system

Abstract

We provide the system. [Solution] A system comprising means for analyzing user inquiry data using natural language processing, means for converting voice data into text data using speech recognition, means for generating responses based on the content of inquiries using artificial intelligence, means for evaluating the user's emotional state using token analysis, means for routing inquiries to human staff according to the appropriate situation, means for integrating with a customer relationship management system and improving individualized service using customer data, and means for responding to inquiries through multiple communication channels.

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 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 as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, many companies are required to communicate with customers efficiently and with high quality. However, in conventional call center systems, there are problems such as difficulty in quickly responding when the inquiry content is complex or when multilingual support is required. Also, it is difficult to appropriately understand and respond to the emotions of customers, which may lead to a decrease in customer satisfaction. Furthermore, improving business efficiency while reducing operation costs is also a challenge for many companies.

Means for Solving the Problems

[0005] This invention provides a system that analyzes user inquiries using natural language processing and converts speech data into text using speech recognition. Furthermore, it utilizes artificial intelligence technology to generate responses based on the content of the inquiry and evaluates the user's emotional state through token analysis, thereby routing inquiries to appropriate human representatives. By integrating this system with a customer relationship management system, it can improve personalized service by utilizing inquiry history and customer information. It can also handle inquiries through multiple communication channels and provide multilingual support. As a result, it becomes possible to respond to customer needs efficiently and accurately, leading to improved customer satisfaction and reduced operational costs.

[0006] "Natural language processing" is a technology aimed at enabling computers to understand and process human language.

[0007] "Speech recognition" is a technology that allows computers to understand human speech and convert it into text data.

[0008] Artificial intelligence is a technology that imitates human intellectual activity to perform learning and problem-solving.

[0009] "Token analysis" is a technique that divides text into words and phrases and analyzes their content.

[0010] "Routing" is the process of distributing inquiries and tasks to the appropriate person or system.

[0011] A "customer relationship management system" is a system that allows companies to efficiently manage and optimize their relationships with their customers.

[0012] "Multilingual support" refers to the ability to provide services and support in multiple languages.

[0013] A "communication channel" refers to the medium or method used for sending and receiving information. [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.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of the 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, the numbered 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, the numbered 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, the numbered 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 is implemented as a call center system that operates on a general computer environment. This system utilizes natural language processing, speech recognition, and artificial intelligence technologies.

[0036] System programs and their processes

[0037] User Inquiry Flow

[0038] The user initiates an inquiry via voice or text. This inquiry is received via a voice communication channel or text chat.

[0039] Processing of audio data by the device

[0040] In the case of voice channels, the terminal sends the received voice data to the speech recognition engine, which converts the speech to text. The converted text data is then used directly for analysis.

[0041] Server-based natural language processing and response generation

[0042] The server receives text data provided by the terminal, analyzes it using a natural language processing engine to understand the user's intent, and then an artificial intelligence agent automatically generates the optimal response based on the analysis results.

[0043] Integration with Customer Relationship Management (CRM) systems

[0044] When providing generated responses, the server uses past inquiry history and customer information obtained from the customer relationship management system to provide personalized service to each user. This ensures that the information best suits the user's needs.

[0045] Sentiment assessment using token analysis

[0046] The server uses token analysis to assess the user's emotional state. If it determines that the user is in a negative emotional state, a flag is set to enable intervention by a human agent if necessary.

[0047] Specific example

[0048] For example, if a user expresses dissatisfaction with a product, the system accurately understands the complaint and suggests the best solution based on data from similar past cases. In addition, if the user requires it, they are appropriately routed to a human representative for further support.

[0049] Through these processes, the invention is implemented in a way that enhances customer satisfaction while maintaining a balance between operational efficiency and operational costs. The system is multilingual, allowing for flexible service to both domestic and international customers.

[0050] The following describes the processing flow.

[0051] Step 1:

[0052] The user initiates an inquiry via voice or text. In the case of voice, the device receives voice data through the microphone and prepares to pass it on to the speech recognition engine.

[0053] Step 2:

[0054] The device uses a speech recognition engine to convert received speech data into text. In the case of a text channel, the text entered by the user is used as is.

[0055] Step 3:

[0056] The server receives text data sent from the terminal and analyzes it using a natural language processing engine. This analysis helps understand the intent behind the user's inquiry.

[0057] Step 4:

[0058] Based on the analysis results, the server's artificial intelligence agent generates the optimal response. This response includes solutions and information regarding the user's inquiry.

[0059] Step 5:

[0060] The server performs token analysis to evaluate the user's emotional state. The analyzed emotional information is used to determine the content of the response and the priority of the response.

[0061] Step 6:

[0062] Based on the sentiment assessment results and the response content, the server decides whether to route the request to a human agent as needed. If the request involves strong negative emotions or a complex inquiry, it will be forwarded to a human agent.

[0063] Step 7:

[0064] The terminal presents the generated response received from the server to the user. In the case of audio output, the text is converted to speech and played for the user; in the case of text output, it is displayed directly on the screen.

[0065] Step 8:

[0066] The user can ask further questions or request additional information regarding the response provided. The terminal then sends these inputs back to the server and initiates any necessary additional processing.

[0067] (Example 1)

[0068] 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."

[0069] In today's communication environment, there is a demand for quick and accurate responses to diverse inquiries from users. However, conventional systems may not adequately address the diversity of inquiries and changes in emotional states. Furthermore, the inability to effectively utilize users' past inquiry history results in insufficient individualized support. Moreover, accurately assessing users' emotional states and providing appropriate responses is difficult. Solving these challenges is essential.

[0070] 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.

[0071] In this invention, the server includes means for analyzing user inquiry information using information processing technology, means for converting voice information into text information using voice conversion technology, and means for generating a response based on the inquiry content using intelligent technology. This makes it possible to respond quickly and accurately to a variety of user inquiries.

[0072] "Information processing technology" refers to the technology used to collect, analyze, and manage data using computers, and to process digital information efficiently.

[0073] "User" refers to a person who accesses the system and enters inquiry information.

[0074] "Inquiry information" refers to text or audio data submitted by users through the system for confirmation or requests.

[0075] "Voice conversion technology" refers to technology that converts voice input data into text-based data.

[0076] "Intelligent technology" refers to technologies that use artificial intelligence to automate data analysis and decision-making.

[0077] "Unit analysis" is a method of analyzing the overall sentiment and intent from the information of each unit by subdividing text data.

[0078] "Routing" refers to the process by which the system determines the appropriate recipient and forwards inquiries to the correct person in charge.

[0079] A "customer management system" is a system that centrally manages customer information and history data to improve the quality of customer service.

[0080] "Communication means" refers to the medium, such as voice channels or text channels, used to send and receive inquiry information.

[0081] "Information acquisition technology" refers to the technology used to extract useful information from accumulated data and utilize it for decision-making.

[0082] "Past inquiry history" refers to a record of inquiries that users have made in the past, which can be used to improve customer service.

[0083] This invention is implemented as an inquiry handling system that operates in a wide range of communication environments. It uses a general information processing system to handle user inquiries made via voice or text.

[0084] Users initiate inquiries via voice or text through their mobile devices or computers. For example, if they want to know more about a product, they can send a prompt such as, "Please tell me more about this product."

[0085] When a terminal receives a voice inquiry, it converts the voice data into text data using "voice conversion technology." This process utilizes general-purpose speech recognition capabilities. An example of such software is a general-purpose speech recognition engine.

[0086] The server analyzes the text data received from the terminal using "information processing technology." This analysis accurately extracts the user's intent and generates an appropriate response. Natural language processing libraries may be used for the analysis. Artificial intelligence is used to further optimize the response and provide intelligent answers. Specifically, generative AI models are used to prepare natural and appropriate responses to user inquiries.

[0087] The server evaluates the analysis results and the user's emotional state, and sets the route as needed. For example, if a user expresses dissatisfaction, a decision is made to take appropriate action based on that emotion. This is done using a token analysis-based emotion evaluation system.

[0088] Integration with the customer management system allows for the use of customer information, including past inquiry history, further improving personalized service. This enables the provision of customized services for each user.

[0089] For example, when a user inquires about the availability of a product, the system refers to past purchase history and inquiries to quickly provide updated information on the availability, as well as related suggestions. An example of a prompt might be a specific inquiry such as, "I would like to know the availability of the new product." In this way, the user experience is streamlined and satisfaction is improved.

[0090] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0091] Step 1:

[0092] The user initiates an inquiry. The inquiry is provided as input, either in voice or text format. The user uses a mobile device or PC to input a prompt such as, "Please tell me the details of the product." This inquiry is received via a voice communication channel or text chat. The output is recorded as voice or text data.

[0093] Step 2:

[0094] When a terminal receives voice data, it uses a speech recognition engine to convert the voice data into text data. Specifically, the speech recognition engine analyzes the voice waveform and identifies patterns that correspond to characters. This converts the voice-based inquiry into accurate text format. The text data then becomes input data for natural language processing.

[0095] Step 3:

[0096] The server analyzes the text data received from the terminal using a natural language processing engine. Text data is provided as input. The natural language processing engine performs syntactic and semantic analysis to interpret the user's intent. The output of this step is metadata about the analyzed user intent. Specifically, the engine extracts noun phrases and identifies the focus of the query.

[0097] Step 4:

[0098] The server generates a response using a generative AI model based on the analyzed user intent. The input is metadata about the user's intent, and the output is a naturally constructed response sentence. The generative AI model creates an appropriate response to provide the user with the most relevant information by referring to available databases and learning from responses to similar past queries.

[0099] Step 5:

[0100] The server integrates with the customer management system to match past inquiry history and customer information. User IDs and past inquiry information are referenced as input. This enables the provision of personalized responses. The output is a personalized response synchronized with the historical data. Specifically, relevant information is retrieved from the history database and incorporated into the response.

[0101] Step 6:

[0102] The server uses token analysis to assess the user's emotional state. Input includes the user's overall conversation content and emotional indicators. The server uses an emotional analysis model to evaluate the emotional tone within the text and identify positive or negative elements. The output generates data regarding the emotional state. If necessary, if the emotion is determined to be negative, transfer to a human agent is considered.

[0103] Step 7:

[0104] The server provides the user with a final response based on the generated response and the results of sentiment analysis. It uses the generated text response and sentiment evaluation data as input. The server scrutinizes the necessary information to provide the optimal response tailored to the user's needs. The output is the final response message sent to the user. Specifically, the optimized response is sent through the communication channel.

[0105] (Application Example 1)

[0106] 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."

[0107] With the diversification of communication environments, consumers want to obtain information about specific products and services instantly, but traditional inquiry systems have struggled to respond quickly and accurately to diverse customer needs. In particular, when inquiries are made via voice or text, it is necessary to provide individually tailored information and related product recommendations, but current systems struggle to do this efficiently. This can lead to decreased customer satisfaction and negatively impact a company's competitiveness.

[0108] 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.

[0109] In this invention, the server includes means for analyzing user inquiry information using natural language processing, means for converting speech information into text information using speech recognition, and means for generating a response based on the inquiry content using artificial intelligence. This enables a quick and accurate response to user inquiries, and allows for the provision of appropriate information and the suggestion of related products based on individual customer information.

[0110] "Natural language processing" is a technology for analyzing user inquiries, and it is a method for computers to understand human language and analyze its meaning.

[0111] "Speech recognition" is a technology that converts speech information into text information, a method by which a computer identifies human speech and outputs it as text.

[0112] "Artificial intelligence" is the ability of a computer to generate responses based on user inquiries, and is a technology that imitates the intellectual activities performed by humans.

[0113] "Token analysis" is a technique used to evaluate a user's psychological state. It involves breaking down utterances into individual units and analyzing elements such as emotions.

[0114] A "customer information management system" is a database system that centrally manages customer information and facilitates personalized service for users.

[0115] "Information and communication means" refers to technologies for sending and receiving information in various ways, including voice and text, and is the method used when users exchange information through various devices.

[0116] "Purchase history" refers to a record of products and services that a user has purchased in the past, and is important information that is referenced when providing individualized support.

[0117] "Inquiry history" refers to a record of questions and requests that a user has made in the past, and is information that is used as reference when generating responses.

[0118] The system for implementing this invention utilizes speech recognition, natural language processing, and artificial intelligence to analyze user inquiries and generate optimal responses. Specifically, the server converts the voice information provided by the user into text information using a general API, which is a speech recognition technology. This converted text information is then analyzed using natural language processing technology to understand the user's intent, and an artificial intelligence model is used to generate the optimal response.

[0119] Furthermore, the server is integrated with a customer information management system, providing personalized responses based on users' purchase and inquiry history. In this way, it is possible to recommend appropriate information and related products to each user.

[0120] The server can also use token analysis to assess the user's emotions from their conversations and route inquiries to the appropriate personnel as needed. This process allows for an understanding of the user's emotional state and the provision of appropriate support.

[0121] For example, if a user asks "Will this item be delivered soon?" via voice, the server converts the voice into text, analyzes the content, and determines that the user is requesting expedited delivery. Based on this, and taking into account past inquiry and purchase history, it quickly generates a specific response such as "This item is usually delivered within 2-3 days" and provides it to the user.

