and adjust the response recognition method based on the estimated user emotions. For example, if the user is nervous, the recognition unit can provide a simple, highly visible recognition method. Furthermore, if the user is relaxed, the recognition unit can provide detailed recognition options and suggest a customizable recognition method. Furthermore, if the user is in a hurry, the recognition unit can provide a quick recognition method and simplify response recognition. This enables more appropriate recognition by adjusting the response recognition method 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 recognition unit can be performed using, for example, AI, or without AI. For example, the recognition unit can input user emotion data into the generation AI and have the generation AI adjust the response recognition method.

[0081] The recognition unit can improve the recognition algorithm by referring to past response data when recognizing a response. For example, the recognition unit selects an optimal recognition algorithm based on past response data. The recognition unit can also apply the most efficient recognition algorithm from the past response data. The recognition unit can also analyze past response data and optimize the recognition algorithm. In this way, the recognition algorithm can be optimized by referring to the past response data. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input past response data to a generation AI and cause the generation AI to improve the recognition algorithm.

[0082] The recognition unit can improve the recognition accuracy based on the attribute information of the callee when recognizing a response. For example, if the callee is a restaurant, the recognition unit applies a recognition algorithm specialized for reservations. Furthermore, if the callee is a hospital, the recognition unit can also apply a recognition algorithm specialized for medical care. Furthermore, the recognition unit can also apply an optimal recognition algorithm based on the attribute information of the callee. This enables more appropriate recognition by improving the recognition accuracy based on the attribute information of the callee. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input the attribute data of the callee into the generation AI and have the generation AI improve the recognition accuracy.

[0083] The recognition unit can estimate the user's emotion and adjust the display method of the recognition results based on the estimated user emotion. For example, if the user is nervous, the recognition unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the recognition unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the recognition unit can provide a display method that focuses on the main points. This allows for more appropriate display by adjusting the display method of the recognition results according to the user's emotion. The emotion estimation is realized 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 recognition unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the recognition unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the recognition results.

[0084] The recognition unit can improve the recognition accuracy based on the geographic distribution of the call recipients when recognizing a response. For example, if the call recipients are concentrated in a specific region, the recognition unit can improve the recognition accuracy by taking into account the dialects and accents of that region. Furthermore, if the call recipients are spread over a wide area, the recognition unit can also improve the recognition accuracy by taking into account the characteristics of each region. Furthermore, the recognition unit can apply an optimal recognition algorithm based on the geographic distribution of the call recipients. This enables more appropriate recognition by improving the recognition accuracy based on the geographic distribution of the call recipients. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input geographic distribution data of the call recipients to the generation AI and have the generation AI improve the recognition accuracy.

[0085] The recognition unit can improve recognition accuracy by referring to literature related to the callee when recognizing a response. For example, if the callee is a restaurant, the recognition unit can improve recognition accuracy by referring to literature related to menus and reservation systems. Furthermore, if the callee is a hospital, the recognition unit can improve recognition accuracy by referring to literature related to medical terminology and medical treatment systems. Furthermore, the recognition unit can improve recognition accuracy by referring to literature related to the callee's industry and characteristics. This enables more appropriate recognition by improving recognition accuracy based on the callee's literature related to the callee. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input data on literature related to the callee into the generation AI and have the generation AI improve recognition accuracy.

[0086] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated user emotions. For example, if the user is nervous, the notification unit can provide a notification in a calm voice. If the user is relaxed, the notification unit can also provide a notification in a cheerful voice. If the user is in a hurry, the notification unit can also provide a quick and concise notification. This allows for more appropriate notification by adjusting the notification method 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 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-mentioned processing in the notification unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the notification unit can input the user's emotion data into the generation AI and have the generation AI adjust the notification method.

[0087] The notification unit can select a notification method by referring to past notification history when sending a notification. For example, the notification unit selects the optimal notification method based on notification methods used by the user in the past. The notification unit can also select the most efficient notification method from the user's past notification history. The notification unit can also analyze the user's past notification history and suggest the optimal notification method. In this way, the optimal notification method can be selected by referring to the past notification history. 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 the user's past notification data into a generation AI and have the generation AI select a notification method.

[0088] The notification unit can customize the notification content based on the user's current situation at the time of notification. For example, when the user is in a meeting, the notification unit can provide a quiet notification method. Furthermore, when the user is on the move, the notification unit can also provide a visually easy-to-understand notification method. Furthermore, the notification unit can customize optimal notification content based on the user's current situation. This enables more appropriate notifications by customizing the notification content based on the user's current situation. 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 the user's current situation data into a generation AI and have the generation AI customize the notification content.

