system

Abstract

The system according to this embodiment aims to create customized materials based on customer inquiries. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a creation unit. The collection unit collects customer inquiries. The analysis unit analyzes the data collected by the collection unit and determines the characteristics of the customer's needs. The creation unit creates customized materials based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, 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, it has not been sufficiently done to create individualized materials based on the content of customer inquiries, and there is room for improvement.

[0005] The system according to the embodiment aims to create customized materials based on the content of customer inquiries.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a creation unit. The collection unit collects the content of customer inquiries. The analysis unit analyzes the data collected by the collection unit and determines the needs characteristics of the customers. The creation unit creates customized materials based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can create customized materials based on customer inquiries. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The customer satisfaction improvement system according to an embodiment of the present invention is a system that enhances customer satisfaction by collecting customer inquiries received by a company, analyzing the characteristics of the customer's needs, and creating customized materials. The customer satisfaction improvement system improves customer satisfaction by collecting and analyzing customer inquiries and creating customized materials. For example, when a customer makes an inquiry to a company by phone or email, the customer satisfaction improvement system collects the content of that inquiry. The collected data includes which service the customer inquired about and what problems or questions they have. Next, the customer satisfaction improvement system analyzes the collected data and determines the characteristics of the customer's needs. For example, if a customer is interested in a particular service, it suggests benefits and usage methods related to that service. Also, if a customer is using multiple services, it can suggest how to use those services in combination. Finally, the customer satisfaction improvement system creates customized materials based on the analysis results. For example, if a customer is seeking a detailed explanation of a particular service, it creates materials that explain how to use that service and its benefits in detail. Also, if a customer is using multiple services, it can create materials that explain how to use those services in combination. The created materials can be sent by email or printed and mailed. This makes it easier for customers to understand the best way to use the service and the benefits for them, thereby improving their satisfaction. For example, when a customer inquires about a specific service, receiving materials that explain the benefits and usage of that service in detail makes it easier for them to understand how to use the service. Furthermore, if a customer uses multiple services, receiving materials that explain how to use those services in combination allows them to understand how to use them more effectively. Thus, a customer satisfaction improvement system can enhance customer satisfaction by collecting and analyzing customer inquiries and creating customized materials.

[0029] The customer satisfaction improvement system according to this embodiment comprises a collection unit, an analysis unit, and a creation unit. The collection unit collects customer inquiries. Customer inquiries include, for example, questions about products and complaints about services, but are not limited to these examples. The collection unit collects the content of inquiries made by customers to the company, for example, by telephone or email. The collection unit can collect inquiry content using telephone recordings or email text analysis. For example, when a customer makes an inquiry by telephone, the collection unit records the content of the call and converts it into text data. The collection unit can also analyze the content of an email when a customer makes an inquiry and save it as text data. The analysis unit analyzes the data collected by the collection unit and determines the characteristics of customer needs. The analysis unit analyzes the collected data based on, for example, the algorithm used and the accuracy of the analysis. For example, the analysis unit uses natural language processing technology to analyze customer inquiries and determine the characteristics of customer needs. The analysis unit can also use machine learning algorithms to analyze patterns in customer inquiries and determine the characteristics of customer needs. For example, the analysis department clusters customer inquiries and assigns specific need characteristics to each cluster. The creation department creates customized materials based on the analysis results obtained by the analysis department. The creation department can customize materials based on customer attributes or specific needs. For example, if a customer requests a detailed explanation of a particular service, the creation department can create materials that explain how to use that service and its benefits in detail. The creation department can also create materials that explain how to use multiple services in combination if the customer is using multiple services. The creation department can send the created materials by email or print them out and mail them. For example, the creation department can attach the created materials to an email in PDF format and send them. Alternatively, the creation department can print the created materials and mail them to the customer. In this way, the customer satisfaction improvement system according to this embodiment can improve customer satisfaction by collecting and analyzing customer inquiries and creating customized materials.

[0030] The collection unit collects customer inquiries. These inquiries include, but are not limited to, product-related questions and service-related complaints. The collection unit collects inquiries made by customers, for example, by phone or email. Specifically, it can collect inquiry content using phone recordings and email text analysis. For example, when a customer makes an inquiry by phone, the collection unit records the conversation and converts it into text data using speech recognition technology. This speech recognition technology can be pre-trained with specific industry terms and product names to accurately transcribe customer speech and minimize misrecognition. The collection unit can also analyze emails made by customers using natural language processing technology and save the content as text data. This analysis includes not only the email body but also the subject line and attachments, collecting information to more accurately understand the customer's intent. Furthermore, the collection unit can also receive customer inquiries through chatbots and web forms. This ensures that inquiries are collected centrally, regardless of the method used by the customer, and used for subsequent analysis and responses. The data collection unit stores this data in a secure database and uses appropriate encryption technology to protect privacy. This allows the data collection unit to efficiently collect customer inquiries through various means and smoothly provide the data to the analysis unit in the next step.

[0031] The analysis department analyzes the data collected by the data collection department to determine the characteristics of customer needs. The analysis department analyzes the collected data based, for example, on the algorithms used and the accuracy of the analysis. Specifically, it uses natural language processing technology to analyze customer inquiries and determine their characteristics. Natural language processing technology can extract keywords and phrases from text data and analyze customer intent and emotions. For example, if a customer inquires, "I don't know how to use the product," the analysis department extracts the keyword "how to use" and determines that the customer is seeking information on how to operate the product. Furthermore, machine learning algorithms can be used to analyze patterns in customer inquiries and determine customer characteristics. For example, the analysis department can cluster customer inquiries based on past inquiry data and assign specific characteristics to each cluster. This allows for a more accurate understanding of the problems customers are facing and the information they are seeking. In addition, the analysis department can combine customer attribute information (age, gender, region, etc.) and past purchase history to determine more detailed characteristics of customer needs. For example, if many inquiries come from younger customers, responses that consider the specific needs and trends of that generation are required. This allows the analysis department to analyze the collected data from multiple perspectives and determine customer needs and characteristics with high accuracy.

[0032] The creation department creates customized materials based on the analysis results obtained by the analysis department. For example, the creation department can customize materials based on customer attributes or specific needs. Specifically, if a customer requests detailed information about a particular service, the creation department will create materials that explain how to use that service and its benefits in detail. Based on the customer needs characteristics provided by the analysis department, the creation department selects the most relevant information and creates materials in a format that is easy for the customer to understand. For example, if a customer inquires about how to use a product, the creation department will create materials that include illustrated manuals and video links explaining the operating procedures. Furthermore, if a customer uses multiple services, the creation department can create materials explaining how to use those services in combination. For example, if a customer uses both service A and service B, the creation department will provide materials explaining how the two services work together and their synergistic effects. The creation department can send the created materials via email or print them out and mail them. For example, the creation department can send the created materials as PDF attachments via email. Alternatively, the creation department can print the materials and mail them to the customer. In addition, the creation department can collect customer feedback and continuously improve the content and format of the materials. For example, a survey can be used to check whether customers are satisfied with the content of the materials and whether they found them easy to understand, and the materials can be improved based on the results. This allows the creation department to provide customized materials that meet customer needs and improve customer satisfaction.

