system

Abstract

The system according to the embodiment aims to enable users to efficiently and accurately communicate what they want to say over the phone. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a call unit, and a voice recognition unit. The reception unit receives input of the content that a user wants to convey over the phone. The generation unit analyzes the content received by the reception unit and places a call to a specified telephone number. The call unit makes a call based on the conversation content generated by the generation unit. The voice recognition unit analyzes the content of the conversation carried out by the call 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 technology has had the problem that it is difficult and time-consuming for users to accurately communicate what they want to say over the phone.

[0005] The system according to the embodiment aims to enable users to efficiently and accurately 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 call unit, and a voice recognition unit. The reception unit receives input of the content that the user wants to convey over the phone. The generation unit analyzes the content received by the reception unit and places a call to the specified telephone number. The call unit makes a call based on the conversation content generated by the generation unit. The voice recognition unit analyzes the content of the conversation made by the call unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to efficiently and accurately 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 in which a user inputs the content they want to communicate over the phone in advance, and a generation AI uses that information to call a specified number, conducts the conversation using voice recognition technology, and executes tasks such as making a reservation. In the AI ​​phone app, a user inputs the content they want to communicate over the phone, and the generation AI analyzes that information, calls the specified number, and conducts the conversation using voice recognition technology to execute tasks such as making a reservation. For example, in an AI phone app, a user inputs the content they want to communicate over the phone within the app, such as a restaurant reservation or a hospital appointment. This information is input into the generation AI. The generation AI then analyzes the input information and calls the specified number. The generation AI then engages in a conversation with the caller based on the content input by the user. For example, in the case of a restaurant reservation, the generation AI would say to the caller, "Please make a reservation on the XX day at XX time." Furthermore, the generation AI uses AI voice recognition technology to analyze the caller's response. For example, if the caller responds, "We're fully booked that day," the generation AI provides feedback on that information to the user. Furthermore, if the person on the other end of the phone responds with "Your reservation has been accepted at ____ time on ____ date," the generating AI will notify the user of that information. This mechanism allows users to perform tasks such as making reservations through the generating AI without having to make the call themselves. This allows the AI ​​phone app to reduce user effort and perform tasks efficiently. For example, the generating AI can automatically make and carry out the call without the user having to make the call themselves, saving time and effort. In addition, by using voice recognition technology, the responding person's response can be accurately analyzed and feedback can be provided to the user. This allows users to perform tasks such as making reservations quickly and accurately.

[0029] An AI phone app according to an embodiment includes a reception unit, a generation unit, a call unit, and a voice recognition unit. The reception unit receives input of phone call content from a user. Phone call content input by a user within the app includes, but is not limited to, restaurant reservations, hospital appointments, messages, instructions, and questions. The reception unit receives phone call content by, for example, text input by the user within the app. The reception unit can also receive dictated content from the user using voice input. For example, the user may use a microphone to input voice, and the voice input may be converted into text data for acceptance. The generation unit uses a generation AI to analyze the content received by the reception unit and place a call to a specified phone number. The generation AI, for example, uses a text generation AI (e.g., LLM) to analyze the content input by the user and generate appropriate conversation content. The generation unit can also use the generation AI to generate conversation content for executing a task specified by the user. For example, the generation AI may generate conversation content regarding a restaurant reservation and place a call to a specified phone number. The call unit makes a call based on the conversation content generated by the generation unit. The call unit, for example, uses AI to convert the generated conversation content into voice and place a call to a specified telephone number. The call unit can also use AI to have a conversation with the other party at the other end of the call. For example, the call unit converts the generated conversation content into voice and transmits it to the other party at the other end of the call. The voice recognition unit analyzes the content of the conversation made by the call unit. For example, the voice recognition unit uses AI to analyze the other party's response and provide feedback to the user. The voice recognition unit can also use AI to convert the other party's response into text data and notify the user. For example, if the other party at the other end of the call responds, "We're fully booked that day," the voice recognition unit converts the content into text data and provides feedback to the user. As a result, the AI ​​phone app according to the embodiment can efficiently input the content a user wants to convey over the phone, call the specified telephone number, conduct a conversation, and analyze the content.

[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 prioritize suggesting 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. This improves input efficiency by suggesting the optimal input method based on the user's past input history. 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 history data into a generation AI and have the generation AI suggest the optimal input method.

[0031] The reception unit can perform filtering based on the user's current situation or area of ​​interest when acquiring the input content. The reception unit, for example, preferentially displays related reservation content according to the user's current situation. The reception unit can also filter and display related information based on the user's area of ​​interest. The reception unit can also suggest optimal input content based on the user's current situation and area of ​​interest. This makes it possible to provide highly relevant information by filtering based on the user's current situation and area 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 and area of ​​interest data to the generation AI and have the generation AI perform filtering.

[0032] When acquiring input content, the reception unit can select an appropriate acquisition means depending on the user's input method. For example, if the user selects voice input, the reception unit can acquire the input content using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also acquire the input content using text analysis technology. Furthermore, if the user selects image input, the reception unit can also acquire the input content using image recognition technology. This improves input accuracy by selecting the optimal acquisition means depending on the user's input method. Some or all of the above-described 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 input method data to the generation AI and cause the generation AI to select the optimal acquisition means.

[0033] When acquiring input content, the reception unit can prioritize acquiring highly relevant content by taking into account the user's geographical location information. For example, the reception unit can prioritize displaying reservation details for nearby restaurants based on the user's current location. The reception unit can also filter and display related information based on the user's geographical location information. The reception unit can also suggest optimal 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 by taking into account 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 information data to the generation AI and cause the generation AI to acquire highly relevant content.

