System

Abstract

To provide a system that analyzes voice data, automatically generates appropriate responses, and converts them into voice.SOLUTION: A system according to an embodiment includes an analysis unit, a generation unit, and a voice conversion unit. The analysis unit analyzes voice data. The generation unit generates a response based on the data analyzed by the analysis unit. The voice conversion unit converts the response generated by the generation unit into voice.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques do not automate the process of analyzing voice data and generating and vocalizing appropriate responses, leaving room for improvement.

[0005] The system according to the embodiment aims to analyze voice data and automatically generate and voice appropriate responses. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a generation unit, and a voice conversion unit. The analysis unit analyzes voice data. The generation unit generates a response based on the data analyzed by the analysis unit. The voice conversion unit voices the response generated by the generation unit. Effect of the Invention

[0007] An embodiment of the system can analyze the voice data and automatically generate and voice appropriate responses. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The customer response automation system according to the embodiment of the present invention is a system that uses large-scale voice data to create a voice tempo and intonation model and combines it with a generation AI to fully automatically respond to inquiries from customers and the like. This system first uses large-scale voice data to model the voice tempo and intonation. This model learns the rhythm and intonation of natural conversation through analysis of voice data. Next, a generation AI is used to generate a response to the customer's inquiry. This generation AI is fine-tuned in advance and has knowledge of specific business and services. Furthermore, the response content generated by the generation AI is converted into voice using the voice tempo and intonation model. As a result, the text-based response generated by the generation AI is provided to the customer as a natural voice. For example, a quick and accurate response to customer inquiries is possible. In addition, by performing fine tuning, the response content of the generation AI can be made closer to a more accurate one. In this way, the present invention combines large-scale voice data and a generation AI to realize the automation of customer responses, thereby improving business efficiency and customer satisfaction. As a result, the customer response automation system is able to quickly and accurately respond to customer inquiries.

[0029] The customer support automation system according to the embodiment includes an analysis unit, a generation unit, and a voice conversion unit. The analysis unit analyzes voice data. The analysis unit converts the voice data into text data, for example, using a voice recognition technology. The analysis unit can also analyze the contents of the voice data using a natural language processing technology. For example, the analysis unit analyzes the phonemes and phonology of the voice data and models the tempo and intonation of the voice. The generation unit generates a response based on the data analyzed by the analysis unit using a generation AI. The generation unit generates a response, for example, using a text generation AI (for example, LLM). The generation unit can also generate a response with knowledge about a specific business or service, using the generation AI. For example, the generation unit generates an appropriate response to an inquiry about customer support. The voice conversion unit voices the response generated by the generation unit. The voice conversion unit converts the text data into voice data, for example, using a voice synthesis technology. The voice conversion unit can also provide the generated voice data to the customer. For example, the voice conversion unit provides the generated voice data to the customer via telephone or the Internet. This enables the customer response automation system according to the embodiment to respond quickly and accurately to inquiries from customers. Some or all of the above-mentioned processes in the analysis unit, generation unit, and voice conversion unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input voice data to the AI ​​and have the AI ​​analyze the voice data. The generation unit can input data analyzed by the analysis unit to the AI ​​and have the AI ​​generate a response. The voice conversion unit can input the response generated by the generation unit to the AI ​​and have the AI ​​convert it into voice.

[0030] The analysis unit analyzes the voice data. The analysis unit converts the voice data into text data, for example, using a voice recognition technology. Specifically, the voice recognition technology converts a voice signal into digital data, and analyzes the digital data to identify phonemes and phonology. This allows the information contained in the voice data to be extracted in text format. The analysis unit can also analyze the contents of the voice data using a natural language processing technology. The natural language processing technology analyzes the grammatical structure and meaning of the text data, and performs understanding based on the context. For example, the analysis unit analyzes the phonemes and phonology of the voice data, and models the tempo and intonation of the voice. This allows the speaker's emotions and intentions to be grasped more accurately. Furthermore, the analysis unit can also use a filtering technology to remove background noise and echoes from the voice data. This allows the quality of the voice data to be improved, and the accuracy of the analysis to be increased. The analysis unit combines these technologies to analyze the voice data with high accuracy, and outputs it as text data. The analysis unit can share the results of the analysis of the voice data with other systems and departments, and can improve the efficiency of customer support by linking with, for example, a customer support system or database.

[0031] The generation unit uses the generation AI to generate a response based on the data analyzed by the analysis unit. The generation unit generates a response using, for example, a text generation AI (e.g., LLM). Specifically, the generation AI receives text data provided by the analysis unit as input and generates an appropriate response based on the content. The generation AI has learned a large amount of text data and has the ability to understand grammar and context, so it can generate natural and fluent responses. The generation unit can also use the generation AI to generate responses with knowledge of specific operations and services. For example, the generation unit generates an appropriate response to an inquiry about customer support. The generation AI has learned knowledge about specific operations and services in advance and can also respond to specialized questions. Furthermore, the generation unit can evaluate the quality of the generated response and make corrections as necessary. For example, if the response generated by the generation AI is inappropriate, the generation unit reevaluates the content of the response and generates a more appropriate response. The generation unit can also store the generated response in a database and use it as a reference for future inquiries. This allows the generation unit to generate quick and accurate responses and improve the efficiency and quality of customer support.

[0032] The voice conversion unit converts the response generated by the generation unit into voice. The voice conversion unit converts text data into voice data, for example, using a voice synthesis technique. Specifically, the voice synthesis technique receives text data as an input and generates a natural voice based on the content of the text data. The voice synthesis technique generates a natural and easy-to-listen voice by analyzing a combination of phonemes and phonological elements and adding an appropriate intonation and tempo. The voice conversion unit can also provide the generated voice data to a customer. For example, the voice conversion unit provides the generated voice data to a customer via telephone or the Internet. In the case of a telephone call, the voice conversion unit transmits the generated voice data to a telephone line in real time and responds directly to the customer. In the case of the Internet, the voice conversion unit distributes the generated voice data in a streaming format so that the customer can listen to the voice through a web browser or a dedicated application. Furthermore, the voice conversion unit can evaluate the quality of the generated voice data and make corrections as necessary. For example, if the intonation or tempo of the voice is unnatural, the voice conversion unit readjusts the voice synthesis technique to generate a more natural voice. The voice conversion unit can also store the generated voice data in a database and use it as a reference for future inquiries. This enables the voice conversion unit to provide fast and accurate voice responses, improving the efficiency and quality of customer service.

[0033] The generation unit can include an adjustment unit that performs fine tuning. The adjustment unit performs fine tuning of the generation AI. The adjustment unit improves the accuracy of the response by, for example, adjusting parameters of the generation AI. The adjustment unit can also select training data. For example, the adjustment unit selects data related to a specific business or service and uses it for training the generation AI. As a result, the generation unit can make the response content of the generation AI more accurate by performing fine tuning. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the adjustment unit can cause the AI ​​to adjust parameters of the generation AI.