[0033] The data collection unit can collect information when customers contact a company by phone or email. For example, when a customer makes a phone call to a company, the data collection unit can record the call and convert it into text data. For example, the data collection unit can use speech recognition technology to automatically convert the call content into text data. The data collection unit can also analyze the content of emails sent by customers to a company and save them as text data. For example, the data collection unit can use natural language processing technology to analyze email content and extract important information. Furthermore, the data collection unit can collect information when customers make inquiries through a company's website. For example, the data collection unit can automatically collect the information entered into web forms and save it to a database. This allows for accurate collection of customer inquiries. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, when collecting customer inquiries, the data collection unit can use speech recognition technology or natural language processing technology to automatically analyze the inquiry content and save it as text data.

[0034] The analysis unit can analyze collected data and determine customer needs characteristics. For example, the analysis unit can use natural language processing technology to analyze customer inquiries and determine customer needs characteristics. For example, the analysis unit can use text analysis algorithms to extract important keywords from customer inquiries and determine customer needs characteristics based on those keywords. The analysis unit can also use machine learning algorithms to analyze patterns in customer inquiries and determine customer needs characteristics. For example, the analysis unit can use clustering algorithms to cluster customer inquiries and assign specific needs characteristics to each cluster. Furthermore, the analysis unit can analyze customers' past inquiry and purchase history to determine customer needs characteristics. For example, the analysis unit can retrieve customers' past inquiry and purchase history from a database and determine customer needs characteristics based on that. This allows for accurate determination of customer needs characteristics. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, when analyzing customer inquiries, the analysis unit can automatically determine customer needs characteristics using natural language processing technology or machine learning algorithms.

[0035] The creation department can create and provide customized materials to customers based on the analysis results. For example, the creation department can customize materials based on customer attributes or specific needs. For instance, if a customer requests detailed information about a particular service, the creation department can create materials explaining how to use that service and its benefits in detail. Furthermore, if a customer uses multiple services, the creation department can create materials explaining how to use those services in combination. The creation department can send the created materials via email or print them out and mail them. For example, the creation department can attach the created materials to an email in PDF format and send them. Alternatively, the creation department can print the created materials and mail them to the customer. This allows the creation department to provide customers with the most suitable materials. Some or all of the above processes in the creation department may be performed using AI, for example, or not. For example, the creation department can automatically create customized materials based on customer attributes and needs and send them via email.

[0036] The creation unit can send the created documents by email or by printing and mailing them. For example, the creation unit can attach the created documents to an email in PDF format and send them. For example, the creation unit can retrieve the customer's email address from a database and automatically send the created documents by email. Alternatively, the creation unit can print the created documents and mail them to the customer. For example, the creation unit can retrieve the customer's address from a database and automatically print and mail the created documents. This allows for the rapid provision of documents to customers. Some or all of the above processes in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can retrieve the customer's email address or address from a database and automatically send or mail the created documents.

[0037] The data collection unit can analyze a customer's past inquiry history and select the optimal data collection method. For example, the data collection unit can prioritize suggesting inquiry methods (such as phone or email) that the customer has frequently used in the past. For example, the data collection unit can retrieve a customer's past inquiry history from a database and select the optimal data collection method. The data collection unit can also select the optimal question format based on the content of the customer's past inquiries. For example, the data collection unit can analyze the content of the customer's past inquiries and suggest the optimal question format. Furthermore, the data collection unit can collect inquiry content at the optimal timing based on the customer's past inquiry history. For example, the data collection unit can analyze the customer's past inquiry history and suggest the optimal collection timing. This allows the optimal data collection method to be selected based on the customer's past inquiry history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the customer's past inquiry history into a generating AI and have the generating AI select the optimal data collection method.

[0038] The collection unit can filter inquiries based on the customer's current usage and areas of interest when collecting them. For example, the collection unit can prioritize collecting inquiries related to services the customer is currently using. For example, the collection unit can obtain the customer's current usage from a database and filter related inquiries. The collection unit can also filter related inquiries based on the customer's areas of interest. For example, the collection unit can analyze the customer's areas of interest and prioritize collecting related inquiries. Furthermore, the collection unit can collect the most suitable inquiries by considering the customer's current usage. For example, the collection unit can analyze the customer's current usage and suggest the most suitable inquiries. This allows the collection of the most suitable inquiries based on the customer's current usage and areas of interest. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the customer's current usage and areas of interest into a generating AI and have the generating AI perform the filtering.

[0039] The collection unit can prioritize collecting highly relevant inquiries by considering the customer's geographical location information when collecting inquiry content. For example, if a customer is in a specific region, the collection unit will prioritize collecting inquiries related to that region. For example, the collection unit can obtain the customer's geographical location information from GPS data and filter relevant inquiries. The collection unit can also suggest the most relevant inquiries based on the customer's geographical location information. For example, the collection unit can prioritize collecting inquiries related to the customer's current location. Furthermore, if the customer is on the move, the collection unit can prioritize collecting inquiries related to their current location. For example, the collection unit can analyze the customer's movement pattern and suggest the most relevant inquiries. This allows for the collection of the most relevant inquiries based on the customer's geographical location information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the customer's geographical location information into a generating AI and have the generating AI collect highly relevant inquiries.

[0040] The collection unit can analyze the customer's social media activity and collect relevant content when collecting inquiry information. For example, the collection unit can analyze the customer's social media posts and collect relevant inquiry information. For example, the collection unit can analyze the customer's social media account and collect the posts as text data. The collection unit can also suggest the most appropriate inquiry content based on the customer's social media activity history. For example, the collection unit can analyze the customer's social media activity history and prioritize the collection of relevant inquiry content. Furthermore, the collection unit can analyze the customer's areas of interest on social media and collect relevant inquiry content. For example, the collection unit can analyze the customer's areas of interest on social media and suggest the most appropriate inquiry content. This allows for the collection of the most appropriate inquiry content based on the customer's social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the customer's social media activity into a generating AI and have the generating AI collect relevant inquiry content.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the inquiry during the analysis. For example, the analysis unit can perform a detailed analysis on inquiries of high importance. For example, the analysis unit can analyze the customer's inquiry, determine its importance, and perform a detailed analysis. The analysis unit can also perform a concise analysis on inquiries of low importance. For example, the analysis unit can analyze the customer's inquiry, determine its importance, and perform a concise analysis. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the inquiry. For example, the analysis unit can analyze the customer's inquiry and adjust the level of detail of the analysis based on its importance. This allows for analysis at the optimal level of detail according to the importance of the inquiry. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the customer's inquiry into a generating AI and have the generating AI perform the importance determination and adjustment of the level of detail of the analysis.

