system

Abstract

The system according to this embodiment aims to automatically categorize user inquiries into appropriate categories. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a determination unit, and a transfer unit. The reception unit receives inquiries from users. The analysis unit analyzes the inquiries received by the reception unit. The determination unit determines a category based on the content analyzed by the analysis unit. The transfer unit transfers the inquiries to the appropriate person based on the category determined by the determination unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, when a user selects an inquiry category, they often get lost, and there is a risk of input trouble and category selection errors.

[0005] The system according to the embodiment aims to automatically assign the user's inquiry content to an appropriate category.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a determination unit, and a transfer unit. The reception unit receives inquiries from users. The analysis unit analyzes the inquiries received by the reception unit. The determination unit determines a category based on the analysis performed by the analysis unit. The transfer unit transfers the inquiries to the appropriate personnel based on the category determined by the determination unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically categorize user inquiries into appropriate categories. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F 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. Also, 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 automated inquiry sorting system according to an embodiment of the present invention is a system in which AI analyzes the content of inquiries entered by users and automatically sorts them into appropriate categories. In this automated inquiry sorting system, users input inquiry content, the AI ​​analyzes the content to determine the appropriate category, and automatically sorts the inquiry content. This system eliminates the effort required for users to select categories and reduces the likelihood of miscategorizing inquiries. Furthermore, staff members receive inquiries sorted into the correct categories, enabling quick and appropriate responses. In addition, the AI ​​can learn from the analysis results of inquiries and improve its accuracy. As a result, more accurate category determination becomes possible as the system is used more extensively. This system can handle various inquiry methods, such as web forms and email. For example, it can automatically sort inquiries not only from web forms but also from emails. Thus, the AI-powered automated inquiry sorting system improves user convenience and streamlines inquiry handling. As a result, the automated inquiry sorting system can efficiently analyze user inquiries and automatically sort them into appropriate categories.

[0029] The automated inquiry distribution system according to this embodiment comprises a reception unit, an analysis unit, a determination unit, and a forwarding unit. The reception unit receives inquiries from users. The reception unit supports inquiry methods such as web forms and emails. When receiving inquiries, the reception unit can save the user's input as digital data. The analysis unit analyzes the inquiries received by the reception unit. The analysis unit analyzes the inquiries using, for example, natural language processing technology. The analysis unit extracts keywords and performs contextual analysis of the inquiries to understand their intent. The determination unit determines the category based on the analysis performed by the analysis unit. The determination unit determines the appropriate category based on the analysis results. Based on the analysis results of the inquiries, the determination unit automatically distributes them to pre-set categories. The forwarding unit forwards the inquiries to the appropriate personnel based on the category determined by the determination unit. The forwarding unit forwards the inquiries to the appropriate personnel via email or system notification based on the determined category. The forwarding unit quickly forwards the inquiries to the appropriate personnel to encourage appropriate action. As a result, the automated inquiry sorting system according to this embodiment can efficiently analyze the content of user inquiries and automatically sort them into the appropriate category.

[0030] The reception department receives inquiries from users. The reception department handles various inquiry methods, such as web forms and email. Specifically, web forms provide users with text boxes and options to enter their inquiries, and the inquiry is sent to the reception department when a submit button is pressed. In the case of email, the reception department receives the email when the user sends it to a specified email address. The reception department can save user input as digital data when receiving inquiries. For example, input from web forms is automatically saved to a database, and email inquiries are saved to the mail server. This allows the reception department to reliably receive user inquiries and centrally manage them as digital data. Furthermore, the reception department also has a function to send an automated response message upon receiving an inquiry. For example, immediately after a user submits an inquiry, the reception department sends an automated response message such as, "Your inquiry has been received. A representative will respond as soon as they have reviewed it." This allows the user to confirm that their inquiry has been successfully received, providing reassurance. The reception department also has a function to monitor the receipt and processing status of inquiries in real time, making it easier for system administrators to understand the status of inquiries. This allows the reception department to efficiently and reliably receive user inquiries and smoothly hand them over to the next processing step.

[0031] The analysis unit analyzes the content of inquiries received by the reception unit. The analysis unit analyzes the content of inquiries using, for example, natural language processing technology. Specifically, it uses natural language processing technology to extract keywords and perform contextual analysis of the inquiry content to understand the intent of the inquiry. For example, if a user asks, "How do I return a product?", the analysis unit extracts keywords such as "return" and "method" and understands from the context that it is an inquiry about the return procedure. The analysis unit uses AI to classify the content of inquiries and provides information to sort them into the appropriate category. The AI ​​learns from past inquiry data and can perform highly accurate analysis of new inquiries. For example, it uses machine learning algorithms to extract characteristics of the inquiry content and identify similar inquiry patterns. This allows the analysis unit to accurately grasp the intent of the inquiry content and provide the information necessary for the next processing step. Furthermore, the analysis unit can also perform sentiment analysis of the inquiry content. For example, if a user's inquiry contains anger or dissatisfaction, the analysis unit detects that emotion and indicates that it needs to be addressed as a priority. This allows the analysis unit to promote responses that take the user's emotions into consideration, contributing to improved customer satisfaction. The analysis unit can improve the overall efficiency of the system by analyzing inquiry content in real time and providing information quickly and accurately.

[0032] The classification unit determines the category based on the content analyzed by the analysis unit. For example, the classification unit determines the appropriate category based on the analysis results. Specifically, it automatically sorts the inquiry content into pre-set categories based on keywords and contextual information provided by the analysis unit. For example, an inquiry containing keywords such as "return" and "method" will be sorted into the "return procedure" category. The classification unit can improve the accuracy of category determination using AI. The AI ​​learns from past inquiry data and can determine the category of new inquiries with high accuracy. For example, it uses a machine learning algorithm to extract the characteristics of the inquiry content and automatically select the most appropriate category. This allows the classification unit to sort inquiries into categories quickly and accurately. Furthermore, the classification unit can flexibly handle inquiries that span multiple categories. For example, if an inquiry is related to multiple categories, such as "Please tell me about the return method and exchange procedure for products," the classification unit can sort it into both the "return procedure" and "exchange procedure" categories. This allows the classification unit to handle complex inquiries and sort them into the appropriate category. The classification unit stores the category determination results in a database and provides the information necessary for subsequent processing steps. This allows the classification unit to efficiently and accurately determine the category of the query content, thereby improving the overall processing efficiency of the system.

[0033] The forwarding unit forwards inquiries to the appropriate person based on the category determined by the judgment unit. For example, the forwarding unit forwards emails or sends in-system notifications to the appropriate person based on the determined category. Specifically, based on the category information provided by the judgment unit, it automatically selects the appropriate person and quickly forwards the inquiry. For example, an inquiry categorized as "return procedures" is forwarded via email to the person in charge of returns procedures. In addition, notifications can be displayed in real time on the person's dashboard using in-system notifications. This allows the person in charge to quickly check the inquiry and take appropriate action. The forwarding unit can also consider the priority of the inquiry when forwarding it. For example, if sentiment analysis shows that the user has strong dissatisfaction, the forwarding unit will prioritize forwarding that inquiry to the appropriate person to encourage a quick response. Furthermore, the forwarding unit can save the forwarding history to a database and track the processing status of inquiries. This allows system administrators to understand the processing status of each inquiry in real time and adjust responses as needed. The forwarding unit can reliably transmit information using multiple communication methods. For example, in addition to email notifications, it can use SMS and chat tools in combination to ensure that important information reaches the appropriate person. This allows the forwarding unit to quickly and reliably transfer inquiries to the appropriate person in charge, facilitating a proper response.

[0034] The automated inquiry routing system includes a learning unit that learns from the analysis results of inquiry content and improves accuracy. The learning unit uses AI to learn from the analysis results of inquiry content and improve analysis accuracy. For example, the learning unit learns from the analysis results of inquiry content using a machine learning algorithm. The learning unit uses a dataset of inquiry content to train the AI ​​to improve analysis accuracy. The learning unit periodically learns from the analysis results of inquiry content and improves analysis accuracy. This improves the accuracy of inquiry content analysis. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input the analysis results of inquiry content into the AI, and the AI ​​can learn to improve analysis accuracy.