[0034] When acquiring input content, the reception unit can analyze the user's social media activity and acquire related content. The reception unit, for example, analyzes the user's social media posts and suggests related reservation content. The reception unit can also acquire related information based on the user's social media check-in information. The reception unit can also acquire related content by referring to the activities of the user's friends on social media. This makes it easier to acquire related content by analyzing the user's social media activity. Some or all of the above-described 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 social media data into the generation AI and cause the generation AI to acquire related content.

[0035] The reception unit can customize the acquisition method by reflecting the user's past feedback when acquiring input content. The reception unit can, for example, suggest an optimal acquisition method based on feedback provided by the user in the past. The reception unit can also customize the acquisition method for the input content by reflecting the user's past feedback. The reception unit can also preferentially acquire related information based on the user's past feedback. This makes it easier to customize the acquisition method by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the acquisition method.

[0036] The generation unit can adjust the level of detail of the conversation content based on the importance of the task during generation. For example, the generation unit generates detailed conversation content for an important task. The generation unit can also generate standard conversation content for a general task. The generation unit can also generate concise conversation content for a simple task. This allows for more appropriate conversation by adjusting the level of detail of the conversation content based on the importance of the task. Some or all of the above-described 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 task importance data into the generation AI and cause the generation AI to adjust the level of detail of the conversation content.

[0037] The generation unit can apply different generation algorithms depending on the task category during generation. 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, the generation unit can select and apply an appropriate generation algorithm for other tasks. In this way, applying different generation algorithms depending on the task category enables more appropriate conversation. 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 task category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0038] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit, for example, analyzes the user's past generation results and generates optimal conversation content. The generation unit can also adjust the generation algorithm based on the user's past generation results. The generation unit can also improve the accuracy of generation by referring to the user's past generation results. In this way, the accuracy of generation is improved by referring to the user's past generation results. 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 past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0039] The generation unit can determine the generation priority based on the submission time of the task at the time of generation. For example, the generation unit gives top priority to generation of an urgent task. The generation unit can also give priority to generation of a task with an approaching deadline. The generation unit can also postpone generation of a task with a distant deadline. In this way, determining the generation priority based on the submission time of the task enables more appropriate conversation. 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 task submission time data into the generation AI and have the generation AI determine the generation priority.

[0040] The generation unit can adjust the order of generation based on the relevance of the tasks during generation. For example, the generation unit prioritizes the generation of highly relevant tasks. The generation unit can also postpone the generation of less relevant tasks. The generation unit can also determine the optimal generation order based on the relevance of the tasks. This allows for more appropriate conversation by adjusting the generation order based on the relevance of the tasks. Some or all of the above-described processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input task relevance data into the generation AI and cause the generation AI to adjust the generation order.

[0041] The generation unit can adjust the use of technical terminology during generation according to the user's level of expertise. For example, if the user has specialized knowledge, the generation unit generates conversation content that uses a lot of technical terminology. Furthermore, if the user has general knowledge, the generation unit can also generate conversation content that uses standard terminology. Furthermore, if the user is a beginner, the generation unit can also generate conversation content that uses simple terminology. This allows for more appropriate conversation by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0042] During a call, the call unit can adjust the speed of the call based on the response speed of the other party. For example, if the other party's response is slow, the call unit slows down the speed of the call. Furthermore, if the other party's response is fast, the call unit can also speed up the speed of the call. Furthermore, the call unit can adjust the optimal speed of the call based on the response speed of the other party. This allows for a more appropriate call by adjusting the speed of the call based on the response speed of the other party. Some or all of the above-mentioned processing in the call unit may be performed using AI, for example, or may be performed without using AI. For example, the call unit can input data on the other party's response speed into a generation AI and have the generation AI adjust the speed of the call.

[0043] The call unit can customize the call content taking into account the attribute information of the other party during a call. For example, if the other party is a business person, the call unit can provide call content suitable for business. Furthermore, if the other party is a general user, the call unit can also provide general call content. Furthermore, the call unit can customize optimal call content based on the attribute information of the other party. This enables more appropriate calls by customizing the call content taking into account the attribute information of the other party. Some or all of the above-mentioned processing in the call unit may be performed using, for example, AI, or may be performed without using AI. For example, the call unit can input the attribute information data of the other party into a generation AI and have the generation AI customize the call content.

[0044] During a call, the call unit can improve the accuracy of the call by referring to the other party's past response history. The call unit, for example, analyzes the other party's past response history and provides optimal call content. The call unit can also adjust the way the call proceeds based on the other party's past response history. The call unit can also improve the accuracy of the call by referring to the other party's past response history. In this way, the accuracy of the call is improved by referring to the other party's past response history. Some or all of the above-mentioned processing in the call unit may be performed using, for example, AI, or may be performed without using AI. For example, the call unit can input the other party's past response history data into a generation AI and have the generation AI improve the accuracy of the call.

[0045] The call unit can adjust the call content during a call by taking into account the geographical location information of the other party. For example, if the other party is far away, the call unit can simplify the call content. Furthermore, if the other party is nearby, the call unit can also provide detailed call content. Furthermore, the call unit can adjust the optimal call content based on the geographical location information of the other party. As a result, by adjusting the call content by taking into account the geographical location information of the other party, a more appropriate call can be made. Some or all of the above-described processing in the call unit may be performed using, for example, AI, or may be performed without using AI. For example, the call unit can input the geographical location information data of the other party into a generation AI and have the generation AI adjust the call content.

[0046] During a call, the call unit can improve the accuracy of the call by referring to the other party's related literature. The call unit can provide optimal call content, for example, based on information previously provided by the other party. The call unit can also adjust the way the call proceeds by referring to the other party's related literature. The call unit can also improve the accuracy of the call by referring to the other party's related literature. In this way, the accuracy of the call is improved by referring to the other party's related literature. Some or all of the above-mentioned processing in the call unit can be performed, for example, using AI, or can be performed without using AI. For example, the call unit can input the other party's related literature data into a generation AI and have the generation AI improve the accuracy of the call.