[0042] 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. For instance, the analysis unit can analyze customer inquiries, determine their technical nature, and apply a specialized analysis algorithm. Furthermore, the analysis unit can apply an analysis algorithm that prioritizes customer satisfaction to service-related inquiries. For example, the analysis unit can analyze customer inquiries, determine their service nature, and apply an analysis algorithm that prioritizes customer satisfaction. In addition, the analysis unit can select the optimal analysis algorithm depending on the category of the inquiry. For example, the analysis unit can analyze customer inquiries and select the optimal analysis algorithm based on the category. This allows the analysis unit to apply the most suitable analysis algorithm depending on the category of the inquiry. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input customer inquiries into a generating AI and have the generating AI perform category determination and analysis algorithm selection.

[0043] The analysis department can determine the priority of the analysis based on when the inquiry content was submitted. For example, the analysis department can prioritize the analysis of recently submitted inquiries. For example, the analysis department can obtain the submission dates of customer inquiries from the database and prioritize the analysis of recently submitted content. The analysis department can also postpone older inquiries. For example, the analysis department can analyze the submission dates of customer inquiries and postpone older content. Furthermore, the analysis department can adjust the priority of the analysis in stages based on the submission dates. For example, the analysis department can analyze the submission dates of customer inquiries and adjust the priority in stages. This allows for analysis to be performed with the optimal priority based on the submission dates of the inquiries. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input the submission dates of customer inquiries into a generating AI and have the generating AI determine the priority.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the inquiry content during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant inquiries. For example, the analysis unit can analyze customer inquiries and prioritize the analysis of highly relevant content. The analysis unit can also postpone the analysis of less relevant inquiries. For example, the analysis unit can analyze customer inquiries and postpone the analysis of less relevant content. Furthermore, the analysis unit can adjust the order of analysis step by step based on the relevance of the inquiry content. For example, the analysis unit can analyze customer inquiries and adjust the order of analysis based on relevance. This allows for analysis to be performed in the optimal order based on the relevance of the inquiry content. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input customer inquiries into a generating AI and have the generating AI perform relevance determination and adjustment of the analysis order.

[0045] The creation unit can analyze past customer inquiries and select the optimal method for creating documents. For example, the creation unit can create optimal documents based on information previously requested by the customer. For example, the creation unit can retrieve past customer inquiries from a database and create optimal documents. The creation unit can also select the optimal document format based on past customer inquiries. For example, the creation unit can analyze past customer inquiries and propose the optimal document format. Furthermore, the creation unit can propose the optimal document content based on the customer's past inquiry history. For example, the creation unit can analyze the customer's past inquiry history and propose the optimal document content. This allows for the creation of documents in the most optimal way based on the customer's past inquiries. Some or all of the above processes in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input past customer inquiries into a generation AI and have the generation AI select the optimal document creation method.

[0046] The creation unit can customize the content of materials based on the customer's current usage when creating them. For example, the creation unit can create materials related to the services the customer is currently using. For example, the creation unit can retrieve the customer's current usage from a database and create relevant materials. The creation unit can also suggest the most suitable material content considering the customer's current usage. For example, the creation unit can analyze the customer's current usage and suggest the most suitable material content. Furthermore, the creation unit can create customized materials based on the customer's current usage. For example, the creation unit can analyze the customer's current usage and create customized materials. This allows for the creation of materials with optimal content based on the customer's current usage. Some or all of the above processes in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input the customer's current usage into a generation AI and have the generation AI perform the material customization.

[0047] The creation unit can select the optimal method for creating materials by considering the customer's geographical location information when creating materials. For example, if the customer is in a specific region, the creation unit can create materials related to that region. For example, the creation unit can obtain the customer's geographical location information from GPS data and create relevant materials. The creation unit can also select the optimal material format based on the customer's geographical location information. For example, the creation unit can create materials related to the customer's current location. Furthermore, if the customer is on the move, the creation unit can also create materials related to their current location. For example, the creation unit can analyze the customer's movement patterns and propose the optimal material format. This allows for the creation of materials in the most optimal way based on the customer's geographical location information. Some or all of the above processes in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input the customer's geographical location information into a generating AI and have the generating AI select the optimal material creation method.

[0048] The creation unit can analyze the customer's social media activity and propose content for the materials when creating them. For example, the creation unit can analyze the customer's social media posts and create relevant materials. For example, the creation unit can analyze the customer's social media accounts, collect the posts as text data, and create relevant materials. The creation unit can also propose optimal material content based on the customer's social media activity history. For example, the creation unit can analyze the customer's social media activity history and propose optimal material content. Furthermore, the creation unit can analyze the customer's areas of interest on social media and create relevant materials. For example, the creation unit can analyze the customer's areas of interest on social media and create relevant materials. This allows for the creation of materials with optimal content based on the customer's social media activity. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input the customer's social media activity into a generating AI and have the generating AI propose material content.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The customer satisfaction improvement system can also be equipped with a prediction unit. This unit predicts future inquiries based on the customer's past inquiry history and behavioral data. For example, it can analyze the types of inquiries a customer has frequently made in the past and predict what kind of inquiry they are likely to make next. It can also predict the products or services a customer is likely to purchase next based on their purchase history. Furthermore, it can predict what actions a customer is likely to take next based on their behavioral data. This allows for a proactive understanding of customer needs and appropriate responses.

[0051] The customer satisfaction improvement system can also include a personalization section. This personalization section provides individually customized services and benefits based on customer attributes and past behavioral data. For example, the personalization section can suggest optimal services and benefits based on customer attribute data such as age, gender, and place of residence. It can also provide individually customized services and benefits based on the customer's past purchase and inquiry history. Furthermore, the personalization section can analyze customer behavioral data in real time and suggest the most suitable services and benefits on the spot. This allows for the provision of optimal services to each individual customer, thereby improving satisfaction.

[0052] The customer satisfaction improvement system can also be equipped with a real-time response unit. This unit has the function of responding immediately to customer inquiries. For example, when a customer makes an inquiry by phone, the real-time response unit can provide an appropriate answer on the spot. It can also provide an immediate response when a customer makes an inquiry via chat. Furthermore, the real-time response unit can analyze the content of the customer's inquiry in real time and propose the optimal solution. This reduces customer waiting times and improves customer satisfaction.

[0053] The customer satisfaction improvement system can also be equipped with a behavior prediction unit. This unit predicts future customer behavior based on past customer behavior data. For example, it can analyze what services a customer has used in the past and predict which services they are likely to use next. It can also predict which products a customer is likely to purchase next based on their purchase history. Furthermore, it can predict which pages a customer is likely to view next based on their website browsing history. This allows for proactive understanding of customer needs and appropriate responses.