[0035] The automated inquiry distribution system includes a means-response unit that handles various inquiry methods such as web forms and email. The means-response unit provides functions to handle various inquiry methods. For example, the means-response unit has a function to receive inquiries via web forms. The means-response unit also has a function to receive inquiries via email. The means-response unit can take appropriate action depending on the inquiry method. This allows it to handle various inquiry methods. Some or all of the above-described processing in the means-response unit may be performed using AI or not. For example, the means-response unit can input the inquiry content received via web form or email into AI, and the AI ​​can perform analysis to take appropriate action.

[0036] The automated inquiry routing system includes a recording unit that records the results of the analysis of inquiry content. The recording unit stores the analysis results of the inquiry content in a database. The recording unit records the analysis results of the inquiry content as digital data, for example. The recording unit saves the analysis results of the inquiry content so that they can be referenced later. The recording unit can also periodically back up the analysis results of the inquiry content. This allows the analysis results of the inquiry content to be referenced later by recording them. Some or all of the above processing in the recording unit may be performed using AI, or it may be performed without using AI. For example, the recording unit can perform recording by inputting the analysis results of the inquiry content into AI, which then saves them to the database.

[0037] The reception department can handle inquiries via various means, such as web forms and email. For example, the reception department has the functionality to receive inquiries via web forms. The reception department also has the functionality to receive inquiries via email. The reception department can provide appropriate responses depending on the method of inquiry. This allows it to handle a variety of inquiry methods. Some or all of the above-mentioned processes in the reception department may be performed using AI, or they may not. For example, the reception department can input the inquiry content received via web form or email into AI, and the AI ​​can perform analysis to provide an appropriate response.

[0038] The analysis unit can analyze the query content using natural language processing techniques. For example, the analysis unit can extract keywords from the query content using morphological analysis. The analysis unit can also understand the context of the query content using grammatical analysis. The analysis unit can also grasp the intent of the query content using semantic analysis. As a result, the accuracy of query content analysis is improved by using natural language processing techniques. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can input the query content into AI, and the AI ​​can grasp the intent of the query content by performing analysis using natural language processing techniques.

[0039] The judgment unit can determine the appropriate category based on the analysis results. For example, the judgment unit can automatically assign the content to a pre-set category based on the analysis results. The judgment unit can also select the optimal category based on the analysis results of the inquiry content. The judgment unit can understand the intent of the inquiry content and assign it to the appropriate category. This allows it to determine the appropriate category based on the analysis results. Some or all of the above processes in the judgment unit may be performed using AI or not. For example, the judgment unit can assign the inquiry content to the appropriate category by inputting the analysis results into AI, which then selects the optimal category.

[0040] The forwarding unit can forward inquiries to the appropriate person based on the determined category. For example, the forwarding unit can forward emails to the appropriate person based on the determined category. The forwarding unit can also forward inquiries to the appropriate person using in-system notifications. The forwarding unit can quickly forward inquiries to the appropriate person and prompt appropriate action. This allows forwarding to the appropriate person based on the determined category. Some or all of the above processes in the forwarding unit may be performed using AI or not. For example, the forwarding unit can input the inquiry content into the AI ​​based on the determined category, and the AI ​​can forward it to the appropriate person, enabling a quick response.

[0041] The reception desk can analyze a user's past inquiry history and suggest the most suitable input method. For example, the reception desk can automatically display as suggestions the type of inquiry the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest the type of inquiry the user will use at a specific time of day based on their past inquiry history. This allows the reception desk to suggest the most suitable input method based on the user's past inquiry history. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can improve user convenience by inputting the user's past inquiry history into AI, which then suggests the most suitable input method.

[0042] The reception desk can filter inquiries based on the user's current situation and areas of interest when the user enters their inquiry. For example, when the user enters their current situation, the reception desk can prioritize displaying relevant inquiries. The reception desk can also automatically filter relevant inquiries based on the user's areas of interest. If the user is in a specific situation, the reception desk can prioritize displaying inquiries related to that situation. This allows for filtering inquiries based on the user's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can improve user convenience by inputting the user's current situation and areas of interest into the AI, which then filters relevant inquiries.

[0043] The reception desk can prioritize receiving inquiries that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize receiving inquiries related to that region. The reception desk can also automatically filter relevant inquiries based on the user's geographical location. If the user is on the move, the reception desk can also prioritize receiving the most relevant inquiries based on their current location. This allows for the priority of receiving inquiries that are highly relevant based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can improve user convenience by inputting the user's geographical location into AI, which then filters relevant inquiries.

[0044] The reception desk can analyze the user's social media activity when receiving inquiries and accept relevant content. For example, the reception desk can analyze the user's current interests from their social media activity and prioritize accepting relevant inquiries. The reception desk can also automatically filter relevant inquiries based on what the user has mentioned on social media. The reception desk can also analyze the user's social media activity and suggest the most appropriate inquiry. This allows the reception desk to accept relevant content based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can improve user convenience by inputting the user's social media activity into AI, which can then filter relevant inquiries.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the inquiry content during the analysis. For example, the analysis unit performs a detailed analysis for inquiries of high importance. The analysis unit can also perform a simplified analysis for inquiries of low importance. The analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the inquiry content. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the inquiry content into the AI, and the AI ​​can adjust the level of detail of the analysis to provide an appropriate response according to the inquiry content.

[0046] The analysis unit can apply different analysis algorithms depending on the category of the inquiry during analysis. For example, the analysis unit can apply a specialized analysis algorithm to technical inquiries. The analysis unit can also apply a standard analysis algorithm to general inquiries. The analysis unit can select the optimal analysis algorithm according to the category of the inquiry. This allows the optimal analysis algorithm to be applied according to the category of the inquiry. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the category of the inquiry into the AI, and the AI ​​can select the optimal analysis algorithm to provide an appropriate response according to the inquiry.

[0047] The analysis unit can determine the priority of analysis based on when the inquiry content was submitted. For example, the analysis unit can prioritize the analysis of urgent inquiries. The analysis unit can also analyze regular inquiries with a standard priority. The analysis unit can dynamically adjust the priority of analysis according to when the inquiry content was submitted. This allows the analysis priority to be determined according to when the inquiry content was submitted. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the submission date of the inquiry content into the AI, and the AI ​​can determine the priority of analysis, thereby enabling appropriate responses according to the inquiry content.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the inquiry content during analysis. For example, the analysis unit can prioritize the analysis of highly relevant inquiry content. The analysis unit can also postpone the analysis of less relevant inquiry content. The analysis unit can dynamically adjust the order of analysis according to the relevance of the inquiry content. This allows the order of analysis to be adjusted according to the relevance of the inquiry content. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the inquiry content into the AI, and the AI ​​can adjust the order of analysis to provide an appropriate response according to the inquiry content.

[0049] The judgment unit can improve the accuracy of its judgment by considering the interrelationships of the inquiry content during the judgment process. For example, if multiple inquiry contents are related, the judgment unit will integrate them and make a judgment. The judgment unit can also analyze the interrelationships of the inquiry contents to make a highly accurate judgment. The judgment unit can determine the optimal category based on the interrelationships of the inquiry contents. This improves the accuracy of the judgment by considering the interrelationships of the inquiry contents. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input the interrelationships of the inquiry contents into the AI, and the AI ​​can determine the optimal category, thereby enabling appropriate responses according to the inquiry content.

[0050] The judgment unit can make a judgment while considering the attribute information of the person submitting the inquiry. For example, the judgment unit can determine the optimal category based on the submitter's attribute information (age, gender, etc.). The judgment unit can also analyze the submitter's attribute information to make a highly accurate judgment. The judgment unit can also adjust the criteria for category determination based on the submitter's attribute information. This allows for a highly accurate judgment by considering the attribute information of the person submitting the inquiry. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input the submitter's attribute information into AI, and the AI ​​can determine the optimal category, enabling appropriate responses according to the inquiry.

[0051] The judgment unit can make a judgment while considering the geographical distribution of the inquiry content. For example, the judgment unit can determine the category for each region for inquiries related to a specific region. The judgment unit can also analyze the geographical distribution of the inquiry content and determine the optimal category. The judgment unit can also adjust the criteria for category determination based on the geographical distribution. This allows for highly accurate judgments by considering the geographical distribution of the inquiry content. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input the geographical distribution of the inquiry content into the AI, and the AI ​​can determine the optimal category, enabling appropriate responses according to the inquiry content.

