system

Abstract

The system according to this embodiment aims to provide an appropriate script in real time when scheduling appointments. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, and a presentation unit. The reception unit inputs client information. The analysis unit analyzes the audio during telemarketing in real time based on the client information input by the reception unit. The presentation unit presents an appropriate script based on the information analyzed by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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 the conventional technology, there is a problem that it is difficult to present an appropriate script in real time when adjusting an appointment.

[0005] The system according to the embodiment aims to present an appropriate script in real time when adjusting an appointment.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a presentation unit. The reception unit inputs client information. The analysis unit analyzes the voice during tele-appointment in real time based on the client information input by the reception unit. The presentation unit presents an appropriate script based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can present an appropriate script in real time when scheduling an appointment. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. 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).

[0015] 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 only A, only B, or a combination of A and B. Further, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

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

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The appointment scheduling support system according to an embodiment of the present invention is a system that takes client information as input, analyzes voice in real time, and presents an appropriate script. This system is particularly useful for salespeople in new business divisions of large companies and startups looking for new clients. These salespeople need to obtain appointments through cold calls, telemarketing, or client inquiry forms because they do not have leads, but they are often turned away at reception or asked to send materials, and are unable to secure an appointment. This system learns information about the clients to be scheduled in advance and analyzes the voice during telemarketing in real time. It then provides a script that tells the user what to say next and how to respond. This solves the problem of not knowing how to get an appointment, and by accumulating history, it teaches the user the appointment-getting method that suits them. For example, the user inputs information about the client they want to schedule an appointment with into the system. Next, the system analyzes the voice during telemarketing in real time and presents an appropriate script. For example, if the client says, "Please send me some materials," the system will present a script such as, "Before I send you the materials, could I talk to you for a bit?" Furthermore, the system learns the user's habits and past history, and teaches them the most suitable appointment-setting methods. For example, based on the user's past successful appointment-setting methods, it suggests what approach to take next. This mechanism improves the success rate of appointment setting and supports salespeople who have sales skills but are unable to secure appointments. As a result, it eliminates the situation where good products cannot be sold, making Japan and the world a better place. With this, the appointment scheduling support system allows for efficient appointment scheduling by inputting client information, analyzing voice in real time, and presenting appropriate scripts.

[0029] The appointment scheduling support system according to this embodiment comprises a reception unit, an analysis unit, and a presentation unit. The reception unit inputs client information. Client information includes, but is not limited to, names, contact information, and past transaction history. The reception unit can, for example, manually input client information. The reception unit can also automatically acquire client information. For example, the reception unit can acquire information by scraping it from the client's website. The analysis unit analyzes the audio during the telemarketing call in real time based on the client information input by the reception unit. The analysis unit can, for example, use speech recognition technology to transcribe the client's statements into text and analyze that text. The analysis unit can, for example, use deep learning to analyze the audio. The analysis unit can also use HMM (Hidden Markov Model) to analyze the audio. The presentation unit presents an appropriate script based on the information analyzed by the analysis unit. The presentation unit presents a script indicating what to say next based on the client's statements. The presentation unit, for example, if a client says, "Please send me the materials," will present a script such as, "Before I send you the materials, could I have a brief chat with you?" The presentation unit can also provide a script on what to say next based on the client's statements. For example, if a client says, "I'm busy right now," it will present a script such as, "Could you spare some time later?" In this way, the appointment scheduling support system can efficiently schedule appointments by inputting client information, analyzing voice in real time, and presenting appropriate scripts.

[0030] The reception department inputs client information. This information includes, but is not limited to, names, contact information, and past transaction history. The reception department can, for example, manually input client information. Alternatively, it can automatically retrieve client information. For example, it can scrape information from the client's website. Specifically, in the case of manual input, an operator inputs the client's name, contact information, and past transaction history through a dedicated interface. In the case of automatic retrieval, the reception department uses scraping techniques to collect the necessary information from the client's website or publicly available databases. Scraping is the process of analyzing the HTML structure of a webpage and extracting specific information. For example, it can access the client's website, identify the section containing the name and contact information, and automatically retrieve that information. It is also possible to retrieve client information using an API. Using an API allows for direct interaction with the client's system, enabling the acquisition of the latest information in real time. This allows the reception department to efficiently and accurately collect client information using both manual input and automatic retrieval methods. Furthermore, the reception department stores the collected client information in a database, making it accessible to the analysis and presentation departments. This allows for smoother information sharing across the entire system, improving the efficiency of appointment scheduling.

[0031] The analysis unit analyzes the audio during telemarketing calls in real time based on client information entered by the reception unit. For example, the analysis unit uses speech recognition technology to transcribe the client's statements into text and then analyzes that text. The analysis unit can also analyze audio using deep learning, for example. Furthermore, the analysis unit can analyze audio using a Hidden Markov Model (HMM). Specifically, speech recognition technology is the process of transcribing the client's statements into text in real time, while deep learning models learn from large amounts of audio data to achieve highly accurate speech recognition. For example, if a client says, "Please send me the materials," the speech recognition technology transcribes this statement into text, and the analysis unit analyzes that text to understand the client's intent. HMM is a technology that models the temporal fluctuations of audio signals and is used to improve the accuracy of speech recognition. The analysis unit combines these technologies to analyze the client's statements with high accuracy and determine what should be said next. In addition, the analysis unit can refer to past interactions and transaction history with clients to understand their needs and interests. This allows the analysis unit to analyze the client's statements in real time and respond appropriately and quickly.

[0032] The presentation unit presents appropriate scripts based on the information analyzed by the analysis unit. For example, the presentation unit can provide a script indicating what to say next based on the client's statements. For instance, if a client says, "Please send me the materials," the presentation unit might suggest, "Before I send you the materials, could I have a brief chat with you?" The presentation unit can also provide a script indicating what to say next based on the client's statements. For example, if a client says, "I'm busy right now," the presentation unit might suggest, "Could you spare some time later?" Specifically, the presentation unit selects the most suitable script from a pre-prepared set based on the text data provided by the analysis unit and presents it to the operator. The scripts are dynamically generated according to the client's statements, past transaction history, and current situation. For example, if a client says, "I'd like to know more about the price," the presentation unit might suggest, "Before I give you details about the price, what use case are you considering?" The presentation unit also provides guidelines and points to note for operators when responding according to the scripts. This allows operators to communicate smoothly with clients and increases the success rate of appointment scheduling. Furthermore, the presentation unit can collect operator feedback and continuously improve the accuracy and effectiveness of the script. This allows the presentation unit to always provide the latest information and optimal scripts, improving the efficiency and success rate of appointment scheduling.