[0054] The customer satisfaction improvement system can also include a content recommendation function. This function recommends the most relevant content based on the customer's past behavior data and inquiries. For example, it can analyze pages the customer has previously viewed and inquiries they have made, and recommend relevant content. It can also recommend relevant products and services based on the customer's purchase history. Furthermore, it can recommend content based on the customer's areas of interest. This allows the system to provide customers with useful information and improve their satisfaction.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The collection department collects customer inquiries. These inquiries include questions about products and complaints about services. The collection department collects the content of inquiries made by customers to the company by phone or email. Specifically, it collects inquiry content using phone recordings and email text analysis. For example, when a customer makes an inquiry by phone, the conversation is recorded and converted into text data. Similarly, when a customer makes an inquiry by email, the content of the email is analyzed and saved as text data. Step 2: The analysis unit analyzes the data collected by the collection unit and determines the characteristics of customer needs. The analysis unit uses natural language processing technology and machine learning algorithms to analyze the content of customer inquiries. For example, it clusters the content of customer inquiries and assigns specific needs characteristics to each cluster. Step 3: The creation team creates customized materials based on the analysis results obtained by the analysis team. The creation team customizes the materials according to the customer's attributes and specific needs. For example, if a customer requests a detailed explanation of a particular service, they will create materials that explain how to use that service and its benefits in detail. Also, if a customer uses multiple services, they will create materials that explain how to use those services in combination. The created materials can be sent by email or printed and mailed.

[0057] (Example of form 2) The customer satisfaction improvement system according to an embodiment of the present invention is a system that enhances customer satisfaction by collecting customer inquiries received by a company, analyzing the characteristics of the customer's needs, and creating customized materials. The customer satisfaction improvement system improves customer satisfaction by collecting and analyzing customer inquiries and creating customized materials. For example, when a customer makes an inquiry to a company by phone or email, the customer satisfaction improvement system collects the content of that inquiry. The collected data includes which service the customer inquired about and what problems or questions they have. Next, the customer satisfaction improvement system analyzes the collected data and determines the characteristics of the customer's needs. For example, if a customer is interested in a particular service, it suggests benefits and usage methods related to that service. Also, if a customer is using multiple services, it can suggest how to use those services in combination. Finally, the customer satisfaction improvement system creates customized materials based on the analysis results. For example, if a customer is seeking a detailed explanation of a particular service, it creates materials that explain how to use that service and its benefits in detail. Also, if a customer is using multiple services, it can create materials that explain how to use those services in combination. The created materials can be sent by email or printed and mailed. This makes it easier for customers to understand the best way to use the service and the benefits for them, thereby improving their satisfaction. For example, when a customer inquires about a specific service, receiving materials that explain the benefits and usage of that service in detail makes it easier for them to understand how to use the service. Furthermore, if a customer uses multiple services, receiving materials that explain how to use those services in combination allows them to understand how to use them more effectively. Thus, a customer satisfaction improvement system can enhance customer satisfaction by collecting and analyzing customer inquiries and creating customized materials.

[0058] The customer satisfaction improvement system according to this embodiment comprises a collection unit, an analysis unit, and a creation unit. The collection unit collects customer inquiries. Customer inquiries include, for example, questions about products and complaints about services, but are not limited to these examples. The collection unit collects the content of inquiries made by customers to the company, for example, by telephone or email. The collection unit can collect inquiry content using telephone recordings or email text analysis. For example, when a customer makes an inquiry by telephone, the collection unit records the content of the call and converts it into text data. The collection unit can also analyze the content of an email when a customer makes an inquiry and save it as text data. The analysis unit analyzes the data collected by the collection unit and determines the characteristics of customer needs. The analysis unit analyzes the collected data based on, for example, the algorithm used and the accuracy of the analysis. For example, the analysis unit uses natural language processing technology to analyze customer inquiries and determine the characteristics of customer needs. The analysis unit can also use machine learning algorithms to analyze patterns in customer inquiries and determine the characteristics of customer needs. For example, the analysis department clusters customer inquiries and assigns specific need characteristics to each cluster. The creation department creates customized materials based on the analysis results obtained by the analysis department. The creation department can customize materials based on customer attributes or specific needs. For example, if a customer requests a detailed explanation of a particular service, the creation department can create materials that explain how to use that service and its benefits in detail. The creation department can also create materials that explain how to use multiple services in combination if the customer is using multiple services. The creation department can send the created materials by email or print them out and mail them. For example, the creation department can attach the created materials to an email in PDF format and send them. Alternatively, the creation department can print the created materials and mail them to the customer. In this way, the customer satisfaction improvement system according to this embodiment can improve customer satisfaction by collecting and analyzing customer inquiries and creating customized materials.

[0059] The collection unit collects customer inquiries. These inquiries include, but are not limited to, product-related questions and service-related complaints. The collection unit collects inquiries made by customers, for example, by phone or email. Specifically, it can collect inquiry content using phone recordings and email text analysis. For example, when a customer makes an inquiry by phone, the collection unit records the conversation and converts it into text data using speech recognition technology. This speech recognition technology can be pre-trained with specific industry terms and product names to accurately transcribe customer speech and minimize misrecognition. The collection unit can also analyze emails made by customers using natural language processing technology and save the content as text data. This analysis includes not only the email body but also the subject line and attachments, collecting information to more accurately understand the customer's intent. Furthermore, the collection unit can also receive customer inquiries through chatbots and web forms. This ensures that inquiries are collected centrally, regardless of the method used by the customer, and used for subsequent analysis and responses. The data collection unit stores this data in a secure database and uses appropriate encryption technology to protect privacy. This allows the data collection unit to efficiently collect customer inquiries through various means and smoothly provide the data to the analysis unit in the next step.

[0060] The analysis department analyzes the data collected by the data collection department to determine the characteristics of customer needs. The analysis department analyzes the collected data based, for example, on the algorithms used and the accuracy of the analysis. Specifically, it uses natural language processing technology to analyze customer inquiries and determine their characteristics. Natural language processing technology can extract keywords and phrases from text data and analyze customer intent and emotions. For example, if a customer inquires, "I don't know how to use the product," the analysis department extracts the keyword "how to use" and determines that the customer is seeking information on how to operate the product. Furthermore, machine learning algorithms can be used to analyze patterns in customer inquiries and determine customer characteristics. For example, the analysis department can cluster customer inquiries based on past inquiry data and assign specific characteristics to each cluster. This allows for a more accurate understanding of the problems customers are facing and the information they are seeking. In addition, the analysis department can combine customer attribute information (age, gender, region, etc.) and past purchase history to determine more detailed characteristics of customer needs. For example, if many inquiries come from younger customers, responses that consider the specific needs and trends of that generation are required. This allows the analysis department to analyze the collected data from multiple perspectives and determine customer needs and characteristics with high accuracy.