[0052] The judgment unit can improve the accuracy of its judgment by referring to relevant literature related to the inquiry content during the judgment process. For example, the judgment unit can refer to relevant literature to accurately determine the category of the inquiry content. The judgment unit can also analyze literature related to the inquiry content and determine the optimal category. The judgment unit can also adjust the criteria for category determination based on the relevant literature. This allows for highly accurate determination of the category of the inquiry content by referring to relevant literature. Some or all of the above processes in the judgment unit may be performed using AI or not. For example, the judgment unit can input relevant literature into AI, and the AI ​​can determine the optimal category, enabling it to take appropriate action according to the inquiry content.

[0053] The forwarding unit can determine the forwarding priority based on the importance of the inquiry content during forwarding. For example, the forwarding unit can prioritize forwarding inquiries with high importance. The forwarding unit can also forward inquiries with low importance using the normal priority. The forwarding unit can dynamically adjust the forwarding priority according to the importance of the inquiry content. This allows the forwarding priority to be determined according to the importance of the inquiry content. Some or all of the above processing in the forwarding unit may be performed using AI or not. For example, the forwarding unit can input the importance of the inquiry content into the AI, and the AI ​​can determine the forwarding priority, thereby enabling appropriate responses according to the inquiry content.

[0054] The forwarding unit can apply different forwarding methods depending on the category of the inquiry during forwarding. For example, the forwarding unit forwards technical inquiries to specialists. The forwarding unit can also apply standard forwarding methods to general inquiries. The forwarding unit can select the optimal forwarding method depending on the category of the inquiry. This ensures that the most appropriate forwarding method is applied according to the category of the inquiry. Some or all of the above processing in the forwarding unit may be performed using AI or not. For example, the forwarding unit can input the category of the inquiry into the AI, and the AI ​​can select the optimal forwarding method to provide an appropriate response according to the inquiry.

[0055] The forwarding unit can adjust the forwarding order based on when the inquiry content was submitted. For example, the forwarding unit can prioritize forwarding urgent inquiries. The forwarding unit can also forward regular inquiries in a standard order. The forwarding unit can dynamically adjust the forwarding order according to when the inquiry content was submitted. This allows the forwarding order to be adjusted according to when the inquiry content was submitted. Some or all of the above processing in the forwarding unit may be performed using AI or not. For example, the forwarding unit can input the submission date of the inquiry content into the AI, and the AI ​​can adjust the forwarding order to provide an appropriate response according to the inquiry content.

[0056] The forwarding unit can select a forwarding method based on the relevance of the inquiry content during forwarding. For example, the forwarding unit can select the optimal forwarding method for highly relevant inquiries. The forwarding unit can also apply a standard forwarding method to less relevant inquiries. The forwarding unit can dynamically adjust the forwarding method according to the relevance of the inquiry content. This allows for the selection of the optimal forwarding method according to the relevance of the inquiry content. Some or all of the above processing in the forwarding unit may be performed using AI or not. For example, the forwarding unit can input the relevance of the inquiry content into the AI, and the AI ​​can select the optimal forwarding method, thereby enabling appropriate responses according to the inquiry content.

[0057] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can analyze past learning data and select the optimal learning algorithm. The learning unit can also dynamically adjust the learning algorithm based on past learning data. The learning unit can improve the accuracy of the learning algorithm by referring to past learning data. As a result, the accuracy of the learning algorithm is improved by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI or not. For example, the learning unit can improve the accuracy of learning by inputting past learning data into AI, which then selects the optimal learning algorithm.

[0058] The learning unit can weight the training data based on the submission timing of inquiries during training. For example, the learning unit can assign a higher weight to urgent inquiries. The learning unit can also assign a standard weight to regular inquiries. The learning unit can dynamically adjust the weighting of the training data according to the submission timing of inquiries. This improves the accuracy of training by weighting the training data according to the submission timing of inquiries. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input the submission timing of inquiries into the AI, and the AI ​​can adjust the weighting of the training data to improve the accuracy of training.

[0059] The means response unit can select the optimal means by referring to the user's past inquiry means history when selecting a response means. For example, the means response unit may prioritize selecting inquiry means previously used by the user. The means response unit can also analyze the user's past inquiry means history and select the optimal means. The means response unit can dynamically adjust the response means based on the user's past inquiry means history. This allows the optimal means to be selected based on the user's past inquiry means history. Some or all of the above processing in the means response unit may be performed using AI or not. For example, the means response unit can improve user convenience by inputting the user's past inquiry means history into AI, which then selects the optimal means.

[0060] The means-response unit can select the optimal means by considering the user's device information when selecting a means to respond to. For example, if the user is using a smartphone, the means-response unit will select the optimal means to respond to a smartphone. If the user is using a tablet, the means-response unit can also select the optimal means to respond to a tablet. If the user is using a desktop, the means-response unit can also select the optimal means to respond to a desktop. This allows the optimal means to be selected based on the user's device information. Some or all of the above processing in the means-response unit may be performed using AI or not. For example, the means-response unit can input the user's device information into AI, and the AI ​​can select the optimal means to improve user convenience.

[0061] The recording unit can adjust the level of detail in the recording based on the importance of the inquiry. For example, the recording unit will record detailed information for high-importance inquiries. The recording unit can also record simplified information for low-importance inquiries. The recording unit can dynamically adjust the level of detail in the recording according to the importance of the inquiry. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the importance of the inquiry into the AI, and the AI ​​can adjust the level of detail in the recording to provide an appropriate response to the inquiry.

[0062] The recording unit can adjust the order of recordings based on when the inquiry content was submitted. For example, the recording unit prioritizes recording urgent inquiries. The recording unit can also record regular inquiries in a standard order. The recording unit can dynamically adjust the order of recordings according to when the inquiry content was submitted. This allows the order of recordings to be adjusted according to when the inquiry content was submitted. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the submission date of the inquiry content into the AI, and the AI ​​can adjust the order of recordings to provide an appropriate response according to the inquiry content.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The automated inquiry routing system can analyze a user's past inquiry history and suggest the most appropriate category. For example, it can prioritize displaying categories that the user has frequently inquired about in the past. It can also suggest relevant categories based on keywords the user has used in the past. It can even predict and suggest categories to be used at specific times of the day based on the user's past inquiry history. This improves user convenience by suggesting the most appropriate category based on the user's past inquiry history. Some or all of the above processing at the reception desk may be performed using AI or not. For example, the reception desk can input the user's past inquiry history into the AI, and the AI ​​can suggest the most appropriate category, thereby improving user convenience.

[0065] The automated inquiry routing system can prioritize suggesting highly relevant categories by considering the user's geographical location. For example, if a user is in a specific region, categories related to that region will be displayed preferentially. It can also automatically filter relevant categories based on the user's geographical location. If a user is on the move, it can prioritize suggesting the most suitable categories based on their current location. This improves user convenience by suggesting highly relevant categories based on the user's geographical location. Some or all of the above processing at the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location into the AI, which can then filter relevant categories to improve user convenience.

[0066] The automated inquiry routing system can analyze a user's social media activity and suggest relevant categories. For example, it can analyze a user's current interests from their social media activity and prioritize suggesting relevant categories. It can also automatically filter relevant categories based on what the user has mentioned on social media. It can analyze a user's social media activity and suggest the most suitable category. This improves user convenience by suggesting relevant categories based on the user's social media activity. Some or all of the above processing at the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity into the AI, which can then filter relevant categories to improve user convenience.

[0067] The automated inquiry routing system can analyze a user's past inquiry history and suggest the optimal input method. For example, it can automatically display as suggestions the type of inquiry the user has frequently entered in the past. It can also prioritize suggesting input methods the user has used in the past (voice, text, etc.). It can also predict and suggest the type of inquiry a user will use at a specific time of day based on their past inquiry history. This improves user convenience by suggesting the optimal input method based on the user's past inquiry history. Some or all of the above processing at the reception desk may be performed using AI or not. For example, the reception desk can improve user convenience by inputting the user's past inquiry history into the AI, which then suggests the optimal input method.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The reception desk receives inquiries from users. The reception desk can handle inquiries via various means, such as web forms and email, and can save the user's input as digital data. Step 2: The analysis unit analyzes the content of the inquiry received by the reception unit. The analysis unit analyzes the content of the inquiry using, for example, natural language processing technology, extracts keywords and performs contextual analysis to understand the intent of the inquiry. Step 3: The determination unit determines the category based on the content analyzed by the analysis unit. The determination unit determines the appropriate category based on the analysis results and automatically assigns the inquiry content to a pre-set category based on the analysis results. Step 4: The forwarding unit forwards the inquiry to the appropriate person based on the category determined by the determination unit. For example, the forwarding unit forwards the inquiry to the appropriate person via email or system notification based on the determined category, promptly forwarding the inquiry to the appropriate person and encouraging appropriate action.