[0033] The learning unit can pre-learn client information. For example, the learning unit can store client information in a database and learn using machine learning algorithms. For example, the learning unit can learn about a client's interests and preferences based on their past transaction history. The learning unit can also collect information from the client's website and social media to learn about the client's latest trends. For example, the learning unit can collect product information and news articles from the client's website to understand the client's latest trends. By pre-learning client information, the learning unit can present more appropriate scripts. Some or all of the above processes in the learning unit may be performed using AI, for example, or not. For example, the learning unit can store client information in a database and learn using an AI model.

[0034] The history unit can learn the user's habits and past history. For example, the history unit can store the user's call records in a database and learn using machine learning algorithms. For example, the history unit can learn the user's speaking patterns and frequently used phrases. The history unit can also learn successful appointment-setting methods based on the user's past appointment-setting history. For example, the history unit can analyze the user's past call records and extract successful appointment-setting methods. As a result, the history unit can present individually optimized scripts by learning the user's habits and past history. Some or all of the above processing in the history unit may be performed using AI, for example, or not using AI. For example, the history unit can store the user's call records in a database and learn using an AI model.

[0035] The teaching unit can instruct users on how to schedule appointments based on a provided script. For example, the teaching unit can instruct users on speaking techniques and questioning methods. For instance, the teaching unit might advise users that "it's important to ask clear questions to clients." The teaching unit can also provide users with specific scripts and instruct them on how to schedule appointments based on those scripts. For example, the teaching unit might provide users with specific scripts such as "Now, say this." In this way, the teaching unit improves the user's appointment-setting skills by instructing them on how to schedule appointments based on the provided scripts. Some or all of the above processes in the teaching unit may be performed using AI, for example, or not. For example, the teaching unit can use an AI model to instruct users on how to schedule appointments.

[0036] The analysis unit can transcribe the client's statements into text using speech recognition technology and analyze that text. The analysis unit can analyze speech using, for example, deep learning. For example, the analysis unit transcribes the client's statements into text using speech recognition technology and analyzes that text. The analysis unit can also analyze speech using HMM (Hidden Markov Model). For example, the analysis unit transcribes the client's statements into text using speech recognition technology and analyzes that text. As a result, the analysis unit can accurately transcribe the client's statements into text using speech recognition technology, improving the accuracy of the analysis. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can use an AI model to transcribe the client's statements into text using speech recognition technology and analyze that text.

[0037] The presentation unit can provide a script indicating what to say next based on the client's statements. For example, if the client says, "Please send me the materials," the presentation unit might suggest, "Could I speak with you briefly before sending the materials?" The presentation unit can also provide a script indicating what to say next based on the client's statements. For example, if the client says, "I'm busy right now," the presentation unit might suggest, "Could you spare some time later?" This improves the success rate of securing appointments by allowing the presentation unit to provide a script indicating what to say next based on the client's statements. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not. For example, the presentation unit can use an AI model to provide a script indicating what to say next based on the client's statements.

[0038] The reception desk can analyze the user's past input history when client information is entered and select the optimal input method. For example, the reception desk may prioritize suggesting input methods that the user has frequently used in the past. The reception desk can also suggest the optimal input method for a specific time period based on the user's past input history. For example, the reception desk may analyze the user's past input history and suggest the most efficient input method. In this way, the reception desk can provide the user with the optimal input method by analyzing past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk may use an AI model to analyze the user's past input history and select the optimal input method.

[0039] The reception desk can filter client information based on the user's current projects and areas of interest when the user enters client information. For example, the reception desk can prioritize displaying client information related to the project the user is currently working on. The reception desk can also filter relevant client information based on the user's areas of interest. For example, the reception desk can suggest the most relevant client information according to the progress of the user's current project. In this way, the reception desk can prioritize providing highly relevant information by filtering based on the current project and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can use an AI model to filter based on the user's current projects and areas of interest.

[0040] The reception unit can prioritize inputting highly relevant information by considering the user's geographical location when client information is entered. For example, the reception unit can prioritize displaying client information that is close to the user's current location. The reception unit can also filter highly relevant client information based on the user's geographical location. For example, the reception unit can suggest the most suitable client information based on the user's current location. In this way, the reception unit can efficiently collect highly relevant information by considering geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can use an AI model to prioritize inputting highly relevant information by considering the user's geographical location.

[0041] The reception desk can analyze a user's social media activity and input relevant information when client information is entered. For example, the reception desk can extract relevant client information from the user's social media activity. The reception desk can also suggest optimal client information based on the user's social media activity. For example, the reception desk can analyze the user's social media activity and prioritize displaying highly relevant client information. This allows the reception desk to efficiently collect highly relevant client information by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can use an AI model to analyze the user's social media activity and input relevant information.

[0042] The analysis unit can improve the accuracy of its analysis by referring to the client's past speech history during speech analysis. For example, the analysis unit may prioritize the analysis of specific keywords based on the client's past speech history. The analysis unit can also improve the accuracy of its analysis by extracting specific patterns from the client's past speech history. For example, the analysis unit may refer to the client's past speech history and perform analysis based on context. In this way, the analysis unit improves the accuracy of its speech analysis by referring to past speech history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may use an AI model to improve the accuracy of its analysis by referring to the client's past speech history.

[0043] The analysis unit can perform voice analysis while considering the client's attribute information. For example, the analysis unit can adjust the parameters of voice analysis based on the client's age and gender. The analysis unit can also prioritize the analysis of specific technical terms based on the client's occupation and industry. For example, the analysis unit can analyze specific expressions based on the client's region and culture. This improves the accuracy of voice analysis by considering the client's attribute information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use an AI model to perform analysis while considering the client's attribute information.

[0044] The analysis unit can perform speech analysis while considering the geographical distribution of the client. For example, the analysis unit can analyze the dialect and accent of a specific region based on the client's geographical distribution. The analysis unit can also analyze regionally specific expressions while considering the client's geographical distribution. For example, the analysis unit can perform analysis while considering the culture and customs of a specific region based on the client's geographical distribution. As a result, the accuracy of speech analysis is improved by considering geographical distribution. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use an AI model to perform analysis while considering the client's geographical distribution.

[0045] The analysis unit can improve the accuracy of its analysis by referring to the client's relevant literature during speech analysis. For example, the analysis unit may prioritize the analysis of specific technical terms based on the client's relevant literature. The analysis unit can also improve the accuracy of its analysis by extracting specific patterns from the client's relevant literature. For example, the analysis unit may refer to the client's relevant literature and perform analysis based on context. This improves the accuracy of speech analysis by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may use an AI model to improve the accuracy of its analysis by referring to the client's relevant literature.

[0046] The presentation unit can adjust the level of detail in the script based on the client's importance when presenting it. For example, the presentation unit can provide a detailed script to important clients, and a concise script to less important clients. For example, the presentation unit can dynamically adjust the level of detail in the script based on the client's importance. This allows the presentation unit to efficiently provide information by adjusting the level of detail in the script based on the client's importance. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit can use an AI model to adjust the level of detail in the script based on the client's importance.