[0122] Specific examples of prompts for the generating AI model include, "If a user asks whether a particular product is returnable, the AI ​​model will provide a detailed explanation of the product's return policy and share relevant updates." Based on these prompts, the AI ​​model generates a detailed and accurate response and provides it to the user.

[0123] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0124] Step 1:

[0125] The device receives voice inquiries from the user. The input is voice data, which is sent to the speech recognition engine to convert the voice into text data. The output is the converted text data. Specifically, the device uses a microphone to collect the user's voice and calls the speech recognition API.

[0126] Step 2:

[0127] The server receives text data and uses a natural language processing engine to analyze the user's intent. The input is the text data obtained in step 1, and the output is the analyzed intent information. In this process, the server uses vocabulary and contextual information to analyze the user's inquiry in detail.

[0128] Step 3:

[0129] The server generates the optimal response using an artificial intelligence model based on the analyzed intent information. The input is the analysis result from step 2, and the output is the generated response message. Here, a generative AI model is used to create a natural language response to the user's inquiry.

[0130] Step 4:

[0131] The server integrates with the customer information management system to retrieve user purchase and inquiry history. Input is user identification information, and output is the retrieved history information. Based on this, it personalizes response messages and adds appropriate information.

[0132] Step 5:

[0133] The server uses token analysis to assess the user's emotional state. The input is the original voice or text data, and the output is emotional assessment data. By analyzing and evaluating emotional attributes such as positive or negative, the server considers routing the user to the appropriate person if necessary.

[0134] Step 6:

[0135] The server sends a final response message to the user. The input is a personalized response message and sentiment rating data, and the output is a clear and appropriate answer to the user. At this point, the user may be routed to a human representative if necessary.

[0136] 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.

[0137] This invention is implemented as an advanced call center system that combines an emotion engine. This system integrates natural language processing, speech recognition, artificial intelligence, and an emotion engine to achieve more comprehensive and personalized customer service.

[0138] System programs and their processes

[0139] Initiating a user inquiry

[0140] The user makes an inquiry via voice or text. In the case of a voice channel, the device captures the audio and converts it to text using a speech recognition engine. This is a preparatory step for sending it to the server.

[0141] Data processing by terminals

[0142] The terminal converts the audio data into text and then sends this data to the server. This initiates the analysis and response generation process.

[0143] Server-based analysis and response generation

[0144] The server uses a natural language processing engine to analyze the received text data and clarify the user's intent. Based on the analysis, artificial intelligence generates the most appropriate response, and further incorporates emotional data obtained by the emotion engine.

[0145] Emotion recognition by an emotion engine

[0146] The emotion engine identifies the user's emotional state based on their tone of voice and expression. For example, if a user is irritated, the emotion engine identifies this and adjusts its response and routing strategy accordingly.

[0147] Specific example

[0148] For example, if a user expresses dissatisfaction with a particular product, the server not only understands the content of the complaint but also uses an emotion engine to confirm that the user has strong emotions. As a result, the system carefully adjusts its response and, if necessary, quickly escalates the issue to a human representative.

[0149] Integration with customer relationship management systems

[0150] The server is integrated with a customer relationship management system, which allows it to refer to past customer history and provide more personalized responses to user inquiries.

[0151] Through the above process, this system can quickly and accurately answer complex user inquiries and improve customer satisfaction through an approach utilizing an emotion engine. The system also features multilingual capabilities, providing high-quality service to customers in various countries and regions.

[0152] The following describes the processing flow.

[0153] Step 1:

[0154] The user initiates an inquiry via voice or text. In the case of a voice inquiry, the device uses the microphone to capture voice data and prepares to send it to the speech recognition engine.

[0155] Step 2:

[0156] The audio data is converted into text data by the device's speech recognition engine. This text data is then sent to a server for analysis.

[0157] Step 3:

[0158] The server receives text data and uses a natural language processing engine to analyze the user's inquiry. This process helps understand the user's intent and the subject of their inquiry.

[0159] Step 4:

[0160] The server uses an emotion engine to analyze the user's emotional state from text data. Specifically, it identifies emotions from voice tone and the words used, and records them in a database.

[0161] Step 5:

[0162] The server utilizes artificial intelligence to generate appropriate responses based on analyzed content and sentiment data. These responses will include specific solutions and information for the user.

[0163] Step 6:

[0164] Based on the emotion engine's evaluation, the server determines whether escalation is necessary. If a high emotional intensity is detected, the request is automatically routed to a human representative.

[0165] Step 7:

[0166] The generated response is sent to the terminal and presented to the user. If it's an audio output, the text is converted to speech; if it's a text output, it's displayed as is.

[0167] Step 8:

[0168] If the user asks further questions about the response provided, the terminal sends this back to the server, and the additional analysis and response generation process is repeated.

[0169] (Example 2)

[0170] 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."

[0171] Modern customer service systems demand the rapid and accurate processing of inquiries, but it is difficult to appropriately assess the user's emotions and generate the optimal response based on that. Furthermore, personalized responses tailored to the individual needs of each user are insufficient, making it a challenge to improve customer satisfaction.

[0172] 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.

[0173] In this invention, the server includes means for analyzing user inquiry information using natural language processing, means for evaluating the user's emotional state using an emotion analysis engine, and means for creating prompt sentences according to the emotional state and adjusting the response using generative AI technology. This makes it possible to provide responses that are in line with the user's emotions and to improve individualized service.

[0174] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[0175] "Speech recognition" is a technology that analyzes speech information and converts it into text information.

[0176] "Intelligent processing" is a technology that uses artificial intelligence to automatically make responses and decisions based on input data.

[0177] An "emotion analysis engine" is a technology that evaluates a user's emotional state based on input information and data.

[0178] "Routing" is the process of sending data or inquiries to the appropriate person or processing path.

[0179] An "information management system" is a system that uses customer history and information to provide efficient and personalized services.

[0180] "Generative AI technology" is a technology that uses artificial intelligence to automatically generate prompt text and content.

[0181] A "prompt message" is a document created by AI generation technology in the form of instructions or questions tailored to a specific task or situation.

[0182] This invention is implemented as an advanced customer service system that combines emotion recognition technology. This system integrates speech recognition, natural language processing, generative AI technology, and emotion analysis to provide comprehensive services to users.

[0183] Users can make inquiries via voice or text. In the case of voice input, the terminal uses speech recognition software to convert the voice data into text data. Specifically, a speech recognition API can be used as the speech recognition engine. The text data is sent to the server via a secure communication protocol.

[0184] The server analyzes the received text data using a natural language processing engine. At this stage, data processing is performed to understand the user's question and intent. After analysis, it uses generative AI technology to generate an appropriate answer.

[0185] Furthermore, the server utilizes an emotion analysis engine to analyze the user's emotions from their voice tone and context. For example, if it determines that the user is irritated, it can prepare a response appropriate to that emotion. The technology used for emotion analysis is expected to be emotion analysis software.

[0186] The server can generate more personalized responses by referencing customer history information and return them to the device. For example, it can provide relevant explanations and update information in response to inquiries about products a user has previously purchased.

[0187] As a concrete example, consider a case where a user is dissatisfied with a particular service. In this case, the generated prompt would be: "The user is dissatisfied with the functionality of product A. The emotion engine is indicating a high stress level. What response should be generated in this case?" Based on this prompt, the generation AI technology generates the most appropriate answer.

[0188] In this way, the system can provide immediate and personalized responses to user inquiries and improve the user experience with emotion-based responses.

[0189] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0190] Step 1:

[0191] The user initiates an inquiry via voice or text. When using the voice channel, the device uses its microphone to capture the user's voice and uses a speech recognition engine to convert this voice data into text data. In this process, the input is voice data, and the output is text data obtained through speech recognition.

[0192] Step 2:

[0193] The terminal sends the converted character data to the server. The input is the character data generated in the previous step, and the server receives this data as output using a secure communication protocol. This allows the server to analyze the data.

[0194] Step 3:

[0195] The server analyzes the received text data using a natural language processing engine. The input is text data, and the output is the analyzed structured data. This analysis involves data processing to clarify the user's intent and the content of their question.

[0196] Step 4:

[0197] The server uses an emotion analysis engine to evaluate the user's emotional state based on the analyzed data. The input here is naturally language processing-analyzed data, and the output is metadata indicating the emotional state. The type and intensity of emotion are identified from voice tone and context.

[0198] Step 5:

[0199] The server uses generative AI technology to generate prompts based on the user's emotional state and the nature of the inquiry, creating an appropriate response. The inputs here are the emotional state and analyzed data, while the output is the response to the user. The generated prompts serve as instructions for obtaining a specific answer.

[0200] Step 6:

[0201] The server references customer information to provide personalized responses based on past history. The input is customer history data retrieved from the CRM system, and the output is the customized response.

[0202] Step 7:

[0203] Finally, the server sends the generated response to the terminal, which then presents its contents to the user. The input is the generated response text, and the output is the response message presented to the user. The terminal uses speech synthesis software and, if a voice response is required, communicates to the user in a natural voice.

[0204] (Application Example 2)

[0205] 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 device 14 will be referred to as the "terminal."

[0206] In recent years, there has been a growing demand for measures to enhance customer satisfaction in physical stores, but there is a lack of effective means to accurately grasp customer emotions in real time and respond appropriately. Therefore, there is a need to develop systems that can provide rapid and effective service tailored to customer needs and emotional states.

[0207] 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.

[0208] In this invention, the server includes means for analyzing user inquiry data using natural language processing, means for converting voice data into text data using speech recognition, and means for providing an output interface to a visual device and displaying support information in real time based on the user's emotional state. This makes it possible to understand the user's emotional state and present the optimal customer service method through the visual device.

[0209] "Natural language processing" is the technology that enables computers to understand, analyze, and generate human language.

[0210] "Speech recognition" is a technology that analyzes speech signals and converts them into text or data.

[0211] Artificial intelligence is a technology that gives computer systems the ability to learn, reason, and make judgments like humans.

[0212] "Token analysis" is a technique that breaks down linguistic data into its constituent elements and analyzes their meaning and emotional state.

[0213] A "customer information management system" is a system that centrally manages customer data and uses that data to improve individual customer service.

[0214] A "visual device" is a device that displays information and presents it to the user visually.

[0215] A "communication method" refers to the means or protocols used to send and receive information.

[0216] "Routing" is the process of distributing information and processing to the appropriate person or system.

[0217] The system for realizing this invention is centered around an application in which staff use smart devices to interact with customers in a physical store. Specifically, a server handles the main information processing, while terminals collect input data, send it to the server, and provide an interface with the user.

[0218] The server uses a natural language processing engine to analyze voice and text data from the user and extract their intent. Voice data is converted into text data using speech recognition software on the terminal. Speech recognition APIs such as Google® Cloud Speech-to-Text are utilized in this process. The analyzed data is further evaluated through token analysis using an emotion recognition engine to assess the user's emotional state. Tools such as IBM Watson® Tone Analyzer are used in this step.

[0219] Next, the server utilizes artificial intelligence to generate the optimal response for the user based on the analysis results. During this process, integration with the customer information management system is performed to reference past visitor data. The generated information is then presented to staff in real time via visual devices.

[0220] The terminal primarily utilizes visual devices worn by staff, such as smart glasses. Sentiment analysis results and response suggestions transmitted from the server are displayed on the screen, providing real-time feedback to staff. This allows staff to understand the customer's emotional state and respond appropriately in a timely manner.

[0221] For example, if a cafe staff member is wearing smart glasses, the server can analyze what a customer says when complaining during a wait and display a message on their visual device suggesting "prompt service." This would encourage the staff to take action to provide prompt service while considering the customer's emotional state.

[0222] An example of a prompt to input into a generative AI model is: "Design a support tool that analyzes customer emotions based on their statements and presents their current emotional state and the optimal customer service style accordingly."

[0223] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0224] Step 1:

[0225] The device captures the user's voice. The input is the user's voice signal, which the device uses the Google Cloud Speech-to-Text API to convert the voice data into text data. The text data is the output.

[0226] Step 2:

[0227] The server receives the converted text data. The input is text data, and the server uses a natural language processing engine to analyze the text and extract the user's intent. The intent data extracted through this process becomes the output.

[0228] Step 3:

[0229] The server performs emotion recognition based on the analyzed intent data. The input is intent data, and the IBM Watson Tone Analyzer is used to evaluate the user's emotional state. Emotional data as an evaluation result is output.

[0230] Step 4:

[0231] The server generates responses based on sentiment data and intent data. The input is sentiment data and intent data, and artificial intelligence designs the most appropriate response. The generated response data is the output. The server also references historical data from the customer information management system to further personalize the response.

[0232] Step 5:

[0233] The server sends the generated response data to the visual device. The input is the response data, and the information is displayed in real time on the smart glasses of the terminal. The final output is the information presented to the staff visually.

[0234] Through the above processing steps, a system is created that quickly proposes the most suitable service to staff in response to the user's emotions.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] [Second Embodiment]

[0239] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0240] 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.