[0034] The voice conversion unit may include a providing unit that provides the generated voice to the customer. The providing unit provides the generated voice to the customer. The providing unit provides the voice to the customer, for example, via telephone. The providing unit may also provide the voice to the customer via the Internet. For example, the providing unit provides the voice through a website or a mobile app. As a result, by providing the generated voice to the customer, it becomes possible to respond in a natural voice. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit may input the generated voice data to AI and cause the AI ​​to provide the voice.

[0035] The analysis unit can analyze multiple pieces of voice data and model the tempo and intonation of the voice. The analysis unit analyzes multiple pieces of voice data, such as telephone voices and recorded voices. The analysis unit analyzes voice waveforms and extracts rhythm patterns. For example, the analysis unit analyzes phonemes and phonology of the voice data and models the tempo and intonation of the voice. This makes it possible to learn the rhythm and intonation of natural conversation by analyzing large-scale voice data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input voice data to AI and have the AI ​​execute analysis of the voice data.

[0036] The generation unit can use a generation AI having knowledge about a specific business or service. The generation unit uses a generation AI having knowledge about a specific business or service, such as customer support or medical consultation. The generation unit uses the generation AI to generate an appropriate response to an inquiry about a specific business or service. For example, the generation unit generates an appropriate response to an inquiry about customer support. In this way, by using a generation AI having knowledge about a specific business or service, a more appropriate response can be generated. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the generation unit can cause an AI to execute a generation AI having knowledge about a specific business or service.

[0037] The analysis unit can improve the analysis accuracy by taking into account a specific accent or dialect when analyzing the voice data. For example, when analyzing voice data having an accent of a specific region, the analysis unit applies an accent model of the region. In addition, when analyzing voice data having a specific dialect, the analysis unit can also perform the analysis by taking into account the characteristics of the dialect. Furthermore, when analyzing voice data containing a mixture of multiple accents and dialects, the analysis unit can also perform the analysis by integrating the characteristics of each. As a result, the analysis accuracy is improved by taking into account the specific accent or dialect. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input voice data having a specific accent or dialect to AI and have AI perform the analysis.

[0038] The analysis unit can perform a filter process to remove background noise when analyzing the voice data. The analysis unit applies filtering to remove background noise, for example, before analyzing the voice data. The analysis unit can also perform filtering to remove noise in a specific frequency band. Furthermore, the analysis unit can also perform filtering to remove dynamically changing background noise in real time. This improves the analysis accuracy by removing background noise. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the voice data to AI and cause the AI ​​to remove the background noise.

[0039] The analysis unit can adjust the analysis method based on the geographical location information of the user when analyzing the voice data. For example, when the user is in a specific area, the analysis unit performs the analysis taking into account the accent or dialect of that area. In addition, when the user is moving, the analysis unit can also perform the analysis taking into account the accent or dialect of the destination area. Furthermore, when the user is in different areas, the analysis unit can also perform the analysis by integrating the characteristics of each area. This makes it possible to adjust the analysis method by taking into account the geographical location information of the user. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the geographical location information of the user to AI and cause the AI ​​to adjust the analysis method.

[0040] When analyzing the voice data, the analysis unit can analyze the social media activity of the user and preferentially analyze related voice data. For example, the analysis unit preferentially analyzes voice data related to a specific topic from the social media activity of the user. The analysis unit can also preferentially analyze voice data related to a specific event from the social media activity of the user. Furthermore, the analysis unit can also preferentially analyze voice data related to a specific person from the social media activity of the user. In this way, by analyzing the social media activity of the user, related voice data can be preferentially analyzed. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the social media activity data of the user to AI and cause the AI ​​to perform an analysis to determine the priority order of related voice data.

[0041] The generation unit can adjust the level of detail of the response based on the importance of the inquiry content when generating the response. For example, the generation unit generates a detailed response to an inquiry with a high level of importance. The generation unit can also generate a concise response to an inquiry with a low level of importance. Furthermore, the generation unit can dynamically adjust the level of detail of the response according to the importance. This allows a more appropriate response to be provided by adjusting the level of detail of the response based on the importance of the inquiry content. A part or all of the above-mentioned 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 importance of the inquiry content to the AI ​​and cause the AI ​​to execute a process of adjusting the level of detail of the response.

[0042] The generation unit can apply different generation algorithms depending on the category of the inquiry when generating a response. For example, the generation unit applies a specialized generation algorithm to a technical inquiry. The generation unit can also apply a general-purpose generation algorithm to a general inquiry. Furthermore, the generation unit can apply a quick generation algorithm to an urgent inquiry. This allows for a more appropriate response by applying different generation algorithms depending on the category of the inquiry. Some or all of the above-mentioned 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 category of the inquiry to the AI ​​and cause the AI ​​to execute a process of determining the generation algorithm to be applied.

[0043] The generation unit can determine the priority of the replies based on the time of submission of the inquiries when generating the replies. For example, the generation unit generates replies with priority to inquiries submitted recently. The generation unit can also generate replies with priority to inquiries that have been unresolved for a long time. Furthermore, the generation unit can dynamically adjust the priority of the replies according to the time of submission. This allows for a faster response by determining the priority of the replies based on the time of submission of the inquiries. A part or all of the above-mentioned 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 time of submission of the inquiry to the AI ​​and cause the AI ​​to execute a process of determining the priority of the replies.

[0044] The generation unit can adjust the order of replies based on the relevance of the inquiries when generating replies. For example, the generation unit generates replies preferentially for inquiries with high relevance. The generation unit can also generate replies later for inquiries with low relevance. Furthermore, the generation unit can dynamically adjust the order of replies according to the relevance. This allows for a more appropriate reply by adjusting the order of replies based on the relevance of the inquiries. A part or all of the above-mentioned 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 relevance of the inquiries to the AI ​​and cause the AI ​​to execute a process of adjusting the order of replies.

[0045] The voice conversion unit can perform voice filtering to improve the naturalness of the generated voice during voice conversion. The voice conversion unit can, for example, apply a noise reduction filter to the generated voice. The voice conversion unit can also apply an echo canceling filter to the generated voice. Furthermore, the voice conversion unit can also apply a sound quality improvement filter to the generated voice. This improves the naturalness of the generated voice, making it possible to convert the voice into a more natural voice. Some or all of the above-mentioned processing in the voice conversion unit can be performed using, for example, AI, or can be performed without using AI. For example, the voice conversion unit can input the generated voice data to AI and have the AI ​​perform voice filtering.

[0046] The voice conversion unit can improve the accuracy of voice conversion by taking into account a specific accent or dialect when generating voice. For example, when generating voice with an accent of a specific region, the voice conversion unit applies an accent model of the region. In addition, when generating voice with a specific dialect, the voice conversion unit can also perform voice conversion by taking into account the characteristics of the dialect. Furthermore, when generating voice in which multiple accents and dialects are mixed, the voice conversion unit can also perform voice conversion by integrating the characteristics of each. As a result, the accuracy of voice conversion is improved by taking into account a specific accent or dialect. A part or all of the above-mentioned processing in the voice conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice conversion unit can input voice data with a specific accent or dialect to AI and have AI perform voice conversion.