[0047] The script presentation unit can apply different scripting algorithms depending on the client's category when presenting scripts. For example, if the client is a large corporation, the presentation unit will provide a formal script. Alternatively, if the client is a startup, the presentation unit can provide a more casual script. For example, the presentation unit can apply the most suitable scripting algorithm depending on the client's category. This allows the presentation unit to provide the most suitable script for each client category. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not. For example, the presentation unit can use an AI model to apply different scripting algorithms depending on the client's category.

[0048] The presentation unit can determine the priority of scripts based on the timing of the client's statements when presenting scripts. For example, the presentation unit can determine the priority of scripts based on what the client has recently said. The presentation unit can also provide the most suitable script depending on the timing of the client's statements. For example, the presentation unit can dynamically adjust the priority of scripts considering the timing of the client's statements. This allows the presentation unit to efficiently provide information by determining the priority of scripts based on the timing of the client's statements. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit can use an AI model to determine the priority of scripts based on the timing of the client's statements.

[0049] The presentation unit can adjust the order of scripts based on the client's relevance when presenting them. For example, the presentation unit may prioritize displaying the most important scripts based on the client's relevance. The presentation unit can also dynamically adjust the order of scripts, taking the client's relevance into consideration. For example, the presentation unit may provide the most suitable scripts according to the client's relevance. This allows the presentation unit to efficiently provide information by adjusting the order of scripts based on the client's relevance. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not. For example, the presentation unit may use an AI model to adjust the order of scripts based on the client's relevance.

[0050] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also optimize its learning algorithm by extracting specific patterns from past learning data. For example, the learning unit can adjust the parameters of the learning algorithm by referring to past learning data. This improves the accuracy of the learning algorithm by referencing past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can use an AI model to optimize the learning algorithm by referring to past learning data.

[0051] The learning unit can weight the training data based on the timing of client information input during training. For example, the learning unit adjusts the weighting of the training data based on the timing of client information input. The learning unit can also determine the priority of the training data considering the timing of client information input. For example, the learning unit dynamically adjusts the weighting of the training data according to the timing of client information input. This improves the accuracy of training by weighting the training data based on the timing of client information input. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can use an AI model to weight the training data based on the timing of client information input.

[0052] The history unit can optimize the analysis algorithm by referring to past historical data when analyzing historical data. For example, the history unit can select the optimal analysis algorithm based on past historical data. The history unit can also optimize the analysis algorithm by extracting specific patterns from past historical data. For example, the history unit can adjust the parameters of the analysis algorithm by referring to past historical data. In this way, the accuracy of the analysis algorithm is improved by the history unit referring to past historical data. Some or all of the above processes in the history unit may be performed using AI, for example, or without AI. For example, the history unit can use an AI model to optimize the analysis algorithm by referring to past historical data.

[0053] The history unit can weight historical data based on the timing of client information input when analyzing historical data. For example, the history unit adjusts the weighting of historical data based on the timing of client information input. The history unit can also determine the priority of historical data considering the timing of client information input. For example, the history unit dynamically adjusts the weighting of historical data according to the timing of client information input. This improves the accuracy of the analysis by weighting historical data based on the timing of client information input. Some or all of the above processing in the history unit may be performed using AI, for example, or without AI. For example, the history unit can use an AI model to weight historical data based on the timing of client information input.

[0054] The teaching unit can select the optimal teaching method by referring to the user's past appointment acquisition history during the teaching process. For example, the teaching unit selects the optimal teaching method based on the user's past appointment acquisition history. The teaching unit can also optimize the teaching method by extracting specific patterns from the user's past appointment acquisition history. For example, the teaching unit adjusts the parameters of the teaching method by referring to the user's past appointment acquisition history. This improves the accuracy of the teaching method by referring to the past appointment acquisition history. Some or all of the above processing in the teaching unit may be performed using AI, for example, or without AI. For example, the teaching unit can use an AI model to select the optimal teaching method by referring to the user's past appointment acquisition history.

[0055] The teaching unit can select the optimal teaching method during teaching, taking into account the user's geographical location information. For example, the teaching unit selects the optimal teaching method based on the user's geographical location information. The teaching unit can also determine the teaching priority, taking into account the user's geographical location information. For example, the teaching unit adjusts the parameters of the teaching method according to the user's geographical location information. In this way, the teaching unit can provide the optimal teaching method by taking geographical location information into account. Some or all of the above processing in the teaching unit may be performed using AI, for example, or without using AI. For example, the teaching unit can use an AI model to select the optimal teaching method, taking into account the user's geographical location information.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The reception desk can analyze the user's past input history when they enter client information and select the optimal input method. For example, it can prioritize suggesting input methods that the user has frequently used in the past. It can also suggest the optimal input method for a specific time period based on the user's past input history. For example, it can analyze the user's past input history and suggest the most efficient input method. In this way, the reception desk can provide the user with the optimal input method by analyzing past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can use an AI model to analyze the user's past input history and select the optimal input method.

[0058] The analysis unit can improve the accuracy of its analysis by referring to the client's past speech history during speech analysis. For example, it can prioritize the analysis of specific keywords based on the client's past speech history. It can also improve the accuracy of its analysis by extracting specific patterns from the client's past speech history. For example, it can perform analysis based on context by referring to the client's past speech history. In this way, the analysis unit improves the accuracy of its speech analysis by referring to past speech history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may use an AI model to improve the accuracy of its analysis by referring to the client's past speech history.

[0059] The presentation unit can adjust the level of detail in the script based on the client's importance when presenting it. For example, it can provide a detailed script to important clients and a concise script to less important clients. For example, it can dynamically adjust the level of detail in the script based on the client's importance. This allows the presentation unit to efficiently provide information by adjusting the level of detail in the script based on the client's importance. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit can use an AI model to adjust the level of detail in the script based on the client's importance.

[0060] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, it can select the optimal learning algorithm based on past learning data. It can also optimize the learning algorithm by extracting specific patterns from past learning data. For example, it can adjust the parameters of the learning algorithm by referring to past learning data. In this way, the learning unit improves the accuracy of the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can use an AI model to optimize the learning algorithm by referring to past learning data.

[0061] The teaching unit can select the optimal teaching method by referring to the user's past appointment history during the teaching process. For example, it can select the optimal teaching method based on the user's past appointment history. It can also optimize the teaching method by extracting specific patterns from the user's past appointment history. For example, it can adjust the parameters of the teaching method by referring to the user's past appointment history. This improves the accuracy of the teaching method by allowing the teaching unit to refer to past appointment history. Some or all of the above processing in the teaching unit may be performed using AI, for example, or without AI. For example, the teaching unit can use an AI model to select the optimal teaching method by referring to the user's past appointment history.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk enters client information. Client information includes, for example, name, contact information, and past transaction history. The reception desk can manually enter client information. Alternatively, client information can be automatically retrieved, for example, by scraping information from the client's website. Step 2: The analysis unit analyzes the audio during the telemarketing call in real time based on the client information entered by the reception unit. The analysis unit uses speech recognition technology to transcribe the client's statements into text and then analyzes that text. Technologies such as deep learning and HMM (Hidden Markov Model) are used for the analysis. Step 3: The presentation unit presents an appropriate script based on the information analyzed by the analysis unit. The presentation unit provides a script indicating what to say next based on the client's statements. For example, if the client says, "Please send me the materials," the presentation unit will present a script such as, "Could I speak with you briefly before sending the materials?" Also, if the client says, "I'm busy right now," the presentation unit will present a script such as, "Could you spare some time later?"