[0089] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated user emotions. For example, if the user is feeling stressed, the notification unit can prioritize important notifications. The notification unit can also provide detailed notifications if the user is relaxed. The notification unit can also quickly determine the content of notifications if the user is in a hurry. This enables more appropriate notifications by determining the priority of notifications 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 notification unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the notification unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of notifications.

[0090] The notification unit can select a notification method based on the user's geographical location information when notifying the user. For example, when the user is in a specific location, the notification unit provides a notification method appropriate for that location. Furthermore, when the user is moving, the notification unit can also provide a visually easy-to-understand notification method. Furthermore, the notification unit can select the optimal notification method based on the user's geographical location information. This enables more appropriate notification by selecting the optimal notification method based on the user's geographical location information. 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 the user's geographical location data to a generation AI and have the generation AI select a notification method.

[0091] The notification unit can analyze the user's social media activity and adjust the notification content at the time of notification. For example, the notification unit analyzes the user's social media activity and adjusts the relevant notification content. The notification unit can also suggest optimal notification content based on the user's social media activity. The notification unit can also optimize the notification content taking the user's social media activity into consideration. This enables more appropriate notifications by adjusting the notification content based on the user's social media activity. 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 the user's social media data into a generation AI and have the generation AI adjust the notification content. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, transmission unit, recognition unit, and notification unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives voice input or text input from a user. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a conversation using a generation AI. The transmission unit makes a call using the communication I / F 44 of the smart device 14. The recognition unit is realized by the specific processing unit 290 of the data processing device 12 and recognizes the response of the person on the other end of the call using AI voice recognition technology. The notification unit notifies the user of the response using the output device 40 of the smart device 14. The reception unit can estimate the user's emotion and adjust the priority of the input content based on the estimated emotion. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, transmission unit, recognition unit, and notification unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives voice input or text input from the user. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a conversation using a generation AI. The transmission unit makes a call using the communication I / F 44 of the smart glasses 214. The recognition unit is realized by the specific processing unit 290 of the data processing device 12 and recognizes the response of the person on the other end of the call using AI voice recognition technology. The notification unit notifies the user of the response using the speaker 240 of the smart glasses 214. The reception unit can estimate the user's emotion and adjust the priority of the input content based on the estimated emotion. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, transmission unit, recognition unit, and notification unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and receives voice input and text input from the user. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a conversation using a generation AI. The transmission unit makes a call using the communication I / F 44 of the headset-type terminal 314. The recognition unit is realized by the specific processing unit 290 of the data processing device 12 and recognizes the response of the person on the other end of the call using AI voice recognition technology. The notification unit notifies the user of the response using the speaker 240 of the headset-type terminal 314. The reception unit can estimate the user's emotion and adjust the priority of the input content based on the estimated emotion. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, transmission unit, recognition unit, and notification unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives voice input or text input from a user. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a conversation using a generation AI. The transmission unit makes a call using the communication I / F 44 of the robot 414. The recognition unit is realized by the specific processing unit 290 of the data processing device 12 and recognizes the response of the person on the other end of the call using AI voice recognition technology. The notification unit notifies the user of the response using the speaker 240 of the robot 414. The reception unit can estimate the user's emotion and adjust the priority of the input content based on the estimated emotion.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The reception unit can also monitor the user's health condition and adjust the priority of input content based on the health condition. For example, if the user has data indicating poor health, it can prioritize input of emergency medical appointments. Alternatively, if the user is healthy, it can prioritize input of regular tasks. Furthermore, it can also suggest an appropriate input method based on the user's health condition. This allows for more appropriate input by adjusting the priority of input content according to the user's health condition.

[0094] The receiving unit can also estimate the user's emotions and adjust the feedback method for the input content based on the estimated user emotions. For example, if the user is feeling stressed, concise and clear feedback can be provided. If the user is relaxed, detailed feedback can be provided. Furthermore, if the user is in a hurry, quick feedback can be provided. In this way, more appropriate feedback can be provided by adjusting the feedback method for the input content according to the user's emotions.

[0095] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display content that the user has frequently input in the past as candidates. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest content that will be used in a specific time period based on the user's past input history. In this way, it is possible to suggest the optimal input method for the user by analyzing the past input history.

[0096] The reception unit can perform filtering based on the user's current situation and areas of interest. For example, based on the user's current situation, related appointments and tasks can be preferentially displayed. In addition, based on the user's areas of interest, related information can be filtered to simplify the input content. Furthermore, based on the user's current situation and areas of interest, unnecessary information can be excluded and the input content can be optimized. In this way, by filtering the input content based on the user's situation and areas of interest, more appropriate information can be provided.

[0097] The reception unit can estimate the user's emotions and adjust the confirmation method for the input content based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible confirmation method can be provided. Alternatively, if the user is relaxed, detailed confirmation options can be provided and a customizable confirmation method can be suggested. Furthermore, if the user is in a hurry, a quick confirmation method can be provided, simplifying the confirmation of the input content. In this way, by adjusting the confirmation method for the input content according to the user's emotions, more appropriate confirmation can be achieved.