[0070] (Example of form 2) The automated inquiry sorting system according to an embodiment of the present invention is a system in which AI analyzes the content of inquiries entered by users and automatically sorts them into appropriate categories. In this automated inquiry sorting system, users input inquiry content, the AI ​​analyzes the content to determine the appropriate category, and automatically sorts the inquiry content. This system eliminates the effort required for users to select categories and reduces the likelihood of miscategorizing inquiries. Furthermore, staff members receive inquiries sorted into the correct categories, enabling quick and appropriate responses. In addition, the AI ​​can learn from the analysis results of inquiries and improve its accuracy. As a result, more accurate category determination becomes possible as the system is used more extensively. This system can handle various inquiry methods, such as web forms and email. For example, it can automatically sort inquiries not only from web forms but also from emails. Thus, the AI-powered automated inquiry sorting system improves user convenience and streamlines inquiry handling. As a result, the automated inquiry sorting system can efficiently analyze user inquiries and automatically sort them into appropriate categories.

[0071] The automated inquiry distribution system according to this embodiment comprises a reception unit, an analysis unit, a determination unit, and a forwarding unit. The reception unit receives inquiries from users. The reception unit supports inquiry methods such as web forms and emails. When receiving inquiries, the reception unit can save the user's input as digital data. The analysis unit analyzes the inquiries received by the reception unit. The analysis unit analyzes the inquiries using, for example, natural language processing technology. The analysis unit extracts keywords and performs contextual analysis of the inquiries to understand their intent. The determination unit determines the category based on the analysis performed by the analysis unit. The determination unit determines the appropriate category based on the analysis results. Based on the analysis results of the inquiries, the determination unit automatically distributes them to pre-set categories. The forwarding unit forwards the inquiries to the appropriate personnel based on the category determined by the determination unit. The forwarding unit forwards the inquiries to the appropriate personnel via email or system notification based on the determined category. The forwarding unit quickly forwards the inquiries to the appropriate personnel to encourage appropriate action. As a result, the automated inquiry sorting system according to this embodiment can efficiently analyze the content of user inquiries and automatically sort them into the appropriate category.

[0072] The reception department receives inquiries from users. The reception department handles various inquiry methods, such as web forms and email. Specifically, web forms provide users with text boxes and options to enter their inquiries, and the inquiry is sent to the reception department when a submit button is pressed. In the case of email, the reception department receives the email when the user sends it to a specified email address. The reception department can save user input as digital data when receiving inquiries. For example, input from web forms is automatically saved to a database, and email inquiries are saved to the mail server. This allows the reception department to reliably receive user inquiries and centrally manage them as digital data. Furthermore, the reception department also has a function to send an automated response message upon receiving an inquiry. For example, immediately after a user submits an inquiry, the reception department sends an automated response message such as, "Your inquiry has been received. A representative will respond as soon as they have reviewed it." This allows the user to confirm that their inquiry has been successfully received, providing reassurance. The reception department also has a function to monitor the receipt and processing status of inquiries in real time, making it easier for system administrators to understand the status of inquiries. This allows the reception department to efficiently and reliably receive user inquiries and smoothly hand them over to the next processing step.

[0073] The analysis unit analyzes the content of inquiries received by the reception unit. The analysis unit analyzes the content of inquiries using, for example, natural language processing technology. Specifically, it uses natural language processing technology to extract keywords and perform contextual analysis of the inquiry content to understand the intent of the inquiry. For example, if a user asks, "How do I return a product?", the analysis unit extracts keywords such as "return" and "method" and understands from the context that it is an inquiry about the return procedure. The analysis unit uses AI to classify the content of inquiries and provides information to sort them into the appropriate category. The AI ​​learns from past inquiry data and can perform highly accurate analysis of new inquiries. For example, it uses machine learning algorithms to extract characteristics of the inquiry content and identify similar inquiry patterns. This allows the analysis unit to accurately grasp the intent of the inquiry content and provide the information necessary for the next processing step. Furthermore, the analysis unit can also perform sentiment analysis of the inquiry content. For example, if a user's inquiry contains anger or dissatisfaction, the analysis unit detects that emotion and indicates that it needs to be addressed as a priority. This allows the analysis unit to promote responses that take the user's emotions into consideration, contributing to improved customer satisfaction. The analysis unit can improve the overall efficiency of the system by analyzing inquiry content in real time and providing information quickly and accurately.

[0074] The classification unit determines the category based on the content analyzed by the analysis unit. For example, the classification unit determines the appropriate category based on the analysis results. Specifically, it automatically sorts the inquiry content into pre-set categories based on keywords and contextual information provided by the analysis unit. For example, an inquiry containing keywords such as "return" and "method" will be sorted into the "return procedure" category. The classification unit can improve the accuracy of category determination using AI. The AI ​​learns from past inquiry data and can determine the category of new inquiries with high accuracy. For example, it uses a machine learning algorithm to extract the characteristics of the inquiry content and automatically select the most appropriate category. This allows the classification unit to sort inquiries into categories quickly and accurately. Furthermore, the classification unit can flexibly handle inquiries that span multiple categories. For example, if an inquiry is related to multiple categories, such as "Please tell me about the return method and exchange procedure for products," the classification unit can sort it into both the "return procedure" and "exchange procedure" categories. This allows the classification unit to handle complex inquiries and sort them into the appropriate category. The classification unit stores the category determination results in a database and provides the information necessary for subsequent processing steps. This allows the classification unit to efficiently and accurately determine the category of the query content, thereby improving the overall processing efficiency of the system.

[0075] The forwarding unit forwards inquiries to the appropriate person based on the category determined by the judgment unit. For example, the forwarding unit forwards emails or sends in-system notifications to the appropriate person based on the determined category. Specifically, based on the category information provided by the judgment unit, it automatically selects the appropriate person and quickly forwards the inquiry. For example, an inquiry categorized as "return procedures" is forwarded via email to the person in charge of returns procedures. In addition, notifications can be displayed in real time on the person's dashboard using in-system notifications. This allows the person in charge to quickly check the inquiry and take appropriate action. The forwarding unit can also consider the priority of the inquiry when forwarding it. For example, if sentiment analysis shows that the user has strong dissatisfaction, the forwarding unit will prioritize forwarding that inquiry to the appropriate person to encourage a quick response. Furthermore, the forwarding unit can save the forwarding history to a database and track the processing status of inquiries. This allows system administrators to understand the processing status of each inquiry in real time and adjust responses as needed. The forwarding unit can reliably transmit information using multiple communication methods. For example, in addition to email notifications, it can use SMS and chat tools in combination to ensure that important information reaches the appropriate person. This allows the forwarding unit to quickly and reliably transfer inquiries to the appropriate person in charge, facilitating a proper response.

[0076] The automated inquiry routing system includes a learning unit that learns from the analysis results of inquiry content and improves accuracy. The learning unit uses AI to learn from the analysis results of inquiry content and improve analysis accuracy. For example, the learning unit learns from the analysis results of inquiry content using a machine learning algorithm. The learning unit uses a dataset of inquiry content to train the AI ​​to improve analysis accuracy. The learning unit periodically learns from the analysis results of inquiry content and improves analysis accuracy. This improves the accuracy of inquiry content analysis. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input the analysis results of inquiry content into the AI, and the AI ​​can learn to improve analysis accuracy.

[0077] The automated inquiry distribution system includes a means-response unit that handles various inquiry methods such as web forms and email. The means-response unit provides functions to handle various inquiry methods. For example, the means-response unit has a function to receive inquiries via web forms. The means-response unit also has a function to receive inquiries via email. The means-response unit can take appropriate action depending on the inquiry method. This allows it to handle various inquiry methods. Some or all of the above-described processing in the means-response unit may be performed using AI or not. For example, the means-response unit can input the inquiry content received via web form or email into AI, and the AI ​​can perform analysis to take appropriate action.