[0064] (Example of form 2) The appointment scheduling support system according to an embodiment of the present invention is a system that takes client information as input, analyzes voice in real time, and presents an appropriate script. This system is particularly useful for salespeople in new business divisions of large companies and startups looking for new clients. These salespeople need to obtain appointments through cold calls, telemarketing, or client inquiry forms because they do not have leads, but they are often turned away at reception or asked to send materials, and are unable to secure an appointment. This system learns information about the clients to be scheduled in advance and analyzes the voice during telemarketing in real time. It then provides a script that tells the user what to say next and how to respond. This solves the problem of not knowing how to get an appointment, and by accumulating history, it teaches the user the appointment-getting method that suits them. For example, the user inputs information about the client they want to schedule an appointment with into the system. Next, the system analyzes the voice during telemarketing in real time and presents an appropriate script. For example, if the client says, "Please send me some materials," the system will present a script such as, "Before I send you the materials, could I talk to you for a bit?" Furthermore, the system learns the user's habits and past history, and teaches them the most suitable appointment-setting methods. For example, based on the user's past successful appointment-setting methods, it suggests what approach to take next. This mechanism improves the success rate of appointment setting and supports salespeople who have sales skills but are unable to secure appointments. As a result, it eliminates the situation where good products cannot be sold, making Japan and the world a better place. With this, the appointment scheduling support system allows for efficient appointment scheduling by inputting client information, analyzing voice in real time, and presenting appropriate scripts.

[0065] The appointment scheduling support system according to this embodiment comprises a reception unit, an analysis unit, and a presentation unit. The reception unit inputs client information. Client information includes, but is not limited to, names, contact information, and past transaction history. The reception unit can, for example, manually input client information. The reception unit can also automatically acquire client information. For example, the reception unit can acquire information by scraping it from the client's website. The analysis unit analyzes the audio during the telemarketing call in real time based on the client information input by the reception unit. The analysis unit can, for example, use speech recognition technology to transcribe the client's statements into text and analyze that text. The analysis unit can, for example, use deep learning to analyze the audio. The analysis unit can also use HMM (Hidden Markov Model) to analyze the audio. The presentation unit presents an appropriate script based on the information analyzed by the analysis unit. The presentation unit presents a script indicating what to say next based on the client's statements. The presentation unit, for example, if a client says, "Please send me the materials," will present a script such as, "Before I send you the materials, could I have a brief chat with you?" The presentation unit can also provide a script on what to say next based on the client's statements. For example, if a client says, "I'm busy right now," it will present a script such as, "Could you spare some time later?" In this way, the appointment scheduling support system can efficiently schedule appointments by inputting client information, analyzing voice in real time, and presenting appropriate scripts.

[0066] The reception department inputs client information. This information includes, but is not limited to, names, contact information, and past transaction history. The reception department can, for example, manually input client information. Alternatively, it can automatically retrieve client information. For example, it can scrape information from the client's website. Specifically, in the case of manual input, an operator inputs the client's name, contact information, and past transaction history through a dedicated interface. In the case of automatic retrieval, the reception department uses scraping techniques to collect the necessary information from the client's website or publicly available databases. Scraping is the process of analyzing the HTML structure of a webpage and extracting specific information. For example, it can access the client's website, identify the section containing the name and contact information, and automatically retrieve that information. It is also possible to retrieve client information using an API. Using an API allows for direct interaction with the client's system, enabling the acquisition of the latest information in real time. This allows the reception department to efficiently and accurately collect client information using both manual input and automatic retrieval methods. Furthermore, the reception department stores the collected client information in a database, making it accessible to the analysis and presentation departments. This allows for smoother information sharing across the entire system, improving the efficiency of appointment scheduling.

[0067] The analysis unit analyzes the audio during telemarketing calls in real time based on client information entered by the reception unit. For example, the analysis unit uses speech recognition technology to transcribe the client's statements into text and then analyzes that text. The analysis unit can also analyze audio using deep learning, for example. Furthermore, the analysis unit can analyze audio using a Hidden Markov Model (HMM). Specifically, speech recognition technology is the process of transcribing the client's statements into text in real time, while deep learning models learn from large amounts of audio data to achieve highly accurate speech recognition. For example, if a client says, "Please send me the materials," the speech recognition technology transcribes this statement into text, and the analysis unit analyzes that text to understand the client's intent. HMM is a technology that models the temporal fluctuations of audio signals and is used to improve the accuracy of speech recognition. The analysis unit combines these technologies to analyze the client's statements with high accuracy and determine what should be said next. In addition, the analysis unit can refer to past interactions and transaction history with clients to understand their needs and interests. This allows the analysis unit to analyze the client's statements in real time and respond appropriately and quickly.

[0068] The presentation unit presents appropriate scripts based on the information analyzed by the analysis unit. For example, the presentation unit can provide a script indicating what to say next based on the client's statements. For instance, if a client says, "Please send me the materials," the presentation unit might suggest, "Before I send you the materials, could I have a brief chat with you?" The presentation unit can also provide a script indicating what to say next based on the client's statements. For example, if a client says, "I'm busy right now," the presentation unit might suggest, "Could you spare some time later?" Specifically, the presentation unit selects the most suitable script from a pre-prepared set based on the text data provided by the analysis unit and presents it to the operator. The scripts are dynamically generated according to the client's statements, past transaction history, and current situation. For example, if a client says, "I'd like to know more about the price," the presentation unit might suggest, "Before I give you details about the price, what use case are you considering?" The presentation unit also provides guidelines and points to note for operators when responding according to the scripts. This allows operators to communicate smoothly with clients and increases the success rate of appointment scheduling. Furthermore, the presentation unit can collect operator feedback and continuously improve the accuracy and effectiveness of the script. This allows the presentation unit to always provide the latest information and optimal scripts, improving the efficiency and success rate of appointment scheduling.

[0069] The learning unit can pre-learn client information. For example, the learning unit can store client information in a database and learn using machine learning algorithms. For example, the learning unit can learn about a client's interests and preferences based on their past transaction history. The learning unit can also collect information from the client's website and social media to learn about the client's latest trends. For example, the learning unit can collect product information and news articles from the client's website to understand the client's latest trends. By pre-learning client information, the learning unit can present more appropriate scripts. Some or all of the above processes in the learning unit may be performed using AI, for example, or not. For example, the learning unit can store client information in a database and learn using an AI model.