[0047] The voice conversion unit can adjust the voice conversion method in consideration of the geographical location information of the user when vocalizing. For example, when the user is in a specific area, the voice conversion unit performs voice conversion in consideration of the accent or dialect of that area. In addition, when the user is moving, the voice conversion unit can also perform voice conversion in consideration of the accent or dialect of the destination area. Furthermore, when the user is in different areas, the voice conversion unit can also perform voice conversion by integrating the characteristics of each area. This makes it possible to adjust the voice conversion method by considering the geographical location information of the user. Part or all of the above-mentioned processing in the voice conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice conversion unit can input the geographical location information of the user to AI and cause the AI ​​to adjust the voice conversion method.

[0048] The voice conversion unit can analyze the user's social media activity and preferentially voice the related voice data at the time of voice conversion. For example, the voice conversion unit preferentially generates voice related to a specific topic from the user's social media activity. The voice conversion unit can also preferentially generate voice related to a specific event from the user's social media activity. Furthermore, the voice conversion unit can also preferentially generate voice related to a specific person from the user's social media activity. In this way, by analyzing the user's social media activity, related voice data can be preferentially voiced. A part or all of the above-mentioned processing in the voice conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice conversion unit inputs the user's social media activity data into AI and causes AI to perform voice conversion that determines the priority order of related voice data.

[0049] The adjustment unit can optimize the generation algorithm by referring to past inquiry data during fine tuning. The adjustment unit, for example, analyzes past inquiry data and optimizes parameters of the generation algorithm. The adjustment unit can also extract a specific pattern from the past inquiry data and reflect it in the generation algorithm. Furthermore, the adjustment unit can improve the accuracy of the generation algorithm based on the past inquiry data. This makes it possible to optimize the generation algorithm by referring to the past inquiry data. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI or may be performed without using AI. For example, the adjustment unit can input past inquiry data to AI and cause AI to execute optimization of the generation algorithm.

[0050] The adjustment unit can weight the learning data based on the time of submission of the inquiry during fine tuning. The adjustment unit, for example, weights recent inquiry data and reflects the weight in the generation algorithm. The adjustment unit can also weight inquiry data that has been unresolved for a long time and reflect the weight in the generation algorithm. Furthermore, the adjustment unit can dynamically adjust the weighting of the learning data according to the submission time. This allows more appropriate adjustment by weighting the learning data based on the time of submission of the inquiry. A part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the adjustment unit can input the time of submission of the inquiry to the AI ​​and cause the AI ​​to perform weighting of the learning data.

[0051] The providing unit can select the optimal voice providing method by referring to the user's past inquiry history when providing the voice. The providing unit selects the optimal voice providing method from, for example, the user's past inquiry history. The providing unit can also analyze the user's past inquiry history and select the voice providing method based on a specific pattern. Furthermore, the providing unit can dynamically adjust the voice providing method based on the user's past inquiry history. Thereby, the optimal voice providing method can be selected by referring to the user's past inquiry history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past inquiry history into AI and cause AI to execute a process of selecting the optimal providing method.

[0052] The providing unit can select the optimal voice providing method in consideration of the device information of the user when providing voice. For example, when the user is using a smartphone, the providing unit selects a voice providing method that matches the screen size. In addition, when the user is using a tablet, the providing unit can also select a voice providing method optimized for a large screen. Furthermore, when the user is using a smartwatch, the providing unit can also select a voice providing method that is simple and has high visibility. In this way, the optimal voice providing method can be selected by considering the device information of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the device information of the user to the AI ​​and cause the AI ​​to execute a process of selecting the optimal providing method.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] When analyzing the voice data, the analysis unit can improve the analysis accuracy by referring to the user's past inquiry history. For example, the analysis unit extracts a specific pattern from the past inquiry history and reflects it in the analysis of the voice data. The analysis unit can also learn the user's speech tendencies based on the past inquiry history and improve the analysis accuracy. Furthermore, the analysis unit can perform analysis with knowledge of specific tasks or services by referring to the past inquiry history. In this way, by referring to the user's past inquiry history, the analysis accuracy is improved and more appropriate responses are possible.

[0055] When providing the generated voice, the voice generating unit can adjust the length of the voice taking into account the remaining battery level of the user's device. For example, when the remaining battery level is low, the voice generating unit can provide a short voice that focuses on the main points. When the remaining battery level is sufficient, the voice generating unit can also provide a voice that includes a detailed explanation. Furthermore, when the remaining battery level is moderate, the voice generating unit can provide a voice of a moderate length. This makes it possible to provide optimal voice by taking into account the remaining battery level of the user's device.

[0056] When analyzing the voice data, the analysis unit can monitor the user's speaking speed in real time and dynamically adjust the analysis method. For example, if the user's speaking speed is fast, the analysis unit can increase the analysis speed. Also, if the user's speaking speed is slow, the analysis unit can decrease the analysis speed. Furthermore, if the user's speaking speed fluctuates, the analysis speed can be dynamically adjusted. This allows for more appropriate analysis by adjusting the analysis method according to the user's speaking speed.

[0057] When analyzing the voice data, the analysis unit can estimate the age group of the user and apply an analysis method according to the age group. For example, the analysis unit performs an analysis that takes into account casual language for younger users. Also, for older users, the analysis unit can perform an analysis that takes into account polite language. Furthermore, the analysis unit can perform an analysis that takes into account the frequency of use of specific words and phrases according to the age group. In this way, by applying an analysis method according to the user's age group, the analysis accuracy can be improved and more appropriate responses can be made.

[0058] When providing the generated voice, the voice generation unit can adjust the frequency band of the voice taking into account the hearing characteristics of the user. For example, if the user has difficulty hearing high-pitched sounds, the voice generation unit can provide voice with emphasis on low-pitched sounds. Also, if the user has difficulty hearing low-pitched sounds, the voice generation unit can provide voice with emphasis on high-pitched sounds. Furthermore, it can also emphasize or suppress a specific frequency band according to the hearing characteristics of the user. This makes it possible to provide optimal voice by taking into account the hearing characteristics of the user.

[0059] When generating a response, the generation unit can maintain consistency of the response by referring to the user's past inquiry history. The generation unit generates a response that matches the content of the past inquiry, for example. Also, the generation unit can generate a response that reflects the user's preferences and tendencies based on the past inquiry history. Furthermore, by referring to the past inquiry history, a response without inconsistencies can be generated. In this way, by referring to the user's past inquiry history, consistency of the response can be maintained, enabling a more appropriate response.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The analysis unit analyzes the voice data. For example, the analysis unit converts the voice data into text data using voice recognition technology. The analysis unit can also analyze the contents of the voice data using natural language processing technology. For example, the analysis unit analyzes the phonemes and phonology of the voice data and models the tempo and intonation of the voice. Step 2: The generation unit uses a generation AI to generate a response based on the data analyzed by the analysis unit. The generation unit generates a response, for example, using a text generation AI (e.g., LLM). The generation unit can also use the generation AI to generate a response with knowledge about a specific business or service. For example, the generation unit generates an appropriate response to a customer support inquiry. Step 3: The voice conversion unit converts the response generated by the generation unit into voice. The voice conversion unit converts the text data into voice data, for example, using voice synthesis technology. The voice conversion unit can also provide the generated voice data to the customer. For example, the voice conversion unit provides the generated voice data to the customer over the telephone or the Internet.