[0241] 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).

[0242] 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.

[0243] 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.

[0244] 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).

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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.

[0249] 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.

[0250] 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".

[0251] This invention is implemented as a call center system that operates on a general computer environment. This system utilizes natural language processing, speech recognition, and artificial intelligence technologies.

[0252] System programs and their processes

[0253] User Inquiry Flow

[0254] The user initiates an inquiry via voice or text. This inquiry is received via a voice communication channel or text chat.

[0255] Processing of audio data by the device

[0256] In the case of voice channels, the terminal sends the received voice data to the speech recognition engine, which converts the speech to text. The converted text data is then used directly for analysis.

[0257] Server-based natural language processing and response generation

[0258] The server receives text data provided by the terminal, analyzes it using a natural language processing engine to understand the user's intent, and then an artificial intelligence agent automatically generates the optimal response based on the analysis results.

[0259] Integration with Customer Relationship Management (CRM) systems

[0260] When providing generated responses, the server uses past inquiry history and customer information obtained from the customer relationship management system to provide personalized service to each user. This ensures that the information best suits the user's needs.

[0261] Sentiment assessment using token analysis

[0262] The server uses token analysis to assess the user's emotional state. If it determines that the user is in a negative emotional state, a flag is set to enable intervention by a human agent if necessary.

[0263] Specific example

[0264] For example, if a user expresses dissatisfaction with a product, the system accurately understands the complaint and suggests the best solution based on data from similar past cases. In addition, if the user requires it, they are appropriately routed to a human representative for further support.

[0265] Through these processes, the invention is implemented in a way that enhances customer satisfaction while maintaining a balance between operational efficiency and operational costs. The system is multilingual, allowing for flexible service to both domestic and international customers.

[0266] The following describes the processing flow.

[0267] Step 1:

[0268] The user initiates an inquiry via voice or text. In the case of voice, the device receives voice data through the microphone and prepares to pass it on to the speech recognition engine.

[0269] Step 2:

[0270] The device uses a speech recognition engine to convert received speech data into text. In the case of a text channel, the text entered by the user is used as is.

[0271] Step 3:

[0272] The server receives text data sent from the terminal and analyzes it using a natural language processing engine. This analysis helps understand the intent behind the user's inquiry.

[0273] Step 4:

[0274] Based on the analysis results, the server's artificial intelligence agent generates the optimal response. This response includes solutions and information regarding the user's inquiry.

[0275] Step 5:

[0276] The server performs token analysis to evaluate the user's emotional state. The analyzed emotional information is used to determine the content of the response and the priority of the response.

[0277] Step 6:

[0278] Based on the result of emotion evaluation and the response content, the server determines the routing to a human agent as needed. In the case of strong negative emotions or complex inquiries, it is transferred to a human agent.

[0279] Step 7:

[0280] The terminal presents the generated response received from the server to the user. In the case of voice output, the text is converted to voice and played for the user to hear, and in the case of text output, it is directly displayed on the screen.

[0281] Step 8:

[0282] The user can request further questions or additional information regarding the presented response. The terminal sends these inputs to the server again and initiates the necessary additional processing.

[0283] (Example 1)

[0284] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0285] In a modern communication environment, it is required to respond quickly and accurately to various inquiries from users. However, conventional systems may not be able to fully handle the diversity of inquiry content and changes in emotional states. Also, it is a challenge that the past inquiry history of users cannot be effectively utilized and individual responses are insufficient. Furthermore, it is difficult to accurately evaluate the emotional state of users and make appropriate responses. It is required to solve these problems.

[0286] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0287] In this invention, the server includes means for analyzing user inquiry information using information processing technology, means for converting voice information into text information using voice conversion technology, and means for generating a response based on the inquiry content using intelligent technology. This makes it possible to respond quickly and accurately to a variety of user inquiries.

[0288] "Information processing technology" refers to the technology used to collect, analyze, and manage data using computers, and to process digital information efficiently.

[0289] "User" refers to a person who accesses the system and enters inquiry information.

[0290] "Inquiry information" refers to text or audio data submitted by users through the system for confirmation or requests.

[0291] "Voice conversion technology" refers to technology that converts voice input data into text-based data.

[0292] "Intelligent technology" refers to technologies that use artificial intelligence to automate data analysis and decision-making.

[0293] "Unit analysis" is a method of analyzing the overall sentiment and intent from the information of each unit by subdividing text data.

[0294] "Routing" refers to the process by which the system determines the appropriate recipient and forwards inquiries to the correct person in charge.

[0295] A "customer management system" is a system that centrally manages customer information and history data to improve the quality of customer service.

[0296] "Communication means" refers to the medium, such as voice channels or text channels, used to send and receive inquiry information.

[0297] "Information acquisition technology" refers to the technology used to extract useful information from accumulated data and utilize it for decision-making.

[0298] "Past inquiry history" refers to a record of inquiries that users have made in the past, which can be used to improve customer service.

[0299] This invention is implemented as an inquiry handling system that operates in a wide range of communication environments. It uses a general information processing system to handle user inquiries made via voice or text.

[0300] Users initiate inquiries via voice or text through their mobile devices or computers. For example, if they want to know more about a product, they can send a prompt such as, "Please tell me more about this product."

[0301] When a terminal receives a voice inquiry, it converts the voice data into text data using "voice conversion technology." This process utilizes general-purpose speech recognition capabilities. An example of such software is a general-purpose speech recognition engine.

[0302] The server analyzes the text data received from the terminal using "information processing technology." This analysis accurately extracts the user's intent and generates an appropriate response. Natural language processing libraries may be used for the analysis. Artificial intelligence is used to further optimize the response and provide intelligent answers. Specifically, generative AI models are used to prepare natural and appropriate responses to user inquiries.

[0303] The server evaluates the analysis results and the user's emotional state, and sets the route as needed. For example, if a user expresses dissatisfaction, a decision is made to take appropriate action based on that emotion. This is done using a token analysis-based emotion evaluation system.

[0304] Through integration with the customer management system, customer information including past inquiry history is utilized, further improving personalized support. This enables the provision of customized services for each user.

[0305] As a specific example, when a user inquires about the inventory status of a product, the system refers to past purchase history and inquiries and promptly provides updated information on the inventory status and relevant proposals. An example of a prompt sentence could be a specific inquiry such as "I would like to know the inventory status of the new product." In this way, the user experience is streamlined and satisfaction is enhanced.

[0306] The flow of the specific process in Example 1 will be described using FIG. 11.

[0307] Step 1:

[0308] The user initiates an inquiry. As input, the inquiry content is provided in the form of voice or text. The user enters a prompt sentence such as "Please tell me the details of the product" using a mobile device or PC. This inquiry is received via a voice communication channel or text chat. The output remains as voice data or text data.

[0309] Step 2:

[0310] When the terminal receives voice data, it converts the voice data into character data using a voice recognition engine. Specifically, the voice recognition engine analyzes the voice waveform and performs a process to identify patterns corresponding to characters. As a result, the inquiry content based on voice is converted into the correct text format. The text data becomes input data for natural language processing.

[0311] Step 3:

[0312] The server analyzes the text data received from the terminal using a natural language processing engine. Text data is provided as input. The natural language processing engine performs syntactic and semantic analysis to interpret the user's intent. The output of this step is metadata about the analyzed user intent. Specifically, the engine extracts noun phrases and identifies the focus of the query.

[0313] Step 4:

[0314] The server generates a response using a generative AI model based on the analyzed user intent. The input is metadata about the user's intent, and the output is a naturally constructed response sentence. The generative AI model creates an appropriate response to provide the user with the most relevant information by referring to available databases and learning from responses to similar past queries.

[0315] Step 5:

[0316] The server integrates with the customer management system to match past inquiry history and customer information. User IDs and past inquiry information are referenced as input. This enables the provision of personalized responses. The output is a personalized response synchronized with the historical data. Specifically, relevant information is retrieved from the history database and incorporated into the response.

[0317] Step 6:

[0318] The server uses token analysis to assess the user's emotional state. Input includes the user's overall conversation content and emotional indicators. The server uses an emotional analysis model to evaluate the emotional tone within the text and identify positive or negative elements. The output generates data regarding the emotional state. If necessary, if the emotion is determined to be negative, transfer to a human agent is considered.

[0319] Step 7:

[0320] The server provides the user with a final response based on the generated response and the results of sentiment analysis. It uses the generated text response and sentiment evaluation data as input. The server scrutinizes the necessary information to provide the optimal response tailored to the user's needs. The output is the final response message sent to the user. Specifically, the optimized response is sent through the communication channel.

[0321] (Application Example 1)

[0322] 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."

[0323] With the diversification of communication environments, consumers want to obtain information about specific products and services instantly, but traditional inquiry systems have struggled to respond quickly and accurately to diverse customer needs. In particular, when inquiries are made via voice or text, it is necessary to provide individually tailored information and related product recommendations, but current systems struggle to do this efficiently. This can lead to decreased customer satisfaction and negatively impact a company's competitiveness.

[0324] 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.

[0325] In this invention, the server includes means for analyzing user inquiry information using natural language processing, means for converting speech information into text information using speech recognition, and means for generating a response based on the inquiry content using artificial intelligence. This enables a quick and accurate response to user inquiries, and allows for the provision of appropriate information and the suggestion of related products based on individual customer information.

[0326] "Natural language processing" is a technology for analyzing user inquiries, and it is a method for computers to understand human language and analyze its meaning.

[0327] "Speech recognition" is a technology that converts speech information into text information, a method by which a computer identifies human speech and outputs it as text.

[0328] "Artificial intelligence" is the ability of a computer to generate responses based on user inquiries, and is a technology that imitates the intellectual activities performed by humans.

[0329] "Token analysis" is a technique used to evaluate a user's psychological state. It involves breaking down utterances into individual units and analyzing elements such as emotions.

[0330] A "customer information management system" is a database system that centrally manages customer information and facilitates personalized service for users.

[0331] "Information and communication means" refers to technologies for sending and receiving information in various ways, including voice and text, and is the method used when users exchange information through various devices.

[0332] "Purchase history" refers to a record of products and services that a user has purchased in the past, and is important information that is referenced when providing individualized support.

[0333] "Inquiry history" refers to a record of questions and requests that a user has made in the past, and is information that is used as reference when generating responses.

[0334] The system for implementing this invention utilizes speech recognition, natural language processing, and artificial intelligence to analyze user inquiries and generate optimal responses. Specifically, the server converts the voice information provided by the user into text information using a general API, which is a speech recognition technology. This converted text information is then analyzed using natural language processing technology to understand the user's intent, and an artificial intelligence model is used to generate the optimal response.

[0335] Furthermore, the server is integrated with a customer information management system, providing personalized responses based on users' purchase and inquiry history. In this way, it is possible to recommend appropriate information and related products to each user.

[0336] The server can also use token analysis to assess the user's emotions from their conversations and route inquiries to the appropriate personnel as needed. This process allows for an understanding of the user's emotional state and the provision of appropriate support.

[0337] For example, if a user asks "Will this item be delivered soon?" via voice, the server converts the voice into text, analyzes the content, and determines that the user is requesting expedited delivery. Based on this, and taking into account past inquiry and purchase history, it quickly generates a specific response such as "This item is usually delivered within 2-3 days" and provides it to the user.

[0338] Specific examples of prompts for the generating AI model include, "If a user asks whether a particular product is returnable, the AI ​​model will provide a detailed explanation of the product's return policy and share relevant updates." Based on these prompts, the AI ​​model generates a detailed and accurate response and provides it to the user.

[0339] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0340] Step 1:

[0341] The device receives voice inquiries from the user. The input is voice data, which is sent to the speech recognition engine to convert the voice into text data. The output is the converted text data. Specifically, the device uses a microphone to collect the user's voice and calls the speech recognition API.

[0342] Step 2:

[0343] The server receives text data and uses a natural language processing engine to analyze the user's intent. The input is the text data obtained in step 1, and the output is the analyzed intent information. In this process, the server uses vocabulary and contextual information to analyze the user's inquiry in detail.

[0344] Step 3:

[0345] The server generates the optimal response using an artificial intelligence model based on the analyzed intent information. The input is the analysis result from step 2, and the output is the generated response message. Here, a generative AI model is used to create a natural language response to the user's inquiry.

[0346] Step 4:

[0347] The server integrates with the customer information management system to retrieve user purchase and inquiry history. Input is user identification information, and output is the retrieved history information. Based on this, it personalizes response messages and adds appropriate information.

[0348] Step 5:

[0349] The server uses token analysis to assess the user's emotional state. The input is the original voice or text data, and the output is emotional assessment data. By analyzing and evaluating emotional attributes such as positive or negative, the server considers routing the user to the appropriate person if necessary.

[0350] Step 6:

[0351] The server sends a final response message to the user. The input is a personalized response message and sentiment rating data, and the output is a clear and appropriate answer to the user. At this point, the user may be routed to a human representative if necessary.

[0352] 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.

[0353] This invention is implemented as an advanced call center system that combines an emotion engine. This system integrates natural language processing, speech recognition, artificial intelligence, and an emotion engine to achieve more comprehensive and personalized customer service.

