system

Abstract

We provide the system. [Solution] A speech recognition means for converting audio signals into text data, A natural language processing means that analyzes the aforementioned text data and generates a summary, An evaluation means that evaluates the content of the inquiry based on the summary above and generates an automated response or determines an appropriate forwarding destination, A response providing means that generates and provides the aforementioned automated response in voice, or forwards the communication to the determined forwarding destination, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In a conventional call center, there were cases where users could not accurately select an inquiry window or multiple transfers or redials were required due to incorrect selections. Such procedural complexity increased the burden on customers, caused a waste of time, and further led to the cause of claims. In addition, there was also a problem that operators had to grasp the content of each inquiry, resulting in a high workload. 【Means for Solving the Problems】 【0005】 This invention utilizes speech recognition means to convert speech signals into text data and natural language processing means to analyze the text data and summarize the inquiry. Furthermore, it includes evaluation means to generate an automated response based on the summarized content or to determine the appropriate contact point for forwarding the inquiry, and response provision means to provide the generated automated response by voice or to forward the inquiry, thereby automating the inquiry process. As a result, users can avoid complicated selections by simply making a voice declaration and receive a quick and appropriate response, thus reducing the overall burden on the user. 【0006】 "Voice signal" refers to the sound energy emitted by a user through a telephone. 【0007】 "Text data" refers to audio signals converted into non-acoustic textual information. 【0008】 "Speech recognition means" refers to a technology or system that analyzes speech signals and converts them into corresponding text data. 【0009】 "Natural language processing means" refers to technologies or systems that analyze text data, understand its meaning and intent, and then generate summaries. 【0010】 "Evaluation means" means a system or technology for generating an automated response based on summarized inquiry content or for determining an appropriate forwarding destination. 【0011】 "Automated response" refers to a system that automatically provides predefined or dynamically generated answers based on the content of an inquiry. 【0012】 "Response provision means" means a system that has the function of conveying an automated response to the user in voice or other format. 【0013】 "Transferring" refers to the process of moving a user's inquiry to the appropriate department. [Brief explanation of the drawing] 【0014】 [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined. 【Mode for Carrying Out the Invention】 【0015】 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. 【0016】 First, the terms used in the following description will be explained. 【0017】 In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like. 【0018】 In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0019】 In the following embodiments, a labeled 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), or magnetic tapes, and the like. 【0020】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0021】 In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0022】 [First Embodiment] 【0023】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0024】 As shown in Figure 1, the 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. 【0025】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0026】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0027】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0028】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. 【0029】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54. 【0030】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0031】 As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0032】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0033】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0034】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0035】 This invention relates to a system for efficiently automating call center inquiry processing. This system functions by combining speech recognition, natural language processing, automated response generation, and transfer. 【0036】 When a user calls a common inquiry number, the terminal receives the call and records the audio. The recorded audio is sent to a server, where speech recognition capabilities convert the audio signal into text data. This text data is then analyzed by natural language processing capabilities within the server to summarize the inquiry. 【0037】 Based on the summarized inquiry content, the server uses an evaluation mechanism to generate an automated response on the server and provide it to the user as voice if the inquiry is simple. If the inquiry is more complex, the server selects the most appropriate contact person to forward the call to. The terminal then forwards the user's call to this designated contact person, ensuring they receive appropriate support. 【0038】 As a concrete example, consider a case where a user reports that their internet connection is slow. In this case, speech recognition technology converts this into text data, "Slow internet connection." This text data is then analyzed by natural language processing technology, and the problem category is summarized as "Technical Support." The server then uses evaluation technology to search for an appropriate destination and forwards the information to the technical support team. As a result, the user can receive technical support smoothly. 【0039】 This system allows users to receive quick and accurate responses through voice-based reporting, and also significantly improves the operational efficiency of call centers. 【0040】 The following describes the processing flow. 【0041】 Step 1: 【0042】 The user calls a common inquiry number. The terminal receives the call, activates the system, and records the audio. 【0043】 Step 2: 【0044】 The device sends the recorded audio data to the server. This data is then passed to the speech recognition system. 【0045】 Step 3: 【0046】 The server uses speech recognition to convert the received audio data into text data. At this stage, the audio becomes non-acoustic textual information. 【0047】 Step 4: 【0048】 The server uses natural language processing to analyze text data and summarize the query. This analysis generates summarized information. 【0049】 Step 5: 【0050】 The server uses an evaluation tool to assess the summarized content. If the content is simple, it starts a process to generate an automated response on the spot. 【0051】 Step 6: 【0052】 When the server generates an automated response, it uses either a predefined response database or a dynamic response generation algorithm. 【0053】 Step 7: 【0054】 If the user has a simple inquiry, the server generates an automated response and sends it back to the terminal, which then uses speech synthesis technology to provide the response to the user. 【0055】 Step 8: 【0056】 For complex inquiries, the server determines the appropriate contact person based on a list of forwarding destinations and selects the appropriate recipient. 【0057】 Step 9: 【0058】 The terminal, following the server's instructions, forwards the user's call to the selected forwarding destination, allowing the user to speak with the appropriate representative. 【0059】 Step 10: 【0060】 The server logs details of all processing steps. This log includes audio data, text data, responses, and destinations. 【0061】 (Example 1) 【0062】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0063】 In call centers, handling inquiries requires speed and accuracy due to the increasing volume of calls and the diversity of issues. However, traditional manual responses are often understaffed, leading to delays in responses and incorrect transfers. This results in challenges such as decreased customer satisfaction and increased operating costs. 【0064】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0065】 In this invention, the server includes communication means for converting voice data into digital data using conversion means and transmitting it to a central device; information processing means for analyzing the digital data and performing content analysis; and selection means for evaluating the information based on the content analysis and generating an automatic response or determining an appropriate forwarding destination. This enables faster and more accurate query processing. 【0066】 "Means for converting audio data" refers to a device or software that converts an audio signal into digital data. 【0067】 "Communication means" refers to a method or device for transmitting data to another device or system. 【0068】 A "central unit" is the main computing unit used for processing data and issuing commands. 【0069】 "Information processing means" refers to software or algorithms used to analyze input data and extract or understand specific information. 【0070】 "Content analysis" is the process of analyzing digital data in detail and understanding its content. 【0071】 A "selection tool" is a method or device for determining the optimal option from multiple choices. 【0072】 "Automated response" refers to a mechanism that autonomously generates appropriate responses to user inquiries. 【0073】 "Determining the forwarding destination" is the process of selecting the appropriate contact point or department to handle the inquiry based on its content. 【0074】 This invention is a system for efficiently handling voice-based inquiries in a call center. The system's functionality is primarily comprised of terminals, a server, and users. 【0075】 The user initiates an inquiry by calling a common inquiry number. The terminal receives the voice signal from this user and generates voice data. This voice data is sent to a server, where it is converted into digital data by a voice data conversion means. For example, a general-purpose voice recognition platform can be used as the voice recognition software. 【0076】 Next, the server analyzes this digital data using information processing tools to understand the query. For example, tools such as "spaCy" or "BERT" could be used as natural language processing technologies. This extracts the gist of the query and classifies it into appropriate categories. 【0077】 The server initiates a process to either generate an automated response based on the extracted information, or to determine the appropriate department to forward the information to using a selection method. If an automated response is possible, the server uses speech synthesis technology to generate and provide the response as audio. For speech synthesis, services such as "Amazon Polly" can be used. 【0078】 On the other hand, if the inquiry is complex and requires specialized handling, the server will determine the most appropriate department based on the selected method. The terminal will then forward the user's call to the designated department according to the server's instructions. 【0079】 For example, if a user reports that their internet connection is slow, speech recognition technology converts this into digital text. Then, natural language processing technology classifies the issue as requiring technical support, and the process of transferring the user to the technical support team is initiated. 【0080】 An example of a prompt message generated using a generative AI model might be: "A user called the call center and reported an internet connection problem. Please provide quick and accurate instructions on how to address this situation." 【0081】 This system allows users to receive efficient, quick, and appropriate responses, and also improves the operational efficiency of call centers. 【0082】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0083】 Step 1: 【0084】 The user calls a common inquiry number. At this point, the user communicates their inquiry via voice. The terminal receives this voice signal in real time and records it as digital audio data. The input here is the user's voice, and the output is digital audio data. The terminal formats this appropriately and sends it to the server. 【0085】 Step 2: 【0086】 The server receives audio data transmitted from the terminal. The server uses a conversion mechanism to perform a speech recognition process. In this process, the audio data is converted into text data. The input is digital audio data, and the output is text data. The server uses speech recognition software to perform this conversion. 【0087】 Step 3: 【0088】 The server analyzes the text data generated by speech recognition using information processing tools. This analysis involves understanding the content and summarizing it as needed. For example, keyphrase extraction and topic modeling are performed. The input is the text data generated in the previous step, and the output is summarized or categorized data. The server performs this processing using natural language processing techniques. 【0089】 Step 4: 【0090】 The server generates an automated response or, if necessary, selects an appropriate forwarding destination based on the analysis results. This decision is made using selection methods, and if an automated response is applicable, the server prepares the response. The input is categorized data, and the output is an automated response message or a forwarding instruction. The server performs evaluation using rule-based or machine learning models. 【0091】 Step 5: 【0092】 Depending on the situation, the server converts the generated automated response into speech using speech synthesis technology and sends it to the terminal. If forwarding is necessary, the server specifies the forwarding destination and issues instructions to the terminal. Input is the automated response message or forwarding instruction, and output is the voice response or forwarding setting. A TTS engine is used for speech synthesis. 【0093】 Step 6: 【0094】 The terminal either provides the user with an audio response from the server or forwards the user's call to a designated destination. Here, the terminal ultimately provides feedback to the user or connects them to a support representative. The input is the audio response or forwarding instruction from the server, and the output is the response to the user and the call forwarding. The terminal then completes the entire process. 【0095】 (Application Example 1) 【0096】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0097】 In modern homes, there is a growing demand for quick and efficient use of home appliances and information services through voice control. However, existing home appliances have limited functionality and struggle to seamlessly integrate with multiple devices and information sources. 【0098】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0099】 In this invention, the server includes speech recognition means for converting voice signals into text data, natural language processing means for analyzing the text data and generating summaries, and a home assistance device that has the function of executing responses or instructions in cooperation with home appliances or information sources. This enables users to efficiently execute a variety of responses and functions through voice control within their homes. 【0100】 "Speech recognition means" refers to a device or program that converts speech signals into text data. 【0101】 "Natural language processing methods" refer to technologies and methods that analyze text data and perform meaning summarization and extraction. 【0102】 "Evaluation means" refers to a function that determines the content of an inquiry based on the analyzed text data and generates an automated response or determines the appropriate forwarding destination. 【0103】 "Response provision means" refers to a part of a system that has the function of providing a generated automated response by voice, or forwarding the communication to a determined destination. 【0104】 "Home-use assistive devices" refer to devices or machines that can respond to or receive instructions via voice within a home environment and can interact with home appliances and information sources. 【0105】 This invention is a voice assistance system for use in the home. The server uses the Google® Speech-to-Text API as a speech recognition tool to convert voice signals transmitted from a home terminal into text data. The text data is then analyzed by the spaCy library as a natural language processing tool to summarize the user's intentions and requests. 【0106】 The server further classifies user requests based on analysis results using evaluation tools and generates appropriate automated responses. This information can also be linked with home assistive devices to control various household appliances. Therefore, users can easily operate home appliances and obtain information through voice commands within their homes. 【0107】 As a concrete example, consider a scenario where a user says, "Turn off the living room lights." This voice is captured by the device and sent to the server. The server transcribes the voice into text and identifies it as a command related to lighting operation. The home assistance device then works in conjunction with the lighting control system to perform the requested operation. This allows users to easily manage their home environment through voice commands. 【0108】 This example demonstrates how a generative AI model can be used to provide appropriate prompts in response to user voice requests. For instance, by generating a prompt such as, "Please tell me the procedure for operating an electrical appliance using voice commands," the system can derive the corresponding procedure. In this way, a flexible system can be provided that is easily integrated with specific usage scenarios. 【0109】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0110】 Step 1: 【0111】 The user gives voice commands to a device within their home. A voice input device captures this voice and generates data as an audio signal. The input is the user's voice, and the output is an audio signal. 