[0062] (Example 2) The customer response automation system according to the embodiment of the present invention is a system that uses large-scale voice data to create a voice tempo and intonation model and combines it with a generation AI to fully automatically respond to inquiries from customers and the like. This system first uses large-scale voice data to model the voice tempo and intonation. This model learns the rhythm and intonation of natural conversation through analysis of voice data. Next, a generation AI is used to generate a response to the customer's inquiry. This generation AI is fine-tuned in advance and has knowledge of specific business and services. Furthermore, the response content generated by the generation AI is converted into voice using the voice tempo and intonation model. As a result, the text-based response generated by the generation AI is provided to the customer as a natural voice. For example, a quick and accurate response to customer inquiries is possible. In addition, by performing fine tuning, the response content of the generation AI can be made closer to a more accurate one. In this way, the present invention combines large-scale voice data and a generation AI to realize the automation of customer responses, thereby improving business efficiency and customer satisfaction. As a result, the customer response automation system is able to quickly and accurately respond to customer inquiries.

[0063] The customer support automation system according to the embodiment includes an analysis unit, a generation unit, and a voice conversion unit. The analysis unit analyzes voice data. The analysis unit converts the voice data into text data, for example, using a voice recognition technology. The analysis unit can also analyze the contents of the voice data using a natural language processing technology. For example, the analysis unit analyzes the phonemes and phonology of the voice data and models the tempo and intonation of the voice. The generation unit generates a response based on the data analyzed by the analysis unit using a generation AI. The generation unit generates a response, for example, using a text generation AI (for example, LLM). The generation unit can also generate a response with knowledge about a specific business or service, using the generation AI. For example, the generation unit generates an appropriate response to an inquiry about customer support. The voice conversion unit voices the response generated by the generation unit. The voice conversion unit converts the text data into voice data, for example, using a voice synthesis technology. The voice conversion unit can also provide the generated voice data to the customer. For example, the voice conversion unit provides the generated voice data to the customer via telephone or the Internet. This enables the customer response automation system according to the embodiment to respond quickly and accurately to inquiries from customers. Some or all of the above-mentioned processes in the analysis unit, generation unit, and voice conversion unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input voice data to the AI ​​and have the AI ​​analyze the voice data. The generation unit can input data analyzed by the analysis unit to the AI ​​and have the AI ​​generate a response. The voice conversion unit can input the response generated by the generation unit to the AI ​​and have the AI ​​convert it into voice.

[0064] The analysis unit analyzes the voice data. The analysis unit converts the voice data into text data, for example, using a voice recognition technology. Specifically, the voice recognition technology converts a voice signal into digital data, and analyzes the digital data to identify phonemes and phonology. This allows the information contained in the voice data to be extracted in text format. The analysis unit can also analyze the contents of the voice data using a natural language processing technology. The natural language processing technology analyzes the grammatical structure and meaning of the text data, and performs understanding based on the context. For example, the analysis unit analyzes the phonemes and phonology of the voice data, and models the tempo and intonation of the voice. This allows the speaker's emotions and intentions to be grasped more accurately. Furthermore, the analysis unit can also use a filtering technology to remove background noise and echoes from the voice data. This allows the quality of the voice data to be improved, and the accuracy of the analysis to be increased. The analysis unit combines these technologies to analyze the voice data with high accuracy, and outputs it as text data. The analysis unit can share the results of the analysis of the voice data with other systems and departments, and can improve the efficiency of customer support by linking with, for example, a customer support system or database.

[0065] The generation unit uses the generation AI to generate a response based on the data analyzed by the analysis unit. The generation unit generates a response using, for example, a text generation AI (e.g., LLM). Specifically, the generation AI receives text data provided by the analysis unit as input and generates an appropriate response based on the content. The generation AI has learned a large amount of text data and has the ability to understand grammar and context, so it can generate natural and fluent responses. The generation unit can also use the generation AI to generate responses with knowledge of specific operations and services. For example, the generation unit generates an appropriate response to an inquiry about customer support. The generation AI has learned knowledge about specific operations and services in advance and can also respond to specialized questions. Furthermore, the generation unit can evaluate the quality of the generated response and make corrections as necessary. For example, if the response generated by the generation AI is inappropriate, the generation unit reevaluates the content of the response and generates a more appropriate response. The generation unit can also store the generated response in a database and use it as a reference for future inquiries. This allows the generation unit to generate quick and accurate responses and improve the efficiency and quality of customer support.

[0066] The voice conversion unit converts the response generated by the generation unit into voice. The voice conversion unit converts text data into voice data, for example, using a voice synthesis technique. Specifically, the voice synthesis technique receives text data as an input and generates a natural voice based on the content of the text data. The voice synthesis technique generates a natural and easy-to-listen voice by analyzing a combination of phonemes and phonological elements and adding an appropriate intonation and tempo. The voice conversion unit can also provide the generated voice data to a customer. For example, the voice conversion unit provides the generated voice data to a customer via telephone or the Internet. In the case of a telephone call, the voice conversion unit transmits the generated voice data to a telephone line in real time and responds directly to the customer. In the case of the Internet, the voice conversion unit distributes the generated voice data in a streaming format so that the customer can listen to the voice through a web browser or a dedicated application. Furthermore, the voice conversion unit can evaluate the quality of the generated voice data and make corrections as necessary. For example, if the intonation or tempo of the voice is unnatural, the voice conversion unit readjusts the voice synthesis technique to generate a more natural voice. The voice conversion unit can also store the generated voice data in a database and use it as a reference for future inquiries. This enables the voice conversion unit to provide fast and accurate voice responses, improving the efficiency and quality of customer service.

[0067] The generation unit can include an adjustment unit that performs fine tuning. The adjustment unit performs fine tuning of the generation AI. The adjustment unit improves the accuracy of the response by, for example, adjusting parameters of the generation AI. The adjustment unit can also select training data. For example, the adjustment unit selects data related to a specific business or service and uses it for training the generation AI. As a result, the generation unit can make the response content of the generation AI more accurate by performing fine tuning. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the adjustment unit can cause the AI ​​to adjust parameters of the generation AI.

[0068] The voice conversion unit may include a providing unit that provides the generated voice to the customer. The providing unit provides the generated voice to the customer. The providing unit provides the voice to the customer, for example, via telephone. The providing unit may also provide the voice to the customer via the Internet. For example, the providing unit provides the voice through a website or a mobile app. As a result, by providing the generated voice to the customer, it becomes possible to respond in a natural voice. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit may input the generated voice data to AI and cause the AI ​​to provide the voice.

[0069] The analysis unit can analyze multiple pieces of voice data and model the tempo and intonation of the voice. The analysis unit analyzes multiple pieces of voice data, such as telephone voices and recorded voices. The analysis unit analyzes voice waveforms and extracts rhythm patterns. For example, the analysis unit analyzes phonemes and phonology of the voice data and models the tempo and intonation of the voice. This makes it possible to learn the rhythm and intonation of natural conversation by analyzing large-scale voice data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input voice data to AI and have the AI ​​execute analysis of the voice data.