[0078] The automated inquiry routing system includes a recording unit that records the results of the analysis of inquiry content. The recording unit stores the analysis results of the inquiry content in a database. The recording unit records the analysis results of the inquiry content as digital data, for example. The recording unit saves the analysis results of the inquiry content so that they can be referenced later. The recording unit can also periodically back up the analysis results of the inquiry content. This allows the analysis results of the inquiry content to be referenced later by recording them. Some or all of the above processing in the recording unit may be performed using AI, or it may be performed without using AI. For example, the recording unit can perform recording by inputting the analysis results of the inquiry content into AI, which then saves them to the database.

[0079] The reception department can handle inquiries via various means, such as web forms and email. For example, the reception department has the functionality to receive inquiries via web forms. The reception department also has the functionality to receive inquiries via email. The reception department can provide appropriate responses depending on the method of inquiry. This allows it to handle a variety of inquiry methods. Some or all of the above-mentioned processes in the reception department may be performed using AI, or they may not. For example, the reception department can input the inquiry content received via web form or email into AI, and the AI ​​can perform analysis to provide an appropriate response.

[0080] The analysis unit can analyze the query content using natural language processing techniques. For example, the analysis unit can extract keywords from the query content using morphological analysis. The analysis unit can also understand the context of the query content using grammatical analysis. The analysis unit can also grasp the intent of the query content using semantic analysis. As a result, the accuracy of query content analysis is improved by using natural language processing techniques. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can input the query content into AI, and the AI ​​can grasp the intent of the query content by performing analysis using natural language processing techniques.

[0081] The judgment unit can determine the appropriate category based on the analysis results. For example, the judgment unit can automatically assign the content to a pre-set category based on the analysis results. The judgment unit can also select the optimal category based on the analysis results of the inquiry content. The judgment unit can understand the intent of the inquiry content and assign it to the appropriate category. This allows it to determine the appropriate category based on the analysis results. Some or all of the above processes in the judgment unit may be performed using AI or not. For example, the judgment unit can assign the inquiry content to the appropriate category by inputting the analysis results into AI, which then selects the optimal category.

[0082] The forwarding unit can forward inquiries to the appropriate person based on the determined category. For example, the forwarding unit can forward emails to the appropriate person based on the determined category. The forwarding unit can also forward inquiries to the appropriate person using in-system notifications. The forwarding unit can quickly forward inquiries to the appropriate person and prompt appropriate action. This allows forwarding to the appropriate person based on the determined category. Some or all of the above processes in the forwarding unit may be performed using AI or not. For example, the forwarding unit can input the inquiry content into the AI ​​based on the determined category, and the AI ​​can forward it to the appropriate person, enabling a quick response.

[0083] The reception desk can estimate the user's emotions and adjust the input method for the inquiry based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest a customizable input method. If the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of the inquiry. This allows the input method for the inquiry to be adjusted according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines 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 or not. For example, the reception desk can input user emotion data into a generative AI, which can estimate the emotions and adjust the input method to provide an appropriate response according to the user's emotions.

[0084] The reception desk can analyze a user's past inquiry history and suggest the most suitable input method. For example, the reception desk can automatically display as suggestions the type of inquiry the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest the type of inquiry the user will use at a specific time of day based on their past inquiry history. This allows the reception desk to suggest the most suitable input method based on the user's past inquiry history. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can improve user convenience by inputting the user's past inquiry history into AI, which then suggests the most suitable input method.

[0085] The reception desk can filter inquiries based on the user's current situation and areas of interest when the user enters their inquiry. For example, when the user enters their current situation, the reception desk can prioritize displaying relevant inquiries. The reception desk can also automatically filter relevant inquiries based on the user's areas of interest. If the user is in a specific situation, the reception desk can prioritize displaying inquiries related to that situation. This allows for filtering inquiries based on the user's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can improve user convenience by inputting the user's current situation and areas of interest into the AI, which then filters relevant inquiries.

[0086] The reception desk can estimate the user's emotions and determine the priority of inquiries based on those emotions. For example, if a user has an urgent inquiry, the reception desk will prioritize that inquiry. If the user is relaxed, the reception desk can also process the inquiry with the normal priority. If the user is stressed, the reception desk can process the inquiry quickly. This allows the priority of inquiries to be determined according to 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 reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI, which will estimate the emotions and determine the priority, thereby providing an appropriate response according to the user's emotions.

[0087] The reception desk can prioritize receiving inquiries that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize receiving inquiries related to that region. The reception desk can also automatically filter relevant inquiries based on the user's geographical location. If the user is on the move, the reception desk can also prioritize receiving the most relevant inquiries based on their current location. This allows for the priority of receiving inquiries that are highly relevant based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can improve user convenience by inputting the user's geographical location into AI, which then filters relevant inquiries.

[0088] The reception desk can analyze the user's social media activity when receiving inquiries and accept relevant content. For example, the reception desk can analyze the user's current interests from their social media activity and prioritize accepting relevant inquiries. The reception desk can also automatically filter relevant inquiries based on what the user has mentioned on social media. The reception desk can also analyze the user's social media activity and suggest the most appropriate inquiry. This allows the reception desk to accept relevant content based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can improve user convenience by inputting the user's social media activity into AI, which can then filter relevant inquiries.

[0089] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit provides a simple and easy-to-understand analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is in a hurry, the analysis unit can also provide a concise analysis result. This allows the presentation of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a 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 input user emotion data into a generative AI, which will estimate the emotions and adjust the presentation to provide an appropriate response according to the user's emotions.

[0090] The analysis unit can adjust the level of detail of the analysis based on the importance of the inquiry content during the analysis. For example, the analysis unit performs a detailed analysis for inquiries of high importance. The analysis unit can also perform a simplified analysis for inquiries of low importance. The analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the inquiry content. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the inquiry content into the AI, and the AI ​​can adjust the level of detail of the analysis to provide an appropriate response according to the inquiry content.

[0091] The analysis unit can apply different analysis algorithms depending on the category of the inquiry during analysis. For example, the analysis unit can apply a specialized analysis algorithm to technical inquiries. The analysis unit can also apply a standard analysis algorithm to general inquiries. The analysis unit can select the optimal analysis algorithm according to the category of the inquiry. This allows the optimal analysis algorithm to be applied according to the category of the inquiry. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the category of the inquiry into the AI, and the AI ​​can select the optimal analysis algorithm to provide an appropriate response according to the inquiry.

[0092] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. If the user is excited, the analysis unit can also provide a visually stimulating analysis. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a 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 input user emotion data into a generative AI, which will estimate the emotions and adjust the length of the analysis to provide an appropriate response according to the user's emotions.

[0093] The analysis unit can determine the priority of analysis based on when the inquiry content was submitted. For example, the analysis unit can prioritize the analysis of urgent inquiries. The analysis unit can also analyze regular inquiries with a standard priority. The analysis unit can dynamically adjust the priority of analysis according to when the inquiry content was submitted. This allows the analysis priority to be determined according to when the inquiry content was submitted. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the submission date of the inquiry content into the AI, and the AI ​​can determine the priority of analysis, thereby enabling appropriate responses according to the inquiry content.

[0094] The analysis unit can adjust the order of analysis based on the relevance of the inquiry content during analysis. For example, the analysis unit can prioritize the analysis of highly relevant inquiry content. The analysis unit can also postpone the analysis of less relevant inquiry content. The analysis unit can dynamically adjust the order of analysis according to the relevance of the inquiry content. This allows the order of analysis to be adjusted according to the relevance of the inquiry content. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the inquiry content into the AI, and the AI ​​can adjust the order of analysis to provide an appropriate response according to the inquiry content.

[0095] The judgment unit can estimate the user's emotions and adjust the category determination criteria based on the estimated emotions. For example, if the user is nervous, the judgment unit can apply simple category determination criteria. If the user is relaxed, the judgment unit can also apply detailed category determination criteria. If the user is in a hurry, the judgment unit can also apply rapid category determination criteria. This allows the category determination criteria to be adjusted according to 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 judgment unit may be performed using AI or not. For example, the judgment unit can input user emotion data into a generative AI, which will estimate the emotions and adjust the category determination criteria to provide an appropriate response according to the user's emotions.

[0096] The judgment unit can improve the accuracy of its judgment by considering the interrelationships of the inquiry content during the judgment process. For example, if multiple inquiry contents are related, the judgment unit will integrate them and make a judgment. The judgment unit can also analyze the interrelationships of the inquiry contents to make a highly accurate judgment. The judgment unit can determine the optimal category based on the interrelationships of the inquiry contents. This improves the accuracy of the judgment by considering the interrelationships of the inquiry contents. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input the interrelationships of the inquiry contents into the AI, and the AI ​​can determine the optimal category, thereby enabling appropriate responses according to the inquiry content.