[0047] The call unit can adjust the content of the call taking into account the market value of the other party during the call. For example, if the other party has a high market value, the call unit can provide polite and detailed call content. Furthermore, if the other party has an average market value, the call unit can also provide standard call content. Furthermore, the call unit can adjust the optimal call content based on the other party's market value. This allows for a more appropriate call by adjusting the call content taking into account the other party's market value. Some or all of the above-described processing in the call unit may be performed using, for example, AI, or may be performed without using AI. For example, the call unit can input the other party's market value data into a generation AI and have the generation AI adjust the call content.

[0048] The speech recognition unit can adjust the recognition speed based on the speaking rate of the other party during speech recognition. For example, if the speaking rate of the other party is slow, the speech recognition unit slows down the speech recognition speed. Furthermore, if the speaking rate of the other party is fast, the speech recognition unit can also speed up the speech recognition speed. Furthermore, the speech recognition unit can adjust the optimal speech recognition speed based on the speaking rate of the other party. This enables more appropriate speech recognition by adjusting the recognition speed based on the speaking rate of the other party. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input the speaking rate data of the other party to the generation AI and have the generation AI adjust the recognition speed.

[0049] The speech recognition unit can customize the recognition content during speech recognition by taking into account the attribute information of the other party. For example, if the other party is a business person, the speech recognition unit may prioritize recognizing business terms. Furthermore, if the other party is a general user, the speech recognition unit may also prioritize recognizing general terms. Furthermore, the speech recognition unit can customize the optimal recognition content based on the attribute information of the other party. This enables more appropriate speech recognition by customizing the recognition content by taking into account the attribute information of the other party. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit may input the attribute information data of the other party into the generation AI and cause the generation AI to customize the recognition content.

[0050] During speech recognition, the speech recognition unit can improve the accuracy of recognition by referring to the other party's past speech history. For example, the speech recognition unit analyzes the other party's past speech history and provides optimal recognition content. The speech recognition unit can also adjust the speech recognition algorithm based on the other party's past speech history. The speech recognition unit can also improve the accuracy of recognition by referring to the other party's past speech history. In this way, the recognition accuracy is improved by referring to the other party's past speech history. Some or all of the above-mentioned processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input the other party's past speech history data into a generation AI and cause the generation AI to improve the recognition accuracy.

[0051] The voice recognition unit can adjust the recognition content during voice recognition by taking into account the geographical location information of the other party. For example, the voice recognition unit increases the accuracy of voice recognition when the other party is far away. The voice recognition unit can also use standard voice recognition accuracy when the other party is nearby. The voice recognition unit can also adjust the optimal recognition content based on the geographical location information of the other party. This enables more appropriate voice recognition by adjusting the recognition content by taking into account the geographical location information of the other party. Some or all of the above-mentioned processing in the voice recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice recognition unit can input the geographical location information data of the other party to the generation AI and have the generation AI adjust the recognition content.

[0052] During speech recognition, the speech recognition unit can improve the accuracy of recognition by referring to the other party's related literature. The speech recognition unit provides optimal recognition content, for example, based on information previously provided by the other party. The speech recognition unit can also adjust the speech recognition algorithm by referring to the other party's related literature. The speech recognition unit can also improve the accuracy of recognition by referring to the other party's related literature. In this way, the accuracy of recognition is improved by referring to the other party's related literature. Some or all of the above-mentioned processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input the other party's related literature data into a generation AI and cause the generation AI to improve the accuracy of recognition.

[0053] The voice recognition unit can adjust the recognition content during voice recognition, taking into account the market value of the other party. For example, if the other party has a high market value, the voice recognition unit can provide careful and detailed recognition content. Furthermore, if the other party has an average market value, the voice recognition unit can also provide standard recognition content. Furthermore, the voice recognition unit can adjust the optimal recognition content based on the other party's market value. This enables more appropriate voice recognition by adjusting the recognition content taking into account the other party's market value. Some or all of the above-mentioned processing in the voice recognition unit may be performed, for example, using AI, or may be performed without using AI. For example, the voice recognition unit can input the other party's market value data into the generation AI and have the generation AI adjust the recognition content.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The reception unit can automatically search for related past data based on the user's input and suggest it to the user. For example, if the user inputs a restaurant reservation, the reception unit can display a list of restaurants the user has visited in the past and suggest whether to make another reservation. If the user inputs a hospital reservation, the reception unit can display information about hospitals the user has visited in the past and suggest whether to make another reservation. Furthermore, if the user inputs a specific message, the reception unit can display similar messages sent in the past and suggest whether to reuse them. This makes it possible to make more efficient input by utilizing the user's past data.

[0056] 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 prioritize and 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. This improves input efficiency by suggesting the optimal input method based on the user's past input history.

[0057] When acquiring the input contents, the reception unit can perform filtering based on the user's current situation or areas of interest. For example, related reservation contents can be preferentially displayed depending on the user's current situation. Also, related information can be filtered and displayed based on the user's areas of interest. Furthermore, the reception unit can suggest optimal input contents based on the user's current situation and areas of interest. In this way, highly relevant information can be provided by filtering based on the user's current situation and areas of interest.

[0058] When acquiring input content, the reception unit can select an appropriate acquisition means depending on the user's input method. For example, if the user selects voice input, the input content can be acquired using voice recognition technology. If the user selects text input, the input content can also be acquired using text analysis technology. Furthermore, if the user selects image input, the input content can also be acquired using image recognition technology. This improves input accuracy by selecting the optimal acquisition means depending on the user's input method.