[0070] The generation unit can use a generation AI having knowledge about a specific business or service. The generation unit uses a generation AI having knowledge about a specific business or service, such as customer support or medical consultation. The generation unit uses the generation AI to generate an appropriate response to an inquiry about a specific business or service. For example, the generation unit generates an appropriate response to an inquiry about customer support. In this way, by using a generation AI having knowledge about a specific business or service, a more appropriate response can be generated. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the generation unit can cause an AI to execute a generation AI having knowledge about a specific business or service.

[0071] The analysis unit can estimate the user's emotion and adjust the analysis method of the voice data based on the estimated user's emotion. For example, when the user is feeling stressed, the analysis unit can slow down the tempo of the voice data and make the intonation gentle. In addition, when the user is relaxed, the analysis unit can also speed up the tempo of the voice data and enrich the intonation. Furthermore, when the user is in a hurry, the analysis unit can also speed up the tempo of the voice data and simplify the intonation. This allows for more appropriate analysis by adjusting the analysis method of the voice data according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to an AI and cause the AI ​​to execute emotion estimation.

[0072] The analysis unit can improve the analysis accuracy by taking into account a specific accent or dialect when analyzing the voice data. For example, when analyzing voice data having an accent of a specific region, the analysis unit applies an accent model of the region. In addition, when analyzing voice data having a specific dialect, the analysis unit can also perform the analysis by taking into account the characteristics of the dialect. Furthermore, when analyzing voice data containing a mixture of multiple accents and dialects, the analysis unit can also perform the analysis by integrating the characteristics of each. As a result, the analysis accuracy is improved by taking into account the specific accent or dialect. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input voice data having a specific accent or dialect to AI and have AI perform the analysis.

[0073] The analysis unit can perform a filter process to remove background noise when analyzing the voice data. The analysis unit applies filtering to remove background noise, for example, before analyzing the voice data. The analysis unit can also perform filtering to remove noise in a specific frequency band. Furthermore, the analysis unit can also perform filtering to remove dynamically changing background noise in real time. This improves the analysis accuracy by removing background noise. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the voice data to AI and cause the AI ​​to remove the background noise.

[0074] The analysis unit can estimate the user's emotion and determine the priority of the voice data to be analyzed based on the estimated user's emotion. For example, when the user is feeling stressed, the analysis unit preferentially analyzes voice data for reducing stress. In addition, when the user is relaxed, the analysis unit can also preferentially analyze voice data for maintaining relaxation. Furthermore, when the user is in a hurry, the analysis unit can also preferentially analyze voice data for responding quickly. This enables more appropriate analysis by determining the priority of the voice data to be analyzed according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to an AI and cause the AI ​​to execute emotion estimation.

[0075] The analysis unit can adjust the analysis method based on the geographical location information of the user when analyzing the voice data. For example, when the user is in a specific area, the analysis unit performs the analysis taking into account the accent or dialect of that area. In addition, when the user is moving, the analysis unit can also perform the analysis taking into account the accent or dialect of the destination area. Furthermore, when the user is in different areas, the analysis unit can also perform the analysis by integrating the characteristics of each area. This makes it possible to adjust the analysis method by taking into account the geographical location information of the user. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the geographical location information of the user to AI and cause the AI ​​to adjust the analysis method.

[0076] When analyzing the voice data, the analysis unit can analyze the social media activity of the user and preferentially analyze related voice data. For example, the analysis unit preferentially analyzes voice data related to a specific topic from the social media activity of the user. The analysis unit can also preferentially analyze voice data related to a specific event from the social media activity of the user. Furthermore, the analysis unit can also preferentially analyze voice data related to a specific person from the social media activity of the user. In this way, by analyzing the social media activity of the user, related voice data can be preferentially analyzed. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the social media activity data of the user to AI and cause the AI ​​to perform an analysis to determine the priority order of related voice data.

[0077] The generation unit can estimate the user's emotion and adjust the way of expressing the reply based on the estimated emotion of the user. For example, when the user is stressed, the generation unit generates a reply in a gentle expression. Also, when the user is relaxed, the generation unit can generate a reply in a friendly expression. Furthermore, when the user is in a hurry, the generation unit can generate a reply in a concise and quick expression. This allows a more appropriate reply by adjusting the way of expressing the reply according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 emotion data to the AI ​​and cause the AI ​​to execute emotion estimation.

[0078] The generation unit can adjust the level of detail of the response based on the importance of the inquiry content when generating the response. For example, the generation unit generates a detailed response to an inquiry with a high level of importance. The generation unit can also generate a concise response to an inquiry with a low level of importance. Furthermore, the generation unit can dynamically adjust the level of detail of the response according to the importance. This allows a more appropriate response to be provided by adjusting the level of detail of the response based on the importance of the inquiry content. A part or all of the above-mentioned 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 importance of the inquiry content to the AI ​​and cause the AI ​​to execute a process of adjusting the level of detail of the response.

[0079] The generation unit can apply different generation algorithms depending on the category of the inquiry when generating a response. For example, the generation unit applies a specialized generation algorithm to a technical inquiry. The generation unit can also apply a general-purpose generation algorithm to a general inquiry. Furthermore, the generation unit can apply a quick generation algorithm to an urgent inquiry. This allows for a more appropriate response by applying different generation algorithms depending on the category of the inquiry. Some or all of the above-mentioned 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 category of the inquiry to the AI ​​and cause the AI ​​to execute a process of determining the generation algorithm to be applied.

[0080] The generation unit can estimate the user's emotion and adjust the length of the reply based on the estimated user's emotion. For example, when the user is stressed, the generation unit generates a short and to the point reply. Also, when the user is relaxed, the generation unit can generate a longer reply including a detailed explanation. Furthermore, when the user is in a hurry, the generation unit can generate a quick and concise reply. This allows a more appropriate reply by adjusting the length of the reply according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 emotion data to the AI ​​and cause the AI ​​to execute emotion estimation.

[0081] The generation unit can determine the priority of the replies based on the time of submission of the inquiries when generating the replies. For example, the generation unit generates replies with priority to inquiries submitted recently. The generation unit can also generate replies with priority to inquiries that have been unresolved for a long time. Furthermore, the generation unit can dynamically adjust the priority of the replies according to the time of submission. This allows for a faster response by determining the priority of the replies based on the time of submission of the inquiries. A part or all of the above-mentioned 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 time of submission of the inquiry to the AI ​​and cause the AI ​​to execute a process of determining the priority of the replies.

[0082] The generation unit can adjust the order of replies based on the relevance of the inquiries when generating replies. For example, the generation unit generates replies preferentially for inquiries with high relevance. The generation unit can also generate replies later for inquiries with low relevance. Furthermore, the generation unit can dynamically adjust the order of replies according to the relevance. This allows for a more appropriate reply by adjusting the order of replies based on the relevance of the inquiries. A part or all of the above-mentioned 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 relevance of the inquiries to the AI ​​and cause the AI ​​to execute a process of adjusting the order of replies.