[0354] System programs and their processes

[0355] Initiating a user inquiry

[0356] The user makes an inquiry via voice or text. In the case of a voice channel, the device captures the audio and converts it to text using a speech recognition engine. This is a preparatory step for sending it to the server.

[0357] Data processing by terminals

[0358] The terminal converts the audio data into text and then sends this data to the server. This initiates the analysis and response generation process.

[0359] Server-based analysis and response generation

[0360] The server uses a natural language processing engine to analyze the received text data and clarify the user's intent. Based on the analysis, artificial intelligence generates the most appropriate response, and further incorporates emotional data obtained by the emotion engine.

[0361] Emotion recognition by an emotion engine

[0362] The emotion engine identifies the user's emotional state based on their tone of voice and expression. For example, if a user is irritated, the emotion engine identifies this and adjusts its response and routing strategy accordingly.

[0363] Specific example

[0364] For example, if a user expresses dissatisfaction with a particular product, the server not only understands the content of the complaint but also uses an emotion engine to confirm that the user has strong emotions. As a result, the system carefully adjusts its response and, if necessary, quickly escalates the issue to a human representative.

[0365] Integration with customer relationship management systems

[0366] The server is integrated with a customer relationship management system, which allows it to refer to past customer history and provide more personalized responses to user inquiries.

[0367] Through the above process, this system can quickly and accurately answer complex user inquiries and improve customer satisfaction through an approach utilizing an emotion engine. The system also features multilingual capabilities, providing high-quality service to customers in various countries and regions.

[0368] The following describes the processing flow.

[0369] Step 1:

[0370] The user initiates an inquiry via voice or text. In the case of a voice inquiry, the device uses the microphone to capture voice data and prepares to send it to the speech recognition engine.

[0371] Step 2:

[0372] The audio data is converted into text data by the device's speech recognition engine. This text data is then sent to a server for analysis.

[0373] Step 3:

[0374] The server receives text data and uses a natural language processing engine to analyze the user's inquiry. This process helps understand the user's intent and the subject of their inquiry.

[0375] Step 4:

[0376] The server uses an emotion engine to analyze the user's emotional state from text data. Specifically, it identifies emotions from voice tone and the words used, and records them in a database.

[0377] Step 5:

[0378] The server utilizes artificial intelligence to generate appropriate responses based on analyzed content and sentiment data. These responses will include specific solutions and information for the user.

[0379] Step 6:

[0380] Based on the emotion engine's evaluation, the server determines whether escalation is necessary. If a high emotional intensity is detected, the request is automatically routed to a human representative.

[0381] Step 7:

[0382] The generated response is sent to the terminal and presented to the user. If it's an audio output, the text is converted to speech; if it's a text output, it's displayed as is.

[0383] Step 8:

[0384] If the user asks further questions about the response provided, the terminal sends this back to the server, and the additional analysis and response generation process is repeated.

[0385] (Example 2)

[0386] 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".

[0387] Modern customer service systems demand the rapid and accurate processing of inquiries, but it is difficult to appropriately assess the user's emotions and generate the optimal response based on that. Furthermore, personalized responses tailored to the individual needs of each user are insufficient, making it a challenge to improve customer satisfaction.

[0388] 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.

[0389] In this invention, the server includes means for analyzing user inquiry information using natural language processing, means for evaluating the user's emotional state using an emotion analysis engine, and means for creating prompt sentences according to the emotional state and adjusting the response using generative AI technology. This makes it possible to provide responses that are in line with the user's emotions and to improve individualized service.

[0390] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[0391] "Speech recognition" is a technology that analyzes speech information and converts it into text information.

[0392] "Intelligent processing" is a technology that uses artificial intelligence to automatically make responses and decisions based on input data.

[0393] An "emotion analysis engine" is a technology that evaluates a user's emotional state based on input information and data.

[0394] "Routing" is the process of sending data or inquiries to the appropriate person or processing path.

[0395] An "information management system" is a system that uses customer history and information to provide efficient and personalized services.

[0396] "Generative AI technology" is a technology that uses artificial intelligence to automatically generate prompt text and content.

[0397] A "prompt message" is a document created by AI generation technology in the form of instructions or questions tailored to a specific task or situation.

[0398] This invention is implemented as an advanced customer service system that combines emotion recognition technology. This system integrates speech recognition, natural language processing, generative AI technology, and emotion analysis to provide comprehensive services to users.

[0399] Users can make inquiries via voice or text. In the case of voice input, the terminal uses speech recognition software to convert the voice data into text data. Specifically, a speech recognition API can be used as the speech recognition engine. The text data is sent to the server via a secure communication protocol.

[0400] The server analyzes the received text data using a natural language processing engine. At this stage, data processing is performed to understand the user's question and intent. After analysis, it uses generative AI technology to generate an appropriate answer.

[0401] Furthermore, the server utilizes an emotion analysis engine to analyze the user's emotions from their voice tone and context. For example, if it determines that the user is irritated, it can prepare a response appropriate to that emotion. The technology used for emotion analysis is expected to be emotion analysis software.

[0402] The server can generate more personalized responses by referencing customer history information and return them to the device. For example, it can provide relevant explanations and update information in response to inquiries about products a user has previously purchased.

[0403] As a concrete example, consider a case where a user is dissatisfied with a particular service. In this case, the generated prompt would be: "The user is dissatisfied with the functionality of product A. The emotion engine is indicating a high stress level. What response should be generated in this case?" Based on this prompt, the generation AI technology generates the most appropriate answer.

[0404] In this way, the system can provide immediate and personalized responses to user inquiries and improve the user experience with emotion-based responses.

[0405] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0406] Step 1:

[0407] The user initiates an inquiry via voice or text. When using the voice channel, the device uses its microphone to capture the user's voice and uses a speech recognition engine to convert this voice data into text data. In this process, the input is voice data, and the output is text data obtained through speech recognition.

[0408] Step 2:

[0409] The terminal sends the converted character data to the server. The input is the character data generated in the previous step, and the server receives this data as output using a secure communication protocol. This allows the server to analyze the data.

[0410] Step 3:

[0411] The server analyzes the received text data using a natural language processing engine. The input is text data, and the output is the analyzed structured data. This analysis involves data processing to clarify the user's intent and the content of their question.

[0412] Step 4:

[0413] The server uses an emotion analysis engine to evaluate the user's emotional state based on the analyzed data. The input here is naturally language processing-analyzed data, and the output is metadata indicating the emotional state. The type and intensity of emotion are identified from voice tone and context.

[0414] Step 5:

[0415] The server uses generative AI technology to generate prompts based on the user's emotional state and the nature of the inquiry, creating an appropriate response. The inputs here are the emotional state and analyzed data, while the output is the response to the user. The generated prompts serve as instructions for obtaining a specific answer.

[0416] Step 6:

[0417] The server references customer information to provide personalized responses based on past history. The input is customer history data retrieved from the CRM system, and the output is the customized response.

[0418] Step 7:

[0419] Finally, the server sends the generated response to the terminal, which then presents its contents to the user. The input is the generated response text, and the output is the response message presented to the user. The terminal uses speech synthesis software and, if a voice response is required, communicates to the user in a natural voice.

[0420] (Application Example 2)

[0421] 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."

[0422] In recent years, there has been a growing demand for measures to enhance customer satisfaction in physical stores, but there is a lack of effective means to accurately grasp customer emotions in real time and respond appropriately. Therefore, there is a need to develop systems that can provide rapid and effective service tailored to customer needs and emotional states.

[0423] 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.

[0424] In this invention, the server includes means for analyzing user inquiry data using natural language processing, means for converting voice data into text data using speech recognition, and means for providing an output interface to a visual device and displaying support information in real time based on the user's emotional state. This makes it possible to understand the user's emotional state and present the optimal customer service method through the visual device.

[0425] "Natural language processing" is the technology that enables computers to understand, analyze, and generate human language.

[0426] "Speech recognition" is a technology that analyzes speech signals and converts them into text or data.

[0427] Artificial intelligence is a technology that gives computer systems the ability to learn, reason, and make judgments like humans.

[0428] "Token analysis" is a technique that breaks down linguistic data into its constituent elements and analyzes their meaning and emotional state.

[0429] A "customer information management system" is a system that centrally manages customer data and uses that data to improve individual customer service.

[0430] A "visual device" is a device that displays information and presents it to the user visually.

[0431] A "communication method" refers to the means or protocols used to send and receive information.

[0432] "Routing" is the process of distributing information and processing to the appropriate person or system.

[0433] The system for realizing this invention is centered around an application in which staff use smart devices to interact with customers in a physical store. Specifically, a server handles the main information processing, while terminals collect input data, send it to the server, and provide an interface with the user.

[0434] The server uses a natural language processing engine to analyze voice and text data from the user and extract their intent. Voice data is converted into text data using speech recognition software on the terminal. Speech recognition APIs such as Google Cloud Speech-to-Text are utilized in this process. The analyzed data is further evaluated through token analysis using an emotion recognition engine to assess the user's emotional state. Tools such as IBM Watson Tone Analyzer are used in this step.

[0435] Next, the server utilizes artificial intelligence to generate the optimal response for the user based on the analysis results. During this process, integration with the customer information management system is performed to reference past visitor data. The generated information is then presented to staff in real time via visual devices.

[0436] The terminal primarily utilizes visual devices worn by staff, such as smart glasses. Sentiment analysis results and response suggestions transmitted from the server are displayed on the screen, providing real-time feedback to staff. This allows staff to understand the customer's emotional state and respond appropriately in a timely manner.

[0437] For example, if a cafe staff member is wearing smart glasses, the server can analyze what a customer says when complaining during a wait and display a message on their visual device suggesting "prompt service." This would encourage the staff to take action to provide prompt service while considering the customer's emotional state.

[0438] An example of a prompt to input into a generative AI model is: "Design a support tool that analyzes customer emotions based on their statements and presents their current emotional state and the optimal customer service style accordingly."

[0439] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0440] Step 1:

[0441] The device captures the user's voice. The input is the user's voice signal, which the device uses the Google Cloud Speech-to-Text API to convert the voice data into text data. The text data is the output.

[0442] Step 2:

[0443] The server receives the converted text data. The input is text data, and the server uses a natural language processing engine to analyze the text and extract the user's intent. The intent data extracted through this process becomes the output.

[0444] Step 3:

[0445] The server performs emotion recognition based on the analyzed intent data. The input is intent data, and the IBM Watson Tone Analyzer is used to evaluate the user's emotional state. Emotional data as an evaluation result is output.

[0446] Step 4:

[0447] The server generates responses based on sentiment data and intent data. The input is sentiment data and intent data, and artificial intelligence designs the most appropriate response. The generated response data is the output. The server also references historical data from the customer information management system to further personalize the response.

[0448] Step 5:

[0449] The server sends the generated response data to the visual device. The input is the response data, and the information is displayed in real time on the smart glasses of the terminal. The final output is the information presented to the staff visually.

[0450] Through the above processing steps, a system is created that quickly proposes the most suitable service to staff in response to the user's emotions.

[0451] 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.

[0452] 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.

[0453] 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.

[0454] [Third Embodiment]

[0455] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0456] 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.

[0457] 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).

[0458] 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.

[0459] 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.

[0460] 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).

[0461] 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.

[0462] 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.

[0463] 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.

[0464] 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.

[0465] 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.

[0466] 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".

[0467] This invention is implemented as a call center system that operates on a general computer environment. This system utilizes natural language processing, speech recognition, and artificial intelligence technologies.

[0468] System programs and their processes

[0469] User Inquiry Flow

[0470] The user initiates an inquiry via voice or text. This inquiry is received via a voice communication channel or text chat.

[0471] Processing of audio data by the device

[0472] In the case of voice channels, the terminal sends the received voice data to the speech recognition engine, which converts the speech to text. The converted text data is then used directly for analysis.

[0473] Server-based natural language processing and response generation

[0474] The server receives text data provided by the terminal, analyzes it using a natural language processing engine to understand the user's intent, and then an artificial intelligence agent automatically generates the optimal response based on the analysis results.

[0475] Integration with Customer Relationship Management (CRM) systems

[0476] When providing generated responses, the server uses past inquiry history and customer information obtained from the customer relationship management system to provide personalized service to each user. This ensures that the information best suits the user's needs.

[0477] Sentiment assessment using token analysis

[0478] The server uses token analysis to assess the user's emotional state. If it determines that the user is in a negative emotional state, a flag is set to enable intervention by a human agent if necessary.

[0479] Specific example

[0480] For example, if a user expresses dissatisfaction with a product, the system accurately understands the complaint and suggests the best solution based on data from similar past cases. In addition, if the user requires it, they are appropriately routed to a human representative for further support.

[0481] Through these processes, the invention is implemented in a way that enhances customer satisfaction while maintaining a balance between operational efficiency and operational costs. The system is multilingual, allowing for flexible service to both domestic and international customers.

[0482] The following describes the processing flow.