[0059] When acquiring input contents, the reception unit can prioritize acquiring highly relevant contents taking into consideration the user's geographical location information. For example, reservation details for nearby restaurants can be displayed preferentially based on the user's current location. Also, related information can be filtered and displayed based on the user's geographical location information. Furthermore, optimal input contents can be suggested based on the user's current location. In this way, by prioritizing acquisition of highly relevant contents taking into consideration the user's geographical location information, more appropriate information can be provided.

[0060] 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 posts and suggest related reservation content. The reception unit can also acquire related information based on the user's social media check-in information. Furthermore, the reception unit can acquire related content by referring to the activities of the user's friends on social media. This makes it easier to acquire related content by analyzing the user's social media activity.

[0061] When acquiring input content, the reception unit can customize the acquisition method by reflecting the user's past feedback. For example, the reception unit can suggest the optimal acquisition method based on feedback provided by the user in the past. The reception unit can also customize the acquisition method of input content by reflecting the user's past feedback. Furthermore, the reception unit can prioritize acquisition of related information based on the user's past feedback. This makes it easier to customize the acquisition method by reflecting the user's past feedback.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The reception unit accepts input of the content that the user wants to convey over the phone. The content that the user inputs within the app to convey over the phone includes, for example, restaurant reservations, hospital appointments, messages, instructions, questions, etc. The reception unit accepts the content that the user wants to convey over the phone by inputting text within the app. It can also accept content that the user dictates using voice input. For example, the user can input voice using a microphone, and the content is converted into text data and accepted. Step 2: The generation unit uses a generation AI to analyze the content received by the reception unit and place a call to the specified telephone number. The generation AI, for example, uses a text generation AI (e.g., LLM) to analyze the content entered by the user and generate appropriate conversation content. The generation unit can also use the generation AI to generate conversation content for performing a task specified by the user. For example, the generation AI generates conversation content regarding making a restaurant reservation and places a call to the specified telephone number. Step 3: The call unit makes a call based on the conversation content generated by the generation unit. The call unit, for example, uses AI to convert the generated conversation content into voice and makes a call to the specified telephone number. The call unit can also use AI to have a conversation with the person at the other end of the phone call. For example, the call unit converts the generated conversation content into voice and transmits it to the person at the other end of the phone call. Step 4: The voice recognition unit analyzes the content of the conversation carried out by the call unit. For example, the voice recognition unit uses AI to analyze the response of the person on the other end of the phone and provides feedback to the user. The voice recognition unit can also use AI to convert the response of the person on the other end of the phone into text data and notify the user. For example, if the person on the other end of the phone responds, "We are fully booked that day," the voice recognition unit converts the content into text data and provides feedback to the user.

[0064] (Example 2) The AI ​​phone app of an embodiment of the present invention is a system in which a user inputs the content they want to communicate over the phone in advance, and a generation AI uses that information to call a specified number, conducts the conversation using voice recognition technology, and executes tasks such as making a reservation. In the AI ​​phone app, a user inputs the content they want to communicate over the phone, and the generation AI analyzes that information, calls the specified number, and conducts the conversation using voice recognition technology to execute tasks such as making a reservation. For example, in an AI phone app, a user inputs the content they want to communicate over the phone within the app, such as a restaurant reservation or a hospital appointment. This information is input into the generation AI. The generation AI then analyzes the input information and calls the specified number. The generation AI then engages in a conversation with the caller based on the content input by the user. For example, in the case of a restaurant reservation, the generation AI would say to the caller, "Please make a reservation on the XX day at XX time." Furthermore, the generation AI uses AI voice recognition technology to analyze the caller's response. For example, if the caller responds, "We're fully booked that day," the generation AI provides feedback on that information to the user. Furthermore, if the person on the other end of the phone responds with "Your reservation has been accepted at ____ time on ____ date," the generating AI will notify the user of that information. This mechanism allows users to perform tasks such as making reservations through the generating AI without having to make the call themselves. This allows the AI ​​phone app to reduce user effort and perform tasks efficiently. For example, the generating AI can automatically make and carry out the call without the user having to make the call themselves, saving time and effort. In addition, by using voice recognition technology, the responding person's response can be accurately analyzed and feedback can be provided to the user. This allows users to perform tasks such as making reservations quickly and accurately.

[0065] An AI phone app according to an embodiment includes a reception unit, a generation unit, a call unit, and a voice recognition unit. The reception unit receives input of phone call content from a user. Phone call content input by a user within the app includes, but is not limited to, restaurant reservations, hospital appointments, messages, instructions, and questions. The reception unit receives phone call content by, for example, text input by the user within the app. The reception unit can also receive dictated content from the user using voice input. For example, the user may use a microphone to input voice, and the voice input may be converted into text data for acceptance. The generation unit uses a generation AI to analyze the content received by the reception unit and place a call to a specified phone number. The generation AI, for example, uses a text generation AI (e.g., LLM) to analyze the content input by the user and generate appropriate conversation content. The generation unit can also use the generation AI to generate conversation content for executing a task specified by the user. For example, the generation AI may generate conversation content regarding a restaurant reservation and place a call to a specified phone number. The call unit makes a call based on the conversation content generated by the generation unit. The call unit, for example, uses AI to convert the generated conversation content into voice and place a call to a specified telephone number. The call unit can also use AI to have a conversation with the other party at the other end of the call. For example, the call unit converts the generated conversation content into voice and transmits it to the other party at the other end of the call. The voice recognition unit analyzes the content of the conversation made by the call unit. For example, the voice recognition unit uses AI to analyze the other party's response and provide feedback to the user. The voice recognition unit can also use AI to convert the other party's response into text data and notify the user. For example, if the other party at the other end of the call responds, "We're fully booked that day," the voice recognition unit converts the content into text data and provides feedback to the user. As a result, the AI ​​phone app according to the embodiment can efficiently input the content a user wants to convey over the phone, call the specified telephone number, conduct a conversation, and analyze the content.