[0070] The history unit can learn the user's habits and past history. For example, the history unit can store the user's call records in a database and learn using machine learning algorithms. For example, the history unit can learn the user's speaking patterns and frequently used phrases. The history unit can also learn successful appointment-setting methods based on the user's past appointment-setting history. For example, the history unit can analyze the user's past call records and extract successful appointment-setting methods. As a result, the history unit can present individually optimized scripts by learning the user's habits and past history. Some or all of the above processing in the history unit may be performed using AI, for example, or not using AI. For example, the history unit can store the user's call records in a database and learn using an AI model.

[0071] The teaching unit can instruct users on how to schedule appointments based on a provided script. For example, the teaching unit can instruct users on speaking techniques and questioning methods. For instance, the teaching unit might advise users that "it's important to ask clear questions to clients." The teaching unit can also provide users with specific scripts and instruct them on how to schedule appointments based on those scripts. For example, the teaching unit might provide users with specific scripts such as "Now, say this." In this way, the teaching unit improves the user's appointment-setting skills by instructing them on how to schedule appointments based on the provided scripts. Some or all of the above processes in the teaching unit may be performed using AI, for example, or not. For example, the teaching unit can use an AI model to instruct users on how to schedule appointments.

[0072] The analysis unit can transcribe the client's statements into text using speech recognition technology and analyze that text. The analysis unit can analyze speech using, for example, deep learning. For example, the analysis unit transcribes the client's statements into text using speech recognition technology and analyzes that text. The analysis unit can also analyze speech using HMM (Hidden Markov Model). For example, the analysis unit transcribes the client's statements into text using speech recognition technology and analyzes that text. As a result, the analysis unit can accurately transcribe the client's statements into text using speech recognition technology, improving the accuracy of the analysis. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can use an AI model to transcribe the client's statements into text using speech recognition technology and analyze that text.

[0073] The presentation unit can provide a script indicating what to say next based on the client's statements. For example, if the client says, "Please send me the materials," the presentation unit might suggest, "Could I speak with you briefly before sending the materials?" The presentation unit can also provide a script indicating what to say next based on the client's statements. For example, if the client says, "I'm busy right now," the presentation unit might suggest, "Could you spare some time later?" This improves the success rate of securing appointments by allowing the presentation unit to provide a script indicating what to say next based on the client's statements. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not. For example, the presentation unit can use an AI model to provide a script indicating what to say next based on the client's statements.

[0074] The reception desk can estimate the user's emotions and adjust the timing of client information input based on the estimated emotions. For example, if the user is stressed, the reception desk can delay the input timing to help them relax. Conversely, if the user is relaxed, the reception desk can speed up the input timing to efficiently collect information. For example, if the user is in a hurry, the reception desk can optimize the input timing to quickly collect information. In this way, the reception desk can reduce user stress and efficiently collect information by adjusting the input timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can use an AI model to estimate the user's emotions and adjust the input timing based on the estimated emotions.

[0075] The reception desk can analyze the user's past input history when client information is entered and select the optimal input method. For example, the reception desk may prioritize suggesting input methods that the user has frequently used in the past. The reception desk can also suggest the optimal input method for a specific time period based on the user's past input history. For example, the reception desk may analyze the user's past input history and suggest the most efficient input method. In this way, the reception desk can provide the user with the optimal input method by analyzing past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk may use an AI model to analyze the user's past input history and select the optimal input method.

[0076] The reception desk can filter client information based on the user's current projects and areas of interest when the user enters client information. For example, the reception desk can prioritize displaying client information related to the project the user is currently working on. The reception desk can also filter relevant client information based on the user's areas of interest. For example, the reception desk can suggest the most relevant client information according to the progress of the user's current project. In this way, the reception desk can prioritize providing highly relevant information by filtering based on the current project and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can use an AI model to filter based on the user's current projects and areas of interest.

[0077] The reception desk can estimate the user's emotions and prioritize the client information to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk may postpone less important information. Conversely, if the user is relaxed, the reception desk may prioritize the input of more important information. For example, if the user is in a hurry, the reception desk may prioritize the input of the most important information. This allows the reception desk to efficiently collect important information by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk may use an AI model to estimate the user's emotions and prioritize information based on the estimated emotions.

[0078] The reception unit can prioritize inputting highly relevant information by considering the user's geographical location when client information is entered. For example, the reception unit can prioritize displaying client information that is close to the user's current location. The reception unit can also filter highly relevant client information based on the user's geographical location. For example, the reception unit can suggest the most suitable client information based on the user's current location. In this way, the reception unit can efficiently collect highly relevant information by considering geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can use an AI model to prioritize inputting highly relevant information by considering the user's geographical location.

[0079] The reception desk can analyze a user's social media activity and input relevant information when client information is entered. For example, the reception desk can extract relevant client information from the user's social media activity. The reception desk can also suggest optimal client information based on the user's social media activity. For example, the reception desk can analyze the user's social media activity and prioritize displaying highly relevant client information. This allows the reception desk to efficiently collect highly relevant client information by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can use an AI model to analyze the user's social media activity and input relevant information.

[0080] The analysis unit can estimate the user's emotions and adjust the speech analysis method based on the estimated emotions. For example, if the user is nervous, the analysis unit can enhance noise filtering to improve the accuracy of the speech analysis. Conversely, if the user is relaxed, the analysis unit can prioritize the speed of the speech analysis. For example, if the user is in a hurry, the analysis unit prioritizes real-time speech analysis. This improves the accuracy of the analysis by adjusting the speech analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can use an AI model to estimate the user's emotions and adjust the speech analysis method based on the estimated emotions.

[0081] The analysis unit can improve the accuracy of its analysis by referring to the client's past speech history during speech analysis. For example, the analysis unit may prioritize the analysis of specific keywords based on the client's past speech history. The analysis unit can also improve the accuracy of its analysis by extracting specific patterns from the client's past speech history. For example, the analysis unit may refer to the client's past speech history and perform analysis based on context. In this way, the analysis unit improves the accuracy of its speech analysis by referring to past speech history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may use an AI model to improve the accuracy of its analysis by referring to the client's past speech history.

[0082] The analysis unit can perform voice analysis while considering the client's attribute information. For example, the analysis unit can adjust the parameters of voice analysis based on the client's age and gender. The analysis unit can also prioritize the analysis of specific technical terms based on the client's occupation and industry. For example, the analysis unit can analyze specific expressions based on the client's region and culture. This improves the accuracy of voice analysis by considering the client's attribute information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use an AI model to perform analysis while considering the client's attribute information.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. It can also provide a display method that includes detailed information if the user is relaxed. For example, if the user is in a hurry, the analysis unit can provide a concise display method. This improves visibility by adjusting the display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can use an AI model to estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions.