[0483] Step 1:

[0484] The user initiates an inquiry via voice or text. In the case of voice, the device receives voice data through the microphone and prepares to pass it on to the speech recognition engine.

[0485] Step 2:

[0486] The device uses a speech recognition engine to convert received speech data into text. In the case of a text channel, the text entered by the user is used as is.

[0487] Step 3:

[0488] The server receives text data sent from the terminal and analyzes it using a natural language processing engine. This analysis helps understand the intent behind the user's inquiry.

[0489] Step 4:

[0490] Based on the analysis results, the server's artificial intelligence agent generates the optimal response. This response includes solutions and information regarding the user's inquiry.

[0491] Step 5:

[0492] The server performs token analysis to evaluate the user's emotional state. The analyzed emotional information is used to determine the content of the response and the priority of the response.

[0493] Step 6:

[0494] Based on the sentiment assessment results and the response content, the server decides whether to route the request to a human agent as needed. If the request involves strong negative emotions or a complex inquiry, it will be forwarded to a human agent.

[0495] Step 7:

[0496] The terminal presents the generated response received from the server to the user. In the case of audio output, the text is converted to speech and played for the user; in the case of text output, it is displayed directly on the screen.

[0497] Step 8:

[0498] The user can ask further questions or request additional information regarding the response provided. The terminal then sends these inputs back to the server and initiates any necessary additional processing.

[0499] (Example 1)

[0500] 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."

[0501] In today's communication environment, there is a demand for quick and accurate responses to diverse inquiries from users. However, conventional systems may not adequately address the diversity of inquiries and changes in emotional states. Furthermore, the inability to effectively utilize users' past inquiry history results in insufficient individualized support. Moreover, accurately assessing users' emotional states and providing appropriate responses is difficult. Solving these challenges is essential.

[0502] 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.

[0503] In this invention, the server includes means for analyzing user inquiry information using information processing technology, means for converting voice information into text information using voice conversion technology, and means for generating a response based on the inquiry content using intelligent technology. This makes it possible to respond quickly and accurately to a variety of user inquiries.

[0504] "Information processing technology" refers to the technology used to collect, analyze, and manage data using computers, and to process digital information efficiently.

[0505] "User" refers to a person who accesses the system and enters inquiry information.

[0506] "Inquiry information" refers to text or audio data submitted by users through the system for confirmation or requests.

[0507] "Voice conversion technology" refers to technology that converts voice input data into text-based data.

[0508] "Intelligent technology" refers to technologies that use artificial intelligence to automate data analysis and decision-making.

[0509] "Unit analysis" is a method of analyzing the overall sentiment and intent from the information of each unit by subdividing text data.

[0510] "Routing" refers to the process by which the system determines the appropriate recipient and forwards inquiries to the correct person in charge.

[0511] A "customer management system" is a system that centrally manages customer information and history data to improve the quality of customer service.

[0512] "Communication means" refers to the medium, such as voice channels or text channels, used to send and receive inquiry information.

[0513] "Information acquisition technology" refers to the technology used to extract useful information from accumulated data and utilize it for decision-making.

[0514] "Past inquiry history" refers to a record of inquiries that users have made in the past, which can be used to improve customer service.

[0515] This invention is implemented as an inquiry handling system that operates in a wide range of communication environments. It uses a general information processing system to handle user inquiries made via voice or text.

[0516] Users initiate inquiries via voice or text through their mobile devices or computers. For example, if they want to know more about a product, they can send a prompt such as, "Please tell me more about this product."

[0517] When a terminal receives a voice inquiry, it converts the voice data into text data using "voice conversion technology." This process utilizes general-purpose speech recognition capabilities. An example of such software is a general-purpose speech recognition engine.

[0518] The server analyzes the text data received from the terminal using "information processing technology." This analysis accurately extracts the user's intent and generates an appropriate response. Natural language processing libraries may be used for the analysis. Artificial intelligence is used to further optimize the response and provide intelligent answers. Specifically, generative AI models are used to prepare natural and appropriate responses to user inquiries.

[0519] The server evaluates the analysis results and the user's emotional state, and sets the route as needed. For example, if a user expresses dissatisfaction, a decision is made to take appropriate action based on that emotion. This is done using a token analysis-based emotion evaluation system.

[0520] Integration with the customer management system allows for the use of customer information, including past inquiry history, further improving personalized service. This enables the provision of customized services for each user.

[0521] For example, when a user inquires about the availability of a product, the system refers to past purchase history and inquiries to quickly provide updated information on the availability, as well as related suggestions. An example of a prompt might be a specific inquiry such as, "I would like to know the availability of the new product." In this way, the user experience is streamlined and satisfaction is improved.

[0522] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0523] Step 1:

[0524] The user initiates an inquiry. The inquiry is provided as input, either in voice or text format. The user uses a mobile device or PC to input a prompt such as, "Please tell me the details of the product." This inquiry is received via a voice communication channel or text chat. The output is recorded as voice or text data.

[0525] Step 2:

[0526] When a terminal receives voice data, it uses a speech recognition engine to convert the voice data into text data. Specifically, the speech recognition engine analyzes the voice waveform and identifies patterns that correspond to characters. This converts the voice-based inquiry into accurate text format. The text data then becomes input data for natural language processing.

[0527] Step 3:

[0528] The server analyzes the text data received from the terminal using a natural language processing engine. Text data is provided as input. The natural language processing engine performs syntactic and semantic analysis to interpret the user's intent. The output of this step is metadata about the analyzed user intent. Specifically, the engine extracts noun phrases and identifies the focus of the query.

[0529] Step 4:

[0530] The server generates a response using a generative AI model based on the analyzed user intent. The input is metadata about the user's intent, and the output is a naturally constructed response sentence. The generative AI model creates an appropriate response to provide the user with the most relevant information by referring to available databases and learning from responses to similar past queries.

[0531] Step 5:

[0532] The server integrates with the customer management system to match past inquiry history and customer information. User IDs and past inquiry information are referenced as input. This enables the provision of personalized responses. The output is a personalized response synchronized with the historical data. Specifically, relevant information is retrieved from the history database and incorporated into the response.

[0533] Step 6:

[0534] The server uses token analysis to assess the user's emotional state. Input includes the user's overall conversation content and emotional indicators. The server uses an emotional analysis model to evaluate the emotional tone within the text and identify positive or negative elements. The output generates data regarding the emotional state. If necessary, if the emotion is determined to be negative, transfer to a human agent is considered.

[0535] Step 7:

[0536] The server provides the user with a final response based on the generated response and the results of sentiment analysis. It uses the generated text response and sentiment evaluation data as input. The server scrutinizes the necessary information to provide the optimal response tailored to the user's needs. The output is the final response message sent to the user. Specifically, the optimized response is sent through the communication channel.

[0537] (Application Example 1)

[0538] 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."

[0539] With the diversification of communication environments, consumers want to obtain information about specific products and services instantly, but traditional inquiry systems have struggled to respond quickly and accurately to diverse customer needs. In particular, when inquiries are made via voice or text, it is necessary to provide individually tailored information and related product recommendations, but current systems struggle to do this efficiently. This can lead to decreased customer satisfaction and negatively impact a company's competitiveness.

[0540] 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.

[0541] In this invention, the server includes means for analyzing user inquiry information using natural language processing, means for converting speech information into text information using speech recognition, and means for generating a response based on the inquiry content using artificial intelligence. This enables a quick and accurate response to user inquiries, and allows for the provision of appropriate information and the suggestion of related products based on individual customer information.

[0542] "Natural language processing" is a technology for analyzing user inquiries, and it is a method for computers to understand human language and analyze its meaning.

[0543] "Speech recognition" is a technology that converts speech information into text information, a method by which a computer identifies human speech and outputs it as text.

[0544] "Artificial intelligence" is the ability of a computer to generate responses based on user inquiries, and is a technology that imitates the intellectual activities performed by humans.

[0545] "Token analysis" is a technique used to evaluate a user's psychological state. It involves breaking down utterances into individual units and analyzing elements such as emotions.

[0546] A "customer information management system" is a database system that centrally manages customer information and facilitates personalized service for users.

[0547] "Information and communication means" refers to technologies for sending and receiving information in various ways, including voice and text, and is the method used when users exchange information through various devices.

[0548] "Purchase history" refers to a record of products and services that a user has purchased in the past, and is important information that is referenced when providing individualized support.

[0549] "Inquiry history" refers to a record of questions and requests that a user has made in the past, and is information that is used as reference when generating responses.

[0550] The system for implementing this invention utilizes speech recognition, natural language processing, and artificial intelligence to analyze user inquiries and generate optimal responses. Specifically, the server converts the voice information provided by the user into text information using a general API, which is a speech recognition technology. This converted text information is then analyzed using natural language processing technology to understand the user's intent, and an artificial intelligence model is used to generate the optimal response.

[0551] Furthermore, the server is integrated with a customer information management system, providing personalized responses based on users' purchase and inquiry history. In this way, it is possible to recommend appropriate information and related products to each user.

[0552] The server can also use token analysis to assess the user's emotions from their conversations and route inquiries to the appropriate personnel as needed. This process allows for an understanding of the user's emotional state and the provision of appropriate support.

[0553] For example, if a user asks "Will this item be delivered soon?" via voice, the server converts the voice into text, analyzes the content, and determines that the user is requesting expedited delivery. Based on this, and taking into account past inquiry and purchase history, it quickly generates a specific response such as "This item is usually delivered within 2-3 days" and provides it to the user.

[0554] Specific examples of prompts for the generating AI model include, "If a user asks whether a particular product is returnable, the AI ​​model will provide a detailed explanation of the product's return policy and share relevant updates." Based on these prompts, the AI ​​model generates a detailed and accurate response and provides it to the user.

[0555] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0556] Step 1:

[0557] The device receives voice inquiries from the user. The input is voice data, which is sent to the speech recognition engine to convert the voice into text data. The output is the converted text data. Specifically, the device uses a microphone to collect the user's voice and calls the speech recognition API.

[0558] Step 2:

[0559] The server receives text data and uses a natural language processing engine to analyze the user's intent. The input is the text data obtained in step 1, and the output is the analyzed intent information. In this process, the server uses vocabulary and contextual information to analyze the user's inquiry in detail.

[0560] Step 3:

[0561] The server generates the optimal response using an artificial intelligence model based on the analyzed intent information. The input is the analysis result from step 2, and the output is the generated response message. Here, a generative AI model is used to create a natural language response to the user's inquiry.

[0562] Step 4:

[0563] The server integrates with the customer information management system to retrieve user purchase and inquiry history. Input is user identification information, and output is the retrieved history information. Based on this, it personalizes response messages and adds appropriate information.

[0564] Step 5:

[0565] The server uses token analysis to assess the user's emotional state. The input is the original voice or text data, and the output is emotional assessment data. By analyzing and evaluating emotional attributes such as positive or negative, the server considers routing the user to the appropriate person if necessary.

[0566] Step 6:

[0567] The server sends a final response message to the user. The input is a personalized response message and sentiment rating data, and the output is a clear and appropriate answer to the user. At this point, the user may be routed to a human representative if necessary.

[0568] 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.

[0569] This invention is implemented as an advanced call center system that combines an emotion engine. This system integrates natural language processing, speech recognition, artificial intelligence, and an emotion engine to achieve more comprehensive and personalized customer service.

[0570] System programs and their processes

[0571] Initiating a user inquiry

[0572] The user makes an inquiry via voice or text. In the case of a voice channel, the device captures the audio and converts it to text using a speech recognition engine. This is a preparatory step for sending it to the server.

[0573] Data processing by terminals

[0574] The terminal converts the audio data into text and then sends this data to the server. This initiates the analysis and response generation process.

[0575] Server-based analysis and response generation

[0576] The server uses a natural language processing engine to analyze the received text data and clarify the user's intent. Based on the analysis, artificial intelligence generates the most appropriate response, and further incorporates emotional data obtained by the emotion engine.

[0577] Emotion recognition by an emotion engine

[0578] The emotion engine identifies the user's emotional state based on their tone of voice and expression. For example, if a user is irritated, the emotion engine identifies this and adjusts its response and routing strategy accordingly.

[0579] Specific example

[0580] For example, if a user expresses dissatisfaction with a particular product, the server not only understands the content of the complaint but also uses an emotion engine to confirm that the user has strong emotions. As a result, the system carefully adjusts its response and, if necessary, quickly escalates the issue to a human representative.

[0581] Integration with customer relationship management systems

[0582] The server is integrated with a customer relationship management system, which allows it to refer to past customer history and provide more personalized responses to user inquiries.

[0583] Through the above process, this system can quickly and accurately answer complex user inquiries and improve customer satisfaction through an approach utilizing an emotion engine. The system also features multilingual capabilities, providing high-quality service to customers in various countries and regions.

[0584] The following describes the processing flow.

[0585] Step 1:

[0586] The user initiates an inquiry via voice or text. In the case of a voice inquiry, the device uses the microphone to capture voice data and prepares to send it to the speech recognition engine.

[0587] Step 2:

[0588] The audio data is converted into text data by the device's speech recognition engine. This text data is then sent to a server for analysis.