[0066] The reception unit can estimate the user's emotions and prioritize input content based on the estimated user emotions. For example, if the user is nervous, the reception unit can prioritize input of important reservation details. Furthermore, if the user is relaxed, the reception unit can also prompt the user to input detailed information. Furthermore, if the user is in a hurry, the reception unit can also prompt the user to input only the most important 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, 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, or can be performed without using an AI. For example, the reception unit can input the user's voice data into the generation AI and cause the generation AI to estimate the user's emotions.

[0067] 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 prioritize suggesting 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. This improves input efficiency by suggesting the optimal input method based on the user's past input history. 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 history data into a generation AI and have the generation AI suggest the optimal input method.

[0068] The reception unit can perform filtering based on the user's current situation or area of ​​interest when acquiring the input content. The reception unit, for example, preferentially displays related reservation content according to the user's current situation. The reception unit can also filter and display related information based on the user's area of ​​interest. The reception unit can also suggest optimal input content based on the user's current situation and area of ​​interest. This makes it possible to provide highly relevant information by filtering based on the user's current situation and area 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 and area of ​​interest data to the generation AI and have the generation AI perform filtering.

[0069] When acquiring input content, the reception unit can select an appropriate acquisition means depending on the user's input method. For example, if the user selects voice input, the reception unit can acquire the input content using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also acquire the input content using text analysis technology. Furthermore, if the user selects image input, the reception unit can also acquire the input content using image recognition technology. This improves input accuracy by selecting the optimal acquisition means depending on the user's input method. Some or all of the above-described 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 input method data to the generation AI and cause the generation AI to select the optimal acquisition means.

[0070] 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, the reception unit can provide a simple confirmation method when the user is nervous. The reception unit can also provide a detailed confirmation method when the user is relaxed. The reception unit can also provide a quick confirmation method when the user is in a hurry. 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 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 reception unit can be performed using AI, for example, or without 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.

[0071] When acquiring input content, the reception unit can prioritize acquiring highly relevant content by taking into account the user's geographical location information. For example, the reception unit can prioritize displaying reservation details for nearby restaurants based on the user's current location. The reception unit can also filter and display related information based on the user's geographical location information. The reception unit can also suggest optimal 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 by taking into account 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 information data to the generation AI and cause the generation AI to acquire highly relevant content.

[0072] When acquiring input content, the reception unit can analyze the user's social media activity and acquire related content. The reception unit, for example, analyzes the user's social media posts and suggests related reservation content. The reception unit can also acquire related information based on the user's social media check-in information. The reception unit can also acquire related content by referring to the activities of the user's friends on social media. This makes it easier to acquire related content by analyzing the user's social media activity. Some or all of the above-described 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 social media data into the generation AI and cause the generation AI to acquire related content.

[0073] The reception unit can customize the acquisition method by reflecting the user's past feedback when acquiring input content. The reception unit can, for example, suggest an optimal acquisition method based on feedback provided by the user in the past. The reception unit can also customize the acquisition method for the input content by reflecting the user's past feedback. The reception unit can also preferentially acquire related information based on the user's past feedback. This makes it easier to customize the acquisition method by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the acquisition method.

[0074] The generation unit can estimate the user's emotions and adjust the expression method of the generated conversation content based on the estimated user emotions. For example, if the user is nervous, the generation unit can use a polite and calm expression method. Furthermore, if the user is relaxed, the generation unit can use a casual expression method. Furthermore, if the user is in a hurry, the generation unit can use a concise and quick expression method. This allows for more appropriate conversation by adjusting the expression method of the conversation 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 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 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 expression method of the conversation content.

[0075] The generation unit can adjust the level of detail of the conversation content based on the importance of the task during generation. For example, the generation unit generates detailed conversation content for an important task. The generation unit can also generate standard conversation content for a general task. The generation unit can also generate concise conversation content for a simple task. This allows for more appropriate conversation by adjusting the level of detail of the conversation content based on the importance of the task. Some or all of the above-described 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 task importance data into the generation AI and cause the generation AI to adjust the level of detail of the conversation content.

[0076] The generation unit can apply different generation algorithms depending on the task category during generation. 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, the generation unit can select and apply an appropriate generation algorithm for other tasks. In this way, applying different generation algorithms depending on the task category enables more appropriate conversation. 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 task category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0077] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit, for example, analyzes the user's past generation results and generates optimal conversation content. The generation unit can also adjust the generation algorithm based on the user's past generation results. The generation unit can also improve the accuracy of generation by referring to the user's past generation results. In this way, the accuracy of generation is improved by referring to the user's past generation results. 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 past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0078] The generation unit can estimate the user's emotions and adjust the length of the conversation content to be generated based on the estimated user's emotions. For example, if the user is nervous, the generation unit can generate short, to-the-point conversation content. Furthermore, if the user is relaxed, the generation unit can generate detailed conversation content. Furthermore, if the user is in a hurry, the generation unit can generate concise, quick conversation content. This allows for more appropriate conversation by adjusting the length of the conversation 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 generation unit may be performed using, for example, an AI, or may 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 length of the conversation content.

[0079] The generation unit can determine the generation priority based on the submission time of the task at the time of generation. For example, the generation unit gives top priority to generation of an urgent task. The generation unit can also give priority to generation of a task with an approaching deadline. The generation unit can also postpone generation of a task with a distant deadline. In this way, determining the generation priority based on the submission time of the task enables more appropriate conversation. 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 task submission time data into the generation AI and have the generation AI determine the generation priority.

[0080] The generation unit can adjust the order of generation based on the relevance of the tasks during generation. For example, the generation unit prioritizes the generation of highly relevant tasks. The generation unit can also postpone the generation of less relevant tasks. The generation unit can also determine the optimal generation order based on the relevance of the tasks. This allows for more appropriate conversation by adjusting the generation order based on the relevance of the tasks. Some or all of the above-described processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input task relevance data into the generation AI and cause the generation AI to adjust the generation order.