[0084] The analysis unit can perform speech analysis while considering the geographical distribution of the client. For example, the analysis unit can analyze the dialect and accent of a specific region based on the client's geographical distribution. The analysis unit can also analyze regionally specific expressions while considering the client's geographical distribution. For example, the analysis unit can perform analysis while considering the culture and customs of a specific region based on the client's geographical distribution. As a result, the accuracy of speech analysis is improved by considering geographical distribution. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use an AI model to perform analysis while considering the client's geographical distribution.

[0085] The analysis unit can improve the accuracy of its analysis by referring to the client's relevant literature during speech analysis. For example, the analysis unit may prioritize the analysis of specific technical terms based on the client's relevant literature. The analysis unit can also improve the accuracy of its analysis by extracting specific patterns from the client's relevant literature. For example, the analysis unit may refer to the client's relevant literature and perform analysis based on context. This improves the accuracy of speech analysis by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may use an AI model to improve the accuracy of its analysis by referring to the client's relevant literature.

[0086] The presentation unit can estimate the user's emotions and adjust the way the script is presented based on the estimated emotions. For example, if the user is nervous, the presentation unit can provide a simple and easy-to-read script. Alternatively, if the user is relaxed, it can provide a script with more detailed information. For example, if the user is in a hurry, the presentation unit can provide a concise script. This improves readability by adjusting the script's presentation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can use an AI model to estimate the user's emotions and adjust the script's presentation based on the estimated emotions.

[0087] The presentation unit can adjust the level of detail in the script based on the client's importance when presenting it. For example, the presentation unit can provide a detailed script to important clients, and a concise script to less important clients. For example, the presentation unit can dynamically adjust the level of detail in the script based on the client's importance. This allows the presentation unit to efficiently provide information by adjusting the level of detail in the script based on the client's importance. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit can use an AI model to adjust the level of detail in the script based on the client's importance.

[0088] The script presentation unit can apply different scripting algorithms depending on the client's category when presenting scripts. For example, if the client is a large corporation, the presentation unit will provide a formal script. Alternatively, if the client is a startup, the presentation unit can provide a more casual script. For example, the presentation unit can apply the most suitable scripting algorithm depending on the client's category. This allows the presentation unit to provide the most suitable script for each client category. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not. For example, the presentation unit can use an AI model to apply different scripting algorithms depending on the client's category.

[0089] The presentation unit can estimate the user's emotions and adjust the length of the script based on the estimated emotions. For example, if the user is nervous, the presentation unit can provide a short, concise script. Conversely, if the user is relaxed, the presentation unit can provide a longer script with more detailed explanations. For example, if the user is in a hurry, the presentation unit can provide a short, quick-response script. This improves readability by adjusting the script length based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can use an AI model to estimate the user's emotions and adjust the script length based on the estimated emotions.

[0090] The presentation unit can determine the priority of scripts based on the timing of the client's statements when presenting scripts. For example, the presentation unit can determine the priority of scripts based on what the client has recently said. The presentation unit can also provide the most suitable script depending on the timing of the client's statements. For example, the presentation unit can dynamically adjust the priority of scripts considering the timing of the client's statements. This allows the presentation unit to efficiently provide information by determining the priority of scripts based on the timing of the client's statements. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit can use an AI model to determine the priority of scripts based on the timing of the client's statements.

[0091] The presentation unit can adjust the order of scripts based on the client's relevance when presenting them. For example, the presentation unit may prioritize displaying the most important scripts based on the client's relevance. The presentation unit can also dynamically adjust the order of scripts, taking the client's relevance into consideration. For example, the presentation unit may provide the most suitable scripts according to the client's relevance. This allows the presentation unit to efficiently provide information by adjusting the order of scripts based on the client's relevance. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not. For example, the presentation unit may use an AI model to adjust the order of scripts based on the client's relevance.

[0092] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is relaxed, the learning unit will select detailed training data. If the user is in a hurry, the learning unit can also select concise training data. For example, if the user is excited, the learning unit will select visually stimulating training data. This improves the efficiency of learning by allowing the learning unit to select training data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can use an AI model to estimate the user's emotions and select training data based on the estimated emotions.

[0093] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also optimize its learning algorithm by extracting specific patterns from past learning data. For example, the learning unit can adjust the parameters of the learning algorithm by referring to past learning data. This improves the accuracy of the learning algorithm by referencing past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can use an AI model to optimize the learning algorithm by referring to past learning data.

[0094] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, the learning unit can increase the learning frequency when the user is relaxed, or decrease it when the user is in a hurry. For example, it can adjust the learning frequency when the user is excited. This improves the learning efficiency by adjusting the learning frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can use an AI model to estimate the user's emotions and adjust the learning frequency based on the estimated emotions.

[0095] The learning unit can weight the training data based on the timing of client information input during training. For example, the learning unit adjusts the weighting of the training data based on the timing of client information input. The learning unit can also determine the priority of the training data considering the timing of client information input. For example, the learning unit dynamically adjusts the weighting of the training data according to the timing of client information input. This improves the accuracy of training by weighting the training data based on the timing of client information input. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can use an AI model to weight the training data based on the timing of client information input.

[0096] The history unit can estimate the user's emotions and select history data based on those estimated emotions. For example, if the user is relaxed, the history unit will select detailed history data. If the user is in a hurry, the history unit can also select concise history data. For example, if the user is excited, the history unit will select visually stimulating history data. This improves the efficiency of the history unit by selecting history data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the history unit may be performed using AI, for example, or not using AI. For example, the history unit can use an AI model to estimate the user's emotions and select history data based on those estimated emotions.

[0097] The history unit can optimize the analysis algorithm by referring to past historical data when analyzing historical data. For example, the history unit can select the optimal analysis algorithm based on past historical data. The history unit can also optimize the analysis algorithm by extracting specific patterns from past historical data. For example, the history unit can adjust the parameters of the analysis algorithm by referring to past historical data. In this way, the accuracy of the analysis algorithm is improved by the history unit referring to past historical data. Some or all of the above processes in the history unit may be performed using AI, for example, or without AI. For example, the history unit can use an AI model to optimize the analysis algorithm by referring to past historical data.

[0098] The history section can estimate the user's emotions and prioritize historical data based on those emotions. For example, if the user is relaxed, the history section will prioritize displaying high-importance historical data. Conversely, if the user is in a hurry, the history section can postpone displaying less important historical data. For example, if the user is excited, the history section will prioritize displaying visually stimulating historical data. This allows the history section to efficiently collect important information by prioritizing historical data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the history section may be performed using AI or not. For example, the history section can use an AI model to estimate the user's emotions and prioritize historical data based on those emotions.