[0061] The creation department creates customized materials based on the analysis results obtained by the analysis department. For example, the creation department can customize materials based on customer attributes or specific needs. Specifically, if a customer requests detailed information about a particular service, the creation department will create materials that explain how to use that service and its benefits in detail. Based on the customer needs characteristics provided by the analysis department, the creation department selects the most relevant information and creates materials in a format that is easy for the customer to understand. For example, if a customer inquires about how to use a product, the creation department will create materials that include illustrated manuals and video links explaining the operating procedures. Furthermore, if a customer uses multiple services, the creation department can create materials explaining how to use those services in combination. For example, if a customer uses both service A and service B, the creation department will provide materials explaining how the two services work together and their synergistic effects. The creation department can send the created materials via email or print them out and mail them. For example, the creation department can send the created materials as PDF attachments via email. Alternatively, the creation department can print the materials and mail them to the customer. In addition, the creation department can collect customer feedback and continuously improve the content and format of the materials. For example, a survey can be used to check whether customers are satisfied with the content of the materials and whether they found them easy to understand, and the materials can be improved based on the results. This allows the creation department to provide customized materials that meet customer needs and improve customer satisfaction.

[0062] The data collection unit can collect information when customers contact a company by phone or email. For example, when a customer makes a phone call to a company, the data collection unit can record the call and convert it into text data. For example, the data collection unit can use speech recognition technology to automatically convert the call content into text data. The data collection unit can also analyze the content of emails sent by customers to a company and save them as text data. For example, the data collection unit can use natural language processing technology to analyze email content and extract important information. Furthermore, the data collection unit can collect information when customers make inquiries through a company's website. For example, the data collection unit can automatically collect the information entered into web forms and save it to a database. This allows for accurate collection of customer inquiries. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, when collecting customer inquiries, the data collection unit can use speech recognition technology or natural language processing technology to automatically analyze the inquiry content and save it as text data.

[0063] The analysis unit can analyze collected data and determine customer needs characteristics. For example, the analysis unit can use natural language processing technology to analyze customer inquiries and determine customer needs characteristics. For example, the analysis unit can use text analysis algorithms to extract important keywords from customer inquiries and determine customer needs characteristics based on those keywords. The analysis unit can also use machine learning algorithms to analyze patterns in customer inquiries and determine customer needs characteristics. For example, the analysis unit can use clustering algorithms to cluster customer inquiries and assign specific needs characteristics to each cluster. Furthermore, the analysis unit can analyze customers' past inquiry and purchase history to determine customer needs characteristics. For example, the analysis unit can retrieve customers' past inquiry and purchase history from a database and determine customer needs characteristics based on that. This allows for accurate determination of customer needs characteristics. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, when analyzing customer inquiries, the analysis unit can automatically determine customer needs characteristics using natural language processing technology or machine learning algorithms.

[0064] The creation department can create and provide customized materials to customers based on the analysis results. For example, the creation department can customize materials based on customer attributes or specific needs. For instance, if a customer requests detailed information about a particular service, the creation department can create materials explaining how to use that service and its benefits in detail. Furthermore, if a customer uses multiple services, the creation department can create materials explaining how to use those services in combination. The creation department can send the created materials via email or print them out and mail them. For example, the creation department can attach the created materials to an email in PDF format and send them. Alternatively, the creation department can print the created materials and mail them to the customer. This allows the creation department to provide customers with the most suitable materials. Some or all of the above processes in the creation department may be performed using AI, for example, or not. For example, the creation department can automatically create customized materials based on customer attributes and needs and send them via email.

[0065] The creation unit can send the created documents by email or by printing and mailing them. For example, the creation unit can attach the created documents to an email in PDF format and send them. For example, the creation unit can retrieve the customer's email address from a database and automatically send the created documents by email. Alternatively, the creation unit can print the created documents and mail them to the customer. For example, the creation unit can retrieve the customer's address from a database and automatically print and mail the created documents. This allows for the rapid provision of documents to customers. Some or all of the above processes in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can retrieve the customer's email address or address from a database and automatically send or mail the created documents.

[0066] The collection unit can estimate the customer's emotions and adjust the timing of inquiry collection based on the estimated emotions. For example, if the customer is stressed, the collection unit can collect the inquiry quickly to reduce the customer's burden. For example, the collection unit can analyze the customer's voice data to detect signs of stress. Also, if the customer is relaxed, the collection unit can collect the inquiry slowly to obtain detailed information. For example, the collection unit can analyze the customer's facial expression data to detect signs of relaxation. Furthermore, if the customer is in a hurry, the collection unit can collect the inquiry concisely to obtain the minimum necessary information. For example, the collection unit can analyze the customer's text data to detect signs of urgency. This allows for the collection of inquiry content at the optimal timing according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input customer voice data and facial expression data into a generating AI, allowing the AI ​​to perform emotion estimation.

[0067] The data collection unit can analyze a customer's past inquiry history and select the optimal data collection method. For example, the data collection unit can prioritize suggesting inquiry methods (such as phone or email) that the customer has frequently used in the past. For example, the data collection unit can retrieve a customer's past inquiry history from a database and select the optimal data collection method. The data collection unit can also select the optimal question format based on the content of the customer's past inquiries. For example, the data collection unit can analyze the content of the customer's past inquiries and suggest the optimal question format. Furthermore, the data collection unit can collect inquiry content at the optimal timing based on the customer's past inquiry history. For example, the data collection unit can analyze the customer's past inquiry history and suggest the optimal collection timing. This allows the optimal data collection method to be selected based on the customer's past inquiry history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the customer's past inquiry history into a generating AI and have the generating AI select the optimal data collection method.

[0068] The collection unit can filter inquiries based on the customer's current usage and areas of interest when collecting them. For example, the collection unit can prioritize collecting inquiries related to services the customer is currently using. For example, the collection unit can obtain the customer's current usage from a database and filter related inquiries. The collection unit can also filter related inquiries based on the customer's areas of interest. For example, the collection unit can analyze the customer's areas of interest and prioritize collecting related inquiries. Furthermore, the collection unit can collect the most suitable inquiries by considering the customer's current usage. For example, the collection unit can analyze the customer's current usage and suggest the most suitable inquiries. This allows the collection of the most suitable inquiries based on the customer's current usage and areas of interest. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the customer's current usage and areas of interest into a generating AI and have the generating AI perform the filtering.

[0069] The data collection unit can estimate the customer's emotions and determine the priority of inquiries to collect based on the estimated emotions. For example, if the customer is feeling anxious, the data collection unit can prioritize collecting urgent inquiries. For example, the data collection unit can analyze the customer's voice data to detect signs of anxiety. Also, if the customer is relaxed, the data collection unit can prioritize collecting detailed inquiries. For example, the data collection unit can analyze the customer's facial expression data to detect signs of relaxation. Furthermore, if the customer is in a hurry, the data collection unit can prioritize collecting concise inquiries. For example, the data collection unit can analyze the customer's text data to detect signs of urgency. This allows for the collection of inquiries with the optimal priority according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input customer voice data and facial expression data into a generating AI, allowing the AI ​​to perform emotion estimation.