[0097] The judgment unit can make a judgment while considering the attribute information of the person submitting the inquiry. For example, the judgment unit can determine the optimal category based on the submitter's attribute information (age, gender, etc.). The judgment unit can also analyze the submitter's attribute information to make a highly accurate judgment. The judgment unit can also adjust the criteria for category determination based on the submitter's attribute information. This allows for a highly accurate judgment by considering the attribute information of the person submitting the inquiry. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input the submitter's attribute information into AI, and the AI ​​can determine the optimal category, enabling appropriate responses according to the inquiry.

[0098] The judgment unit can estimate the user's emotions and adjust the display order of the judgment results based on the estimated emotions. For example, if the user is nervous, the judgment unit provides a simple and highly visible display order. If the user is relaxed, the judgment unit can also provide a display order that includes detailed information. If the user is in a hurry, the judgment unit can also provide a display order that gets straight to the point. This allows the display order of the judgment results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input user emotion data into a generative AI, which will estimate the emotions and adjust the display order to provide an appropriate response according to the user's emotions.

[0099] The judgment unit can make a judgment while considering the geographical distribution of the inquiry content. For example, the judgment unit can determine the category for each region for inquiries related to a specific region. The judgment unit can also analyze the geographical distribution of the inquiry content and determine the optimal category. The judgment unit can also adjust the criteria for category determination based on the geographical distribution. This allows for highly accurate judgments by considering the geographical distribution of the inquiry content. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input the geographical distribution of the inquiry content into the AI, and the AI ​​can determine the optimal category, enabling appropriate responses according to the inquiry content.

[0100] The judgment unit can improve the accuracy of its judgment by referring to relevant literature related to the inquiry content during the judgment process. For example, the judgment unit can refer to relevant literature to accurately determine the category of the inquiry content. The judgment unit can also analyze literature related to the inquiry content and determine the optimal category. The judgment unit can also adjust the criteria for category determination based on the relevant literature. This allows for highly accurate determination of the category of the inquiry content by referring to relevant literature. Some or all of the above processes in the judgment unit may be performed using AI or not. For example, the judgment unit can input relevant literature into AI, and the AI ​​can determine the optimal category, enabling it to take appropriate action according to the inquiry content.

[0101] The transfer unit can estimate the user's emotions and adjust the transfer method based on the estimated emotions. For example, if the user has an urgent inquiry, the transfer unit will quickly transfer them to the appropriate person. If the user is relaxed, the transfer unit can also transfer them to the appropriate person using the normal transfer method. If the user is stressed, the transfer unit can also transfer them to the appropriate person quickly and carefully. This allows the transfer method to be adjusted according to 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 transfer unit may be performed using AI or not. For example, the transfer unit can input user emotion data into a generative AI, which will estimate the emotions and adjust the transfer method to provide an appropriate response according to the user's emotions.

[0102] The forwarding unit can determine the forwarding priority based on the importance of the inquiry content during forwarding. For example, the forwarding unit can prioritize forwarding inquiries with high importance. The forwarding unit can also forward inquiries with low importance using the normal priority. The forwarding unit can dynamically adjust the forwarding priority according to the importance of the inquiry content. This allows the forwarding priority to be determined according to the importance of the inquiry content. Some or all of the above processing in the forwarding unit may be performed using AI or not. For example, the forwarding unit can input the importance of the inquiry content into the AI, and the AI ​​can determine the forwarding priority, thereby enabling appropriate responses according to the inquiry content.

[0103] The forwarding unit can apply different forwarding methods depending on the category of the inquiry during forwarding. For example, the forwarding unit forwards technical inquiries to specialists. The forwarding unit can also apply standard forwarding methods to general inquiries. The forwarding unit can select the optimal forwarding method depending on the category of the inquiry. This ensures that the most appropriate forwarding method is applied according to the category of the inquiry. Some or all of the above processing in the forwarding unit may be performed using AI or not. For example, the forwarding unit can input the category of the inquiry into the AI, and the AI ​​can select the optimal forwarding method to provide an appropriate response according to the inquiry.

[0104] The transfer unit can estimate the user's emotions and adjust the timing of the transfer based on the estimated emotions. For example, if the user has an urgent inquiry, the transfer unit will transfer the call quickly. If the user is relaxed, the transfer unit can transfer the call at a normal time. If the user is stressed, the transfer unit can transfer the call quickly and carefully. This allows the timing of the transfer to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the transfer unit may be performed using AI or not. For example, the transfer unit can input user emotion data into a generative AI, which will estimate the emotions and adjust the timing of the transfer, thereby providing an appropriate response according to the user's emotions.

[0105] The forwarding unit can adjust the forwarding order based on when the inquiry content was submitted. For example, the forwarding unit can prioritize forwarding urgent inquiries. The forwarding unit can also forward regular inquiries in a standard order. The forwarding unit can dynamically adjust the forwarding order according to when the inquiry content was submitted. This allows the forwarding order to be adjusted according to when the inquiry content was submitted. Some or all of the above processing in the forwarding unit may be performed using AI or not. For example, the forwarding unit can input the submission date of the inquiry content into the AI, and the AI ​​can adjust the forwarding order to provide an appropriate response according to the inquiry content.

[0106] The forwarding unit can select a forwarding method based on the relevance of the inquiry content during forwarding. For example, the forwarding unit can select the optimal forwarding method for highly relevant inquiries. The forwarding unit can also apply a standard forwarding method to less relevant inquiries. The forwarding unit can dynamically adjust the forwarding method according to the relevance of the inquiry content. This allows for the selection of the optimal forwarding method according to the relevance of the inquiry content. Some or all of the above processing in the forwarding unit may be performed using AI or not. For example, the forwarding unit can input the relevance of the inquiry content into the AI, and the AI ​​can select the optimal forwarding method, thereby enabling appropriate responses according to the inquiry content.

[0107] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is nervous, the learning unit can select simple training data. If the user is relaxed, the learning unit can also select detailed training data. If the user is in a hurry, the learning unit can also select data that allows for rapid learning. This improves the accuracy of learning by selecting training data according to 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 learning unit may be performed using AI or not. For example, the learning unit can input user emotion data into a generative AI, which will estimate the emotions and select training data, thereby enabling appropriate responses according to the user's emotions.

[0108] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can analyze past learning data and select the optimal learning algorithm. The learning unit can also dynamically adjust the learning algorithm based on past learning data. The learning unit can improve the accuracy of the learning algorithm by referring to past learning data. As a result, the accuracy of the learning algorithm is improved by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI or not. For example, the learning unit can improve the accuracy of learning by inputting past learning data into AI, which then selects the optimal learning algorithm.

[0109] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is nervous, the learning unit can set a lower learning frequency. If the user is relaxed, the learning unit can also set a higher learning frequency. If the user is in a hurry, the learning unit can perform learning quickly. This allows the learning frequency to be adjusted according to 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 learning unit may be performed using AI or not. For example, the learning unit can input user emotion data into a generative AI, which will estimate the emotions and adjust the learning frequency to provide an appropriate response according to the user's emotions.

[0110] The learning unit can weight the training data based on the submission timing of inquiries during training. For example, the learning unit can assign a higher weight to urgent inquiries. The learning unit can also assign a standard weight to regular inquiries. The learning unit can dynamically adjust the weighting of the training data according to the submission timing of inquiries. This improves the accuracy of training by weighting the training data according to the submission timing of inquiries. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input the submission timing of inquiries into the AI, and the AI ​​can adjust the weighting of the training data to improve the accuracy of training.

[0111] The response unit can estimate the user's emotions and select appropriate response measures based on the estimated emotions. For example, if the user is nervous, the response unit may select a simple response measure. If the user is relaxed, the response unit may also select a more detailed response measure. If the user is in a hurry, the response unit may also select a measure that allows for a quick response. This enables appropriate responses by selecting response measures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the response unit may be performed using AI or not. For example, the response unit can input user emotion data into a generative AI, which will estimate the emotions and select appropriate response measures to match the user's emotions.