【0112】 Step 2: 【0113】 The device sends the captured audio signal to the server. The server uses the Google Speech-to-Text API to convert the audio signal into text data. The input is an audio signal, and the output is text data. Speech recognition is performed in this step, and the data is processed to clarify the user's intent. 【0114】 Step 3: 【0115】 The server analyzes the acquired text data using spaCy, a natural language processing tool, to summarize the instructions and identify categories. The input is text data, and the output is summarized instructions and category information. Here, data analysis using natural language processing is performed. 【0116】 Step 4: 【0117】 The server uses an evaluation tool to generate an appropriate automated response to the summarized instructions, or instructs the home assistive device to perform the action. The input consists of summarized instructions and category information, while the output is a specific operational instruction or response. The evaluation process may also utilize a generative AI model. 【0118】 Step 5: 【0119】 Home assistive devices execute operation instructions received from a server, for example, to operate household appliances. The input is the operation instructions from the server, and the output is the physical execution of the operation on the appliance. This allows the user's voice commands to be reflected in actual operation. 【0120】 Through the above processing steps, a system is realized that allows users to efficiently control devices and information services within their homes through voice commands. 【0121】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0122】 This invention is a system for efficiently and flexibly handling user inquiries in a call center. This system combines speech recognition, natural language processing, automatic response generation, and transfer functions with an emotion engine that recognizes the user's emotions. 【0123】 When a user calls a common inquiry number, the terminal immediately records the audio. This audio data is sent to a server and converted into text data by speech recognition. At the same time, an emotion engine also receives the audio signal and analyzes the user's emotions based on their speech. 【0124】 The analyzed text data is then analyzed in detail using natural language processing tools to summarize the query. Using this summary and sentiment information obtained by the sentiment engine, the server uses evaluation tools to generate an appropriate automated response to the user's query, or, if necessary, to determine the destination. 【0125】 Based on the analysis results of the emotion engine, the content and tone of responses can be adjusted. For example, if a user is showing signs of frustration, the server will set the response to a gentler tone and begin addressing the issue quickly. If emotions suggesting a potential complaint are detected, the user will be transferred to a more specialized department. 【0126】 For example, if a user reports dissatisfaction with an "unknown charge on a recent invoice," the device sends the recorded audio to the server for speech recognition and sentiment analysis. If natural language processing recognizes the "invoice problem" and the sentiment engine detects "anxiety" and "frustration," the server adjusts its automated response based on evaluation criteria and forwards the case to a specialist team for prompt problem resolution. 【0127】 This system allows users to receive efficient and emotionally sensitive responses, and also improves the quality of service provided by call centers. 【0128】 The following describes the processing flow. 【0129】 Step 1: 【0130】 The user calls a common inquiry number. The terminal receives the call and starts recording the voice message. 【0131】 Step 2: 【0132】 The device sends the recorded audio data to the server. This audio data is then input into the speech recognition system and the emotion engine. 【0133】 Step 3: 【0134】 The server uses speech recognition to convert the audio data into text data. This text data represents the content of the inquiry. 【0135】 Step 4: 【0136】 The server's emotion engine analyzes the voice data to identify the user's emotions. In this process, it determines emotions from the tone of voice and word choice. 【0137】 Step 5: 【0138】 The server analyzes the text data using natural language processing techniques and summarizes the query content. As a result, the query category and importance level are determined. 【0139】 Step 6: 【0140】 The server uses various evaluation tools to generate an automated response based on the summarized content and sentiment analysis results. If a simple response is possible, that response will be selected. 【0141】 Step 7: 【0142】 The server adjusts the tone and content of its response based on the user's emotional state. For example, in urgent situations or when the user is highly dissatisfied, a more considerate response will be selected. 【0143】 Step 8: 【0144】 The terminal receives a response from the server and provides it to the user in voice using speech synthesis technology. 【0145】 Step 9: 【0146】 If the inquiry is deemed complex and requires specialized support, the server consults a list of forwarding destinations and sends an instruction to the terminal to transfer the user's call to the most appropriate contact person. 【0147】 Step 10: 【0148】 The terminal, following instructions from the server, forwards the user's call to the selected forwarding destination. There, the user can interact directly with the representative. 【0149】 Step 11: 【0150】 The server records all processing logs and saves necessary data. This data is then used for subsequent service improvements and analysis. 【0151】 (Example 2) 【0152】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0153】 There is a lack of efficient and emotionally sensitive means to handle user inquiries in call centers. Traditional systems fail to adequately consider user emotions in their responses, and there are also issues with the quality of automated responses and the accuracy of call transfers. 【0154】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0155】 In this invention, the server includes means for converting an audio signal into digital data and extracting emotional information from the audio signal; means for converting the digital data into text data and analyzing the text data to generate a summary; and means for evaluating the content of the inquiry based on the summary and emotional information, and for generating an automated response or determining the destination for forwarding. This enables flexible responses that take into account the user's emotions and prompt, appropriate forwarding. 【0156】 A "voice signal" is an electrical or digital waveform used for information transmission via voice communication. 【0157】 "Digital data" refers to data that represents analog signals using a digital method, making it easy to process and store on computers and communication devices. 【0158】 "Emotional information" refers to data that indicates a user's psychological state, extracted from audio and text, and quantifies or categorizes the user's emotional expressions. 【0159】 "Text data" refers to data that represents natural language in string format and is used by computers to read and process it. 【0160】 A "summary" refers to a shortened expression of the most important points from a given piece of information or a dataset. 【0161】 "Inquiry details" refers to information about questions or requests that users ask the system or service. 【0162】 "Automatic response" refers to a function or the content of a response in which a pre-configured program automatically reacts to and responds to user inquiries. 【0163】 "Transfer destination" refers to the designated recipient or processing location to which a user's inquiry will be transferred to another operator or function as needed. 【0164】 A "generative AI model" refers to an artificial intelligence model that has the ability to generate new data and information based on machine learning. 【0165】 A "prompt" is an instruction or input sentence for an AI model, referring to text that defines the conditions or circumstances under which the model generates a response. 【0166】 This invention is a system for handling user inquiries in a call center in an efficient and emotionally sensitive manner. When a user calls a common inquiry number, a terminal receives the call and immediately records the voice signal. The terminal converts the voice signal into digital data and transmits it to a server via the network. This recorded voice data is converted into text data using speech recognition technology, specifically a common speech recognition API. 【0167】 When this audio data is converted into text data, the server uses an emotion engine to analyze the tone, speed, and volume of the voice, and extract emotional information. This allows the server to understand the user's emotional state and reflect it in subsequent response generation. The server then passes the acquired text data to a natural language processing engine to analyze the query and create a summary. 【0168】 By using a generative AI model and creating appropriate prompts based on this data, the AI ​​generates automated responses. For example, if a user complains of "unknown charges on a recent bill," the emotion engine detects "anxiety" and "frustration." Based on this information, the AI ​​generates a response such as, "We apologize for the confusion. We will contact you as soon as we have more information," and can deliver it to the user in a natural way using speech synthesis technology. 【0169】 As a concrete example, here are some examples of prompt statements that can be input to a generative AI model: 【0170】 User comment: "My delivery didn't arrive on time." 【0171】 Emotional analysis results: "Anxiety," "Irritation" 【0172】 Required response tone: "Gentle" 【0173】 Thus, in this invention, the system can provide efficient responses while taking user emotions into consideration, and contribute to improving the operations of call centers. 【0174】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0175】 Step 1: 【0176】 A user calls a call center, and the terminal receives the call. The terminal records the audio signal at the start of the call and converts it into digital data. The input is an analog audio signal, and the output is obtained as digital audio data. Processing for sending this digital data to the server is performed via a network interface. 【0177】 Step 2: 【0178】 The server receives digital audio data. The server uses a speech recognition engine to convert the digital audio data into text data. The input is digital audio data, and the output is text data that represents the content of the audio as character data. During this conversion process, the speech recognition engine uses a specific algorithm to map phonemes to characters. 【0179】 Step 3: 【0180】 The server inputs text data into a natural language processing engine, which then parses it. The natural language processing engine analyzes the input text data, summarizes its content, and extracts the main topic of the query. This process yields summarized data as output from the input text data. For example, if the input is "There are unknown charges on my recent invoice," a summary such as "Invoice problem" will be generated. 【0181】 Step 4: 【0182】 The server evaluates the inquiry content and generates an appropriate automated response, using emotional information extracted from the voice. An emotional analysis engine is used for evaluation, which quantifies the user's emotional state. Input consists of summary data and emotional information, while output is response data based on the generated prompt. 【0183】 Step 5: 【0184】 The server inputs a prompt into a generative AI model, which then generates an appropriate response. The prompt includes the user's statement, sentiment analysis results, and the required response tone. The input is the prompt, and the output is the response text generated by the AI. A concrete example of a response generated might be, "We apologize for the delay. We will contact you as soon as we have confirmed the delivery status." 【0185】 Step 6: 【0186】 The server converts the generated response text into speech using a speech synthesis engine and provides the response to the user via the terminal. In this process, the input is the response text, and the output is synthesized speech. Finally, the terminal delivers this synthesized speech to the user via the call line. 【0187】 (Application Example 2) 【0188】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal". 【0189】 In brick-and-mortar retail settings, employees are required to quickly and accurately understand customer emotions and respond accordingly. However, it is not easy for employees to consistently grasp customer emotions and provide the optimal response. This can lead to a decline in customer satisfaction. 【0190】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0191】 In this invention, the server includes speech recognition means for converting audio signals into text data, natural language processing means for analyzing the text data and generating a summary, emotion recognition means for analyzing the user's emotions and adjusting the content and tone of the response based on the analysis results, and visual output means for outputting the analysis results through the display of visual information. This enables employees in physical stores to analyze customers' emotions in real time and take appropriate action. 【0192】 "Speech recognition means" refers to technology for analyzing speech signals and converting them into corresponding text data. 【0193】 "Natural language processing methods" are technologies that analyze text data, summarize its content, and understand the meaning and intent of the information. 【0194】 "Evaluation methods" refer to technologies that analyze information based on the content of an inquiry and either generate an automated response or determine the appropriate destination for forwarding the information. 【0195】 "Emotion recognition means" refers to technology that extracts a user's emotions from voice or text and adjusts the content and tone of the response according to the analysis results. 【0196】 "Response provision means" refers to technology for providing a generated automated response in voice or for forwarding the communication to a determined destination. 【0197】 "Visual output means" refers to a device or technology for presenting information to a user by visually displaying the analysis results. 【0198】 "Recording means" refers to a device or technology for storing process data generated using speech recognition means, natural language processing means, evaluation means, and response provision means. 【0199】 This invention is a system for providing customer service in stores, utilizing speech recognition, natural language processing, and sentiment analysis as its underlying technologies. The server effectively integrates these functions, processing voice data to analyze customer emotions and generating appropriate responses. 【0200】 First, when a user interacts with a store staff member, the audio is recorded by the device and sent to a server. The Google Cloud Speech-to-Text API is used for speech recognition, converting the audio signal into text data. Next, this text data is analyzed using IBM Watson® Tone Analyzer to extract the user's emotional state. Based on this analysis, natural language processing technology is used to summarize the inquiry and generate specific response suggestions. 【0201】 The generated response suggestions are integrated with information obtained through emotion recognition, and the tone and content of the actual response are adjusted to reflect the customer's emotions. This adjustment result is displayed on the screen of smart glasses such as Google Glass®, and serves as a reference for on-site staff to provide optimal customer service. 【0202】 For example, if a customer expresses concern about a product, the system recognizes this concern and notifies staff to provide a detailed explanation regarding the product warranty. This system is expected to improve customer satisfaction. 【0203】 An example of a prompt statement is as follows: 【0204】 "Analyze the user's voice input and recognize their emotional state. Perform text analysis and emotion recognition, and demonstrate how to display feedback on smart glasses based on the results, indicating how employees should interact with customers." 【0205】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0206】 Step 1: 【0207】 The terminal records the conversation between the user and the store staff. The recorded audio data is input and sent to the server in its original format. 【0208】 Step 2: 【0209】 The server uses the Google Cloud Speech-to-Text API to convert received audio data into text data. The input is audio data, and the output is the converted text data. In this conversion process, the audio signal is analyzed to generate the corresponding string. 【0210】 Step 3: 【0211】 The server uses IBM Watson's Tone Analyzer to analyze text data and extract user sentiment information. The input for this step is text data, and the output is data indicating the user's emotional state. Natural language processing is performed here to evaluate the emotional elements within the text. 【0212】 Step 4: 【0213】 The server analyzes text data and summarizes the query using natural language processing techniques. The input is text data, and the output is the summarized query. This process extracts the meaning and important parts of the information and generates a summary. 【0214】 Step 5: 【0215】 The server generates an appropriate automated response based on the summarized inquiry content and sentiment state. The inputs here are the summarized content and sentiment data, and the output is the automated response data. The response tone is adjusted to take sentiment data into consideration, creating a customer-friendly response. 