[0070] The collection unit can prioritize collecting highly relevant inquiries by considering the customer's geographical location information when collecting inquiry content. For example, if a customer is in a specific region, the collection unit will prioritize collecting inquiries related to that region. For example, the collection unit can obtain the customer's geographical location information from GPS data and filter relevant inquiries. The collection unit can also suggest the most relevant inquiries based on the customer's geographical location information. For example, the collection unit can prioritize collecting inquiries related to the customer's current location. Furthermore, if the customer is on the move, the collection unit can prioritize collecting inquiries related to their current location. For example, the collection unit can analyze the customer's movement pattern and suggest the most relevant inquiries. This allows for the collection of the most relevant inquiries based on the customer's geographical location information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the customer's geographical location information into a generating AI and have the generating AI collect highly relevant inquiries.

[0071] The collection unit can analyze the customer's social media activity and collect relevant content when collecting inquiry information. For example, the collection unit can analyze the customer's social media posts and collect relevant inquiry information. For example, the collection unit can analyze the customer's social media account and collect the posts as text data. The collection unit can also suggest the most appropriate inquiry content based on the customer's social media activity history. For example, the collection unit can analyze the customer's social media activity history and prioritize the collection of relevant inquiry content. Furthermore, the collection unit can analyze the customer's areas of interest on social media and collect relevant inquiry content. For example, the collection unit can analyze the customer's areas of interest on social media and suggest the most appropriate inquiry content. This allows for the collection of the most appropriate inquiry content based on the customer's social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the customer's social media activity into a generating AI and have the generating AI collect relevant inquiry content.

[0072] The analysis unit can estimate the customer's emotions and adjust the expression of the analysis based on the estimated emotions. For example, if the customer is feeling anxious, the analysis unit can use a reassuring expression. For instance, it can analyze the customer's voice data to detect signs of anxiety and select a reassuring expression. Furthermore, if the customer is relaxed, the analysis unit can use an expression that includes detailed information. For example, it can analyze the customer's facial expression data to detect signs of relaxation and select an expression that includes detailed information. Additionally, if the customer is in a hurry, the analysis unit can use a concise and to-the-point expression. For example, it can analyze the customer's text data to detect signs of urgency and select a concise and to-the-point expression. This allows the analysis to be performed using the most appropriate expression according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input customer voice data and facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of the inquiry during the analysis. For example, the analysis unit can perform a detailed analysis on inquiries of high importance. For example, the analysis unit can analyze the customer's inquiry, determine its importance, and perform a detailed analysis. The analysis unit can also perform a concise analysis on inquiries of low importance. For example, the analysis unit can analyze the customer's inquiry, determine its importance, and perform a concise analysis. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the inquiry. For example, the analysis unit can analyze the customer's inquiry and adjust the level of detail of the analysis based on its importance. This allows for analysis at the optimal level of detail according to the importance of the inquiry. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the customer's inquiry into a generating AI and have the generating AI perform the importance determination and adjustment of the level of detail of the analysis.

[0074] 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. For instance, the analysis unit can analyze customer inquiries, determine their technical nature, and apply a specialized analysis algorithm. Furthermore, the analysis unit can apply an analysis algorithm that prioritizes customer satisfaction to service-related inquiries. For example, the analysis unit can analyze customer inquiries, determine their service nature, and apply an analysis algorithm that prioritizes customer satisfaction. In addition, the analysis unit can select the optimal analysis algorithm depending on the category of the inquiry. For example, the analysis unit can analyze customer inquiries and select the optimal analysis algorithm based on the category. This allows the analysis unit to apply the most suitable analysis algorithm depending on the category of the inquiry. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input customer inquiries into a generating AI and have the generating AI perform category determination and analysis algorithm selection.

[0075] The analysis unit can estimate the customer's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the customer is in a hurry, the analysis unit can perform a short, concise analysis. For instance, it can analyze the customer's voice data to detect signs of urgency and perform a short, concise analysis. Furthermore, if the customer is relaxed, the analysis unit can perform a detailed analysis. For example, it can analyze the customer's facial expression data to detect signs of relaxation and perform a detailed analysis. Additionally, if the customer is excited, the analysis unit can add visually stimulating effects to the analysis. For example, it can analyze the customer's text data to detect signs of excitement and add visually stimulating effects. This allows for analysis of the optimal length according to the customer's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input customer voice data and facial expression data into a generating AI and have the generating AI perform emotion estimation and adjust the length of the analysis.

[0076] The analysis department can determine the priority of the analysis based on when the inquiry content was submitted. For example, the analysis department can prioritize the analysis of recently submitted inquiries. For example, the analysis department can obtain the submission dates of customer inquiries from the database and prioritize the analysis of recently submitted content. The analysis department can also postpone older inquiries. For example, the analysis department can analyze the submission dates of customer inquiries and postpone older content. Furthermore, the analysis department can adjust the priority of the analysis in stages based on the submission dates. For example, the analysis department can analyze the submission dates of customer inquiries and adjust the priority in stages. This allows for analysis to be performed with the optimal priority based on the submission dates of the inquiries. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input the submission dates of customer inquiries into a generating AI and have the generating AI determine the priority.

[0077] The analysis unit can adjust the order of analysis based on the relevance of the inquiry content during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant inquiries. For example, the analysis unit can analyze customer inquiries and prioritize the analysis of highly relevant content. The analysis unit can also postpone the analysis of less relevant inquiries. For example, the analysis unit can analyze customer inquiries and postpone the analysis of less relevant content. Furthermore, the analysis unit can adjust the order of analysis step by step based on the relevance of the inquiry content. For example, the analysis unit can analyze customer inquiries and adjust the order of analysis based on relevance. This allows for analysis to be performed in the optimal order based on the relevance of the inquiry content. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input customer inquiries into a generating AI and have the generating AI perform relevance determination and adjustment of the analysis order.

[0078] The creation unit can estimate the customer's emotions and adjust the method of creating materials based on the estimated emotions. For example, if the customer is feeling anxious, the creation unit can create reassuring materials. For example, the creation unit can analyze the customer's voice data, detect signs of anxiety, and create reassuring materials. Also, if the customer is relaxed, the creation unit can create materials containing detailed information. For example, the creation unit can analyze the customer's facial expression data, detect signs of relaxation, and create materials containing detailed information. Furthermore, if the customer is in a hurry, the creation unit can create concise and to-the-point materials. For example, the creation unit can analyze the customer's text data, detect signs of urgency, and create concise and to-the-point materials. This allows for the creation of materials in the most optimal way according to the customer'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-described processes in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input customer voice data and facial expression data into a generating AI and have the generating AI perform emotion estimation and adjust the method of creating the materials.