[0083] The voice conversion unit can estimate the user's emotion and adjust the vocalization expression method based on the estimated user's emotion. For example, when the user is stressed, the voice conversion unit performs vocalization in a calm voice. Also, when the user is relaxed, the voice conversion unit can perform vocalization in a friendly voice. Furthermore, when the user is in a hurry, the voice conversion unit can perform vocalization in a quick and concise voice. This allows for more appropriate vocalization by adjusting the vocalization expression method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the voice conversion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the voice conversion unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute emotion estimation.

[0084] The voice conversion unit can perform voice filtering to improve the naturalness of the generated voice during voice conversion. The voice conversion unit can, for example, apply a noise reduction filter to the generated voice. The voice conversion unit can also apply an echo canceling filter to the generated voice. Furthermore, the voice conversion unit can also apply a sound quality improvement filter to the generated voice. This improves the naturalness of the generated voice, making it possible to convert the voice into a more natural voice. Some or all of the above-mentioned processing in the voice conversion unit can be performed using, for example, AI, or can be performed without using AI. For example, the voice conversion unit can input the generated voice data to AI and have the AI ​​perform voice filtering.

[0085] The voice conversion unit can improve the accuracy of voice conversion by taking into account a specific accent or dialect when generating voice. For example, when generating voice with an accent of a specific region, the voice conversion unit applies an accent model of the region. In addition, when generating voice with a specific dialect, the voice conversion unit can also perform voice conversion by taking into account the characteristics of the dialect. Furthermore, when generating voice in which multiple accents and dialects are mixed, the voice conversion unit can also perform voice conversion by integrating the characteristics of each. As a result, the accuracy of voice conversion is improved by taking into account a specific accent or dialect. A part or all of the above-mentioned processing in the voice conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice conversion unit can input voice data with a specific accent or dialect to AI and have AI perform voice conversion.

[0086] The voice conversion unit can estimate the user's emotion and determine the priority of voice conversion based on the estimated user's emotion. For example, when the user is feeling stressed, the voice conversion unit preferentially generates a voice for reducing stress. In addition, when the user is relaxed, the voice conversion unit can also preferentially generate a voice for maintaining relaxation. Furthermore, when the user is in a hurry, the voice conversion unit can also preferentially generate a voice for responding quickly. This enables more appropriate voice conversion by determining the priority of voice conversion according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the voice conversion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the voice conversion unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute emotion estimation.

[0087] The voice conversion unit can adjust the voice conversion method in consideration of the geographical location information of the user when vocalizing. For example, when the user is in a specific area, the voice conversion unit performs voice conversion in consideration of the accent or dialect of that area. In addition, when the user is moving, the voice conversion unit can also perform voice conversion in consideration of the accent or dialect of the destination area. Furthermore, when the user is in different areas, the voice conversion unit can also perform voice conversion by integrating the characteristics of each area. This makes it possible to adjust the voice conversion method by considering the geographical location information of the user. Part or all of the above-mentioned processing in the voice conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice conversion unit can input the geographical location information of the user to AI and cause the AI ​​to adjust the voice conversion method.

[0088] The voice conversion unit can analyze the user's social media activity and preferentially voice the related voice data at the time of voice conversion. For example, the voice conversion unit preferentially generates voice related to a specific topic from the user's social media activity. The voice conversion unit can also preferentially generate voice related to a specific event from the user's social media activity. Furthermore, the voice conversion unit can also preferentially generate voice related to a specific person from the user's social media activity. In this way, by analyzing the user's social media activity, related voice data can be preferentially voiced. A part or all of the above-mentioned processing in the voice conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice conversion unit inputs the user's social media activity data into AI and causes AI to perform voice conversion that determines the priority order of related voice data.

[0089] The adjustment unit can estimate the user's emotion and adjust the fine-tuning parameters based on the estimated user's emotion. For example, when the user is feeling stressed, the adjustment unit adjusts the parameters for reducing stress. In addition, when the user is relaxed, the adjustment unit can also adjust the parameters for maintaining relaxation. Furthermore, when the user is in a hurry, the adjustment unit can adjust the parameters for responding quickly. This allows for more appropriate adjustment by adjusting the fine-tuning parameters according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the adjustment unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute emotion estimation.

[0090] The adjustment unit can optimize the generation algorithm by referring to past inquiry data during fine tuning. The adjustment unit, for example, analyzes past inquiry data and optimizes parameters of the generation algorithm. The adjustment unit can also extract a specific pattern from the past inquiry data and reflect it in the generation algorithm. Furthermore, the adjustment unit can improve the accuracy of the generation algorithm based on the past inquiry data. This makes it possible to optimize the generation algorithm by referring to the past inquiry data. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI or may be performed without using AI. For example, the adjustment unit can input past inquiry data to AI and cause AI to execute optimization of the generation algorithm.

[0091] The adjustment unit can estimate the user's emotion and adjust the frequency of fine tuning based on the estimated user's emotion. For example, when the user is stressed, the adjustment unit frequently performs fine tuning to reduce stress. Also, when the user is relaxed, the adjustment unit can reduce the frequency of fine tuning to maintain relaxation. Furthermore, when the user is in a hurry, the adjustment unit can frequently perform fine tuning to respond quickly. This allows for more appropriate adjustment by adjusting the frequency of fine tuning according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the adjustment unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute emotion estimation.

[0092] The adjustment unit can weight the learning data based on the time of submission of the inquiry during fine tuning. The adjustment unit, for example, weights recent inquiry data and reflects the weight in the generation algorithm. The adjustment unit can also weight inquiry data that has been unresolved for a long time and reflect the weight in the generation algorithm. Furthermore, the adjustment unit can dynamically adjust the weighting of the learning data according to the submission time. This allows more appropriate adjustment by weighting the learning data based on the time of submission of the inquiry. A part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the adjustment unit can input the time of submission of the inquiry to the AI ​​and cause the AI ​​to perform weighting of the learning data.

[0093] The providing unit can estimate the user's emotion and adjust the method of providing the voice based on the estimated emotion of the user. For example, when the user is stressed, the providing unit provides the voice in a calm voice. Also, when the user is relaxed, the providing unit can provide the voice in a friendly voice. Furthermore, when the user is in a hurry, the providing unit can provide the voice in a quick and concise voice. This allows for more appropriate voice provision by adjusting the method of providing the voice according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute emotion estimation.

[0094] The providing unit can select the optimal voice providing method by referring to the user's past inquiry history when providing the voice. The providing unit selects the optimal voice providing method from, for example, the user's past inquiry history. The providing unit can also analyze the user's past inquiry history and select the voice providing method based on a specific pattern. Furthermore, the providing unit can dynamically adjust the voice providing method based on the user's past inquiry history. Thereby, the optimal voice providing method can be selected by referring to the user's past inquiry history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past inquiry history into AI and cause AI to execute a process of selecting the optimal providing method.