【0216】 Step 6: 【0217】 The server displays the generated automated response on the smart glasses' display, providing staff with visual feedback. The input is the automated response data, and the output is the visual display on the smart glasses. Here, the visibility and speed of the feedback are emphasized, and the information is provided in a way that staff can easily understand. 【0218】 The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data. 【0219】 Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0220】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14. 【0221】 [Second Embodiment] 【0222】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0223】 As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server. 【0224】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0225】 The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52. 【0226】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0227】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0228】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0229】 Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0230】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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. 【0231】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0232】 In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0233】 Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0234】 This invention relates to a system for efficiently automating call center inquiry processing. This system functions by combining speech recognition, natural language processing, automated response generation, and transfer. 【0235】 When a user calls a common inquiry number, the terminal receives the call and records the audio. The recorded audio is sent to a server, where speech recognition capabilities convert the audio signal into text data. This text data is then analyzed by natural language processing capabilities within the server to summarize the inquiry. 【0236】 Based on the summarized inquiry content, the server uses an evaluation mechanism to generate an automated response on the server and provide it to the user as voice if the inquiry is simple. If the inquiry is more complex, the server selects the most appropriate contact person to forward the call to. The terminal then forwards the user's call to this designated contact person, ensuring they receive appropriate support. 【0237】 As a concrete example, consider a case where a user reports that their internet connection is slow. In this case, speech recognition technology converts this into text data, "Slow internet connection." This text data is then analyzed by natural language processing technology, and the problem category is summarized as "Technical Support." The server then uses evaluation technology to search for an appropriate destination and forwards the information to the technical support team. As a result, the user can receive technical support smoothly. 【0238】 This system allows users to receive quick and accurate responses through voice-based reporting, and also significantly improves the operational efficiency of call centers. 【0239】 The following describes the processing flow. 【0240】 Step 1: 【0241】 The user calls a common inquiry number. The terminal receives the call, activates the system, and records the audio. 【0242】 Step 2: 【0243】 The device sends the recorded audio data to the server. This data is then passed to the speech recognition system. 【0244】 Step 3: 【0245】 The server uses speech recognition to convert the received audio data into text data. At this stage, the audio becomes non-acoustic textual information. 【0246】 Step 4: 【0247】 The server uses natural language processing to analyze text data and summarize the query. This analysis generates summarized information. 【0248】 Step 5: 【0249】 The server uses an evaluation tool to assess the summarized content. If the content is simple, it starts a process to generate an automated response on the spot. 【0250】 Step 6: 【0251】 When the server generates an automated response, it uses either a predefined response database or a dynamic response generation algorithm. 【0252】 Step 7: 【0253】 If the user has a simple inquiry, the server generates an automated response and sends it back to the terminal, which then uses speech synthesis technology to provide the response to the user. 【0254】 Step 8: 【0255】 For complex inquiries, the server determines the appropriate contact person based on a list of forwarding destinations and selects the appropriate recipient. 【0256】 Step 9: 【0257】 The terminal, following the server's instructions, forwards the user's call to the selected forwarding destination, allowing the user to speak with the appropriate representative. 【0258】 Step 10: 【0259】 The server logs details of all processing steps. This log includes audio data, text data, responses, and destinations. 【0260】 (Example 1) 【0261】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0262】 In call centers, handling inquiries requires speed and accuracy due to the increasing volume of calls and the diversity of issues. However, traditional manual responses are often understaffed, leading to delays in responses and incorrect transfers. This results in challenges such as decreased customer satisfaction and increased operating costs. 【0263】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0264】 In this invention, the server includes communication means for converting voice data into digital data using conversion means and transmitting it to a central device; information processing means for analyzing the digital data and performing content analysis; and selection means for evaluating the information based on the content analysis and generating an automatic response or determining an appropriate forwarding destination. This enables faster and more accurate query processing. 【0265】 "Means for converting audio data" refers to a device or software that converts an audio signal into digital data. 【0266】 "Communication means" refers to a method or device for transmitting data to another device or system. 【0267】 A "central unit" is the main computing unit used for processing data and issuing commands. 【0268】 "Information processing means" refers to software or algorithms used to analyze input data and extract or understand specific information. 【0269】 "Content analysis" is the process of analyzing digital data in detail and understanding its content. 【0270】 A "selection tool" is a method or device for determining the optimal option from multiple choices. 【0271】 "Automated response" refers to a mechanism that autonomously generates appropriate responses to user inquiries. 【0272】 "Determining the forwarding destination" is the process of selecting the appropriate contact point or department to handle the inquiry based on its content. 【0273】 This invention is a system for efficiently handling voice-based inquiries in a call center. The system's functionality is primarily comprised of terminals, a server, and users. 【0274】 The user initiates an inquiry by calling a common inquiry number. The terminal receives the voice signal from this user and generates voice data. This voice data is sent to a server, where it is converted into digital data by a voice data conversion means. For example, a general-purpose voice recognition platform can be used as the voice recognition software. 【0275】 Next, the server analyzes this digital data using information processing tools to understand the query. For example, tools such as "spaCy" or "BERT" could be used as natural language processing technologies. This extracts the gist of the query and classifies it into appropriate categories. 【0276】 The server initiates a process to either generate an automated response based on the extracted information, or to determine the appropriate department to forward the information to using a selection method. If an automated response is possible, the server uses speech synthesis technology to generate and provide the response as audio. For speech synthesis, services such as "Amazon Polly" can be used. 【0277】 On the other hand, when the inquiry is complex and requires specialized handling, the server determines the optimal responsible department by means of selection. The terminal transfers the call from the user to the designated responsible window according to the instructions of the server. 【0278】 As a specific example, when a user reports that "the Internet connection is slow", it is converted into digital text "slow Internet connection" by voice recognition means. Then, it is classified as "technical support" as the category of the problem by natural language processing means, and the transfer procedure to the technical support team is executed. 【0279】 As an example of a prompt sentence using a generative AI model, something like "A user called the call center and reported a problem with the Internet connection. Please teach me the quick and accurate response procedure for this situation." can be considered. 【0280】 With this system, the user can receive an appropriate response efficiently and quickly, and the operating efficiency of the call center is also improved. 【0281】 The flow of the specific process in Example 1 will be described using FIG. 11. 【0282】 Step 1: 【0283】 The user calls a common inquiry number. At this point, the user conveys the inquiry content through voice. The terminal receives this voice signal in real time and records it as digital voice data. The input here is the user's voice, and the output is digital voice data. The terminal formats this appropriately and sends it to the server. 【0284】 Step 2: 【0285】 The server receives the voice data sent from the terminal. The server uses conversion means to execute a voice recognition process on the voice data. In this process, the voice data is converted into text data. The input is digital voice data and the output is text data. The server performs this conversion by leveraging voice recognition software. 【0286】 Step 3: 【0287】 The server analyzes the text data generated by voice recognition using information processing means. In this analysis, the content is understood and summarized as needed. For example, keyphrase extraction and topic modeling are performed. The input is the text data generated in the previous step and the output is the summarized or categorized data. The server performs this processing using natural language processing technology. 【0288】 Step 4: 【0289】 Based on the analysis result, the server generates an automatic response or selects an appropriate transfer destination if necessary. This determination is made using selection means, and when an automatic response is applicable, the server prepares the response. The input is the categorized data and the output is an automatic response message or a transfer instruction. The server conducts an evaluation based on a rule-based or machine learning model. 【0290】 Step 5: 【0291】 Depending on the situation, the server converts the generated automatic response into voice using voice synthesis technology and sends it to the terminal. If transfer is necessary, the server designates the transfer destination and issues an instruction to the terminal. The input is an automatic response message or a transfer instruction and the output is a voice response or a transfer setting. A TTS engine is used for voice synthesis. 【0292】 Step 6: 【0293】 The terminal either provides the user with an audio response from the server or forwards the user's call to a designated destination. Here, the terminal ultimately provides feedback to the user or connects them to a support representative. The input is the audio response or forwarding instruction from the server, and the output is the response to the user and the call forwarding. The terminal then completes the entire process. 【0294】 (Application Example 1) 【0295】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0296】 In modern homes, there is a growing demand for quick and efficient use of home appliances and information services through voice control. However, existing home appliances have limited functionality and struggle to seamlessly integrate with multiple devices and information sources. 【0297】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0298】 In this invention, the server includes speech recognition means for converting voice signals into text data, natural language processing means for analyzing the text data and generating summaries, and a home assistance device that has the function of executing responses or instructions in cooperation with home appliances or information sources. This enables users to efficiently execute a variety of responses and functions through voice control within their homes. 【0299】 "Speech recognition means" refers to a device or program that converts speech signals into text data. 【0300】 "Natural language processing methods" refer to technologies and methods that analyze text data and perform meaning summarization and extraction. 【0301】 "Evaluation means" refers to a function that determines the content of an inquiry based on the analyzed text data and generates an automated response or determines the appropriate forwarding destination. 【0302】 "Response provision means" refers to a part of a system that has the function of providing a generated automated response by voice, or forwarding the communication to a determined destination. 【0303】 "Home-use assistive devices" refer to devices or machines that can respond to or receive instructions via voice within a home environment and can interact with home appliances and information sources. 【0304】 This invention is a voice assistance system for use in the home. The server uses the Google Speech-to-Text API as a speech recognition tool to convert voice signals transmitted from a home device into text data. The text data is then parsed by the spaCy library as a natural language processing tool to summarize the user's intent and requests. 【0305】 The server further classifies user requests based on analysis results using evaluation tools and generates appropriate automated responses. This information can also be linked with home assistive devices to control various household appliances. Therefore, users can easily operate home appliances and obtain information through voice commands within their homes. 【0306】 As a concrete example, consider a scenario where a user says, "Turn off the living room lights." This voice is captured by the device and sent to the server. The server transcribes the voice into text and identifies it as a command related to lighting operation. The home assistance device then works in conjunction with the lighting control system to perform the requested operation. This allows users to easily manage their home environment through voice commands. 【0307】 An example of an appropriate prompt sentence corresponding to a voice request from a user is shown using a generative AI model. For example, by generating a prompt sentence such as "Please teach me the procedure when a user requests the operation of an electrical product by voice", the system can derive the corresponding procedure. In this way, a flexible system that can easily cooperate with specific usage scenarios can be provided. 【0308】 The flow of the specific process in Application Example 1 will be described using FIG. 12. 【0309】 Step 1: 【0310】 The user gives a voice instruction to a terminal in the home. The voice input device captures this voice and generates data as a voice signal. The input is the user's voice, and the output is a voice signal. 【0311】 Step 2: 【0312】 The terminal sends the captured voice signal to the server. The server uses the Google Speech-to-Text API to convert the voice signal into text data. The input is a voice signal, and the output is text data. Voice recognition is performed in this step, and data processing for clarifying the user's intention is performed. 【0313】 Step 3: 【0314】 The server analyzes the acquired text data using spaCy, which is a natural language processing means, summarizes the instruction content, and identifies the category. The input is text data, and the output is the summarized instruction content and category information. Here, data analysis by natural language processing is performed. 【0315】 Step 4: 【0316】 The server uses an evaluation tool to generate an appropriate automated response to the summarized instructions, or instructs the home assistive device to perform the action. The input consists of summarized instructions and category information, while the output is a specific operational instruction or response. The evaluation process may also utilize a generative AI model. 【0317】 Step 5: 【0318】 Home assistive devices execute operation instructions received from a server, for example, to operate household appliances. The input is the operation instructions from the server, and the output is the physical execution of the operation on the appliance. This allows the user's voice commands to be reflected in actual operation. 【0319】 Through the above processing steps, a system is realized that allows users to efficiently control devices and information services within their homes through voice commands. 【0320】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0321】 This invention is a system for efficiently and flexibly handling user inquiries in a call center. This system combines speech recognition, natural language processing, automatic response generation, and transfer functions with an emotion engine that recognizes the user's emotions. 【0322】 When a user calls a common inquiry number, the terminal immediately records the audio. This audio data is sent to a server and converted into text data by speech recognition. At the same time, an emotion engine also receives the audio signal and analyzes the user's emotions based on their speech. 【0323】 The analyzed text data is then analyzed in detail using natural language processing tools to summarize the query. Using this summary and sentiment information obtained by the sentiment engine, the server uses evaluation tools to generate an appropriate automated response to the user's query, or, if necessary, to determine the destination. 