[0112] The means response unit can select the optimal means by referring to the user's past inquiry means history when selecting a response means. For example, the means response unit may prioritize selecting inquiry means previously used by the user. The means response unit can also analyze the user's past inquiry means history and select the optimal means. The means response unit can dynamically adjust the response means based on the user's past inquiry means history. This allows the optimal means to be selected based on the user's past inquiry means history. Some or all of the above processing in the means response unit may be performed using AI or not. For example, the means response unit can improve user convenience by inputting the user's past inquiry means history into AI, which then selects the optimal means.

[0113] The response unit can estimate the user's emotions and determine the priority of response measures based on the estimated emotions. For example, if the user has an urgent inquiry, the response unit will select a priority response measure. If the user is relaxed, the response unit can also select a response measure with normal priority. If the user is stressed, the response unit can also select a response measure that is quick and courteous. This allows the priority of response measures to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input user emotion data into a generative AI, which will estimate the emotions and determine the priority of response measures, thereby providing an appropriate response according to the user's emotions.

[0114] The means-response unit can select the optimal means by considering the user's device information when selecting a means to respond to. For example, if the user is using a smartphone, the means-response unit will select the optimal means to respond to a smartphone. If the user is using a tablet, the means-response unit can also select the optimal means to respond to a tablet. If the user is using a desktop, the means-response unit can also select the optimal means to respond to a desktop. This allows the optimal means to be selected based on the user's device information. Some or all of the above processing in the means-response unit may be performed using AI or not. For example, the means-response unit can input the user's device information into AI, and the AI ​​can select the optimal means to improve user convenience.

[0115] The recording unit can estimate the user's emotions and adjust the recording method based on the estimated emotions. For example, if the user is nervous, the recording unit can provide a simple recording method. If the user is relaxed, the recording unit can also provide a detailed recording method. If the user is in a hurry, the recording unit can also provide a method that allows for quick recording. This allows the recording method to be adjusted according to 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 recording unit may be performed using AI or not. For example, the recording unit can input user emotion data into a generative AI, which will estimate the emotions and adjust the recording method to provide an appropriate response according to the user's emotions.

[0116] The recording unit can adjust the level of detail in the recording based on the importance of the inquiry. For example, the recording unit will record detailed information for high-importance inquiries. The recording unit can also record simplified information for low-importance inquiries. The recording unit can dynamically adjust the level of detail in the recording according to the importance of the inquiry. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the importance of the inquiry into the AI, and the AI ​​can adjust the level of detail in the recording to provide an appropriate response to the inquiry.

[0117] The recording unit can estimate the user's emotions and determine the priority of recordings based on the estimated emotions. For example, if the user has an urgent inquiry, the recording unit will prioritize recording. If the user is relaxed, the recording unit can record with the normal priority. If the user is stressed, the recording unit can record quickly and carefully. This allows the recording unit to determine the priority of recordings according to 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 recording unit may be performed using AI or not. For example, the recording unit can input user emotion data into a generative AI, which will estimate the emotions and determine the priority of recordings, thereby enabling appropriate responses according to the user's emotions.

[0118] The recording unit can adjust the order of recordings based on when the inquiry content was submitted. For example, the recording unit prioritizes recording urgent inquiries. The recording unit can also record regular inquiries in a standard order. The recording unit can dynamically adjust the order of recordings according to when the inquiry content was submitted. This allows the order of recordings to be adjusted according to when the inquiry content was submitted. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the submission date of the inquiry content into the AI, and the AI ​​can adjust the order of recordings to provide an appropriate response according to the inquiry content.

[0119] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0120] An automated inquiry routing system can estimate a user's emotions and dynamically adjust the priority of inquiries based on those emotions. For example, if a user has an urgent inquiry, it will be processed with the highest priority. If the user is relaxed, it can be processed with the normal priority. If the user is stressed, it can be processed quickly. This allows for a quick and appropriate response by adjusting the priority of inquiries according to the user's emotions. Emotion estimation is achieved using 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 at the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI, which will estimate the emotion and adjust the priority to provide an appropriate response according to the user's emotions.

[0121] The automated inquiry routing system can analyze a user's past inquiry history and suggest the most appropriate category. For example, it can prioritize displaying categories that the user has frequently inquired about in the past. It can also suggest relevant categories based on keywords the user has used in the past. It can even predict and suggest categories to be used at specific times of the day based on the user's past inquiry history. This improves user convenience by suggesting the most appropriate category based on the user's past inquiry history. Some or all of the above processing at the reception desk may be performed using AI or not. For example, the reception desk can input the user's past inquiry history into the AI, and the AI ​​can suggest the most appropriate category, thereby improving user convenience.

[0122] The automated inquiry routing system can prioritize suggesting highly relevant categories by considering the user's geographical location. For example, if a user is in a specific region, categories related to that region will be displayed preferentially. It can also automatically filter relevant categories based on the user's geographical location. If a user is on the move, it can prioritize suggesting the most suitable categories based on their current location. This improves user convenience by suggesting highly relevant categories based on the user's geographical location. Some or all of the above processing at the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location into the AI, which can then filter relevant categories to improve user convenience.

[0123] The automated inquiry routing system can analyze a user's social media activity and suggest relevant categories. For example, it can analyze a user's current interests from their social media activity and prioritize suggesting relevant categories. It can also automatically filter relevant categories based on what the user has mentioned on social media. It can analyze a user's social media activity and suggest the most suitable category. This improves user convenience by suggesting relevant categories based on the user's social media activity. Some or all of the above processing at the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity into the AI, which can then filter relevant categories to improve user convenience.

[0124] An automated inquiry routing system can estimate a user's emotions and adjust the way they input their inquiries based on that estimation. For example, if a user is stressed, it can provide a simple interface and minimize the input steps. If a user is relaxed, it can offer detailed input options and suggest customizable input methods. If a user is in a hurry, it can prioritize voice input to allow for quick input of their inquiries. This improves user convenience by adjusting the way inquiries are entered according to the user's emotions. Emotion estimation is achieved 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 at the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI, which can estimate the emotion and adjust the input method to provide an appropriate response according to the user's emotions.

[0125] The automated inquiry routing system 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, it can provide simple and easy-to-read analysis results. If the user is relaxed, it can also provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results. This improves user convenience by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved 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 or not. For example, the analysis unit can input user emotion data into the generative AI, which will estimate the emotions and adjust the display method to provide an appropriate response according to the user's emotions.

[0126] The automated inquiry routing system can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, it can provide a short, concise analysis. If the user is relaxed, it can provide a detailed analysis. If the user is excited, it can provide a visually stimulating analysis. This improves user convenience by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using 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 input user emotion data into a generative AI, which will estimate the emotions and adjust the length of the analysis to provide an appropriate response according to the user's emotions.

[0127] An automated inquiry routing system can estimate a user's emotions and adjust the category determination criteria based on those emotions. For example, if a user is nervous, a simple category determination criterion can be applied. If a user is relaxed, a more detailed category determination criterion can be applied. If a user is in a hurry, a rapid category determination criterion can be applied. This improves user convenience by adjusting the category determination criteria according to the user's emotions. Emotion estimation is achieved using 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 determination unit may be performed using AI or not. For example, the determination unit can input user emotion data into a generative AI, which estimates the emotion and adjusts the category determination criteria to provide an appropriate response according to the user's emotions.

[0128] An automated inquiry routing system can estimate a user's emotions and adjust the transfer method based on the estimated emotions. For example, if a user has an urgent inquiry, they can be quickly transferred to the appropriate person. If the user is relaxed, they can be transferred to the appropriate person using the normal transfer method. If the user is stressed, they can be transferred to the appropriate person quickly and carefully. This allows for a quick and appropriate response by adjusting the transfer method according to the user's emotions. Emotion estimation is achieved using 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 transfer unit may be performed using AI or not. For example, the transfer unit can input user emotion data into a generative AI, which will estimate the emotions and adjust the transfer method to provide an appropriate response according to the user's emotions.

[0129] The automated inquiry routing system can analyze a user's past inquiry history and suggest the optimal input method. For example, it can automatically display as suggestions the type of inquiry the user has frequently entered in the past. It can also prioritize suggesting input methods the user has used in the past (voice, text, etc.). It can also predict and suggest the type of inquiry a user will use at a specific time of day based on their past inquiry history. This improves user convenience by suggesting the optimal input method based on the user's past inquiry history. Some or all of the above processing at the reception desk may be performed using AI or not. For example, the reception desk can improve user convenience by inputting the user's past inquiry history into the AI, which then suggests the optimal input method.