[0095] The providing unit can estimate the user's emotion and determine the priority of voice provision based on the estimated user's emotion. For example, when the user is feeling stressed, the providing unit can provide preferentially a voice for reducing stress. In addition, when the user is relaxed, the providing unit can also provide preferentially a voice for maintaining relaxation. Furthermore, when the user is in a hurry, the providing unit can also provide preferentially a voice for quickly responding. This enables more appropriate voice provision by determining the priority of voice provision according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute emotion estimation.

[0096] The providing unit can select the optimal voice providing method in consideration of the device information of the user when providing voice. For example, when the user is using a smartphone, the providing unit selects a voice providing method that matches the screen size. In addition, when the user is using a tablet, the providing unit can also select a voice providing method optimized for a large screen. Furthermore, when the user is using a smartwatch, the providing unit can also select a voice providing method that is simple and has high visibility. In this way, the optimal voice providing method can be selected by considering the device information of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the device information of the user to the AI ​​and cause the AI ​​to execute a process of selecting the optimal providing method.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] When analyzing the voice data, the analysis unit can improve the analysis accuracy by referring to the user's past inquiry history. For example, the analysis unit extracts a specific pattern from the past inquiry history and reflects it in the analysis of the voice data. The analysis unit can also learn the user's speech tendencies based on the past inquiry history and improve the analysis accuracy. Furthermore, the analysis unit can perform analysis with knowledge of specific tasks or services by referring to the past inquiry history. In this way, by referring to the user's past inquiry history, the analysis accuracy is improved and more appropriate responses are possible.

[0099] The generation unit can estimate the user's emotion and adjust the tone of the reply based on the estimated user's emotion. For example, when the user is angry, the generation unit generates a reply in a calm and subdued tone. When the user is sad, the generation unit can also generate a reply in a gentle tone. Furthermore, when the user is happy, the generation unit can also generate a reply in a bright tone. This allows for a more appropriate reply by adjusting the tone of the reply according to the user's emotion.

[0100] When providing the generated voice, the voice generating unit can adjust the length of the voice taking into account the remaining battery level of the user's device. For example, when the remaining battery level is low, the voice generating unit can provide a short voice that focuses on the main points. When the remaining battery level is sufficient, the voice generating unit can also provide a voice that includes a detailed explanation. Furthermore, when the remaining battery level is moderate, the voice generating unit can provide a voice of a moderate length. This makes it possible to provide optimal voice by taking into account the remaining battery level of the user's device.

[0101] When analyzing the voice data, the analysis unit can monitor the user's speaking speed in real time and dynamically adjust the analysis method. For example, if the user's speaking speed is fast, the analysis unit can increase the analysis speed. Also, if the user's speaking speed is slow, the analysis unit can decrease the analysis speed. Furthermore, if the user's speaking speed fluctuates, the analysis speed can be dynamically adjusted. This allows for more appropriate analysis by adjusting the analysis method according to the user's speaking speed.

[0102] The generation unit can estimate the user's emotions and customize the content of the reply based on the estimated user's emotions. For example, if the user is feeling anxious, the generation unit can generate a reply with content that gives a sense of security. Also, if the user is excited, the generation unit can generate a reply with content that encourages the user to stay calm. Furthermore, if the user is confused, the generation unit can generate a reply with clear and easy-to-understand content. This allows for a more appropriate reply by customizing the content of the reply according to the user's emotions.

[0103] When analyzing the voice data, the analysis unit can estimate the age group of the user and apply an analysis method according to the age group. For example, the analysis unit performs an analysis that takes into account casual language for younger users. Also, for older users, the analysis unit can perform an analysis that takes into account polite language. Furthermore, the analysis unit can perform an analysis that takes into account the frequency of use of specific words and phrases according to the age group. In this way, by applying an analysis method according to the user's age group, the analysis accuracy can be improved and more appropriate responses can be made.

[0104] The generation unit can estimate the user's emotion and adjust the timing of the response based on the estimated user's emotion. For example, the generation unit generates a response quickly when the user is impatient. Also, when the user is relaxed, the generation unit can generate a response with a slight delay. Furthermore, when the user is angry, the response can be delayed to give the user time to calm down. In this way, a more appropriate response can be provided by adjusting the timing of the response according to the user's emotion.

[0105] When providing the generated voice, the voice generation unit can adjust the frequency band of the voice taking into account the hearing characteristics of the user. For example, if the user has difficulty hearing high-pitched sounds, the voice generation unit can provide voice with emphasis on low-pitched sounds. Also, if the user has difficulty hearing low-pitched sounds, the voice generation unit can provide voice with emphasis on high-pitched sounds. Furthermore, it can also emphasize or suppress a specific frequency band according to the hearing characteristics of the user. This makes it possible to provide optimal voice by taking into account the hearing characteristics of the user.

[0106] When analyzing the voice data, the analysis unit can estimate the emotion based on the content of the user's speech and adjust the depth of the analysis according to the estimated emotion. For example, when the user makes an emotional statement, the analysis unit performs a detailed analysis. Also, when the user makes a calm statement, the analysis unit can perform a simplified analysis. Furthermore, when the user's emotion fluctuates, the analysis depth can be dynamically adjusted. In this way, by estimating the emotion based on the content of the user's speech and adjusting the analysis depth, more appropriate analysis is possible.

[0107] When generating a response, the generation unit can maintain consistency of the response by referring to the user's past inquiry history. The generation unit generates a response that matches the content of the past inquiry, for example. Also, the generation unit can generate a response that reflects the user's preferences and tendencies based on the past inquiry history. Furthermore, by referring to the past inquiry history, a response without inconsistencies can be generated. In this way, by referring to the user's past inquiry history, consistency of the response can be maintained, enabling a more appropriate response.

[0108] The process flow of the second embodiment will be briefly described below.

[0109] Step 1: The analysis unit analyzes the voice data. For example, the analysis unit converts the voice data into text data using voice recognition technology. The analysis unit can also analyze the contents of the voice data using natural language processing technology. For example, the analysis unit analyzes the phonemes and phonology of the voice data and models the tempo and intonation of the voice. Step 2: The generation unit uses a generation AI to generate a response based on the data analyzed by the analysis unit. The generation unit generates a response, for example, using a text generation AI (e.g., LLM). The generation unit can also use the generation AI to generate a response with knowledge about a specific business or service. For example, the generation unit generates an appropriate response to a customer support inquiry. Step 3: The voice conversion unit converts the response generated by the generation unit into voice. The voice conversion unit converts the text data into voice data, for example, using voice synthesis technology. The voice conversion unit can also provide the generated voice data to the customer. For example, the voice conversion unit provides the generated voice data to the customer over the telephone or the Internet.

[0110] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0111] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0112] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0113] Each of the elements including the above-mentioned analysis unit, generation unit, and voice conversion unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart device 14, and analyzes voice data and converts it into text data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a response based on the analyzed data. The voice conversion unit is realized, for example, by the control unit 46A of the smart device 14, and converts the generated response into voice data and provides it to the customer. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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, the RAM 30, and the 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.

[0117] 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.

[0118] 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 the voice according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0120] 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 performed in a secure state.

[0121] Fig. 4 shows an example of 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.