【0324】 Based on the analysis results of the emotion engine, the content and tone of responses can be adjusted. For example, if a user is showing signs of frustration, the server will set the response to a gentler tone and begin addressing the issue quickly. If emotions suggesting a potential complaint are detected, the user will be transferred to a more specialized department. 【0325】 For example, if a user reports dissatisfaction with an "unknown charge on a recent invoice," the device sends the recorded audio to the server for speech recognition and sentiment analysis. If natural language processing recognizes the "invoice problem" and the sentiment engine detects "anxiety" and "frustration," the server adjusts its automated response based on evaluation criteria and forwards the case to a specialist team for prompt problem resolution. 【0326】 This system allows users to receive efficient and emotionally sensitive responses, and also improves the quality of service provided by call centers. 【0327】 The following describes the processing flow. 【0328】 Step 1: 【0329】 The user calls a common inquiry number. The terminal receives the call and starts recording the voice message. 【0330】 Step 2: 【0331】 The device sends the recorded audio data to the server. This audio data is then input into the speech recognition system and the emotion engine. 【0332】 Step 3: 【0333】 The server uses speech recognition to convert the audio data into text data. This text data represents the content of the inquiry. 【0334】 Step 4: 【0335】 The server's emotion engine analyzes the voice data to identify the user's emotions. In this process, it determines emotions from the tone of voice and word choice. 【0336】 Step 5: 【0337】 The server analyzes the text data using natural language processing techniques and summarizes the query content. As a result, the query category and importance level are determined. 【0338】 Step 6: 【0339】 The server uses various evaluation tools to generate an automated response based on the summarized content and sentiment analysis results. If a simple response is possible, that response will be selected. 【0340】 Step 7: 【0341】 The server adjusts the tone and content of its response based on the user's emotional state. For example, in urgent situations or when the user is highly dissatisfied, a more considerate response will be selected. 【0342】 Step 8: 【0343】 The terminal receives a response from the server and provides it to the user in voice using speech synthesis technology. 【0344】 Step 9: 【0345】 If the inquiry is deemed complex and requires specialized support, the server consults a list of forwarding destinations and sends an instruction to the terminal to transfer the user's call to the most appropriate contact person. 【0346】 Step 10: 【0347】 The terminal, following instructions from the server, forwards the user's call to the selected forwarding destination. There, the user can interact directly with the representative. 【0348】 Step 11: 【0349】 The server records all processing logs and saves necessary data. This data is then used for subsequent service improvements and analysis. 【0350】 (Example 2) 【0351】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0352】 There is a lack of efficient and emotionally sensitive means to handle user inquiries in call centers. Traditional systems fail to adequately consider user emotions in their responses, and there are also issues with the quality of automated responses and the accuracy of call transfers. 【0353】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0354】 In this invention, the server includes means for converting an audio signal into digital data and extracting emotional information from the audio signal; means for converting the digital data into text data and analyzing the text data to generate a summary; and means for evaluating the content of the inquiry based on the summary and emotional information, and for generating an automated response or determining the destination for forwarding. This enables flexible responses that take into account the user's emotions and prompt, appropriate forwarding. 【0355】 A "voice signal" is an electrical or digital waveform used for information transmission via voice communication. 【0356】 "Digital data" refers to data that represents analog signals using a digital method, making it easy to process and store on computers and communication devices. 【0357】 "Emotional information" refers to data that indicates a user's psychological state, extracted from audio and text, and quantifies or categorizes the user's emotional expressions. 【0358】 "Text data" refers to data that represents natural language in string format and is used by computers to read and process it. 【0359】 A "summary" refers to a shortened expression of the most important points from a given piece of information or a dataset. 【0360】 "Inquiry details" refers to information about questions or requests that users ask the system or service. 【0361】 "Automatic response" refers to a function or the content of a response in which a pre-configured program automatically reacts to and responds to user inquiries. 【0362】 "Transfer destination" refers to the designated recipient or processing location to which a user's inquiry will be transferred to another operator or function as needed. 【0363】 A "generative AI model" refers to an artificial intelligence model that has the ability to generate new data and information based on machine learning. 【0364】 A "prompt" is an instruction or input sentence for an AI model, referring to text that defines the conditions or circumstances under which the model generates a response. 【0365】 This invention is a system for handling user inquiries in a call center in an efficient and emotionally sensitive manner. When a user calls a common inquiry number, a terminal receives the call and immediately records the voice signal. The terminal converts the voice signal into digital data and transmits it to a server via the network. This recorded voice data is converted into text data using speech recognition technology, specifically a common speech recognition API. 【0366】 When this audio data is converted into text data, the server uses an emotion engine to analyze the tone, speed, and volume of the voice, and extract emotional information. This allows the server to understand the user's emotional state and reflect it in subsequent response generation. The server then passes the acquired text data to a natural language processing engine to analyze the query and create a summary. 【0367】 By using a generative AI model and creating appropriate prompts based on this data, the AI ​​generates automated responses. For example, if a user complains of "unknown charges on a recent bill," the emotion engine detects "anxiety" and "frustration." Based on this information, the AI ​​generates a response such as, "We apologize for the confusion. We will contact you as soon as we have more information," and can deliver it to the user in a natural way using speech synthesis technology. 【0368】 As a concrete example, here are some examples of prompt statements that can be input to a generative AI model: 【0369】 User comment: "My delivery didn't arrive on time." 【0370】 Emotional analysis results: "Anxiety," "Irritation" 【0371】 Required response tone: "Gentle" 【0372】 Thus, in this invention, the system can provide efficient responses while taking user emotions into consideration, and contribute to improving the operations of call centers. 【0373】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0374】 Step 1: 【0375】 A user calls a call center, and the terminal receives the call. The terminal records the audio signal at the start of the call and converts it into digital data. The input is an analog audio signal, and the output is obtained as digital audio data. Processing for sending this digital data to the server is performed via a network interface. 【0376】 Step 2: 【0377】 The server receives digital audio data. The server uses a speech recognition engine to convert the digital audio data into text data. The input is digital audio data, and the output is text data that represents the content of the audio as character data. During this conversion process, the speech recognition engine uses a specific algorithm to map phonemes to characters. 【0378】 Step 3: 【0379】 The server inputs text data into a natural language processing engine, which then parses it. The natural language processing engine analyzes the input text data, summarizes its content, and extracts the main topic of the query. This process yields summarized data as output from the input text data. For example, if the input is "There are unknown charges on my recent invoice," a summary such as "Invoice problem" will be generated. 【0380】 Step 4: 【0381】 The server evaluates the inquiry content and generates an appropriate automated response, using emotional information extracted from the voice. An emotional analysis engine is used for evaluation, which quantifies the user's emotional state. Input consists of summary data and emotional information, while output is response data based on the generated prompt. 【0382】 Step 5: 【0383】 The server inputs a prompt into a generative AI model, which then generates an appropriate response. The prompt includes the user's statement, sentiment analysis results, and the required response tone. The input is the prompt, and the output is the response text generated by the AI. A concrete example of a response generated might be, "We apologize for the delay. We will contact you as soon as we have confirmed the delivery status." 【0384】 Step 6: 【0385】 The server converts the generated response text into speech using a speech synthesis engine and provides the response to the user via the terminal. In this process, the input is the response text, and the output is synthesized speech. Finally, the terminal delivers this synthesized speech to the user via the call line. 【0386】 (Application Example 2) 【0387】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0388】 In brick-and-mortar retail settings, employees are required to quickly and accurately understand customer emotions and respond accordingly. However, it is not easy for employees to consistently grasp customer emotions and provide the optimal response. This can lead to a decline in customer satisfaction. 【0389】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0390】 In this invention, the server includes speech recognition means for converting audio signals into text data, natural language processing means for analyzing the text data and generating a summary, emotion recognition means for analyzing the user's emotions and adjusting the content and tone of the response based on the analysis results, and visual output means for outputting the analysis results through the display of visual information. This enables employees in physical stores to analyze customers' emotions in real time and take appropriate action. 【0391】 "Speech recognition means" refers to technology for analyzing speech signals and converting them into corresponding text data. 【0392】 "Natural language processing methods" are technologies that analyze text data, summarize its content, and understand the meaning and intent of the information. 【0393】 "Evaluation methods" refer to technologies that analyze information based on the content of an inquiry and either generate an automated response or determine the appropriate destination for forwarding the information. 【0394】 "Emotion recognition means" refers to technology that extracts a user's emotions from voice or text and adjusts the content and tone of the response according to the analysis results. 【0395】 "Response provision means" refers to technology for providing a generated automated response in voice or for forwarding the communication to a determined destination. 【0396】 "Visual output means" refers to a device or technology for presenting information to a user by visually displaying the analysis results. 【0397】 "Recording means" refers to a device or technology for storing process data generated using speech recognition means, natural language processing means, evaluation means, and response provision means. 【0398】 This invention is a system for providing customer service in stores, utilizing speech recognition, natural language processing, and sentiment analysis as its underlying technologies. The server effectively integrates these functions, processing voice data to analyze customer emotions and generating appropriate responses. 【0399】 First, when a user interacts with a store staff member, the audio is recorded by the device and sent to a server. The Google Cloud Speech-to-Text API is used for speech recognition, converting the audio signal into text data. Next, this text data is analyzed using IBM Watson's Tone Analyzer to extract the user's emotional state. Based on this analysis, natural language processing technology is used to summarize the inquiry and generate specific response suggestions. 【0400】 The generated response suggestions are integrated with information obtained through emotion recognition, and the tone and content of the actual response are adjusted to reflect the customer's emotions. This adjustment result is displayed on the screen of smart glasses, such as Google Glass, to help on-site staff provide optimal customer service. 【0401】 For example, if a customer expresses concern about a product, the system recognizes this concern and notifies staff to provide a detailed explanation regarding the product warranty. This system is expected to improve customer satisfaction. 【0402】 An example of a prompt statement is as follows: 【0403】 "Analyze the user's voice input and recognize their emotional state. Perform text analysis and emotion recognition, and demonstrate how to display feedback on smart glasses based on the results, indicating how employees should interact with customers." 【0404】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0405】 Step 1: 【0406】 The terminal records the conversation between the user and the store staff. The recorded audio data is input and sent to the server in its original format. 【0407】 Step 2: 【0408】 The server uses the Google Cloud Speech-to-Text API to convert received audio data into text data. The input is audio data, and the output is the converted text data. In this conversion process, the audio signal is analyzed to generate the corresponding string. 【0409】 Step 3: 【0410】 The server uses IBM Watson's Tone Analyzer to analyze text data and extract user sentiment information. The input for this step is text data, and the output is data indicating the user's emotional state. Natural language processing is performed here to evaluate the emotional elements within the text. 【0411】 Step 4: 【0412】 The server analyzes text data and summarizes the query using natural language processing techniques. The input is text data, and the output is the summarized query. This process extracts the meaning and important parts of the information and generates a summary. 【0413】 Step 5: 【0414】 The server generates an appropriate automated response based on the summarized inquiry content and sentiment state. The inputs here are the summarized content and sentiment data, and the output is the automated response data. The response tone is adjusted to take sentiment data into consideration, creating a customer-friendly response. 【0415】 Step 6: 【0416】 The server displays the generated automated response on the smart glasses' display, providing staff with visual feedback. The input is the automated response data, and the output is the visual display on the smart glasses. Here, the visibility and speed of the feedback are emphasized, and the information is provided in a way that staff can easily understand. 【0417】 The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0418】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0419】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214. 【0420】 [Third Embodiment] 【0421】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0422】 As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server. 【0423】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0424】 The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52. 【0425】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0426】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0427】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0428】 Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0429】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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. 【0430】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0431】 In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0432】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal". 【0433】 This invention relates to a system for efficiently automating call center inquiry processing. This system functions by combining speech recognition, natural language processing, automated response generation, and transfer. 【0434】 When a user calls a common inquiry number, the terminal receives the call and records the audio. The recorded audio is sent to a server, where speech recognition capabilities convert the audio signal into text data. This text data is then analyzed by natural language processing capabilities within the server to summarize the inquiry. 【0435】 Based on the summarized inquiry content, the server uses an evaluation mechanism to generate an automated response on the server and provide it to the user as voice if the inquiry is simple. If the inquiry is more complex, the server selects the most appropriate contact person to forward the call to. The terminal then forwards the user's call to this designated contact person, ensuring they receive appropriate support. 【0436】 As a concrete example, consider a case where a user reports that their internet connection is slow. In this case, speech recognition technology converts this into text data, "Slow internet connection." This text data is then analyzed by natural language processing technology, and the problem category is summarized as "Technical Support." The server then uses evaluation technology to search for an appropriate destination and forwards the information to the technical support team. As a result, the user can receive technical support smoothly. 