[0589] Step 3:

[0590] The server receives text data and uses a natural language processing engine to analyze the user's inquiry. This process helps understand the user's intent and the subject of their inquiry.

[0591] Step 4:

[0592] The server uses an emotion engine to analyze the user's emotional state from text data. Specifically, it identifies emotions from voice tone and the words used, and records them in a database.

[0593] Step 5:

[0594] The server utilizes artificial intelligence to generate appropriate responses based on analyzed content and sentiment data. These responses will include specific solutions and information for the user.

[0595] Step 6:

[0596] Based on the emotion engine's evaluation, the server determines whether escalation is necessary. If a high emotional intensity is detected, the request is automatically routed to a human representative.

[0597] Step 7:

[0598] The generated response is sent to the terminal and presented to the user. If it's an audio output, the text is converted to speech; if it's a text output, it's displayed as is.

[0599] Step 8:

[0600] If the user asks further questions about the response provided, the terminal sends this back to the server, and the additional analysis and response generation process is repeated.

[0601] (Example 2)

[0602] 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."

[0603] Modern customer service systems demand the rapid and accurate processing of inquiries, but it is difficult to appropriately assess the user's emotions and generate the optimal response based on that. Furthermore, personalized responses tailored to the individual needs of each user are insufficient, making it a challenge to improve customer satisfaction.

[0604] 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.

[0605] In this invention, the server includes means for analyzing user inquiry information using natural language processing, means for evaluating the user's emotional state using an emotion analysis engine, and means for creating prompt sentences according to the emotional state and adjusting the response using generative AI technology. This makes it possible to provide responses that are in line with the user's emotions and to improve individualized service.

[0606] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[0607] "Speech recognition" is a technology that analyzes speech information and converts it into text information.

[0608] "Intelligent processing" is a technology that uses artificial intelligence to automatically make responses and decisions based on input data.

[0609] An "emotion analysis engine" is a technology that evaluates a user's emotional state based on input information and data.

[0610] "Routing" is the process of sending data or inquiries to the appropriate person or processing path.

[0611] An "information management system" is a system that uses customer history and information to provide efficient and personalized services.

[0612] "Generative AI technology" is a technology that uses artificial intelligence to automatically generate prompt text and content.

[0613] A "prompt message" is a document created by AI generation technology in the form of instructions or questions tailored to a specific task or situation.

[0614] This invention is implemented as an advanced customer service system that combines emotion recognition technology. This system integrates speech recognition, natural language processing, generative AI technology, and emotion analysis to provide comprehensive services to users.

[0615] Users can make inquiries via voice or text. In the case of voice input, the terminal uses speech recognition software to convert the voice data into text data. Specifically, a speech recognition API can be used as the speech recognition engine. The text data is sent to the server via a secure communication protocol.

[0616] The server analyzes the received text data using a natural language processing engine. At this stage, data processing is performed to understand the user's question and intent. After analysis, it uses generative AI technology to generate an appropriate answer.

[0617] Furthermore, the server utilizes an emotion analysis engine to analyze the user's emotions from their voice tone and context. For example, if it determines that the user is irritated, it can prepare a response appropriate to that emotion. The technology used for emotion analysis is expected to be emotion analysis software.

[0618] The server can generate more personalized responses by referencing customer history information and return them to the device. For example, it can provide relevant explanations and update information in response to inquiries about products a user has previously purchased.

[0619] As a concrete example, consider a case where a user is dissatisfied with a particular service. In this case, the generated prompt would be: "The user is dissatisfied with the functionality of product A. The emotion engine is indicating a high stress level. What response should be generated in this case?" Based on this prompt, the generation AI technology generates the most appropriate answer.

[0620] In this way, the system can provide immediate and personalized responses to user inquiries and improve the user experience with emotion-based responses.

[0621] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0622] Step 1:

[0623] The user initiates an inquiry via voice or text. When using the voice channel, the device uses its microphone to capture the user's voice and uses a speech recognition engine to convert this voice data into text data. In this process, the input is voice data, and the output is text data obtained through speech recognition.

[0624] Step 2:

[0625] The terminal sends the converted character data to the server. The input is the character data generated in the previous step, and the server receives this data as output using a secure communication protocol. This allows the server to analyze the data.

[0626] Step 3:

[0627] The server analyzes the received text data using a natural language processing engine. The input is text data, and the output is the analyzed structured data. This analysis involves data processing to clarify the user's intent and the content of their question.

[0628] Step 4:

[0629] The server uses an emotion analysis engine to evaluate the user's emotional state based on the analyzed data. The input here is naturally language processing-analyzed data, and the output is metadata indicating the emotional state. The type and intensity of emotion are identified from voice tone and context.

[0630] Step 5:

[0631] The server uses generative AI technology to generate prompts based on the user's emotional state and the nature of the inquiry, creating an appropriate response. The inputs here are the emotional state and analyzed data, while the output is the response to the user. The generated prompts serve as instructions for obtaining a specific answer.

[0632] Step 6:

[0633] The server references customer information to provide personalized responses based on past history. The input is customer history data retrieved from the CRM system, and the output is the customized response.

[0634] Step 7:

[0635] Finally, the server sends the generated response to the terminal, which then presents its contents to the user. The input is the generated response text, and the output is the response message presented to the user. The terminal uses speech synthesis software and, if a voice response is required, communicates to the user in a natural voice.

[0636] (Application Example 2)

[0637] 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."

[0638] In recent years, there has been a growing demand for measures to enhance customer satisfaction in physical stores, but there is a lack of effective means to accurately grasp customer emotions in real time and respond appropriately. Therefore, there is a need to develop systems that can provide rapid and effective service tailored to customer needs and emotional states.

[0639] 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.

[0640] In this invention, the server includes means for analyzing user inquiry data using natural language processing, means for converting voice data into text data using speech recognition, and means for providing an output interface to a visual device and displaying support information in real time based on the user's emotional state. This makes it possible to understand the user's emotional state and present the optimal customer service method through the visual device.

[0641] "Natural language processing" is the technology that enables computers to understand, analyze, and generate human language.

[0642] "Speech recognition" is a technology that analyzes speech signals and converts them into text or data.

[0643] Artificial intelligence is a technology that gives computer systems the ability to learn, reason, and make judgments like humans.

[0644] "Token analysis" is a technique that breaks down linguistic data into its constituent elements and analyzes their meaning and emotional state.

[0645] A "customer information management system" is a system that centrally manages customer data and uses that data to improve individual customer service.

[0646] A "visual device" is a device that displays information and presents it to the user visually.

[0647] A "communication method" refers to the means or protocols used to send and receive information.

[0648] "Routing" is the process of distributing information and processing to the appropriate person or system.

[0649] The system for realizing this invention is centered around an application in which staff use smart devices to interact with customers in a physical store. Specifically, a server handles the main information processing, while terminals collect input data, send it to the server, and provide an interface with the user.

[0650] The server uses a natural language processing engine to analyze voice and text data from the user and extract their intent. Voice data is converted into text data using speech recognition software on the terminal. Speech recognition APIs such as Google Cloud Speech-to-Text are utilized in this process. The analyzed data is further evaluated through token analysis using an emotion recognition engine to assess the user's emotional state. Tools such as IBM Watson Tone Analyzer are used in this step.

[0651] Next, the server utilizes artificial intelligence to generate the optimal response for the user based on the analysis results. During this process, integration with the customer information management system is performed to reference past visitor data. The generated information is then presented to staff in real time via visual devices.

[0652] The terminal primarily utilizes visual devices worn by staff, such as smart glasses. Sentiment analysis results and response suggestions transmitted from the server are displayed on the screen, providing real-time feedback to staff. This allows staff to understand the customer's emotional state and respond appropriately in a timely manner.

[0653] For example, if a cafe staff member is wearing smart glasses, the server can analyze what a customer says when complaining during a wait and display a message on their visual device suggesting "prompt service." This would encourage the staff to take action to provide prompt service while considering the customer's emotional state.

[0654] An example of a prompt to input into a generative AI model is: "Design a support tool that analyzes customer emotions based on their statements and presents their current emotional state and the optimal customer service style accordingly."

[0655] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0656] Step 1:

[0657] The device captures the user's voice. The input is the user's voice signal, which the device uses the Google Cloud Speech-to-Text API to convert the voice data into text data. The text data is the output.

[0658] Step 2:

[0659] The server receives the converted text data. The input is text data, and the server uses a natural language processing engine to analyze the text and extract the user's intent. The intent data extracted through this process becomes the output.

[0660] Step 3:

[0661] The server performs emotion recognition based on the analyzed intent data. The input is intent data, and the IBM Watson Tone Analyzer is used to evaluate the user's emotional state. Emotional data as an evaluation result is output.

[0662] Step 4:

[0663] The server generates responses based on sentiment data and intent data. The input is sentiment data and intent data, and artificial intelligence designs the most appropriate response. The generated response data is the output. The server also references historical data from the customer information management system to further personalize the response.

[0664] Step 5:

[0665] The server sends the generated response data to the visual device. The input is the response data, and the information is displayed in real time on the smart glasses of the terminal. The final output is the information presented to the staff visually.

[0666] Through the above processing steps, a system is created that quickly proposes the most suitable service to staff in response to the user's emotions.

[0667] 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.

[0668] 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.

[0669] 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.

[0670] [Fourth Embodiment]

[0671] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0672] 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.

[0673] 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).

[0674] 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.

[0675] 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.

[0676] 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).

[0677] 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.

[0678] 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.

[0679] 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.

[0680] 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.

[0681] 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.

[0682] 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.

[0683] 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".

[0684] This invention is implemented as a call center system that operates on a general computer environment. This system utilizes natural language processing, speech recognition, and artificial intelligence technologies.

[0685] System programs and their processes

[0686] User Inquiry Flow

[0687] The user initiates an inquiry via voice or text. This inquiry is received via a voice communication channel or text chat.

[0688] Processing of audio data by the device

[0689] In the case of voice channels, the terminal sends the received voice data to the speech recognition engine, which converts the speech to text. The converted text data is then used directly for analysis.

[0690] Server-based natural language processing and response generation

[0691] The server receives text data provided by the terminal, analyzes it using a natural language processing engine to understand the user's intent, and then an artificial intelligence agent automatically generates the optimal response based on the analysis results.

[0692] Integration with Customer Relationship Management (CRM) systems

[0693] When providing generated responses, the server uses past inquiry history and customer information obtained from the customer relationship management system to provide personalized service to each user. This ensures that the information best suits the user's needs.

[0694] Sentiment assessment using token analysis

[0695] The server uses token analysis to assess the user's emotional state. If it determines that the user is in a negative emotional state, a flag is set to enable intervention by a human agent if necessary.

[0696] Specific example

[0697] For example, if a user expresses dissatisfaction with a product, the system accurately understands the complaint and suggests the best solution based on data from similar past cases. In addition, if the user requires it, they are appropriately routed to a human representative for further support.

[0698] Through these processes, the invention is implemented in a way that enhances customer satisfaction while maintaining a balance between operational efficiency and operational costs. The system is multilingual, allowing for flexible service to both domestic and international customers.

[0699] The following describes the processing flow.

[0700] Step 1:

[0701] The user initiates an inquiry via voice or text. In the case of voice, the device receives voice data through the microphone and prepares to pass it on to the speech recognition engine.

[0702] Step 2:

[0703] The device uses a speech recognition engine to convert received speech data into text. In the case of a text channel, the text entered by the user is used as is.

[0704] Step 3:

[0705] The server receives text data sent from the terminal and analyzes it using a natural language processing engine. This analysis helps understand the intent behind the user's inquiry.

[0706] Step 4:

[0707] Based on the analysis results, the server's artificial intelligence agent generates the optimal response. This response includes solutions and information regarding the user's inquiry.

[0708] Step 5:

[0709] The server performs token analysis to evaluate the user's emotional state. The analyzed emotional information is used to determine the content of the response and the priority of the response.

[0710] Step 6:

[0711] Based on the sentiment assessment results and the response content, the server decides whether to route the request to a human agent as needed. If the request involves strong negative emotions or a complex inquiry, it will be forwarded to a human agent.

[0712] Step 7:

[0713] The terminal presents the generated response received from the server to the user. In the case of audio output, the text is converted to speech and played for the user; in the case of text output, it is displayed directly on the screen.

[0714] Step 8:

[0715] The user can ask further questions or request additional information regarding the response provided. The terminal then sends these inputs back to the server and initiates any necessary additional processing.

[0716] (Example 1)

[0717] 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".

[0718] In today's communication environment, there is a demand for quick and accurate responses to diverse inquiries from users. However, conventional systems may not adequately address the diversity of inquiries and changes in emotional states. Furthermore, the inability to effectively utilize users' past inquiry history results in insufficient individualized support. Moreover, accurately assessing users' emotional states and providing appropriate responses is difficult. Solving these challenges is essential.

[0719] 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.

[0720] In this invention, the server includes means for analyzing user inquiry information using information processing technology, means for converting voice information into text information using voice conversion technology, and means for generating a response based on the inquiry content using intelligent technology. This makes it possible to respond quickly and accurately to a variety of user inquiries.