[0079] The creation unit can analyze past customer inquiries and select the optimal method for creating documents. For example, the creation unit can create optimal documents based on information previously requested by the customer. For example, the creation unit can retrieve past customer inquiries from a database and create optimal documents. The creation unit can also select the optimal document format based on past customer inquiries. For example, the creation unit can analyze past customer inquiries and propose the optimal document format. Furthermore, the creation unit can propose the optimal document content based on the customer's past inquiry history. For example, the creation unit can analyze the customer's past inquiry history and propose the optimal document content. This allows for the creation of documents in the most optimal way based on the customer's past inquiries. Some or all of the above processes in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input past customer inquiries into a generation AI and have the generation AI select the optimal document creation method.

[0080] The creation unit can customize the content of materials based on the customer's current usage when creating them. For example, the creation unit can create materials related to the services the customer is currently using. For example, the creation unit can retrieve the customer's current usage from a database and create relevant materials. The creation unit can also suggest the most suitable material content considering the customer's current usage. For example, the creation unit can analyze the customer's current usage and suggest the most suitable material content. Furthermore, the creation unit can create customized materials based on the customer's current usage. For example, the creation unit can analyze the customer's current usage and create customized materials. This allows for the creation of materials with optimal content based on the customer's current usage. Some or all of the above processes in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input the customer's current usage into a generation AI and have the generation AI perform the material customization.

[0081] The creation unit can estimate the customer's emotions and prioritize materials based on those emotions. For example, if the customer is feeling anxious, the creation unit can prioritize creating urgent materials. For example, it can analyze the customer's voice data to detect signs of anxiety and create urgent materials. Also, if the customer is relaxed, the creation unit can prioritize creating detailed materials. For example, it can analyze the customer's facial expression data to detect signs of relaxation and create detailed materials. Furthermore, if the customer is in a hurry, the creation unit can prioritize creating concise materials. For example, it can analyze the customer's text data to detect signs of urgency and create concise materials. This allows for the creation of materials with optimal priority according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input customer voice data and facial expression data into a generating AI, which can then perform emotion estimation and determine the priority of materials.

[0082] The creation unit can select the optimal method for creating materials by considering the customer's geographical location information when creating materials. For example, if the customer is in a specific region, the creation unit can create materials related to that region. For example, the creation unit can obtain the customer's geographical location information from GPS data and create relevant materials. The creation unit can also select the optimal material format based on the customer's geographical location information. For example, the creation unit can create materials related to the customer's current location. Furthermore, if the customer is on the move, the creation unit can also create materials related to their current location. For example, the creation unit can analyze the customer's movement patterns and propose the optimal material format. This allows for the creation of materials in the most optimal way based on the customer's geographical location information. Some or all of the above processes in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input the customer's geographical location information into a generating AI and have the generating AI select the optimal material creation method.

[0083] The creation unit can analyze the customer's social media activity and propose content for the materials when creating them. For example, the creation unit can analyze the customer's social media posts and create relevant materials. For example, the creation unit can analyze the customer's social media accounts, collect the posts as text data, and create relevant materials. The creation unit can also propose optimal material content based on the customer's social media activity history. For example, the creation unit can analyze the customer's social media activity history and propose optimal material content. Furthermore, the creation unit can analyze the customer's areas of interest on social media and create relevant materials. For example, the creation unit can analyze the customer's areas of interest on social media and create relevant materials. This allows for the creation of materials with optimal content based on the customer's social media activity. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input the customer's social media activity into a generating AI and have the generating AI propose material content.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The customer satisfaction improvement system can also include a feedback collection unit. This unit collects how customers reacted to the materials provided. For example, it can send a questionnaire to customers after they receive the materials and collect their responses. It can also collect behavioral data on how customers viewed the materials (e.g., which pages they viewed and for how long). Furthermore, the feedback collection unit can estimate the customer's feelings towards the materials and provide the results to the analysis unit. This allows for identifying areas for improvement in the materials based on customer feedback and incorporating those improvements into future material creation.

[0086] The customer satisfaction improvement system can also be equipped with a prediction unit. This unit predicts future inquiries based on the customer's past inquiry history and behavioral data. For example, it can analyze the types of inquiries a customer has frequently made in the past and predict what kind of inquiry they are likely to make next. It can also predict the products or services a customer is likely to purchase next based on their purchase history. Furthermore, it can predict what actions a customer is likely to take next based on their behavioral data. This allows for a proactive understanding of customer needs and appropriate responses.

[0087] The customer satisfaction improvement system can also include a personalization section. This personalization section provides individually customized services and benefits based on customer attributes and past behavioral data. For example, the personalization section can suggest optimal services and benefits based on customer attribute data such as age, gender, and place of residence. It can also provide individually customized services and benefits based on the customer's past purchase and inquiry history. Furthermore, the personalization section can analyze customer behavioral data in real time and suggest the most suitable services and benefits on the spot. This allows for the provision of optimal services to each individual customer, thereby improving satisfaction.

[0088] The customer satisfaction improvement system can also be equipped with an emotion analysis unit. This unit analyzes customer emotions in detail based on customer inquiries and behavioral data. For example, it can analyze customer voice data to estimate what emotions the customer is feeling. It can also analyze customer text data to estimate emotions. Furthermore, it can analyze customer facial expression data to estimate emotions. This allows for responses tailored to customer emotions, thereby improving customer satisfaction.

[0089] The customer satisfaction improvement system can also be equipped with a real-time response unit. This unit has the function of responding immediately to customer inquiries. For example, when a customer makes an inquiry by phone, the real-time response unit can provide an appropriate answer on the spot. It can also provide an immediate response when a customer makes an inquiry via chat. Furthermore, the real-time response unit can analyze the content of the customer's inquiry in real time and propose the optimal solution. This reduces customer waiting times and improves customer satisfaction.

[0090] The customer satisfaction improvement system can also be equipped with an emotional feedback unit. This unit collects information on the emotions customers feel towards the services and materials provided. For example, it can collect facial expression data from customers viewing materials and estimate their emotions. It can also collect audio data from customers using services and estimate their emotions. Furthermore, it can collect text data from customers answering questionnaires and estimate their emotions. This allows for identifying areas for improvement in services and materials based on customer emotions and reflecting those improvements in future offerings.

[0091] The customer satisfaction improvement system can also be equipped with a behavior prediction unit. This unit predicts future customer behavior based on past customer behavior data. For example, it can analyze what services a customer has used in the past and predict which services they are likely to use next. It can also predict which products a customer is likely to purchase next based on their purchase history. Furthermore, it can predict which pages a customer is likely to view next based on their website browsing history. This allows for proactive understanding of customer needs and appropriate responses.