[0081] The generation unit can adjust the use of technical terminology during generation according to the user's level of expertise. For example, if the user has specialized knowledge, the generation unit generates conversation content that uses a lot of technical terminology. Furthermore, if the user has general knowledge, the generation unit can also generate conversation content that uses standard terminology. Furthermore, if the user is a beginner, the generation unit can also generate conversation content that uses simple terminology. This allows for more appropriate conversation by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0082] The call unit can estimate the user's emotions and adjust the timing of the start of the call based on the estimated user's emotions. For example, if the user is nervous, the call unit delays the start of the call until the user is relaxed. Furthermore, if the user is relaxed, the call unit can also start the call immediately. Furthermore, if the user is in a hurry, the call unit can also start the call quickly. This allows for more appropriate calls by adjusting the start 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, 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 call unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the call unit can input the user's voice data into the generation AI and have the generation AI adjust the start timing of the call.

[0083] During a call, the call unit can adjust the speed of the call based on the response speed of the other party. For example, if the other party's response is slow, the call unit slows down the speed of the call. Furthermore, if the other party's response is fast, the call unit can also speed up the speed of the call. Furthermore, the call unit can adjust the optimal speed of the call based on the response speed of the other party. This allows for a more appropriate call by adjusting the speed of the call based on the response speed of the other party. Some or all of the above-mentioned processing in the call unit may be performed using AI, for example, or may be performed without using AI. For example, the call unit can input data on the other party's response speed into a generation AI and have the generation AI adjust the speed of the call.

[0084] The call unit can customize the call content taking into account the attribute information of the other party during a call. For example, if the other party is a business person, the call unit can provide call content suitable for business. Furthermore, if the other party is a general user, the call unit can also provide general call content. Furthermore, the call unit can customize optimal call content based on the attribute information of the other party. This enables more appropriate calls by customizing the call content taking into account the attribute information of the other party. Some or all of the above-mentioned processing in the call unit may be performed using, for example, AI, or may be performed without using AI. For example, the call unit can input the attribute information data of the other party into a generation AI and have the generation AI customize the call content.

[0085] During a call, the call unit can improve the accuracy of the call by referring to the other party's past response history. The call unit, for example, analyzes the other party's past response history and provides optimal call content. The call unit can also adjust the way the call proceeds based on the other party's past response history. The call unit can also improve the accuracy of the call by referring to the other party's past response history. In this way, the accuracy of the call is improved by referring to the other party's past response history. Some or all of the above-mentioned processing in the call unit may be performed using, for example, AI, or may be performed without using AI. For example, the call unit can input the other party's past response history data into a generation AI and have the generation AI improve the accuracy of the call.

[0086] The call unit can estimate the user's emotions and adjust the timing of ending the call based on the estimated user's emotions. For example, if the user is nervous, the call unit continues the call until the user relaxes. Furthermore, if the user is relaxed, the call unit can also end the call at an appropriate time. Furthermore, if the user is in a hurry, the call unit can also end the call quickly. This allows for a more appropriate call by adjusting the timing of ending 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 call unit may be performed using, for example, AI, or may be performed without using AI. For example, the call unit can input the user's voice data into the generation AI and have the generation AI adjust the timing of ending the call.

[0087] The call unit can adjust the call content during a call by taking into account the geographical location information of the other party. For example, if the other party is far away, the call unit can simplify the call content. Furthermore, if the other party is nearby, the call unit can also provide detailed call content. Furthermore, the call unit can adjust the optimal call content based on the geographical location information of the other party. As a result, by adjusting the call content by taking into account the geographical location information of the other party, a more appropriate call can be made. Some or all of the above-described processing in the call unit may be performed using, for example, AI, or may be performed without using AI. For example, the call unit can input the geographical location information data of the other party into a generation AI and have the generation AI adjust the call content.

[0088] During a call, the call unit can improve the accuracy of the call by referring to the other party's related literature. The call unit can provide optimal call content, for example, based on information previously provided by the other party. The call unit can also adjust the way the call proceeds by referring to the other party's related literature. The call unit can also improve the accuracy of the call by referring to the other party's related literature. In this way, the accuracy of the call is improved by referring to the other party's related literature. Some or all of the above-mentioned processing in the call unit can be performed, for example, using AI, or can be performed without using AI. For example, the call unit can input the other party's related literature data into a generation AI and have the generation AI improve the accuracy of the call.

[0089] The call unit can adjust the content of the call taking into account the market value of the other party during the call. For example, if the other party has a high market value, the call unit can provide polite and detailed call content. Furthermore, if the other party has an average market value, the call unit can also provide standard call content. Furthermore, the call unit can adjust the optimal call content based on the other party's market value. This allows for a more appropriate call by adjusting the call content taking into account the other party's market value. Some or all of the above-described processing in the call unit may be performed using, for example, AI, or may be performed without using AI. For example, the call unit can input the other party's market value data into a generation AI and have the generation AI adjust the call content.

[0090] The speech recognition unit can estimate the user's emotions and adjust the accuracy of speech recognition based on the estimated user's emotions. For example, the speech recognition unit can increase the accuracy of speech recognition when the user is nervous. The speech recognition unit can also use standard speech recognition accuracy when the user is relaxed. The speech recognition unit can also perform quick speech recognition when the user is in a hurry. This enables more appropriate speech recognition by adjusting the accuracy of speech recognition 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 speech recognition unit can be performed using AI, for example, or without AI. For example, the speech recognition unit can input the user's voice data to the generation AI and have the generation AI adjust the accuracy of speech recognition.