[0721] "Information processing technology" refers to the technology used to collect, analyze, and manage data using computers, and to process digital information efficiently.

[0722] "User" refers to a person who accesses the system and enters inquiry information.

[0723] "Inquiry information" refers to text or audio data submitted by users through the system for confirmation or requests.

[0724] "Voice conversion technology" refers to technology that converts voice input data into text-based data.

[0725] "Intelligent technology" refers to technologies that use artificial intelligence to automate data analysis and decision-making.

[0726] "Unit analysis" is a method of analyzing the overall sentiment and intent from the information of each unit by subdividing text data.

[0727] "Routing" refers to the process by which the system determines the appropriate recipient and forwards inquiries to the correct person in charge.

[0728] A "customer management system" is a system that centrally manages customer information and history data to improve the quality of customer service.

[0729] "Communication means" refers to the medium, such as voice channels or text channels, used to send and receive inquiry information.

[0730] "Information acquisition technology" refers to the technology used to extract useful information from accumulated data and utilize it for decision-making.

[0731] "Past inquiry history" refers to a record of inquiries that users have made in the past, which can be used to improve customer service.

[0732] This invention is implemented as an inquiry handling system that operates in a wide range of communication environments. It uses a general information processing system to handle user inquiries made via voice or text.

[0733] Users initiate inquiries via voice or text through their mobile devices or computers. For example, if they want to know more about a product, they can send a prompt such as, "Please tell me more about this product."

[0734] When a terminal receives a voice inquiry, it converts the voice data into text data using "voice conversion technology." This process utilizes general-purpose speech recognition capabilities. An example of such software is a general-purpose speech recognition engine.

[0735] The server analyzes the text data received from the terminal using "information processing technology." This analysis accurately extracts the user's intent and generates an appropriate response. Natural language processing libraries may be used for the analysis. Artificial intelligence is used to further optimize the response and provide intelligent answers. Specifically, generative AI models are used to prepare natural and appropriate responses to user inquiries.

[0736] The server evaluates the analysis results and the user's emotional state, and sets the route as needed. For example, if a user expresses dissatisfaction, a decision is made to take appropriate action based on that emotion. This is done using a token analysis-based emotion evaluation system.

[0737] Integration with the customer management system allows for the use of customer information, including past inquiry history, further improving personalized service. This enables the provision of customized services for each user.

[0738] For example, when a user inquires about the availability of a product, the system refers to past purchase history and inquiries to quickly provide updated information on the availability, as well as related suggestions. An example of a prompt might be a specific inquiry such as, "I would like to know the availability of the new product." In this way, the user experience is streamlined and satisfaction is improved.

[0739] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0740] Step 1:

[0741] The user initiates an inquiry. The inquiry is provided as input, either in voice or text format. The user uses a mobile device or PC to input a prompt such as, "Please tell me the details of the product." This inquiry is received via a voice communication channel or text chat. The output is recorded as voice or text data.

[0742] Step 2:

[0743] When a terminal receives voice data, it uses a speech recognition engine to convert the voice data into text data. Specifically, the speech recognition engine analyzes the voice waveform and identifies patterns that correspond to characters. This converts the voice-based inquiry into accurate text format. The text data then becomes input data for natural language processing.

[0744] Step 3:

[0745] The server analyzes the text data received from the terminal using a natural language processing engine. Text data is provided as input. The natural language processing engine performs syntactic and semantic analysis to interpret the user's intent. The output of this step is metadata about the analyzed user intent. Specifically, the engine extracts noun phrases and identifies the focus of the query.

[0746] Step 4:

[0747] The server generates a response using a generative AI model based on the analyzed user intent. The input is metadata about the user's intent, and the output is a naturally constructed response sentence. The generative AI model creates an appropriate response to provide the user with the most relevant information by referring to available databases and learning from responses to similar past queries.

[0748] Step 5:

[0749] The server integrates with the customer management system to match past inquiry history and customer information. User IDs and past inquiry information are referenced as input. This enables the provision of personalized responses. The output is a personalized response synchronized with the historical data. Specifically, relevant information is retrieved from the history database and incorporated into the response.

[0750] Step 6:

[0751] The server uses token analysis to assess the user's emotional state. Input includes the user's overall conversation content and emotional indicators. The server uses an emotional analysis model to evaluate the emotional tone within the text and identify positive or negative elements. The output generates data regarding the emotional state. If necessary, if the emotion is determined to be negative, transfer to a human agent is considered.

[0752] Step 7:

[0753] The server provides the user with a final response based on the generated response and the results of sentiment analysis. It uses the generated text response and sentiment evaluation data as input. The server scrutinizes the necessary information to provide the optimal response tailored to the user's needs. The output is the final response message sent to the user. Specifically, the optimized response is sent through the communication channel.

[0754] (Application Example 1)

[0755] 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".

[0756] With the diversification of communication environments, consumers want to obtain information about specific products and services instantly, but traditional inquiry systems have struggled to respond quickly and accurately to diverse customer needs. In particular, when inquiries are made via voice or text, it is necessary to provide individually tailored information and related product recommendations, but current systems struggle to do this efficiently. This can lead to decreased customer satisfaction and negatively impact a company's competitiveness.

[0757] 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.

[0758] In this invention, the server includes means for analyzing user inquiry information using natural language processing, means for converting speech information into text information using speech recognition, and means for generating a response based on the inquiry content using artificial intelligence. This enables a quick and accurate response to user inquiries, and allows for the provision of appropriate information and the suggestion of related products based on individual customer information.

[0759] "Natural language processing" is a technology for analyzing user inquiries, and it is a method for computers to understand human language and analyze its meaning.

[0760] "Speech recognition" is a technology that converts speech information into text information, a method by which a computer identifies human speech and outputs it as text.

[0761] "Artificial intelligence" is the ability of a computer to generate responses based on user inquiries, and is a technology that imitates the intellectual activities performed by humans.

[0762] "Token analysis" is a technique used to evaluate a user's psychological state. It involves breaking down utterances into individual units and analyzing elements such as emotions.

[0763] A "customer information management system" is a database system that centrally manages customer information and facilitates personalized service for users.

[0764] "Information and communication means" refers to technologies for sending and receiving information in various ways, including voice and text, and is the method used when users exchange information through various devices.

[0765] "Purchase history" refers to a record of products and services that a user has purchased in the past, and is important information that is referenced when providing individualized support.

[0766] "Inquiry history" refers to a record of questions and requests that a user has made in the past, and is information that is used as reference when generating responses.

[0767] The system for implementing this invention utilizes speech recognition, natural language processing, and artificial intelligence to analyze user inquiries and generate optimal responses. Specifically, the server converts the voice information provided by the user into text information using a general API, which is a speech recognition technology. This converted text information is then analyzed using natural language processing technology to understand the user's intent, and an artificial intelligence model is used to generate the optimal response.

[0768] Furthermore, the server is integrated with a customer information management system, providing personalized responses based on users' purchase and inquiry history. In this way, it is possible to recommend appropriate information and related products to each user.

[0769] The server can also use token analysis to assess the user's emotions from their conversations and route inquiries to the appropriate personnel as needed. This process allows for an understanding of the user's emotional state and the provision of appropriate support.

[0770] For example, if a user asks "Will this item be delivered soon?" via voice, the server converts the voice into text, analyzes the content, and determines that the user is requesting expedited delivery. Based on this, and taking into account past inquiry and purchase history, it quickly generates a specific response such as "This item is usually delivered within 2-3 days" and provides it to the user.

[0771] Specific examples of prompts for the generating AI model include, "If a user asks whether a particular product is returnable, the AI ​​model will provide a detailed explanation of the product's return policy and share relevant updates." Based on these prompts, the AI ​​model generates a detailed and accurate response and provides it to the user.

[0772] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0773] Step 1:

[0774] The device receives voice inquiries from the user. The input is voice data, which is sent to the speech recognition engine to convert the voice into text data. The output is the converted text data. Specifically, the device uses a microphone to collect the user's voice and calls the speech recognition API.

[0775] Step 2:

[0776] The server receives text data and uses a natural language processing engine to analyze the user's intent. The input is the text data obtained in step 1, and the output is the analyzed intent information. In this process, the server uses vocabulary and contextual information to analyze the user's inquiry in detail.

[0777] Step 3:

[0778] The server generates the optimal response using an artificial intelligence model based on the analyzed intent information. The input is the analysis result from step 2, and the output is the generated response message. Here, a generative AI model is used to create a natural language response to the user's inquiry.

[0779] Step 4:

[0780] The server integrates with the customer information management system to retrieve user purchase and inquiry history. Input is user identification information, and output is the retrieved history information. Based on this, it personalizes response messages and adds appropriate information.

[0781] Step 5:

[0782] The server uses token analysis to assess the user's emotional state. The input is the original voice or text data, and the output is emotional assessment data. By analyzing and evaluating emotional attributes such as positive or negative, the server considers routing the user to the appropriate person if necessary.

[0783] Step 6:

[0784] The server sends a final response message to the user. The input is a personalized response message and sentiment rating data, and the output is a clear and appropriate answer to the user. At this point, the user may be routed to a human representative if necessary.

[0785] 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.

[0786] This invention is implemented as an advanced call center system that combines an emotion engine. This system integrates natural language processing, speech recognition, artificial intelligence, and an emotion engine to achieve more comprehensive and personalized customer service.

[0787] System programs and their processes

[0788] Initiating a user inquiry

[0789] The user makes an inquiry via voice or text. In the case of a voice channel, the device captures the audio and converts it to text using a speech recognition engine. This is a preparatory step for sending it to the server.

[0790] Data processing by terminals

[0791] The terminal converts the audio data into text and then sends this data to the server. This initiates the analysis and response generation process.

[0792] Server-based analysis and response generation

[0793] The server uses a natural language processing engine to analyze the received text data and clarify the user's intent. Based on the analysis, artificial intelligence generates the most appropriate response, and further incorporates emotional data obtained by the emotion engine.

[0794] Emotion recognition by an emotion engine

[0795] The emotion engine identifies the user's emotional state based on their tone of voice and expression. For example, if a user is irritated, the emotion engine identifies this and adjusts its response and routing strategy accordingly.

[0796] Specific example

[0797] For example, if a user expresses dissatisfaction with a particular product, the server not only understands the content of the complaint but also uses an emotion engine to confirm that the user has strong emotions. As a result, the system carefully adjusts its response and, if necessary, quickly escalates the issue to a human representative.

[0798] Integration with customer relationship management systems

[0799] The server is integrated with a customer relationship management system, which allows it to refer to past customer history and provide more personalized responses to user inquiries.

[0800] Through the above process, this system can quickly and accurately answer complex user inquiries and improve customer satisfaction through an approach utilizing an emotion engine. The system also features multilingual capabilities, providing high-quality service to customers in various countries and regions.

[0801] The following describes the processing flow.

[0802] Step 1:

[0803] The user initiates an inquiry via voice or text. In the case of a voice inquiry, the device uses the microphone to capture voice data and prepares to send it to the speech recognition engine.

[0804] Step 2:

[0805] The audio data is converted into text data by the device's speech recognition engine. This text data is then sent to a server for analysis.

[0806] Step 3:

[0807] The server receives text data and uses a natural language processing engine to analyze the user's inquiry. This process helps understand the user's intent and the subject of their inquiry.

[0808] Step 4:

[0809] The server uses an emotion engine to analyze the user's emotional state from text data. Specifically, it identifies emotions from voice tone and the words used, and records them in a database.

[0810] Step 5:

[0811] The server utilizes artificial intelligence to generate appropriate responses based on analyzed content and sentiment data. These responses will include specific solutions and information for the user.

[0812] Step 6:

[0813] Based on the emotion engine's evaluation, the server determines whether escalation is necessary. If a high emotional intensity is detected, the request is automatically routed to a human representative.

[0814] Step 7:

[0815] The generated response is sent to the terminal and presented to the user. If it's an audio output, the text is converted to speech; if it's a text output, it's displayed as is.

[0816] Step 8:

[0817] If the user asks further questions about the response provided, the terminal sends this back to the server, and the additional analysis and response generation process is repeated.

[0818] (Example 2)

[0819] 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".

[0820] Modern customer service systems demand the rapid and accurate processing of inquiries, but it is difficult to appropriately assess the user's emotions and generate the optimal response based on that. Furthermore, personalized responses tailored to the individual needs of each user are insufficient, making it a challenge to improve customer satisfaction.

[0821] 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.

[0822] In this invention, the server includes means for analyzing user inquiry information using natural language processing, means for evaluating the user's emotional state using an emotion analysis engine, and means for creating prompt sentences according to the emotional state and adjusting the response using generative AI technology. This makes it possible to provide responses that are in line with the user's emotions and to improve individualized service.

[0823] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[0824] "Speech recognition" is a technology that analyzes speech information and converts it into text information.

[0825] "Intelligent processing" is a technology that uses artificial intelligence to automatically make responses and decisions based on input data.