[0092] A customer satisfaction improvement system can also be equipped with an emotion-adaptive unit. This unit adjusts the system's operation according to the customer's emotions. For example, if the customer is stressed, the unit can speed up the system's response time. Conversely, if the customer is relaxed, the unit can slow down the system's response and provide detailed information. Furthermore, if the customer is in a hurry, the unit can simplify the system's response and provide only the essential information. This allows for optimal responses tailored to the customer's emotions, thereby improving satisfaction.

[0093] The customer satisfaction improvement system can also include a content recommendation function. This function recommends the most relevant content based on the customer's past behavior data and inquiries. For example, it can analyze pages the customer has previously viewed and inquiries they have made, and recommend relevant content. It can also recommend relevant products and services based on the customer's purchase history. Furthermore, it can recommend content based on the customer's areas of interest. This allows the system to provide customers with useful information and improve their satisfaction.

[0094] The customer satisfaction improvement system can also be equipped with an emotion monitoring unit. This unit monitors the customer's emotions in real time while they are using the system. For example, it can analyze the customer's voice data in real time to detect changes in their emotions. It can also analyze the customer's facial expression data in real time to detect changes in their emotions. Furthermore, it can analyze the customer's text data in real time to detect changes in their emotions. This allows for real-time, optimal responses tailored to the customer's emotions, thereby improving satisfaction.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The collection department collects customer inquiries. These inquiries include questions about products and complaints about services. The collection department collects the content of inquiries made by customers to the company by phone or email. Specifically, it collects inquiry content using phone recordings and email text analysis. For example, when a customer makes an inquiry by phone, the conversation is recorded and converted into text data. Similarly, when a customer makes an inquiry by email, the content of the email is analyzed and saved as text data. Step 2: The analysis unit analyzes the data collected by the collection unit and determines the characteristics of customer needs. The analysis unit uses natural language processing technology and machine learning algorithms to analyze the content of customer inquiries. For example, it clusters the content of customer inquiries and assigns specific needs characteristics to each cluster. Step 3: The creation team creates customized materials based on the analysis results obtained by the analysis team. The creation team customizes the materials according to the customer's attributes and specific needs. For example, if a customer requests a detailed explanation of a particular service, they will create materials that explain how to use that service and its benefits in detail. Also, if a customer uses multiple services, they will create materials that explain how to use those services in combination. The created materials can be sent by email or printed and mailed.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] For example, the collection unit can collect customer inquiries using the camera 42 and microphone 38B of the smart device 14. The collection unit can also be implemented by the specific processing unit 290 of the data processing device 12, for example, by recording phone calls or analyzing email text. The analysis unit can also be implemented by the specific processing unit 290 of the data processing device 12, for example, by analyzing the collected data using natural language processing technology and machine learning algorithms. The creation unit can also be implemented by the control unit 46A of the smart device 14, for example, by creating customized documents and sending them by email or printing and mailing them. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] For example, the collection unit can collect customer inquiries using the camera 42 and microphone 238 of the smart glasses 214. The collection unit can also be implemented by the specific processing unit 290 of the data processing device 12, for example, by recording phone calls or analyzing email text. The analysis unit can also be implemented by the specific processing unit 290 of the data processing device 12, for example, by analyzing the collected data using natural language processing technology and machine learning algorithms. The creation unit can also be implemented by the control unit 46A of the smart glasses 214, for example, by creating customized materials and sending them by email or printing and mailing them. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] For example, the collection unit can collect customer inquiries using the camera 42 and microphone 238 of the headset terminal 314. The collection unit can also be implemented by the specific processing unit 290 of the data processing device 12, for example, by recording phone calls or analyzing email text. The analysis unit can also be implemented by the specific processing unit 290 of the data processing device 12, for example, by analyzing the collected data using natural language processing technology and machine learning algorithms. The creation unit can also be implemented by the control unit 46A of the headset terminal 314, for example, by creating customized documents and sending them by email or printing and mailing them. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] For example, the collection unit can collect customer inquiries using the camera 42 and microphone 238 of the robot 414. The collection unit can also be implemented by the specific processing unit 290 of the data processing device 12, for example, by recording phone calls or analyzing email text. The analysis unit can also be implemented by the specific processing unit 290 of the data processing device 12, for example, by analyzing the collected data using natural language processing technology and machine learning algorithms. The creation unit can also be implemented by the control unit 46A of the robot 414, for example, by creating customized documents and sending them by email or printing and mailing them. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] (Note 1) The collection department collects customer inquiries, An analysis unit analyzes the data collected by the aforementioned collection unit and determines the characteristics of customer needs, The system includes a creation unit that creates customized materials based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is When customers contact a company by phone or email, the company collects the details of those inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is We analyze the collected data to determine the characteristics of customer needs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned creation unit, Based on the analysis results, we create and provide customized materials to our customers. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned creation unit, Send the created document by email or print it out and send it. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate customer emotions and adjust the timing of collecting inquiries based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the customer's past inquiry history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting inquiries, filtering is performed based on the customer's current usage patterns and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate customer emotions and prioritize the types of inquiries to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting inquiries, the system prioritizes collecting highly relevant information by considering the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting inquiry details, we analyze the customer's social media activity and gather relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is We estimate customer emotions and adjust the way the analysis is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During the analysis, adjust the level of detail based on the importance of the inquiry. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is 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 15) The aforementioned analysis unit is Estimate customer sentiment and adjust the length of the analysis based on the estimated customer sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During the analysis, we prioritize the analysis based on when the inquiry was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, the order of analysis is adjusted based on the relevance of the inquiry content. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned creation unit, We estimate customer emotions and adjust how materials are created based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned creation unit, When creating documents, we analyze past customer inquiries to select the most suitable document creation method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned creation unit, When creating documents, customize the content based on the customer's current usage. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned creation unit, Estimate customer sentiment and prioritize materials based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned creation unit, When creating documents, we select the most suitable document creation method by considering the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned creation unit, When creating materials, we analyze the customer's social media activity and propose content accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0169] 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 collection department collects customer inquiries, An analysis unit analyzes the data collected by the aforementioned collection unit and determines the characteristics of customer needs, The system includes a creation unit that creates customized materials based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is When customers contact a company by phone or email, the company collects the details of those inquiries. The system according to feature 1.

3. The aforementioned analysis unit is We analyze the collected data to determine the characteristics of customer needs. The system according to feature 1.

4. The aforementioned creation unit, Based on the analysis results, we create and provide customized materials to our customers. The system according to feature 1.

5. The aforementioned creation unit, Send the created document by email or print it out and send it. The system according to feature 1.

6. The aforementioned collection unit is We estimate customer emotions and adjust the timing of collecting inquiries based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the customer's past inquiry history and select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting inquiries, filtering is performed based on the customer's current usage patterns and areas of interest. The system according to feature 1.

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