[0099] The history unit can weight historical data based on the timing of client information input when analyzing historical data. For example, the history unit adjusts the weighting of historical data based on the timing of client information input. The history unit can also determine the priority of historical data considering the timing of client information input. For example, the history unit dynamically adjusts the weighting of historical data according to the timing of client information input. This improves the accuracy of the analysis by weighting historical data based on the timing of client information input. Some or all of the above processing in the history unit may be performed using AI, for example, or without AI. For example, the history unit can use an AI model to weight historical data based on the timing of client information input.

[0100] The instruction unit can estimate the user's emotions and adjust the instruction method based on the estimated emotions. For example, if the user is nervous, the instruction unit will give instructions in a calm voice. Conversely, if the user is relaxed, the instruction unit can give instructions in a cheerful voice. For example, if the user is in a hurry, the instruction unit will give instructions quickly and concisely. This improves the efficiency of instruction by allowing the instruction unit to adjust the instruction method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction unit may be performed using AI, for example, or not using AI. For example, the instruction unit can use an AI model to estimate the user's emotions and adjust the instruction method based on the estimated emotions.

[0101] The teaching unit can select the optimal teaching method by referring to the user's past appointment acquisition history during the teaching process. For example, the teaching unit selects the optimal teaching method based on the user's past appointment acquisition history. The teaching unit can also optimize the teaching method by extracting specific patterns from the user's past appointment acquisition history. For example, the teaching unit adjusts the parameters of the teaching method by referring to the user's past appointment acquisition history. This improves the accuracy of the teaching method by referring to the past appointment acquisition history. Some or all of the above processing in the teaching unit may be performed using AI, for example, or without AI. For example, the teaching unit can use an AI model to select the optimal teaching method by referring to the user's past appointment acquisition history.

[0102] The instruction unit can estimate the user's emotions and determine the priority of instructions based on the estimated emotions. For example, if the user is relaxed, the instruction unit will prioritize high-importance instructions. Conversely, if the user is in a hurry, the instruction unit can postpone less important instructions. For example, if the user is excited, the instruction unit will prioritize visually stimulating instructions. In this way, the instruction unit can efficiently provide important information by determining the priority of instructions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction unit may be performed using AI, for example, or not using AI. For example, the instruction unit can use an AI model to estimate the user's emotions and determine the priority of instructions based on the estimated emotions.

[0103] The teaching unit can select the optimal teaching method during teaching, taking into account the user's geographical location information. For example, the teaching unit selects the optimal teaching method based on the user's geographical location information. The teaching unit can also determine the teaching priority, taking into account the user's geographical location information. For example, the teaching unit adjusts the parameters of the teaching method according to the user's geographical location information. In this way, the teaching unit can provide the optimal teaching method by taking geographical location information into account. Some or all of the above processing in the teaching unit may be performed using AI, for example, or without using AI. For example, the teaching unit can use an AI model to select the optimal teaching method, taking into account the user's geographical location information.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The analysis unit can estimate the user's emotions and adjust the speech analysis method based on the estimated emotions. For example, if the user is nervous, noise filtering can be enhanced to improve the accuracy of the speech analysis. If the user is relaxed, the speed of the speech analysis can be prioritized. For example, if the user is in a hurry, real-time speech analysis is prioritized. In this way, the analysis unit improves the accuracy of the analysis by adjusting the speech analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can use an AI model to estimate the user's emotions and adjust the speech analysis method based on the estimated emotions.

[0106] The presentation unit can estimate the user's emotions and adjust the way the script is presented based on those emotions. For example, if the user is nervous, it can provide a simple and easy-to-read script. If the user is relaxed, it can provide a script with more detailed information. If the user is in a hurry, it can provide a script that gets straight to the point. This improves readability by allowing the presentation unit to adjust the script's presentation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI, or not. For example, the presentation unit can use an AI model to estimate the user's emotions and adjust the way the script is presented based on those emotions.

[0107] The reception desk can estimate the user's emotions and adjust the timing of client information input based on the estimated emotions. For example, if the user is stressed, the input timing can be delayed to help them relax. Conversely, if the user is relaxed, the input timing can be sped up to efficiently collect information. For example, if the user is in a hurry, the input timing can be optimized to quickly collect information. In this way, the reception desk can reduce user stress and efficiently collect information by adjusting the input timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can use an AI model to estimate the user's emotions and adjust the input timing based on the estimated emotions.

[0108] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is relaxed, it can select detailed training data. If the user is in a hurry, it can select training data that gets straight to the point. If the user is excited, it can select visually stimulating training data. This improves the efficiency of learning by allowing the learning unit to select training data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can use an AI model to estimate the user's emotions and select training data based on the estimated emotions.

[0109] The history unit can estimate the user's emotions and select history data based on those emotions. For example, if the user is relaxed, it can select detailed history data. If the user is in a hurry, it can select concise history data. If the user is excited, it can select visually stimulating history data. This improves the efficiency of the history unit by selecting history data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the history unit may be performed using AI or not. For example, the history unit can use an AI model to estimate the user's emotions and select history data based on those emotions.

[0110] The reception desk can analyze the user's past input history when they enter client information and select the optimal input method. For example, it can prioritize suggesting input methods that the user has frequently used in the past. It can also suggest the optimal input method for a specific time period based on the user's past input history. For example, it can analyze the user's past input history and suggest the most efficient input method. In this way, the reception desk can provide the user with the optimal input method by analyzing past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can use an AI model to analyze the user's past input history and select the optimal input method.

[0111] The analysis unit can improve the accuracy of its analysis by referring to the client's past speech history during speech analysis. For example, it can prioritize the analysis of specific keywords based on the client's past speech history. It can also improve the accuracy of its analysis by extracting specific patterns from the client's past speech history. For example, it can perform analysis based on context by referring to the client's past speech history. In this way, the analysis unit improves the accuracy of its speech analysis by referring to past speech history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may use an AI model to improve the accuracy of its analysis by referring to the client's past speech history.

[0112] The presentation unit can adjust the level of detail in the script based on the client's importance when presenting it. For example, it can provide a detailed script to important clients and a concise script to less important clients. For example, it can dynamically adjust the level of detail in the script based on the client's importance. This allows the presentation unit to efficiently provide information by adjusting the level of detail in the script based on the client's importance. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit can use an AI model to adjust the level of detail in the script based on the client's importance.

[0113] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, it can select the optimal learning algorithm based on past learning data. It can also optimize the learning algorithm by extracting specific patterns from past learning data. For example, it can adjust the parameters of the learning algorithm by referring to past learning data. In this way, the learning unit improves the accuracy of the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can use an AI model to optimize the learning algorithm by referring to past learning data.