[0122] 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.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0124] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0125] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0126] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0127] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0128] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements including the above-mentioned analysis unit, generation unit, and voice conversion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart glasses 214, and analyzes voice data and converts it into text data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a response based on the analyzed data. The voice conversion unit is realized, for example, by the control unit 46A of the smart glasses 214, and converts the generated response into voice data and provides it to the customer. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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, the RAM 30, and the 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.

[0133] 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.

[0134] 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 the voice according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0136] 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 performed in a secure state.

[0137] 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.

[0138] 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.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0140] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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.

[0141] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0142] 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 voice indicating a user input for 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.

[0143] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0144] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements including the above-mentioned analysis unit, generation unit, and voice conversion unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the headset type terminal 314, and analyzes voice data and converts it into text data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a response based on the analyzed data. The voice conversion unit is realized, for example, by the control unit 46A of the headset type terminal 314, and converts the generated response into voice data and provides it to the customer. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] 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, the RAM 30, and the 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.

[0149] 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.

[0150] 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 the voice according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0152] 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 performed in a secure state.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0154] 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.

[0155] 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.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0157] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0158] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0159] 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 a voice indicating a user input for 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.

[0160] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0161] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0162] Each of the multiple elements including the above-mentioned analysis unit, generation unit, and voice conversion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the robot 414, and analyzes voice data and converts it into text data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a response based on the analyzed data. The voice conversion unit is realized, for example, by the control unit 46A of the robot 414, and converts the generated response into voice data and provides it to the customer. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0163] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0164] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0165] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0166] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0167] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0168] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0170] 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.

[0171] 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 Universal Serial Bus (USB) 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.

[0172] In addition, 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 upon request from the data processing device 12.

[0173] 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.

[0174] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0175] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0176] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0177] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0178] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0179] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0180] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0181] (Appendix 1) an analysis unit that analyzes the voice data; a generation unit that generates a response based on the data analyzed by the analysis unit; a voice generation unit that voices the response generated by the generation unit; Equipped A system characterized by: (Appendix 2) The generation unit is Equipped with an adjustment section for fine tuning 2. The system of claim 1, (Appendix 3) The voice conversion unit is A provision unit is provided for providing the generated voice to the customer. 2. The system of claim 1, (Appendix 4) The analysis unit is Analyze multiple voice data and model the tempo and intonation of the voice 2. The system of claim 1, (Appendix 5) The generation unit is Use generative AI with knowledge of specific tasks and services 2. The system of claim 1, (Appendix 6) The analysis unit is To estimate a user's emotion and adjust a method of analyzing voice data based on the estimated user's emotion. 2. The system of claim 1, (Appendix 7) The analysis unit is When analyzing speech data, take into account specific accents or dialects to improve analysis accuracy 2. The system of claim 1, (Appendix 8) The analysis unit is When analyzing voice data, filtering is performed to remove background noise. 2. The system of claim 1, (Appendix 9) The analysis unit is A user's emotion is estimated, and a priority order of voice data to be analyzed is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 10) The analysis unit is When analyzing voice data, adjust the analysis method based on the user's geographic location information 2. The system of claim 1, (Appendix 11) The analysis unit is When analyzing voice data, prioritize analysis of relevant voice data based on the user's social media activity. 2. The system of claim 1, (Appendix 12) The generation unit is Infer the user's emotions and adjust the way the reply is expressed based on the estimated user's emotions 2. The system of claim 1, (Appendix 13) The generation unit is When generating a response, adjust the level of detail in the response based on the importance of the inquiry 2. The system of claim 1, (Appendix 14) The generation unit is When generating responses, different generation algorithms are applied depending on the category of the query. 2. The system of claim 1, (Appendix 15) The generation unit is Infer user sentiment and adjust the length of responses based on the estimated sentiment 2. The system of claim 1, (Appendix 16) The generation unit is When generating responses, prioritize responses based on when the query was submitted 2. The system of claim 1, (Appendix 17) The generation unit is While generating responses, adjust the order of responses based on the relevance of the query 2. The system of claim 1, (Appendix 18) The voice conversion unit is Estimating a user's emotion and adjusting the speech expression based on the estimated user's emotion 2. The system of claim 1, (Appendix 19) The voice conversion unit is During speech generation, voice filtering is performed to improve the naturalness of the generated voice. 2. The system of claim 1, (Appendix 20) The voice conversion unit is Improve speech accuracy by taking into account specific accents and dialects during speech generation 2. The system of claim 1, (Appendix 21) The voice conversion unit is Estimating a user's emotion and prioritizing speech generation based on the estimated user's emotion 2. The system of claim 1, (Appendix 22) The voice conversion unit is During speech generation, the speech generation method is adjusted taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 23) The voice conversion unit is When converting voice, the system analyzes the user's social media activity and prioritizes relevant voice data for conversion. 2. The system of claim 1, (Appendix 24) The adjustment unit is Estimate user emotions and adjust fine-tuning parameters based on the estimated user emotions. 3. The system of claim 2, (Appendix 25) The adjustment unit is During fine-tuning, the generation algorithm is optimized by referencing past query data. 3. The system of claim 2, (Appendix 26) The adjustment unit is Estimate user emotions and adjust the frequency of fine-tuning based on the estimated user emotions. 3. The system of claim 2, (Appendix 27) The adjustment unit is During fine tuning, weighting of training data based on query submission age 3. The system of claim 2, (Appendix 28) The providing unit is To estimate a user's emotion and adjust a method of providing voice based on the estimated user's emotion 4. The system of claim 3. (Appendix 29) The providing unit is When providing voice guidance, the system selects the optimal method of providing the guidance by referring to the user's past inquiry history. 4. The system of claim 3. (Appendix 30) The providing unit is A user's emotion is estimated, and a priority order for providing voice is determined based on the estimated user's emotion. 4. The system of claim 3. (Appendix 31) The providing unit is When providing voice information, the optimal method of providing the information is selected taking into account the user's device information. 4. The system of claim 3. [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes the voice data; a generation unit that generates a response based on the data analyzed by the analysis unit; a voice generation unit that voices the response generated by the generation unit; Equipped A system characterized by:

2. The generation unit is Equipped with an adjustment section for fine tuning 2. The system of claim 1 .

3. The voice conversion unit is A provision unit is provided for providing the generated voice to the customer.

2. The system of claim 1 .

4. The analysis unit is Analyze multiple voice data and model the tempo and intonation of the voice 2. The system of claim 1 .

5. The generation unit is Use generative AI with knowledge of specific tasks and services 2. The system of claim 1 .

6. The analysis unit is To estimate a user's emotion and adjust a method of analyzing voice data based on the estimated user's emotion.

2. The system of claim 1 .

7. The analysis unit is When analyzing speech data, take into account specific accents or dialects to improve analysis accuracy 2. The system of claim 1 .

8. The analysis unit is When analyzing voice data, filtering is performed to remove background noise.

2. The system of claim 1 .

9. The analysis unit is A user's emotion is estimated, and a priority order of voice data to be analyzed is determined based on the estimated user's emotion.

2. The system of claim 1 .

10. The analysis unit is When analyzing voice data, adjust the analysis method based on the user's geographic location information 2. The system of claim 1 .

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