[0091] The speech recognition unit can adjust the recognition speed based on the speaking rate of the other party during speech recognition. For example, if the speaking rate of the other party is slow, the speech recognition unit slows down the speech recognition speed. Furthermore, if the speaking rate of the other party is fast, the speech recognition unit can also speed up the speech recognition speed. Furthermore, the speech recognition unit can adjust the optimal speech recognition speed based on the speaking rate of the other party. This enables more appropriate speech recognition by adjusting the recognition speed based on the speaking rate of the other party. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input the speaking rate data of the other party to the generation AI and have the generation AI adjust the recognition speed.

[0092] The speech recognition unit can customize the recognition content during speech recognition by taking into account the attribute information of the other party. For example, if the other party is a business person, the speech recognition unit may prioritize recognizing business terms. Furthermore, if the other party is a general user, the speech recognition unit may also prioritize recognizing general terms. Furthermore, the speech recognition unit can customize the optimal recognition content based on the attribute information of the other party. This enables more appropriate speech recognition by customizing the recognition content by taking into account the attribute information of the other party. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit may input the attribute information data of the other party into the generation AI and cause the generation AI to customize the recognition content.

[0093] During speech recognition, the speech recognition unit can improve the accuracy of recognition by referring to the other party's past speech history. For example, the speech recognition unit analyzes the other party's past speech history and provides optimal recognition content. The speech recognition unit can also adjust the speech recognition algorithm based on the other party's past speech history. The speech recognition unit can also improve the accuracy of recognition by referring to the other party's past speech history. In this way, the recognition accuracy is improved by referring to the other party's past speech history. Some or all of the above-mentioned processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input the other party's past speech history data into a generation AI and cause the generation AI to improve the recognition accuracy.

[0094] The speech recognition unit can estimate the user's emotions and adjust the order in which speech recognition results are displayed based on the estimated user's emotions. For example, if the user is nervous, the speech recognition unit can prioritize displaying important results. The speech recognition unit can also display detailed results if the user is relaxed. The speech recognition unit can also quickly display results if the user is in a hurry. This allows for more appropriate results to be displayed by adjusting the order in which speech recognition results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 these examples. Some or all of the above-described processing in the speech recognition unit can be performed using, for example, AI, or without AI. For example, the speech recognition unit can input the user's voice data into the generation AI and have the generation AI adjust the order in which the results are displayed.

[0095] The voice recognition unit can adjust the recognition content during voice recognition by taking into account the geographical location information of the other party. For example, the voice recognition unit increases the accuracy of voice recognition when the other party is far away. The voice recognition unit can also use standard voice recognition accuracy when the other party is nearby. The voice recognition unit can also adjust the optimal recognition content based on the geographical location information of the other party. This enables more appropriate voice recognition by adjusting the recognition content by taking into account the geographical location information of the other party. Some or all of the above-mentioned processing in the voice recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice recognition unit can input the geographical location information data of the other party to the generation AI and have the generation AI adjust the recognition content.

[0096] During speech recognition, the speech recognition unit can improve the accuracy of recognition by referring to the other party's related literature. The speech recognition unit provides optimal recognition content, for example, based on information previously provided by the other party. The speech recognition unit can also adjust the speech recognition algorithm by referring to the other party's related literature. The speech recognition unit can also improve the accuracy of recognition by referring to the other party's related literature. In this way, the accuracy of recognition is improved by referring to the other party's related literature. Some or all of the above-mentioned processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input the other party's related literature data into a generation AI and cause the generation AI to improve the accuracy of recognition.

[0097] The voice recognition unit can adjust the recognition content during voice recognition, taking into account the market value of the other party. For example, if the other party has a high market value, the voice recognition unit can provide careful and detailed recognition content. Furthermore, if the other party has an average market value, the voice recognition unit can also provide standard recognition content. Furthermore, the voice recognition unit can adjust the optimal recognition content based on the other party's market value. This enables more appropriate voice recognition by adjusting the recognition content taking into account the other party's market value. Some or all of the above-mentioned processing in the voice recognition unit may be performed, for example, using AI, or may be performed without using AI. For example, the voice recognition unit can input the other party's market value data into the generation AI and have the generation AI adjust the recognition content. === Hard Collateral 1-1 === Each of the multiple elements including the reception unit, generation unit, call unit, and voice recognition unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives the content to be communicated by phone through text input or voice input by the user within the app. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content received by the reception unit using a generation AI and makes a call to a specified telephone number. The call unit is realized, for example, by the output device 40 of the smart device 14 and converts the generated conversation content into voice and makes a call to a specified telephone number. The voice recognition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of the conversation conducted by the call unit and provides feedback to the user. === Hard Collateral 1-2 === Each of the multiple elements including the reception unit, generation unit, call unit, and voice recognition unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives the content to be communicated by the user via voice input. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content received by the reception unit using a generation AI and makes a call to a specified telephone number. The call unit is realized, for example, by the speaker 240 of the smart glasses 214 and converts the generated conversation content into voice and makes a call to a specified telephone number. The voice recognition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of the conversation conducted by the call unit and provides feedback to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, call unit, and voice recognition 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 microphone 238 of the headset-type terminal 314 and receives the content to be communicated by the user through voice input. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content received by the reception unit using a generation AI and makes a call to a specified telephone number. The call unit is realized, for example, by the speaker 240 of the headset-type terminal 314 and converts the generated conversation content into voice and makes a call to a specified telephone number. The voice recognition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of the conversation conducted by the call unit and provides feedback to the user. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, generation unit, call unit, and voice recognition unit described above 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 microphone 238 of the robot 414 and receives the content that the user wants to communicate over the phone by voice input. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content received by the reception unit using a generation AI and makes a call to a specified telephone number. The call unit is realized, for example, by the speaker 240 of the robot 414 and converts the generated conversation content into voice and makes a call to a specified telephone number. The voice recognition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of the conversation conducted by the call unit and provides feedback to the user.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The reception unit can automatically search for related past data based on the user's input and suggest it to the user. For example, if the user inputs a restaurant reservation, the reception unit can display a list of restaurants the user has visited in the past and suggest whether to make another reservation. If the user inputs a hospital reservation, the reception unit can display information about hospitals the user has visited in the past and suggest whether to make another reservation. Furthermore, if the user inputs a specific message, the reception unit can display similar messages sent in the past and suggest whether to reuse them. This makes it possible to make more efficient input by utilizing the user's past data.