[0826] An "emotion analysis engine" is a technology that evaluates a user's emotional state based on input information and data.

[0827] "Routing" is the process of sending data or inquiries to the appropriate person or processing path.

[0828] An "information management system" is a system that uses customer history and information to provide efficient and personalized services.

[0829] "Generative AI technology" is a technology that uses artificial intelligence to automatically generate prompt text and content.

[0830] A "prompt message" is a document created by AI generation technology in the form of instructions or questions tailored to a specific task or situation.

[0831] This invention is implemented as an advanced customer service system that combines emotion recognition technology. This system integrates speech recognition, natural language processing, generative AI technology, and emotion analysis to provide comprehensive services to users.

[0832] Users can make inquiries via voice or text. In the case of voice input, the terminal uses speech recognition software to convert the voice data into text data. Specifically, a speech recognition API can be used as the speech recognition engine. The text data is sent to the server via a secure communication protocol.

[0833] The server analyzes the received text data using a natural language processing engine. At this stage, data processing is performed to understand the user's question and intent. After analysis, it uses generative AI technology to generate an appropriate answer.

[0834] Furthermore, the server utilizes an emotion analysis engine to analyze the user's emotions from their voice tone and context. For example, if it determines that the user is irritated, it can prepare a response appropriate to that emotion. The technology used for emotion analysis is expected to be emotion analysis software.

[0835] The server can generate more personalized responses by referencing customer history information and return them to the device. For example, it can provide relevant explanations and update information in response to inquiries about products a user has previously purchased.

[0836] As a concrete example, consider a case where a user is dissatisfied with a particular service. In this case, the generated prompt would be: "The user is dissatisfied with the functionality of product A. The emotion engine is indicating a high stress level. What response should be generated in this case?" Based on this prompt, the generation AI technology generates the most appropriate answer.

[0837] In this way, the system can provide immediate and personalized responses to user inquiries and improve the user experience with emotion-based responses.

[0838] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0839] Step 1:

[0840] The user initiates an inquiry via voice or text. When using the voice channel, the device uses its microphone to capture the user's voice and uses a speech recognition engine to convert this voice data into text data. In this process, the input is voice data, and the output is text data obtained through speech recognition.

[0841] Step 2:

[0842] The terminal sends the converted character data to the server. The input is the character data generated in the previous step, and the server receives this data as output using a secure communication protocol. This allows the server to analyze the data.

[0843] Step 3:

[0844] The server analyzes the received text data using a natural language processing engine. The input is text data, and the output is the analyzed structured data. This analysis involves data processing to clarify the user's intent and the content of their question.

[0845] Step 4:

[0846] The server uses an emotion analysis engine to evaluate the user's emotional state based on the analyzed data. The input here is naturally language processing-analyzed data, and the output is metadata indicating the emotional state. The type and intensity of emotion are identified from voice tone and context.

[0847] Step 5:

[0848] The server uses generative AI technology to generate prompts based on the user's emotional state and the nature of the inquiry, creating an appropriate response. The inputs here are the emotional state and analyzed data, while the output is the response to the user. The generated prompts serve as instructions for obtaining a specific answer.

[0849] Step 6:

[0850] The server references customer information to provide personalized responses based on past history. The input is customer history data retrieved from the CRM system, and the output is the customized response.

[0851] Step 7:

[0852] Finally, the server sends the generated response to the terminal, which then presents its contents to the user. The input is the generated response text, and the output is the response message presented to the user. The terminal uses speech synthesis software and, if a voice response is required, communicates to the user in a natural voice.

[0853] (Application Example 2)

[0854] 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".

[0855] In recent years, there has been a growing demand for measures to enhance customer satisfaction in physical stores, but there is a lack of effective means to accurately grasp customer emotions in real time and respond appropriately. Therefore, there is a need to develop systems that can provide rapid and effective service tailored to customer needs and emotional states.

[0856] 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.

[0857] In this invention, the server includes means for analyzing user inquiry data using natural language processing, means for converting voice data into text data using speech recognition, and means for providing an output interface to a visual device and displaying support information in real time based on the user's emotional state. This makes it possible to understand the user's emotional state and present the optimal customer service method through the visual device.

[0858] "Natural language processing" is the technology that enables computers to understand, analyze, and generate human language.

[0859] "Speech recognition" is a technology that analyzes speech signals and converts them into text or data.

[0860] Artificial intelligence is a technology that gives computer systems the ability to learn, reason, and make judgments like humans.

[0861] "Token analysis" is a technique that breaks down linguistic data into its constituent elements and analyzes their meaning and emotional state.

[0862] A "customer information management system" is a system that centrally manages customer data and uses that data to improve individual customer service.

[0863] A "visual device" is a device that displays information and presents it to the user visually.

[0864] A "communication method" refers to the means or protocols used to send and receive information.

[0865] "Routing" is the process of distributing information and processing to the appropriate person or system.

[0866] The system for realizing this invention is centered around an application in which staff use smart devices to interact with customers in a physical store. Specifically, a server handles the main information processing, while terminals collect input data, send it to the server, and provide an interface with the user.

[0867] The server uses a natural language processing engine to analyze voice and text data from the user and extract their intent. Voice data is converted into text data using speech recognition software on the terminal. Speech recognition APIs such as Google Cloud Speech-to-Text are utilized in this process. The analyzed data is further evaluated through token analysis using an emotion recognition engine to assess the user's emotional state. Tools such as IBM Watson Tone Analyzer are used in this step.

[0868] Next, the server utilizes artificial intelligence to generate the optimal response for the user based on the analysis results. During this process, integration with the customer information management system is performed to reference past visitor data. The generated information is then presented to staff in real time via visual devices.

[0869] The terminal primarily utilizes visual devices worn by staff, such as smart glasses. Sentiment analysis results and response suggestions transmitted from the server are displayed on the screen, providing real-time feedback to staff. This allows staff to understand the customer's emotional state and respond appropriately in a timely manner.

[0870] For example, if a cafe staff member is wearing smart glasses, the server can analyze what a customer says when complaining during a wait and display a message on their visual device suggesting "prompt service." This would encourage the staff to take action to provide prompt service while considering the customer's emotional state.

[0871] An example of a prompt to input into a generative AI model is: "Design a support tool that analyzes customer emotions based on their statements and presents their current emotional state and the optimal customer service style accordingly."

[0872] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0873] Step 1:

[0874] The device captures the user's voice. The input is the user's voice signal, which the device uses the Google Cloud Speech-to-Text API to convert the voice data into text data. The text data is the output.

[0875] Step 2:

[0876] The server receives the converted text data. The input is text data, and the server uses a natural language processing engine to analyze the text and extract the user's intent. The intent data extracted through this process becomes the output.

[0877] Step 3:

[0878] The server performs emotion recognition based on the analyzed intent data. The input is intent data, and the IBM Watson Tone Analyzer is used to evaluate the user's emotional state. Emotional data as an evaluation result is output.

[0879] Step 4:

[0880] The server generates responses based on sentiment data and intent data. The input is sentiment data and intent data, and artificial intelligence designs the most appropriate response. The generated response data is the output. The server also references historical data from the customer information management system to further personalize the response.

[0881] Step 5:

[0882] The server sends the generated response data to the visual device. The input is the response data, and the information is displayed in real time on the smart glasses of the terminal. The final output is the information presented to the staff visually.

[0883] Through the above processing steps, a system is created that quickly proposes the most suitable service to staff in response to the user's emotions.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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."

[0893] 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.

[0894] 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.

[0895] 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.

[0896] 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.

[0897] 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.

[0898] 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.

[0899] 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.

[0900] 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.

[0901] 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.

[0902] 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.

[0903] 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.

[0904] 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 to be incorporated by reference.

[0905] The following is further disclosed regarding the embodiments described above.

[0906] (Claim 1)

[0907] A method for analyzing user inquiry data using natural language processing,

[0908] A method for converting speech data into text data using speech recognition,

[0909] A means of generating a response based on the content of an inquiry using artificial intelligence,

[0910] A method for evaluating a user's emotional state using token analysis,

[0911] A means of routing to a human operator according to the appropriate situation,

[0912] A means of integrating with a customer relationship management system and using customer data to improve personalized service,

[0913] A means of responding to inquiries through multiple communication channels,

[0914] A system that includes this.

[0915] (Claim 2)

[0916] The system according to claim 1, wherein artificial intelligence automatically determines whether to provide a response or route to a human representative based on the content of the inquiry and the emotional state.

[0917] (Claim 3)

[0918] The system according to claim 1, which can handle multilingual inquiries from users by combining natural language processing and speech recognition.

[0919] "Example 1"

[0920] (Claim 1)

[0921] A means of analyzing user inquiry information using information processing technology,

[0922] A means of converting speech information into text information using speech conversion technology,

[0923] A means for generating a response based on the content of an inquiry using intelligent technology,

[0924] A means of evaluating the emotional state of users using unit analysis,

[0925] A means of having a human operator set the route according to the appropriate situation,

[0926] A means of integrating with the customer management system and using customer information to improve personalized service,

[0927] A means of responding to inquiries through multiple communication channels,

[0928] A means of analyzing past inquiry history using information acquisition technology and reflecting that in the response content,

[0929] A system that includes this.

[0930] (Claim 2)

[0931] The system according to claim 1, wherein intelligent technology automatically determines whether to provide a response or route the inquiry to a human representative based on the content of the inquiry and the emotional state.

[0932] (Claim 3)

[0933] The system according to claim 1, which can respond to multilingual inquiries from users by combining information processing technology and speech conversion technology.

[0934] "Application Example 1"

[0935] (Claim 1)

[0936] A method for analyzing user inquiry information using natural language processing,

[0937] A method for converting speech information into text information using speech recognition,

[0938] A method for generating responses based on the content of inquiries using artificial intelligence,

[0939] A method for evaluating a user's psychological state using token analysis,

[0940] A method for routing to a human agent according to the appropriate situation,

[0941] Methods for integrating with a customer information management system and using customer information to improve personalized service,

[0942] Methods for responding to inquiries via multiple information and communication methods,

[0943] Methods of providing information to users individually using purchase history and inquiry history,

[0944] A system that includes this.

[0945] (Claim 2)

[0946] The system according to claim 1, wherein artificial intelligence automatically determines whether to provide a response or route to a human representative based on the content of the inquiry, purchase history, and psychological state.

[0947] (Claim 3)

[0948] The system according to claim 1, which can respond to multilingual inquiries from users and present relevant products and information by combining natural language processing and speech recognition.

[0949] "Example 2 of combining an emotion engine"

[0950] (Claim 1)

[0951] A means of analyzing user inquiry information using natural language processing,

[0952] A means of converting speech information into text information using speech recognition,

[0953] A means for generating an answer based on the content of an inquiry using intelligent processing,

[0954] A means of evaluating a user's emotional state using an emotion analysis engine,

[0955] A means of routing to a human operator according to the appropriate situation,

[0956] A means of integrating with the information management system and using customer information to improve personalized service,

[0957] A means of responding to inquiries through multiple communication methods,

[0958] A means of creating prompt sentences that correspond to emotional states using generative AI technology and adjusting responses,

[0959] A system that includes this.

[0960] (Claim 2)

[0961] The system according to claim 1, wherein intelligent processing automatically determines whether to provide a response or route to a human representative based on the content of the inquiry and the emotional state.

[0962] (Claim 3)

[0963] The system according to claim 1, which can handle multilingual inquiries from users by combining natural language processing and speech recognition.

[0964] "Application example 2 when combining with an emotional engine"

[0965] (Claim 1)

[0966] A method for analyzing user inquiry data using natural language processing,

[0967] A method for converting speech data into text data using speech recognition,

[0968] A method for generating responses based on the content of inquiries using artificial intelligence,

[0969] A method for evaluating a user's emotional state using token analysis,

[0970] A method for routing to a human agent according to the appropriate situation,

[0971] Methods to improve personalized service by integrating with customer information management systems and using visitor data,

[0972] A method for providing an output interface to a visual device and displaying support information in real time based on emotional state,

[0973] Methods for handling inquiries via multiple communication methods,

[0974] A system that includes this.

[0975] (Claim 2)

[0976] The system according to claim 1, wherein artificial intelligence automatically determines whether to provide a response or route to a human representative based on the content of the inquiry and the emotional state.

[0977] (Claim 3)

[0978] The system according to claim 1, which can handle multilingual inquiries from users by combining natural language processing and speech recognition. [Explanation of symbols]

[0979] 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

1. A method for analyzing user inquiry data using natural language processing, A method for converting speech data into text data using speech recognition, A means of generating a response based on the content of an inquiry using artificial intelligence, A method for evaluating a user's emotional state using token analysis, A means of routing to a human operator according to the appropriate situation, A means of integrating with a customer relationship management system and using customer data to improve personalized service, A means of responding to inquiries through multiple communication channels, A system that includes this.

2. The system according to claim 1, wherein artificial intelligence automatically determines whether to provide a response or route the user to a human representative based on the content of the inquiry and the emotional state of the user.

3. The system according to claim 1, which can handle multilingual inquiries from users by combining natural language processing and speech recognition.

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