[0098] When acquiring input content, the reception unit can prioritize acquiring highly relevant content based on the user's geographical location information. For example, reservations for nearby restaurants or hospitals can be displayed with priority based on the user's current location. The reception unit can also filter relevant information and simplify the input content by taking the user's geographical location information into consideration. Furthermore, the reception unit can also optimize the input content by excluding unnecessary information based on the user's current location. This allows the reception unit to provide more appropriate information by prioritizing acquisition of highly relevant content based on the user's geographical location information.

[0099] When acquiring input content, the reception unit can analyze the user's social media activity and acquire related content. For example, the reception unit can analyze the user's social media activity and prioritize displaying related reservations and tasks. The reception unit can also filter related information based on the user's social media activity and simplify the input content. Furthermore, the reception unit can exclude unnecessary information and optimize the input content based on the user's social media activity. This allows the reception unit to provide more appropriate information by acquiring related content based on the user's social media activity.

[0100] The generation unit can estimate the user's emotions and adjust the way the conversation is expressed based on the estimated user's emotions. For example, if the user is relaxed, a polite and relaxed way of expression can be used. If the user is in a hurry, a concise and quick way of expression can be used. Furthermore, if the user is excited, a way of expression that adds a visually stimulating effect can be used. In this way, by adjusting the way the conversation is expressed according to the user's emotions, more appropriate conversations can be generated.

[0101] When generating a conversation, the generation unit can adjust the level of detail of the conversation based on the importance of the input content. For example, a detailed conversation can be generated for an important reservation or task. A concise conversation can also be generated for a simple inquiry. Furthermore, the generation unit can adjust the level of detail of the conversation based on the importance of the user's input content to generate an optimal conversation. In this way, by adjusting the level of detail of the conversation based on the importance of the input content, a more appropriate conversation can be generated.

[0102] When generating a conversation, the generation unit can apply different generation algorithms depending on the category of the input content. For example, in the case of a restaurant reservation, a generation algorithm specialized for reservations can be applied. In addition, in the case of a hospital reservation, a generation algorithm specialized for medical care can be applied. Furthermore, for other tasks, the optimal generation algorithm depending on the category can be applied. In this way, by applying different generation algorithms depending on the category of the input content, more appropriate conversations can be generated.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The reception unit receives input from the user. The input from the user includes voice input, text input, gesture input, and the like. For example, the reception unit receives what the user wants to communicate using voice input. It is also possible to receive what the user wants to communicate using text input or gesture input. Step 2: The generation unit generates a conversation based on the information received by the reception unit. The generation unit generates a conversation using generative AI, natural language processing technology, and template-based generation technology. This allows an appropriate conversation to be generated based on the content entered by the user. Step 3: The calling unit makes a call based on the conversation generated by the generating unit. The calling unit can make the call using an internet phone, a mobile phone, a landline phone, or the like. Step 4: The recognition unit recognizes the response of the person on the other end of the call made by the calling unit. The recognition unit recognizes the response using voice recognition technology, text analysis technology, and AI voice recognition technology. Step 5: The notification unit notifies the user of the response recognized by the recognition unit. The notification unit can notify the user of the response using a pop-up notification, a voice notification, an email notification, or the like.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The 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.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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, in order to avoid confusion and to 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input from a user; a generation unit that generates a conversation based on the information received by the reception unit; a calling unit that makes a call based on the conversation generated by the generating unit; a recognition unit that recognizes a response from the other party of a call made by the calling unit; a notification unit that notifies a user of the response recognized by the recognition unit. A system characterized by:

2. The reception unit Inferring user emotions and adjusting the priority of input content based on the estimated user emotions 2. The system of claim 1.

3. The reception unit Analyzes the user's past input history and suggests input methods 2. The system of claim 1.

4. The reception unit As input is captured, it is filtered based on the user's current context and interests.

2. The system of claim 1.

5. The reception unit Inferring user emotions and adjusting input confirmation methods based on the estimated user emotions 2. The system of claim 1.

6. The reception unit When retrieving input, prioritize relevant content based on the user's geographic location.

2. The system of claim 1.

7. The reception unit When capturing input, analyze the user's social media activity to capture relevant content 2. The system of claim 1.

8. The generation unit Estimate the user's emotions and adjust the way the conversation is expressed based on the estimated user emotions.

2. The system of claim 1.

9. The generation unit When generating a conversation, adjust the level of detail based on the importance of the input.

2. The system of claim 1.

10. The generation unit When generating conversations, different generation algorithms are applied depending on the category of the input content.

2. The system of claim 1.

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