【0437】 This system allows users to receive quick and accurate responses through voice-based reporting, and also significantly improves the operational efficiency of call centers. 【0438】 The following describes the processing flow. 【0439】 Step 1: 【0440】 The user calls a common inquiry number. The terminal receives the call, activates the system, and records the audio. 【0441】 Step 2: 【0442】 The device sends the recorded audio data to the server. This data is then passed to the speech recognition system. 【0443】 Step 3: 【0444】 The server uses speech recognition to convert the received audio data into text data. At this stage, the audio becomes non-acoustic textual information. 【0445】 Step 4: 【0446】 The server uses natural language processing to analyze text data and summarize the query. This analysis generates summarized information. 【0447】 Step 5: 【0448】 The server uses an evaluation tool to assess the summarized content. If the content is simple, it starts a process to generate an automated response on the spot. 【0449】 Step 6: 【0450】 When the server generates an automated response, it uses either a predefined response database or a dynamic response generation algorithm. 【0451】 Step 7: 【0452】 If the user has a simple inquiry, the server generates an automated response and sends it back to the terminal, which then uses speech synthesis technology to provide the response to the user. 【0453】 Step 8: 【0454】 For complex inquiries, the server determines the appropriate contact person based on a list of forwarding destinations and selects the appropriate recipient. 【0455】 Step 9: 【0456】 The terminal, following the server's instructions, forwards the user's call to the selected forwarding destination, allowing the user to speak with the appropriate representative. 【0457】 Step 10: 【0458】 The server logs details of all processing steps. This log includes audio data, text data, responses, and destinations. 【0459】 (Example 1) 【0460】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0461】 In call centers, handling inquiries requires speed and accuracy due to the increasing volume of calls and the diversity of issues. However, traditional manual responses are often understaffed, leading to delays in responses and incorrect transfers. This results in challenges such as decreased customer satisfaction and increased operating costs. 【0462】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0463】 In this invention, the server includes communication means for converting voice data into digital data using conversion means and transmitting it to a central device; information processing means for analyzing the digital data and performing content analysis; and selection means for evaluating the information based on the content analysis and generating an automatic response or determining an appropriate forwarding destination. This enables faster and more accurate query processing. 【0464】 "Means for converting audio data" refers to a device or software that converts an audio signal into digital data. 【0465】 "Communication means" refers to a method or device for transmitting data to another device or system. 【0466】 A "central unit" is the main computing unit used for processing data and issuing commands. 【0467】 "Information processing means" refers to software or algorithms used to analyze input data and extract or understand specific information. 【0468】 "Content analysis" is the process of analyzing digital data in detail and understanding its content. 【0469】 A "selection tool" is a method or device for determining the optimal option from multiple choices. 【0470】 "Automated response" refers to a mechanism that autonomously generates appropriate responses to user inquiries. 【0471】 "Determining the forwarding destination" is the process of selecting the appropriate contact point or department to handle the inquiry based on its content. 【0472】 This invention is a system for efficiently handling voice-based inquiries in a call center. The system's functionality is primarily comprised of terminals, a server, and users. 【0473】 The user initiates an inquiry by calling a common inquiry number. The terminal receives the voice signal from this user and generates voice data. This voice data is sent to a server, where it is converted into digital data by a voice data conversion means. For example, a general-purpose voice recognition platform can be used as the voice recognition software. 【0474】 Next, the server analyzes this digital data using information processing tools to understand the query. For example, tools such as "spaCy" or "BERT" could be used as natural language processing technologies. This extracts the gist of the query and classifies it into appropriate categories. 【0475】 The server initiates a process to either generate an automated response based on the extracted information, or to determine the appropriate department to forward the information to using a selection method. If an automated response is possible, the server uses speech synthesis technology to generate and provide the response as audio. For speech synthesis, services such as "Amazon Polly" can be used. 【0476】 On the other hand, if the inquiry is complex and requires specialized handling, the server will determine the most appropriate department based on the selected method. The terminal will then forward the user's call to the designated department according to the server's instructions. 【0477】 For example, if a user reports that their internet connection is slow, speech recognition technology converts this into digital text. Then, natural language processing technology classifies the issue as requiring technical support, and the process of transferring the user to the technical support team is initiated. 【0478】 An example of a prompt message generated using a generative AI model might be: "A user called the call center and reported an internet connection problem. Please provide quick and accurate instructions on how to address this situation." 【0479】 This system allows users to receive efficient, quick, and appropriate responses, and also improves the operational efficiency of call centers. 【0480】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0481】 Step 1: 【0482】 The user calls a common inquiry number. At this point, the user communicates their inquiry via voice. The terminal receives this voice signal in real time and records it as digital audio data. The input here is the user's voice, and the output is digital audio data. The terminal formats this appropriately and sends it to the server. 【0483】 Step 2: 【0484】 The server receives audio data transmitted from the terminal. The server uses a conversion mechanism to perform a speech recognition process. In this process, the audio data is converted into text data. The input is digital audio data, and the output is text data. The server uses speech recognition software to perform this conversion. 【0485】 Step 3: 【0486】 The server analyzes the text data generated by speech recognition using information processing tools. This analysis involves understanding the content and summarizing it as needed. For example, keyphrase extraction and topic modeling are performed. The input is the text data generated in the previous step, and the output is summarized or categorized data. The server performs this processing using natural language processing techniques. 【0487】 Step 4: 【0488】 The server generates an automated response or, if necessary, selects an appropriate forwarding destination based on the analysis results. This decision is made using selection methods, and if an automated response is applicable, the server prepares the response. The input is categorized data, and the output is an automated response message or a forwarding instruction. The server performs evaluation using rule-based or machine learning models. 【0489】 Step 5: 【0490】 Depending on the situation, the server converts the generated automated response into speech using speech synthesis technology and sends it to the terminal. If forwarding is necessary, the server specifies the forwarding destination and issues instructions to the terminal. Input is the automated response message or forwarding instruction, and output is the voice response or forwarding setting. A TTS engine is used for speech synthesis. 【0491】 Step 6: 【0492】 The terminal either provides the user with an audio response from the server or forwards the user's call to a designated destination. Here, the terminal ultimately provides feedback to the user or connects them to a support representative. The input is the audio response or forwarding instruction from the server, and the output is the response to the user and the call forwarding. The terminal then completes the entire process. 【0493】 (Application Example 1) 【0494】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0495】 In modern homes, there is a growing demand for quick and efficient use of home appliances and information services through voice control. However, existing home appliances have limited functionality and struggle to seamlessly integrate with multiple devices and information sources. 【0496】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0497】 In this invention, the server includes speech recognition means for converting voice signals into text data, natural language processing means for analyzing the text data and generating summaries, and a home assistance device that has the function of executing responses or instructions in cooperation with home appliances or information sources. This enables users to efficiently execute a variety of responses and functions through voice control within their homes. 【0498】 "Speech recognition means" refers to a device or program that converts speech signals into text data. 【0499】 "Natural language processing methods" refer to technologies and methods that analyze text data and perform meaning summarization and extraction. 【0500】 "Evaluation means" refers to a function that determines the content of an inquiry based on the analyzed text data and generates an automated response or determines the appropriate forwarding destination. 【0501】 "Response provision means" refers to a part of a system that has the function of providing a generated automated response by voice, or forwarding the communication to a determined destination. 【0502】 "Home-use assistive devices" refer to devices or machines that can respond to or receive instructions via voice within a home environment and can interact with home appliances and information sources. 【0503】 This invention is a voice assistance system for use in the home. The server uses the Google Speech-to-Text API as a speech recognition tool to convert voice signals transmitted from a home device into text data. The text data is then parsed by the spaCy library as a natural language processing tool to summarize the user's intent and requests. 【0504】 The server further classifies user requests based on analysis results using evaluation tools and generates appropriate automated responses. This information can also be linked with home assistive devices to control various household appliances. Therefore, users can easily operate home appliances and obtain information through voice commands within their homes. 【0505】 As a concrete example, consider a scenario where a user says, "Turn off the living room lights." This voice is captured by the device and sent to the server. The server transcribes the voice into text and identifies it as a command related to lighting operation. The home assistance device then works in conjunction with the lighting control system to perform the requested operation. This allows users to easily manage their home environment through voice commands. 【0506】 This example demonstrates how a generative AI model can be used to provide appropriate prompts in response to user voice requests. For instance, by generating a prompt such as, "Please tell me the procedure for operating an electrical appliance using voice commands," the system can derive the corresponding procedure. In this way, a flexible system can be provided that is easily integrated with specific usage scenarios. 【0507】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0508】 Step 1: 【0509】 The user gives voice commands to a device within their home. A voice input device captures this voice and generates data as an audio signal. The input is the user's voice, and the output is an audio signal. 【0510】 Step 2: 【0511】 The device sends the captured audio signal to the server. The server uses the Google Speech-to-Text API to convert the audio signal into text data. The input is an audio signal, and the output is text data. Speech recognition is performed in this step, and the data is processed to clarify the user's intent. 【0512】 Step 3: 【0513】 The server analyzes the acquired text data using spaCy, a natural language processing tool, to summarize the instructions and identify categories. The input is text data, and the output is summarized instructions and category information. Here, data analysis using natural language processing is performed. 【0514】 Step 4: 【0515】 The server uses an evaluation tool to generate an appropriate automated response to the summarized instructions, or instructs the home assistive device to perform the action. The input consists of summarized instructions and category information, while the output is a specific operational instruction or response. The evaluation process may also utilize a generative AI model. 【0516】 Step 5: 【0517】 Home assistive devices execute operation instructions received from a server, for example, to operate household appliances. The input is the operation instructions from the server, and the output is the physical execution of the operation on the appliance. This allows the user's voice commands to be reflected in actual operation. 【0518】 Through the above processing steps, a system is realized that allows users to efficiently control devices and information services within their homes through voice commands. 【0519】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0520】 This invention is a system for efficiently and flexibly handling user inquiries in a call center. This system combines speech recognition, natural language processing, automatic response generation, and transfer functions with an emotion engine that recognizes the user's emotions. 【0521】 When a user calls a common inquiry number, the terminal immediately records the audio. This audio data is sent to a server and converted into text data by speech recognition. At the same time, an emotion engine also receives the audio signal and analyzes the user's emotions based on their speech. 【0522】 The analyzed text data is then analyzed in detail using natural language processing tools to summarize the query. Using this summary and sentiment information obtained by the sentiment engine, the server uses evaluation tools to generate an appropriate automated response to the user's query, or, if necessary, to determine the destination. 【0523】 Based on the analysis results of the emotion engine, the content and tone of responses can be adjusted. For example, if a user is showing signs of frustration, the server will set the response to a gentler tone and begin addressing the issue quickly. If emotions suggesting a potential complaint are detected, the user will be transferred to a more specialized department. 【0524】 For example, if a user reports dissatisfaction with an "unknown charge on a recent invoice," the device sends the recorded audio to the server for speech recognition and sentiment analysis. If natural language processing recognizes the "invoice problem" and the sentiment engine detects "anxiety" and "frustration," the server adjusts its automated response based on evaluation criteria and forwards the case to a specialist team for prompt problem resolution. 【0525】 This system allows users to receive efficient and emotionally sensitive responses, and also improves the quality of service provided by call centers. 【0526】 The following describes the processing flow. 【0527】 Step 1: 【0528】 The user calls a common inquiry number. The terminal receives the call and starts recording the voice message. 【0529】 Step 2: 【0530】 The device sends the recorded audio data to the server. This audio data is then input into the speech recognition system and the emotion engine. 【0531】 Step 3: 【0532】 The server uses speech recognition to convert the audio data into text data. This text data represents the content of the inquiry. 【0533】 Step 4: 【0534】 The server's emotion engine analyzes the voice data to identify the user's emotions. In this process, it determines emotions from the tone of voice and word choice. 【0535】 Step 5: 【0536】 The server analyzes the text data using natural language processing techniques and summarizes the query content. As a result, the query category and importance level are determined. 【0537】 Step 6: 【0538】 The server uses various evaluation tools to generate an automated response based on the summarized content and sentiment analysis results. If a simple response is possible, that response will be selected. 【0539】 Step 7: 【0540】 The server adjusts the tone and content of its response based on the user's emotional state. For example, in urgent situations or when the user is highly dissatisfied, a more considerate response will be selected. 【0541】 Step 8: 【0542】 The terminal receives a response from the server and provides it to the user in voice using speech synthesis technology. 【0543】 Step 9: 【0544】 If the inquiry is deemed complex and requires specialized support, the server consults a list of forwarding destinations and sends an instruction to the terminal to transfer the user's call to the most appropriate contact person. 【0545】 Step 10: 【0546】 The terminal, following instructions from the server, forwards the user's call to the selected forwarding destination. There, the user can interact directly with the representative. 【0547】 Step 11: 【0548】 The server records all processing logs and saves necessary data. This data is then used for subsequent service improvements and analysis. 