[0114] The teaching unit can select the optimal teaching method by referring to the user's past appointment history during the teaching process. For example, it can select the optimal teaching method based on the user's past appointment history. It can also optimize the teaching method by extracting specific patterns from the user's past appointment history. For example, it can adjust the parameters of the teaching method by referring to the user's past appointment history. This improves the accuracy of the teaching method by allowing the teaching unit to refer to past appointment history. Some or all of the above processing in the teaching unit may be performed using AI, for example, or without AI. For example, the teaching unit can use an AI model to select the optimal teaching method by referring to the user's past appointment history.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The reception desk enters client information. Client information includes, for example, name, contact information, and past transaction history. The reception desk can manually enter client information. Alternatively, client information can be automatically retrieved, for example, by scraping information from the client's website. Step 2: The analysis unit analyzes the audio during the telemarketing call in real time based on the client information entered by the reception unit. The analysis unit uses speech recognition technology to transcribe the client's statements into text and then analyzes that text. Technologies such as deep learning and HMM (Hidden Markov Model) are used for the analysis. Step 3: The presentation unit presents an appropriate script based on the information analyzed by the analysis unit. The presentation unit provides a script indicating what to say next based on the client's statements. For example, if the client says, "Please send me the materials," the presentation unit will present a script such as, "Could I speak with you briefly before sending the materials?" Also, if the client says, "I'm busy right now," the presentation unit will present a script such as, "Could you spare some time later?"

[0117] 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.

[0118] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0119] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the reception unit, analysis unit, presentation unit, learning unit, history unit, and teaching unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit can input client information via the control unit 46A of the smart device 14. The analysis unit can analyze voice in real time via the specific processing unit 290 of the data processing unit 12. The presentation unit can present an appropriate script via the control unit 46A of the smart device 14. The learning unit can pre-learn client information via the specific processing unit 290 of the data processing unit 12. The history unit can learn the user's habits and past history via the specific processing unit 290 of the data processing unit 12. The teaching unit can teach how to obtain an appointment via the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] 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.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0124] 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.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0126] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] 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.

[0128] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the reception unit, analysis unit, presentation unit, learning unit, history unit, and teaching unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit can input client information via the control unit 46A of the smart glasses 214. The analysis unit can analyze voice in real time via the specific processing unit 290 of the data processing unit 12. The presentation unit can present an appropriate script via the control unit 46A of the smart glasses 214. The learning unit can pre-learn client information via the specific processing unit 290 of the data processing unit 12. The history unit can learn the user's habits and past history via the specific processing unit 290 of the data processing unit 12. The teaching unit can teach how to make an appointment via the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] 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.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0140] 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.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0142] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] 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.

[0144] 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.

[0145] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the reception unit, analysis unit, presentation unit, learning unit, history unit, and teaching unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit can input client information via the control unit 46A of the headset terminal 314. The analysis unit can analyze voice in real time via the specific processing unit 290 of the data processing unit 12. The presentation unit can present an appropriate script via the control unit 46A of the headset terminal 314. The learning unit can pre-learn client information via the specific processing unit 290 of the data processing unit 12. The history unit can learn the user's habits and past history via the specific processing unit 290 of the data processing unit 12. The teaching unit can teach how to obtain an appointment via the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0156] 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.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0158] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] 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.

[0160] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0161] 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.

[0162] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] 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.

[0167] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] Each of the multiple elements described above, including the reception unit, analysis unit, presentation unit, learning unit, history unit, and teaching unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit can receive client information via the control unit 46A of the robot 414. The analysis unit can analyze voice in real time via the specific processing unit 290 of the data processing unit 12. The presentation unit can present an appropriate script via the control unit 46A of the robot 414. The learning unit can pre-learn client information via the specific processing unit 290 of the data processing unit 12. The history unit can learn the user's habits and past history via the specific processing unit 290 of the data processing unit 12. The teaching unit can teach the robot 414 how to obtain an appointment via the control unit 46A. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0170] 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.

[0171] Figure 9 shows the 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.

[0172] 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.

[0173] 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.

[0174] 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, and motorcycles, 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 based, for example, 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.

[0175] 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."

[0176] 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.

[0177] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0186] 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 other things 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.

[0187] 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.

[0188] (Note 1) A reception area where client information is entered, An analysis unit analyzes the audio during telemarketing in real time based on the client information entered by the reception unit, The system includes a presentation unit that presents an appropriate script based on the information analyzed by the analysis unit. A system characterized by the following features. (Note 2) It includes a learning unit that pre-learns client information. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a history section that learns the user's habits and past history. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a teaching unit that instructs the user on how to obtain an appointment based on a provided script. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Speech recognition technology is used to convert the client's statements into text, and that text is then analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned display unit is, Based on the client's statements, the script will show what to say next. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of client information input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering client information, the system analyzes the user's past input history and selects the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering client information, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of client information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering client information, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering client information, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the voice analysis method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During voice analysis, the system improves the accuracy of the analysis by referencing the client's past speech history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During voice analysis, the analysis is performed while taking into account the client's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During voice analysis, the analysis takes into account the geographical distribution of the clients. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During speech analysis, we improve the accuracy of the analysis by referring to the client's relevant literature. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is, It estimates the user's emotions and adjusts the script's expression based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is, When presenting a script, adjust the level of detail based on the client's importance. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is, When presenting a script, different scripting algorithms are applied depending on the client's category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is, It estimates the user's emotions and adjusts the script length based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is, When presenting scripts, prioritize the scripts based on when the client speaks. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is, When presenting scripts, adjust the order of the scripts based on their relevance to the client. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned learning unit, During training, the training data is weighted based on when the client information was entered. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned history section is, The system estimates the user's emotions and selects historical data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned history section is, When analyzing historical data, refer to past historical data to optimize the analysis algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned history section is, It estimates the user's emotions and prioritizes historical data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned history section is, When analyzing historical data, the data is weighted based on when the client information was entered. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned teaching unit is, It estimates the user's emotions and adjusts the teaching method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned teaching unit is, During instruction, the system selects the optimal instruction method by referring to the user's past appointment booking history. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned teaching unit is, The system estimates the user's emotions and determines the priority of instructions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned teaching unit is, During instruction, the optimal instruction method is selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where client information is entered, An analysis unit analyzes the audio during telemarketing in real time based on the client information entered by the reception unit, The system includes a presentation unit that presents an appropriate script based on the information analyzed by the analysis unit. A system characterized by the following features.

2. It includes a learning unit that pre-learns client information. The system according to feature 1.

3. It includes a history section that learns the user's habits and past history. The system according to feature 1.

4. It includes a teaching unit that instructs the user on how to obtain an appointment based on a provided script. The system according to feature 1.

5. The aforementioned analysis unit, Speech recognition technology is used to convert the client's statements into text, and that text is then analyzed. The system according to feature 1.

6. The aforementioned display unit is, Based on the client's statements, the script will show what to say next. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of client information input based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is When entering client information, the system analyzes the user's past input history and selects the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When entering client information, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

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