[0130] The following briefly describes the processing flow for example form 2.

[0131] Step 1: The reception desk receives inquiries from users. The reception desk can handle inquiries via various means, such as web forms and email, and can save the user's input as digital data. Step 2: The analysis unit analyzes the content of the inquiry received by the reception unit. The analysis unit analyzes the content of the inquiry using, for example, natural language processing technology, extracts keywords and performs contextual analysis to understand the intent of the inquiry. Step 3: The determination unit determines the category based on the content analyzed by the analysis unit. The determination unit determines the appropriate category based on the analysis results and automatically assigns the inquiry content to a pre-set category based on the analysis results. Step 4: The forwarding unit forwards the inquiry to the appropriate person based on the category determined by the determination unit. For example, the forwarding unit forwards the inquiry to the appropriate person via email or system notification based on the determined category, promptly forwarding the inquiry to the appropriate person and encouraging appropriate action.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the reception unit, analysis unit, determination unit, transfer unit, learning unit, means-response unit, and recording unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives inquiries from users. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes the inquiry content. The determination unit is implemented by the identification processing unit 290 of the data processing device 12 and determines a category based on the analysis results. The transfer unit is implemented by the communication I / F 44 of the smart device 14 and transfers the inquiry to the person in charge based on the determined category. The learning unit is implemented by the identification processing unit 290 of the data processing device 12 and learns the analysis results of the inquiry content to improve accuracy. The means-response unit is implemented by the control unit 46A of the smart device 14 and responds to inquiry means such as web forms and email. The recording unit is implemented, for example, by the database 24 of the data processing device 12, and records the results of the analysis of the query content. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0136] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the reception unit, analysis unit, determination unit, transfer unit, learning unit, means-response unit, and recording unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives inquiries from the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the inquiry content. The determination unit is implemented by the identification processing unit 290 of the data processing unit 12 and determines the category based on the analysis results. The transfer unit is implemented by the communication I / F 44 of the smart glasses 214 and transfers the inquiry to the person in charge based on the determined category. The learning unit is implemented by the identification processing unit 290 of the data processing unit 12 and learns the analysis results of the inquiry content to improve accuracy. The means-response unit is implemented by the control unit 46A of the smart glasses 214 and responds to inquiry means such as web forms and email. The recording unit is implemented, for example, by the database 24 of the data processing device 12, and records the results of the analysis of the query content. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0152] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Each of the multiple elements described above, including the reception unit, analysis unit, determination unit, transfer unit, learning unit, means-response unit, and recording 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 is implemented by the microphone 238 of the headset terminal 314 and receives inquiries from the user. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the inquiry content. The determination unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and determines a category based on the analysis results. The transfer unit is implemented by, for example, the communication I / F 44 of the headset terminal 314 and transfers the inquiry to the person in charge based on the determined category. The learning unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and learns the analysis results of the inquiry content to improve accuracy. The means-response unit is implemented by, for example, the control unit 46A of the headset terminal 314 and responds to inquiry means such as web forms and email. The recording unit is implemented, for example, by the database 24 of the data processing device 12, and records the results of the analysis of the query content. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0168] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.).

[0181] 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.

[0182] 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.

[0183] 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.

[0184] Each of the multiple elements described above, including the reception unit, analysis unit, determination unit, transfer unit, learning unit, means-response unit, and recording unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives inquiries from users. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the inquiry content. The determination unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and determines a category based on the analysis results. The transfer unit is implemented by, for example, the communication I / F 44 of the robot 414 and transfers the inquiry to the person in charge based on the determined category. The learning unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and learns the analysis results of the inquiry content to improve accuracy. The means-response unit is implemented by, for example, the control unit 46A of the robot 414 and responds to inquiry means such as web forms and email. The recording unit is implemented, for example, by the database 24 of the data processing device 12, and records the results of the analysis of the query content. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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."

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] (Note 1) The reception department handles inquiries from users, An analysis unit that analyzes the content of inquiries received by the aforementioned reception unit, A determination unit that determines a category based on the content analyzed by the analysis unit, The system includes a transfer unit that transfers information to the person in charge based on the category determined by the determination unit. A system characterized by the following features. (Note 2) It includes a learning unit that learns from the analysis results of the inquiry content and improves accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 3) It is equipped with a means-response section that supports inquiry methods such as web forms and email. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a recording unit for recording the results of the analysis of the inquiry content. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is Supports inquiry methods such as web forms and email. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, The query content is analyzed using natural language processing technology. The system described in Appendix 1, characterized by the features described herein. (Note 7) The determination unit, Determine the appropriate category based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 8) The transfer unit is, Transfer to the appropriate person based on the determined category. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and adjusts the way the inquiry is entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is We analyze the user's past inquiry history and suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users enter their inquiry details, the system filters them based on their current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the inquiries it receives based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When receiving inquiries, the system prioritizes receiving inquiries that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is When receiving an inquiry, the system analyzes the user's social media activity and accepts relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the inquiry. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the inquiry. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on when the inquiry was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the query content. The system described in Appendix 1, characterized by the features described herein. (Note 21) The determination unit, The system estimates the user's emotions and adjusts the category determination criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The determination unit, When making a decision, we improve the accuracy of the decision by considering the interrelationships between the inquiry details. The system described in Appendix 1, characterized by the features described herein. (Note 23) The determination unit, When making a decision, the attribute information of the person who submitted the inquiry will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The determination unit, The system estimates the user's emotions and adjusts the display order of the judgment results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The determination unit, When making a determination, the geographical distribution of the inquiry content is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 26) The determination unit, When making a determination, we refer to relevant literature related to the inquiry to improve the accuracy of the determination. The system described in Appendix 1, characterized by the features described herein. (Note 27) The transfer unit is, It estimates the user's emotions and adjusts the transfer method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The transfer unit is, When forwarding an inquiry, the priority of the forwarding process is determined based on the importance of the inquiry content. The system described in Appendix 1, characterized by the features described herein. (Note 29) The transfer unit is, When forwarding, different forwarding methods are applied depending on the category of the inquiry. The system described in Appendix 1, characterized by the features described herein. (Note 30) The transfer unit is, It estimates the user's emotions and adjusts the timing of transfers based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The transfer unit is, When forwarding inquiries, the order of forwarding will be adjusted based on when the inquiry was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 32) The transfer unit is, When forwarding an inquiry, the forwarding method is selected based on the relevance of the inquiry content. The system described in Appendix 1, characterized by the features described herein. (Note 33) 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 34) 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 35) 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 36) The aforementioned learning unit, During training, the training data is weighted based on when the inquiry was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 37) The means corresponding section is, The system estimates the user's emotions and selects appropriate responses based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The means corresponding section is, When selecting a response method, the most suitable method is selected by referring to the user's past inquiry history. The system described in Appendix 1, characterized by the features described herein. (Note 39) The means corresponding section is, The system estimates the user's emotions and prioritizes appropriate responses based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The means corresponding section is, When selecting a solution, the optimal solution will be chosen by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned recording unit is The system estimates the user's emotions and adjusts the recording method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned recording unit is When recording, adjust the level of detail in the record based on the importance of the inquiry. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned recording unit is The system estimates the user's emotions and prioritizes recordings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned recording unit is When recording, adjust the order of records based on when the inquiry was submitted. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0204] 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. The reception department handles inquiries from users, An analysis unit that analyzes the content of inquiries received by the aforementioned reception unit, A determination unit that determines a category based on the content analyzed by the analysis unit, The system includes a transfer unit that transfers information to the person in charge based on the category determined by the determination unit. A system characterized by the following features.

2. It includes a learning unit that learns from the analysis results of the inquiry content and improves accuracy. The system according to feature 1.

3. It is equipped with a means-response section that supports inquiry methods such as web forms and email. The system according to feature 1.

4. It includes a recording unit for recording the results of the analysis of the inquiry content. The system according to feature 1.

5. The aforementioned reception unit is Supports inquiry methods such as web forms and email. The system according to feature 1.

6. The aforementioned analysis unit, The query content is analyzed using natural language processing technology. The system according to feature 1.

7. The determination unit, Determine the appropriate category based on the analysis results. The system according to feature 1.

8. The transfer unit is, Transfer to the appropriate person based on the determined category. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the user's emotions and adjusts the way the inquiry is entered based on those estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is We analyze the user's past inquiry history and suggest the optimal input method. The system according to feature 1.

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