【0549】 (Example 2) 【0550】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0551】 There is a lack of efficient and emotionally sensitive means to handle user inquiries in call centers. Traditional systems fail to adequately consider user emotions in their responses, and there are also issues with the quality of automated responses and the accuracy of call transfers. 【0552】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0553】 In this invention, the server includes means for converting an audio signal into digital data and extracting emotional information from the audio signal; means for converting the digital data into text data and analyzing the text data to generate a summary; and means for evaluating the content of the inquiry based on the summary and emotional information, and for generating an automated response or determining the destination for forwarding. This enables flexible responses that take into account the user's emotions and prompt, appropriate forwarding. 【0554】 A "voice signal" is an electrical or digital waveform used for information transmission via voice communication. 【0555】 "Digital data" refers to data that represents analog signals using a digital method, making it easy to process and store on computers and communication devices. 【0556】 "Emotional information" refers to data that indicates a user's psychological state, extracted from audio and text, and quantifies or categorizes the user's emotional expressions. 【0557】 "Text data" refers to data that represents natural language in string format and is used by computers to read and process it. 【0558】 A "summary" refers to a shortened expression of the most important points from a given piece of information or a dataset. 【0559】 "Inquiry details" refers to information about questions or requests that users ask the system or service. 【0560】 "Automatic response" refers to a function or the content of a response in which a pre-configured program automatically reacts to and responds to user inquiries. 【0561】 "Transfer destination" refers to the designated recipient or processing location to which a user's inquiry will be transferred to another operator or function as needed. 【0562】 A "generative AI model" refers to an artificial intelligence model that has the ability to generate new data and information based on machine learning. 【0563】 A "prompt" is an instruction or input sentence for an AI model, referring to text that defines the conditions or circumstances under which the model generates a response. 【0564】 This invention is a system for handling user inquiries in a call center in an efficient and emotionally sensitive manner. When a user calls a common inquiry number, a terminal receives the call and immediately records the voice signal. The terminal converts the voice signal into digital data and transmits it to a server via the network. This recorded voice data is converted into text data using speech recognition technology, specifically a common speech recognition API. 【0565】 When this audio data is converted into text data, the server uses an emotion engine to analyze the tone, speed, and volume of the voice, and extract emotional information. This allows the server to understand the user's emotional state and reflect it in subsequent response generation. The server then passes the acquired text data to a natural language processing engine to analyze the query and create a summary. 【0566】 By using a generative AI model and creating appropriate prompts based on this data, the AI ​​generates automated responses. For example, if a user complains of "unknown charges on a recent bill," the emotion engine detects "anxiety" and "frustration." Based on this information, the AI ​​generates a response such as, "We apologize for the confusion. We will contact you as soon as we have more information," and can deliver it to the user in a natural way using speech synthesis technology. 【0567】 As a concrete example, here are some examples of prompt statements that can be input to a generative AI model: 【0568】 User comment: "My delivery didn't arrive on time." 【0569】 Emotional analysis results: "Anxiety," "Irritation" 【0570】 Required response tone: "Gentle" 【0571】 Thus, in this invention, the system can provide efficient responses while taking user emotions into consideration, and contribute to improving the operations of call centers. 【0572】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0573】 Step 1: 【0574】 A user calls a call center, and the terminal receives the call. The terminal records the audio signal at the start of the call and converts it into digital data. The input is an analog audio signal, and the output is obtained as digital audio data. Processing for sending this digital data to the server is performed via a network interface. 【0575】 Step 2: 【0576】 The server receives digital audio data. The server uses a speech recognition engine to convert the digital audio data into text data. The input is digital audio data, and the output is text data that represents the content of the audio as character data. During this conversion process, the speech recognition engine uses a specific algorithm to map phonemes to characters. 【0577】 Step 3: 【0578】 The server inputs text data into a natural language processing engine, which then parses it. The natural language processing engine analyzes the input text data, summarizes its content, and extracts the main topic of the query. This process yields summarized data as output from the input text data. For example, if the input is "There are unknown charges on my recent invoice," a summary such as "Invoice problem" will be generated. 【0579】 Step 4: 【0580】 The server evaluates the inquiry content and generates an appropriate automated response, using emotional information extracted from the voice. An emotional analysis engine is used for evaluation, which quantifies the user's emotional state. Input consists of summary data and emotional information, while output is response data based on the generated prompt. 【0581】 Step 5: 【0582】 The server inputs a prompt into a generative AI model, which then generates an appropriate response. The prompt includes the user's statement, sentiment analysis results, and the required response tone. The input is the prompt, and the output is the response text generated by the AI. A concrete example of a response generated might be, "We apologize for the delay. We will contact you as soon as we have confirmed the delivery status." 【0583】 Step 6: 【0584】 The server converts the generated response text into speech using a speech synthesis engine and provides the response to the user via the terminal. In this process, the input is the response text, and the output is synthesized speech. Finally, the terminal delivers this synthesized speech to the user via the call line. 【0585】 (Application Example 2) 【0586】 Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0587】 In brick-and-mortar retail settings, employees are required to quickly and accurately understand customer emotions and respond accordingly. However, it is not easy for employees to consistently grasp customer emotions and provide the optimal response. This can lead to a decline in customer satisfaction. 【0588】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0589】 In this invention, the server includes speech recognition means for converting audio signals into text data, natural language processing means for analyzing the text data and generating a summary, emotion recognition means for analyzing the user's emotions and adjusting the content and tone of the response based on the analysis results, and visual output means for outputting the analysis results through the display of visual information. This enables employees in physical stores to analyze customers' emotions in real time and take appropriate action. 【0590】 "Speech recognition means" refers to technology for analyzing speech signals and converting them into corresponding text data. 【0591】 "Natural language processing methods" are technologies that analyze text data, summarize its content, and understand the meaning and intent of the information. 【0592】 "Evaluation methods" refer to technologies that analyze information based on the content of an inquiry and either generate an automated response or determine the appropriate destination for forwarding the information. 【0593】 "Emotion recognition means" refers to technology that extracts a user's emotions from voice or text and adjusts the content and tone of the response according to the analysis results. 【0594】 "Response provision means" refers to technology for providing a generated automated response in voice or for forwarding the communication to a determined destination. 【0595】 "Visual output means" refers to a device or technology for presenting information to a user by visually displaying the analysis results. 【0596】 "Recording means" refers to a device or technology for storing process data generated using speech recognition means, natural language processing means, evaluation means, and response provision means. 【0597】 This invention is a system for providing customer service in stores, utilizing speech recognition, natural language processing, and sentiment analysis as its underlying technologies. The server effectively integrates these functions, processing voice data to analyze customer emotions and generating appropriate responses. 【0598】 First, when a user interacts with a store staff member, the audio is recorded by the device and sent to a server. The Google Cloud Speech-to-Text API is used for speech recognition, converting the audio signal into text data. Next, this text data is analyzed using IBM Watson's Tone Analyzer to extract the user's emotional state. Based on this analysis, natural language processing technology is used to summarize the inquiry and generate specific response suggestions. 【0599】 The generated response suggestions are integrated with information obtained through emotion recognition, and the tone and content of the actual response are adjusted to reflect the customer's emotions. This adjustment result is displayed on the screen of smart glasses, such as Google Glass, to help on-site staff provide optimal customer service. 【0600】 For example, if a customer expresses concern about a product, the system recognizes this concern and notifies staff to provide a detailed explanation regarding the product warranty. This system is expected to improve customer satisfaction. 【0601】 An example of a prompt statement is as follows: 【0602】 "Analyze the user's voice input and recognize their emotional state. Perform text analysis and emotion recognition, and demonstrate how to display feedback on smart glasses based on the results, indicating how employees should interact with customers." 【0603】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0604】 Step 1: 【0605】 The terminal records the conversation between the user and the store staff. The recorded audio data is input and sent to the server in its original format. 【0606】 Step 2: 【0607】 The server uses the Google Cloud Speech-to-Text API to convert received audio data into text data. The input is audio data, and the output is the converted text data. In this conversion process, the audio signal is analyzed to generate the corresponding string. 【0608】 Step 3: 【0609】 The server uses IBM Watson's Tone Analyzer to analyze text data and extract user sentiment information. The input for this step is text data, and the output is data indicating the user's emotional state. Natural language processing is performed here to evaluate the emotional elements within the text. 【0610】 Step 4: 【0611】 The server analyzes text data and summarizes the query using natural language processing techniques. The input is text data, and the output is the summarized query. This process extracts the meaning and important parts of the information and generates a summary. 【0612】 Step 5: 【0613】 The server generates an appropriate automated response based on the summarized inquiry content and sentiment state. The inputs here are the summarized content and sentiment data, and the output is the automated response data. The response tone is adjusted to take sentiment data into consideration, creating a customer-friendly response. 【0614】 Step 6: 【0615】 The server displays the generated automated response on the smart glasses' display, providing staff with visual feedback. The input is the automated response data, and the output is the visual display on the smart glasses. Here, the visibility and speed of the feedback are emphasized, and the information is provided in a way that staff can easily understand. 【0616】 The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0617】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0618】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314. 【0619】 [Fourth Embodiment] 【0620】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0621】 As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server. 【0622】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0623】 The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52. 【0624】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0625】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0626】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0627】 The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes. 【0628】 Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0629】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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. 【0630】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0631】 In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0632】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0633】 This invention relates to a system for efficiently automating call center inquiry processing. This system functions by combining speech recognition, natural language processing, automated response generation, and transfer. 【0634】 When a user calls a common inquiry number, the terminal receives the call and records the audio. The recorded audio is sent to a server, where speech recognition capabilities convert the audio signal into text data. This text data is then analyzed by natural language processing capabilities within the server to summarize the inquiry. 【0635】 Based on the summarized inquiry content, the server uses an evaluation mechanism to generate an automated response on the server and provide it to the user as voice if the inquiry is simple. If the inquiry is more complex, the server selects the most appropriate contact person to forward the call to. The terminal then forwards the user's call to this designated contact person, ensuring they receive appropriate support. 【0636】 As a concrete example, consider a case where a user reports that their internet connection is slow. In this case, speech recognition technology converts this into text data, "Slow internet connection." This text data is then analyzed by natural language processing technology, and the problem category is summarized as "Technical Support." The server then uses evaluation technology to search for an appropriate destination and forwards the information to the technical support team. As a result, the user can receive technical support smoothly. 【0637】 This system allows users to receive quick and accurate responses through voice-based reporting, and also significantly improves the operational efficiency of call centers. 【0638】 The following describes the processing flow. 【0639】 Step 1: 【0640】 The user calls a common inquiry number. The terminal receives the call, activates the system, and records the audio. 【0641】 Step 2: 【0642】 The device sends the recorded audio data to the server. This data is then passed to the speech recognition system. 【0643】 Step 3: 【0644】 The server uses speech recognition to convert the received audio data into text data. At this stage, the audio becomes non-acoustic textual information. 【0645】 Step 4: 【0646】 The server uses natural language processing to analyze text data and summarize the query. This analysis generates summarized information. 【0647】 Step 5: 【0648】 The server uses an evaluation tool to assess the summarized content. If the content is simple, it starts a process to generate an automated response on the spot. 【0649】 Step 6: 【0650】 When the server generates an automated response, it uses either a predefined response database or a dynamic response generation algorithm. 【0651】 Step 7: 【0652】 If the user has a simple inquiry, the server generates an automated response and sends it back to the terminal, which then uses speech synthesis technology to provide the response to the user. 【0653】 Step 8: 【0654】 For complex inquiries, the server determines the appropriate contact person based on a list of forwarding destinations and selects the appropriate recipient. 【0655】 Step 9: 【0656】 The terminal, following the server's instructions, forwards the user's call to the selected forwarding destination, allowing the user to speak with the appropriate representative. 【0657】 Step 10: 【0658】 The server logs details of all processing steps. This log includes audio data, text data, responses, and destinations. 【0659】 (Example 1) 【0660】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0661】 In call centers, handling inquiries requires speed and accuracy due to the increasing volume of calls and the diversity of issues. However, traditional manual responses are often understaffed, leading to delays in responses and incorrect transfers. This results in challenges such as decreased customer satisfaction and increased operating costs. 【0662】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0663】 In this invention, the server includes communication means for converting voice data into digital data using conversion means and transmitting it to a central device; information processing means for analyzing the digital data and performing content analysis; and selection means for evaluating the information based on the content analysis and generating an automatic response or determining an appropriate forwarding destination. This enables faster and more accurate query processing. 【0664】 "Means for converting audio data" refers to a device or software that converts an audio signal into digital data. 