[0100] 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, a simple and easy-to-understand confirmation method can be provided. If the user is relaxed, a detailed confirmation method can be provided. Furthermore, if the user is in a hurry, a quick confirmation method can be provided. This allows for more appropriate confirmation by adjusting the confirmation method according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI.

[0101] 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 prioritize and 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. This improves input efficiency by suggesting the optimal input method based on the user's past input history.

[0102] When acquiring the input contents, the reception unit can perform filtering based on the user's current situation or areas of interest. For example, related reservation contents can be preferentially displayed depending on the user's current situation. Also, related information can be filtered and displayed based on the user's areas of interest. Furthermore, the reception unit can suggest optimal input contents based on the user's current situation and areas of interest. In this way, highly relevant information can be provided by filtering based on the user's current situation and areas of interest.

[0103] When acquiring input content, the reception unit can select an appropriate acquisition means depending on the user's input method. For example, if the user selects voice input, the input content can be acquired using voice recognition technology. If the user selects text input, the input content can also be acquired using text analysis technology. Furthermore, if the user selects image input, the input content can also be acquired using image recognition technology. This improves input accuracy by selecting the optimal acquisition means depending on the user's input method.

[0104] 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, a simple confirmation method can be provided. If the user is relaxed, a detailed confirmation method can be provided. Furthermore, if the user is in a hurry, a quick confirmation method can be provided. This allows for more appropriate confirmation by adjusting the confirmation method for the input content according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI.

[0105] When acquiring input contents, the reception unit can prioritize acquiring highly relevant contents taking into consideration the user's geographical location information. For example, reservation details for nearby restaurants can be displayed preferentially based on the user's current location. Also, related information can be filtered and displayed based on the user's geographical location information. Furthermore, optimal input contents can be suggested based on the user's current location. In this way, by prioritizing acquisition of highly relevant contents taking into consideration the user's geographical location information, more appropriate information can be provided.

[0106] 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 posts and suggest related reservation content. The reception unit can also acquire related information based on the user's social media check-in information. Furthermore, the reception unit can acquire related content by referring to the activities of the user's friends on social media. This makes it easier to acquire related content by analyzing the user's social media activity.

[0107] When acquiring input content, the reception unit can customize the acquisition method by reflecting the user's past feedback. For example, the reception unit can suggest the optimal acquisition method based on feedback provided by the user in the past. The reception unit can also customize the acquisition method of input content by reflecting the user's past feedback. Furthermore, the reception unit can prioritize acquisition of related information based on the user's past feedback. This makes it easier to customize the acquisition method by reflecting the user's past feedback.

[0108] The generation unit can estimate the user's emotions and adjust the way the conversation content is expressed based on the estimated user emotions. For example, if the user is nervous, a polite and calm way of expression can be used. If the user is relaxed, a casual way of expression can be used. Furthermore, if the user is in a hurry, a concise and quick way of expression can be used. This allows for more appropriate conversation by adjusting the way the conversation content is expressed according to the user's emotions. Emotion estimation is achieved using an emotion engine or a generation AI.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The reception unit accepts input of the content that the user wants to convey over the phone. The content that the user inputs within the app to convey over the phone includes, for example, restaurant reservations, hospital appointments, messages, instructions, questions, etc. The reception unit accepts the content that the user wants to convey over the phone by inputting text within the app. It can also accept content that the user dictates using voice input. For example, the user can input voice using a microphone, and the content is converted into text data and accepted. Step 2: The generation unit uses a generation AI to analyze the content received by the reception unit and place a call to the specified telephone number. The generation AI, for example, uses a text generation AI (e.g., LLM) to analyze the content entered by the user and generate appropriate conversation content. The generation unit can also use the generation AI to generate conversation content for performing a task specified by the user. For example, the generation AI generates conversation content regarding making a restaurant reservation and places a call to the specified telephone number. Step 3: The call unit makes a call based on the conversation content generated by the generation unit. The call unit, for example, uses AI to convert the generated conversation content into voice and makes a call to the specified telephone number. The call unit can also use AI to have a conversation with the person at the other end of the phone call. For example, the call unit converts the generated conversation content into voice and transmits it to the person at the other end of the phone call. Step 4: The voice recognition unit analyzes the content of the conversation carried out by the call unit. For example, the voice recognition unit uses AI to analyze the response of the person on the other end of the phone and provides feedback to the user. The voice recognition unit can also use AI to convert the response of the person on the other end of the phone into text data and notify the user. For example, if the person on the other end of the phone responds, "We are fully booked that day," the voice recognition unit converts the content into text data and provides feedback to the user.

[0111] 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.

[0112] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] 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.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] 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.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0162] 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.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0181] 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.

[0182] [Explanation of symbols]

[0183] 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 of the content that the user wants to convey by telephone; a generating unit that analyzes the content received by the receiving unit and makes a call to a specified telephone number; a calling unit that makes a call based on the conversation content generated by the generation unit; a voice recognition unit that analyzes the content of the conversation made by the call unit. A system characterized by:

2. The reception unit Estimate the user's emotions and prioritize 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 appropriate 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 situation or interests.

2. The system of claim 1.

5. The reception unit When acquiring input content, select the appropriate acquisition method depending on the user's input method.

2. The system of claim 1.

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

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

2. The system of claim 1.

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

Images