【0665】 "Communication means" refers to a method or device for transmitting data to another device or system. 【0666】 A "central unit" is the main computing unit used for processing data and issuing commands. 【0667】 "Information processing means" refers to software or algorithms used to analyze input data and extract or understand specific information. 【0668】 "Content analysis" is the process of analyzing digital data in detail and understanding its content. 【0669】 A "selection tool" is a method or device for determining the optimal option from multiple choices. 【0670】 "Automated response" refers to a mechanism that autonomously generates appropriate responses to user inquiries. 【0671】 "Determining the forwarding destination" is the process of selecting the appropriate contact point or department to handle the inquiry based on its content. 【0672】 This invention is a system for efficiently handling voice-based inquiries in a call center. The system's functionality is primarily comprised of terminals, a server, and users. 【0673】 The user initiates an inquiry by calling a common inquiry number. The terminal receives the voice signal from this user and generates voice data. This voice data is sent to a server, where it is converted into digital data by a voice data conversion means. For example, a general-purpose voice recognition platform can be used as the voice recognition software. 【0674】 Next, the server analyzes this digital data using information processing tools to understand the query. For example, tools such as "spaCy" or "BERT" could be used as natural language processing technologies. This extracts the gist of the query and classifies it into appropriate categories. 【0675】 The server initiates a process to either generate an automated response based on the extracted information, or to determine the appropriate department to forward the information to using a selection method. If an automated response is possible, the server uses speech synthesis technology to generate and provide the response as audio. For speech synthesis, services such as "Amazon Polly" can be used. 【0676】 On the other hand, if the inquiry is complex and requires specialized handling, the server will determine the most appropriate department based on the selected method. The terminal will then forward the user's call to the designated department according to the server's instructions. 【0677】 For example, if a user reports that their internet connection is slow, speech recognition technology converts this into digital text. Then, natural language processing technology classifies the issue as requiring technical support, and the process of transferring the user to the technical support team is initiated. 【0678】 An example of a prompt message generated using a generative AI model might be: "A user called the call center and reported an internet connection problem. Please provide quick and accurate instructions on how to address this situation." 【0679】 This system allows users to receive efficient, quick, and appropriate responses, and also improves the operational efficiency of call centers. 【0680】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0681】 Step 1: 【0682】 The user calls a common inquiry number. At this point, the user communicates their inquiry via voice. The terminal receives this voice signal in real time and records it as digital audio data. The input here is the user's voice, and the output is digital audio data. The terminal formats this appropriately and sends it to the server. 【0683】 Step 2: 【0684】 The server receives audio data transmitted from the terminal. The server uses a conversion mechanism to perform a speech recognition process. In this process, the audio data is converted into text data. The input is digital audio data, and the output is text data. The server uses speech recognition software to perform this conversion. 【0685】 Step 3: 【0686】 The server analyzes the text data generated by speech recognition using information processing tools. This analysis involves understanding the content and summarizing it as needed. For example, keyphrase extraction and topic modeling are performed. The input is the text data generated in the previous step, and the output is summarized or categorized data. The server performs this processing using natural language processing techniques. 【0687】 Step 4: 【0688】 The server generates an automated response or, if necessary, selects an appropriate forwarding destination based on the analysis results. This decision is made using selection methods, and if an automated response is applicable, the server prepares the response. The input is categorized data, and the output is an automated response message or a forwarding instruction. The server performs evaluation using rule-based or machine learning models. 【0689】 Step 5: 【0690】 Depending on the situation, the server converts the generated automated response into speech using speech synthesis technology and sends it to the terminal. If forwarding is necessary, the server specifies the forwarding destination and issues instructions to the terminal. Input is the automated response message or forwarding instruction, and output is the voice response or forwarding setting. A TTS engine is used for speech synthesis. 【0691】 Step 6: 【0692】 The terminal either provides the user with an audio response from the server or forwards the user's call to a designated destination. Here, the terminal ultimately provides feedback to the user or connects them to a support representative. The input is the audio response or forwarding instruction from the server, and the output is the response to the user and the call forwarding. The terminal then completes the entire process. 【0693】 (Application Example 1) 【0694】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0695】 In modern homes, there is a growing demand for quick and efficient use of home appliances and information services through voice control. However, existing home appliances have limited functionality and struggle to seamlessly integrate with multiple devices and information sources. 【0696】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0697】 In this invention, the server includes speech recognition means for converting voice signals into text data, natural language processing means for analyzing the text data and generating summaries, and a home assistance device that has the function of executing responses or instructions in cooperation with home appliances or information sources. This enables users to efficiently execute a variety of responses and functions through voice control within their homes. 【0698】 "Speech recognition means" refers to a device or program that converts speech signals into text data. 【0699】 "Natural language processing methods" refer to technologies and methods that analyze text data and perform meaning summarization and extraction. 【0700】 "Evaluation means" refers to a function that determines the content of an inquiry based on the analyzed text data and generates an automated response or determines the appropriate forwarding destination. 【0701】 "Response provision means" refers to a part of a system that has the function of providing a generated automated response by voice, or forwarding the communication to a determined destination. 【0702】 "Home-use assistive devices" refer to devices or machines that can respond to or receive instructions via voice within a home environment and can interact with home appliances and information sources. 【0703】 This invention is a voice assistance system for use in the home. The server uses the Google Speech-to-Text API as a speech recognition tool to convert voice signals transmitted from a home device into text data. The text data is then parsed by the spaCy library as a natural language processing tool to summarize the user's intent and requests. 【0704】 The server further classifies user requests based on analysis results using evaluation tools and generates appropriate automated responses. This information can also be linked with home assistive devices to control various household appliances. Therefore, users can easily operate home appliances and obtain information through voice commands within their homes. 【0705】 As a concrete example, consider a scenario where a user says, "Turn off the living room lights." This voice is captured by the device and sent to the server. The server transcribes the voice into text and identifies it as a command related to lighting operation. The home assistance device then works in conjunction with the lighting control system to perform the requested operation. This allows users to easily manage their home environment through voice commands. 【0706】 This example demonstrates how a generative AI model can be used to provide appropriate prompts in response to user voice requests. For instance, by generating a prompt such as, "Please tell me the procedure for operating an electrical appliance using voice commands," the system can derive the corresponding procedure. In this way, a flexible system can be provided that is easily integrated with specific usage scenarios. 【0707】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0708】 Step 1: 【0709】 The user gives voice commands to a device within their home. A voice input device captures this voice and generates data as an audio signal. The input is the user's voice, and the output is an audio signal. 【0710】 Step 2: 【0711】 The device sends the captured audio signal to the server. The server uses the Google Speech-to-Text API to convert the audio signal into text data. The input is an audio signal, and the output is text data. Speech recognition is performed in this step, and the data is processed to clarify the user's intent. 【0712】 Step 3: 【0713】 The server analyzes the acquired text data using spaCy, a natural language processing tool, to summarize the instructions and identify categories. The input is text data, and the output is summarized instructions and category information. Here, data analysis using natural language processing is performed. 【0714】 Step 4: 【0715】 The server uses an evaluation tool to generate an appropriate automated response to the summarized instructions, or instructs the home assistive device to perform the action. The input consists of summarized instructions and category information, while the output is a specific operational instruction or response. The evaluation process may also utilize a generative AI model. 【0716】 Step 5: 【0717】 Home assistive devices execute operation instructions received from a server, for example, to operate household appliances. The input is the operation instructions from the server, and the output is the physical execution of the operation on the appliance. This allows the user's voice commands to be reflected in actual operation. 【0718】 Through the above processing steps, a system is realized that allows users to efficiently control devices and information services within their homes through voice commands. 【0719】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0720】 This invention is a system for efficiently and flexibly handling user inquiries in a call center. This system combines speech recognition, natural language processing, automatic response generation, and transfer functions with an emotion engine that recognizes the user's emotions. 【0721】 When a user calls a common inquiry number, the terminal immediately records the audio. This audio data is sent to a server and converted into text data by speech recognition. At the same time, an emotion engine also receives the audio signal and analyzes the user's emotions based on their speech. 【0722】 The analyzed text data is then analyzed in detail using natural language processing tools to summarize the query. Using this summary and sentiment information obtained by the sentiment engine, the server uses evaluation tools to generate an appropriate automated response to the user's query, or, if necessary, to determine the destination. 【0723】 Based on the analysis results of the emotion engine, the content and tone of responses can be adjusted. For example, if a user is showing signs of frustration, the server will set the response to a gentler tone and begin addressing the issue quickly. If emotions suggesting a potential complaint are detected, the user will be transferred to a more specialized department. 【0724】 For example, if a user reports dissatisfaction with an "unknown charge on a recent invoice," the device sends the recorded audio to the server for speech recognition and sentiment analysis. If natural language processing recognizes the "invoice problem" and the sentiment engine detects "anxiety" and "frustration," the server adjusts its automated response based on evaluation criteria and forwards the case to a specialist team for prompt problem resolution. 【0725】 This system allows users to receive efficient and emotionally sensitive responses, and also improves the quality of service provided by call centers. 【0726】 The following describes the processing flow. 【0727】 Step 1: 【0728】 The user calls a common inquiry number. The terminal receives the call and starts recording the voice message. 【0729】 Step 2: 【0730】 The device sends the recorded audio data to the server. This audio data is then input into the speech recognition system and the emotion engine. 【0731】 Step 3: 【0732】 The server uses speech recognition to convert the audio data into text data. This text data represents the content of the inquiry. 【0733】 Step 4: 【0734】 The server's emotion engine analyzes the voice data to identify the user's emotions. In this process, it determines emotions from the tone of voice and word choice. 【0735】 Step 5: 【0736】 The server analyzes the text data using natural language processing techniques and summarizes the query content. As a result, the query category and importance level are determined. 【0737】 Step 6: 【0738】 The server uses various evaluation tools to generate an automated response based on the summarized content and sentiment analysis results. If a simple response is possible, that response will be selected. 【0739】 Step 7: 【0740】 The server adjusts the tone and content of its response based on the user's emotional state. For example, in urgent situations or when the user is highly dissatisfied, a more considerate response will be selected. 【0741】 Step 8: 【0742】 The terminal receives a response from the server and provides it to the user in voice using speech synthesis technology. 【0743】 Step 9: 【0744】 If the inquiry is deemed complex and requires specialized support, the server consults a list of forwarding destinations and sends an instruction to the terminal to transfer the user's call to the most appropriate contact person. 【0745】 Step 10: 【0746】 The terminal, following instructions from the server, forwards the user's call to the selected forwarding destination. There, the user can interact directly with the representative. 【0747】 Step 11: 【0748】 The server records all processing logs and saves necessary data. This data is then used for subsequent service improvements and analysis. 【0749】 (Example 2) 【0750】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0751】 There is a lack of efficient and emotionally sensitive means to handle user inquiries in call centers. Traditional systems fail to adequately consider user emotions in their responses, and there are also issues with the quality of automated responses and the accuracy of call transfers. 【0752】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0753】 In this invention, the server includes means for converting an audio signal into digital data and extracting emotional information from the audio signal; means for converting the digital data into text data and analyzing the text data to generate a summary; and means for evaluating the content of the inquiry based on the summary and emotional information, and for generating an automated response or determining the destination for forwarding. This enables flexible responses that take into account the user's emotions and prompt, appropriate forwarding. 【0754】 A "voice signal" is an electrical or digital waveform used for information transmission via voice communication. 【0755】 "Digital data" refers to data that represents analog signals using a digital method, making it easy to process and store on computers and communication devices. 【0756】 "Emotional information" refers to data that indicates a user's psychological state, extracted from audio and text, and quantifies or categorizes the user's emotional expressions. 【0757】 "Text data" refers to data that represents natural language in string format and is used by computers to read and process it. 【0758】 A "summary" refers to a shortened expression of the most important points from a given piece of information or a dataset. 【0759】 "Inquiry details" refers to information about questions or requests that users ask the system or service. 【0760】 "Automatic response" refers to a function or the content of a response in which a pre-configured program automatically reacts to and responds to user inquiries. 【0761】 "Transfer destination" refers to the designated recipient or processing location to which a user's inquiry will be transferred to another operator or function as needed. 【0762】 A "generative AI model" refers to an artificial intelligence model that has the ability to generate new data and information based on machine learning. 【0763】 A "prompt" is an instruction or input sentence for an AI model, referring to text that defines the conditions or circumstances under which the model generates a response. 【0764】 This invention is a system for handling user inquiries in a call center in an efficient and emotionally sensitive manner. When a user calls a common inquiry number, a terminal receives the call and immediately records the voice signal. The terminal converts the voice signal into digital data and transmits it to a server via the network. This recorded voice data is converted into text data using speech recognition technology, specifically a common speech recognition API. 【0765】 When this audio data is converted into text data, the server uses an emotion engine to analyze the tone, speed, and volume of the voice, and extract emotional information. This allows the server to understand the user's emotional state and reflect it in subsequent response generation. The server then passes the acquired text data to a natural language processing engine to analyze the query and create a summary. 【0766】 By using a generative AI model and creating appropriate prompts based on this data, the AI ​​generates automated responses. For example, if a user complains of "unknown charges on a recent bill," the emotion engine detects "anxiety" and "frustration." Based on this information, the AI ​​generates a response such as, "We apologize for the confusion. We will contact you as soon as we have more information," and can deliver it to the user in a natural way using speech synthesis technology. 【0767】 As a concrete example, here are some examples of prompt statements that can be input to a generative AI model: 【0768】 User comment: "My delivery didn't arrive on time." 【0769】 Emotional analysis results: "Anxiety," "Irritation" 【0770】 Required response tone: "Gentle" 【0771】 Thus, in this invention, the system can provide efficient responses while taking user emotions into consideration, and contribute to improving the operations of call centers. 【0772】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0773】 Step 1: 【0774】 A user calls a call center, and the terminal receives the call. The terminal records the audio signal at the start of the call and converts it into digital data. The input is an analog audio signal, and the output is obtained as digital audio data. Processing for sending this digital data to the server is performed via a network interface. 【0775】 Step 2: 【0776】 The server receives digital audio data. The server uses a speech recognition engine to convert the digital audio data into text data. The input is digital audio data, and the output is text data that represents the content of the audio as character data. During this conversion process, the speech recognition engine uses a specific algorithm to map phonemes to characters. 【0777】 Step 3: 【0778】 The server inputs text data into a natural language processing engine, which then parses it. The natural language processing engine analyzes the input text data, summarizes its content, and extracts the main topic of the query. This process yields summarized data as output from the input text data. For example, if the input is "There are unknown charges on my recent invoice," a summary such as "Invoice problem" will be generated. 【0779】 Step 4: 【0780】 The server evaluates the inquiry content and generates an appropriate automated response, using emotional information extracted from the voice. An emotional analysis engine is used for evaluation, which quantifies the user's emotional state. Input consists of summary data and emotional information, while output is response data based on the generated prompt. 【0781】 Step 5: 【0782】 The server inputs a prompt into a generative AI model, which then generates an appropriate response. The prompt includes the user's statement, sentiment analysis results, and the required response tone. The input is the prompt, and the output is the response text generated by the AI. A concrete example of a response generated might be, "We apologize for the delay. We will contact you as soon as we have confirmed the delivery status." 【0783】 Step 6: 【0784】 The server converts the generated response text into speech using a speech synthesis engine and provides the response to the user via the terminal. In this process, the input is the response text, and the output is synthesized speech. Finally, the terminal delivers this synthesized speech to the user via the call line. 【0785】 (Application Example 2) 【0786】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0787】 In brick-and-mortar retail settings, employees are required to quickly and accurately understand customer emotions and respond accordingly. However, it is not easy for employees to consistently grasp customer emotions and provide the optimal response. This can lead to a decline in customer satisfaction. 【0788】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0789】 In this invention, the server includes speech recognition means for converting audio signals into text data, natural language processing means for analyzing the text data and generating a summary, emotion recognition means for analyzing the user's emotions and adjusting the content and tone of the response based on the analysis results, and visual output means for outputting the analysis results through the display of visual information. This enables employees in physical stores to analyze customers' emotions in real time and take appropriate action. 【0790】 "Speech recognition means" refers to technology for analyzing speech signals and converting them into corresponding text data. 【0791】 "Natural language processing methods" are technologies that analyze text data, summarize its content, and understand the meaning and intent of the information. 【0792】 "Evaluation methods" refer to technologies that analyze information based on the content of an inquiry and either generate an automated response or determine the appropriate destination for forwarding the information. 【0793】 "Emotion recognition means" refers to technology that extracts a user's emotions from voice or text and adjusts the content and tone of the response according to the analysis results. 【0794】 "Response provision means" refers to technology for providing a generated automated response in voice or for forwarding the communication to a determined destination. 【0795】 "Visual output means" refers to a device or technology for presenting information to a user by visually displaying the analysis results. 【0796】 "Recording means" refers to a device or technology for storing process data generated using speech recognition means, natural language processing means, evaluation means, and response provision means. 【0797】 This invention is a system for providing customer service in stores, utilizing speech recognition, natural language processing, and sentiment analysis as its underlying technologies. The server effectively integrates these functions, processing voice data to analyze customer emotions and generating appropriate responses. 【0798】 First, when a user interacts with a store staff member, the audio is recorded by the device and sent to a server. The Google Cloud Speech-to-Text API is used for speech recognition, converting the audio signal into text data. Next, this text data is analyzed using IBM Watson's Tone Analyzer to extract the user's emotional state. Based on this analysis, natural language processing technology is used to summarize the inquiry and generate specific response suggestions. 【0799】 The generated response suggestions are integrated with information obtained through emotion recognition, and the tone and content of the actual response are adjusted to reflect the customer's emotions. This adjustment result is displayed on the screen of smart glasses, such as Google Glass, to help on-site staff provide optimal customer service. 【0800】 For example, if a customer expresses concern about a product, the system recognizes this concern and notifies staff to provide a detailed explanation regarding the product warranty. This system is expected to improve customer satisfaction. 【0801】 An example of a prompt statement is as follows: 【0802】 "Analyze the user's voice input and recognize their emotional state. Perform text analysis and emotion recognition, and demonstrate how to display feedback on smart glasses based on the results, indicating how employees should interact with customers." 【0803】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0804】 Step 1: 【0805】 The terminal records the conversation between the user and the store staff. The recorded audio data is input and sent to the server in its original format. 【0806】 Step 2: 【0807】 The server uses the Google Cloud Speech-to-Text API to convert received audio data into text data. The input is audio data, and the output is the converted text data. In this conversion process, the audio signal is analyzed to generate the corresponding string. 【0808】 Step 3: 【0809】 The server uses IBM Watson's Tone Analyzer to analyze text data and extract user sentiment information. The input for this step is text data, and the output is data indicating the user's emotional state. Natural language processing is performed here to evaluate the emotional elements within the text. 【0810】 Step 4: 【0811】 The server analyzes text data and summarizes the query using natural language processing techniques. The input is text data, and the output is the summarized query. This process extracts the meaning and important parts of the information and generates a summary. 【0812】 Step 5: 【0813】 The server generates an appropriate automated response based on the summarized inquiry content and sentiment state. The inputs here are the summarized content and sentiment data, and the output is the automated response data. The response tone is adjusted to take sentiment data into consideration, creating a customer-friendly response. 【0814】 Step 6: 【0815】 The server displays the generated automated response on the smart glasses' display, providing staff with visual feedback. The input is the automated response data, and the output is the visual display on the smart glasses. Here, the visibility and speed of the feedback are emphasized, and the information is provided in a way that staff can easily understand. 【0816】 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0817】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0818】 In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0819】 Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion. 【0820】 Figure 9 shows an emotion map 400 in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together. 【0821】 These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression. 【0822】 The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become. 【0823】 Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant. 【0824】 The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more." 【0825】 The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values. 【0826】 The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format. 【0827】 In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data. 【0828】 In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56. 【0829】 Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12. 【0830】 Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56. 【0831】 The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory. 【0832】 The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor. 【0833】 Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources. 【0834】 Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose. 【0835】 The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above. 【0836】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference. 【0837】 The following is further disclosed regarding the embodiments described above. 【0838】 (Claim 1) 【0839】 A speech recognition means for converting audio signals into text data, 【0840】 A natural language processing means that analyzes the aforementioned text data and generates a summary, 【0841】 An evaluation means that evaluates the content of the inquiry based on the summary above and generates an automated response or determines an appropriate forwarding destination, 【0842】 A response providing means that generates and provides the aforementioned automated response in voice, or forwards the communication to the determined forwarding destination, 【0843】 A system that includes this. 【0844】 (Claim 2) 【0845】 The system according to claim 1, further comprising terminal means for recording the aforementioned audio signal and transmitting it to a server. 【0846】 (Claim 3) 【0847】 The system according to claim 1, further comprising recording means for recording process data generated using the speech recognition means, natural language processing means, evaluation means, and response providing means. 【0848】 "Example 1" 【0849】 (Claim 1) 【0850】 A communication means that converts audio data into digital data using a conversion means and transmits it to a central device, 【0851】 Information processing means for analyzing the aforementioned digital data and performing content analysis, 【0852】 A selection means that evaluates the information based on the content analysis and generates an automatic response or determines an appropriate forwarding destination, 【0853】 A supply means that generates and presents the aforementioned automated response, or transfers the connection to the determined transfer destination, 【0854】 A system that includes this. 【0855】 (Claim 2) 【0856】 The system according to claim 1, further comprising an auxiliary device that stores recorded data using the aforementioned communication means and transfers it from a central device. 【0857】 (Claim 3) 【0858】 The system according to claim 1, further comprising storage means for recording operation information generated using the conversion means, information processing means, selection means, and supply means. 【0859】 "Application Example 1" 【0860】 (Claim 1) 【0861】 A speech recognition means for converting audio signals into text data, 【0862】 A natural language processing means that analyzes the aforementioned text data and generates a summary, 【0863】 An evaluation means that evaluates the content of the inquiry based on the summary above and generates an automated response or determines an appropriate forwarding destination, 【0864】 A response providing means that generates and provides the aforementioned automated response in voice, or forwards the communication to the determined forwarding destination, 【0865】 This includes home assistive devices having the function of performing the aforementioned responses or instructions in conjunction with home appliances or information sources, 【0866】 A system that includes this. 【0867】 (Claim 2) 【0868】 The system according to claim 1, further comprising terminal means for recording the aforementioned audio signal and transmitting it to a server. 【0869】 (Claim 3) 【0870】 The system according to claim 1, further comprising recording means for recording process data generated using the speech recognition means, natural language processing means, evaluation means, and response providing means. 【0871】 "Example 2 of combining an emotion engine" 【0872】 (Claim 1) 【0873】 A means for converting an audio signal into digital data and extracting emotional information from the audio signal, 【0874】 A means for converting the aforementioned digital data into text data, and for analyzing the text data to generate a summary, 【0875】 A means for evaluating the content of an inquiry based on the summary and sentiment information, and for generating an automated response or determining the destination for forwarding the response, 【0876】 A means for generating an appropriate response based on a prompt sentence using a generative AI model, 【0877】 Means for converting the aforementioned automated response into voice and providing it, or for forwarding the communication to the determined forwarding destination, 【0878】 A system that includes this. 【0879】 (Claim 2) 【0880】 The system according to claim 1, further comprising a communication device that receives the aforementioned audio signal and transmits it to a server. 【0881】 (Claim 3) 【0882】 The system according to claim 1, further comprising means for recording processing information generated by the voice conversion means, analysis means, evaluation means, response generation means, and communication means. 【0883】 "Application example 2 when combining with an emotional engine" 【0884】 (Claim 1) 【0885】 A speech recognition means for converting audio signals into text data, 【0886】 A natural language processing means that analyzes the aforementioned text data and generates a summary, 【0887】 An evaluation means that evaluates the content of the inquiry based on the summary above and generates an automated response or determines an appropriate forwarding destination, 【0888】 An emotion recognition means that analyzes the user's emotions and adjusts the content and tone of the response based on the analysis results, 【0889】 A response providing means that generates and provides the aforementioned automated response in voice, or forwards the communication to the determined forwarding destination, 【0890】 A visual output means that outputs analysis results through the display of visual information, 【0891】 A system that includes this. 【0892】 (Claim 2) 【0893】 The system according to claim 1, further comprising terminal means for recording the aforementioned audio signal and transmitting it to a server. 【0894】 (Claim 3) 【0895】 The system according to claim 1, further comprising recording means for recording process data generated using the speech recognition means, natural language processing means, evaluation means, emotion recognition means, response providing means, and visual output means. [Explanation of symbols] 【0896】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] A speech recognition means for converting audio signals into text data, A natural language processing means that analyzes the aforementioned text data and generates a summary, An evaluation means that evaluates the content of the inquiry based on the summary above and generates an automated response or determines an appropriate forwarding destination, A response providing means that generates and provides the aforementioned automated response in voice, or forwards the communication to the determined forwarding destination, A system that includes this. [Claim 2] The system according to claim 1, further comprising terminal means for recording the aforementioned audio signal and transmitting it to a server. [Claim 3] The system according to claim 1, further comprising recording means for recording process data generated using the speech recognition means, natural language processing means, evaluation means, and response provision means.

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