system

Abstract

The system according to this embodiment aims to analyze customer complaint data and propose specific improvement measures. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a recommendation unit, and a provision unit. The collection unit collects customer complaint data. The analysis unit performs text analysis on the complaint data collected by the collection unit. The generation unit generates insights based on the frequency, trends, and relevance of the problems extracted by the analysis unit. The recommendation unit proposes an action plan based on the insights generated by the generation unit. The provision unit provides the action plan proposed by the recommendation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 prior art, there was a problem that it was difficult to efficiently analyze claim data from customers and lead to specific improvement measures.

[0005] The system according to the embodiment aims to analyze claim data from customers and propose specific improvement measures.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a recommendation unit, and a provision unit. The collection unit collects customer complaint data. The analysis unit performs text analysis on the complaint data collected by the collection unit. The generation unit generates insights based on the frequency, trends, and relevance of the problems extracted by the analysis unit. The recommendation unit proposes an action plan based on the insights generated by the generation unit. The provision unit provides the action plan proposed by the recommendation unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze customer complaint data and propose specific improvement measures. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI system according to an embodiment of the present invention is a system that analyzes customer complaint data and identifies the problems causing those complaints. The AI ​​system collects customer complaint data, performs text analysis, extracts the frequency, trends, and relevance of problems, and generates insights. This helps companies develop measures to improve their services and products. The AI ​​system also clarifies specific points and process defects that cause complaints and proposes action plans to prevent future complaints. For example, the AI ​​system collects customer complaint data. In this process, it collects detailed data such as the content of the complaint, the date and time of occurrence, and the customer's attributes. For example, it can collect various types of complaint data, such as complaints about product defects or complaints about service responses. Next, the AI ​​performs text analysis on the collected complaint data. The AI ​​analyzes the text data of the complaints and extracts the frequency, trends, and relevance of problems. For example, if there are many complaints about a particular product, it can identify common problems with that product. Furthermore, by analyzing data such as the frequency and timing of complaints and customer attributes, it can grasp the trends of complaints. In addition, the AI ​​clarifies specific points and process defects that cause complaints. For example, it can identify specific problems, such as flaws in product design or defects in the service delivery process. This allows companies to understand the root cause of the problem and take appropriate corrective actions. Finally, AI can suggest action plans to prevent future complaints. For example, it can suggest specific action plans such as reviewing product design or improving the service delivery process. This allows companies to prevent complaints from occurring and improve customer satisfaction. In this way, AI systems can efficiently collect, analyze, generate insights from customer complaint data, suggest action plans, and provide them to customers.

[0029] The AI ​​system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a recommendation unit, and a provision unit. The collection unit collects customer complaint data. The collection unit collects detailed data such as the content of the complaint, the date and time of occurrence, and customer attributes. The collection unit can collect various types of complaint data, such as complaints about product defects and complaints about service responses. The analysis unit performs text analysis on the complaint data collected by the collection unit. The analysis unit analyzes the text data of the complaints and extracts the frequency, trends, and relevance of the problems. For example, if there are many complaints about a particular product, the analysis unit can identify common problems with that product. The analysis unit can also grasp the trends of complaints by analyzing data such as the frequency and timing of complaint occurrences and customer attributes. The generation unit generates insights based on the frequency, trends, and relevance of the problems extracted by the analysis unit. For example, the generation unit generates insights based on the extracted data that can help companies develop improvement measures. The recommendation unit proposes an action plan based on the insights generated by the generation unit. The recommendation department, for example, identifies specific points and process defects that cause complaints and proposes action plans to prevent future complaints. The recommendation department may propose specific action plans, such as reviewing product design or improving service delivery processes. The delivery department provides the action plans proposed by the recommendation department. The delivery department provides the proposed action plans to companies, for example. As a result, the AI ​​system according to the embodiment can efficiently collect, analyze, generate insights from customer complaint data, propose action plans, and provide them.

[0030] The data collection unit collects customer complaint data. This includes detailed data such as the nature of the complaint, the date and time of occurrence, and customer attributes. Specifically, the unit collects data from customer complaint forms, customer support call records, emails, and chat logs. This data includes not only text data but also audio and image data. For example, photos and videos sent by customers when reporting product defects are also collected. The data collection unit centrally manages this data and stores it in a database. Furthermore, the data collection unit allows for flexible configuration of data collection frequency and methods. For example, it can be configured to collect data in real time or periodically. The data collection unit also has features to detect data duplication and omissions and clean the data as needed to ensure data quality. This enables the data collection unit to efficiently and accurately collect customer complaint data, providing high-quality data necessary for subsequent analysis and insight generation.

[0031] The analysis department performs text analysis on the complaint data collected by the collection department. For example, the analysis department analyzes the text data of complaints to extract the frequency, trends, and relevance of problems. Specifically, it uses natural language processing (NLP) techniques to analyze the content of complaints, extract keywords, and perform sentiment analysis. For example, it extracts keywords such as "defect," "delay," and "poor service" from the complaint text and calculates the frequency of each keyword. It also uses sentiment analysis to determine whether a complaint is classified as positive, negative, or neutral. Furthermore, the analysis department grasps the trends of complaints by analyzing data such as the frequency and timing of complaint occurrences and customer attributes. For example, if complaints about a particular product are concentrated at a specific time, there may be a problem with the manufacturing lot or shipping timing of that product. Also, if complaints are frequent in a particular customer segment (e.g., age or region), there may be a problem specific to that customer segment. In this way, the analysis department can analyze the collected complaint data from multiple angles and clarify the root causes and trends of problems.

[0032] The generation unit generates insights based on the frequency, trends, and relevance of problems extracted by the analysis unit. For example, the generation unit generates insights that help companies develop improvement measures based on the extracted data. Specifically, the generation unit uses AI to analyze data and propose root causes of problems and specific actions for improvement. For example, if there are many complaints about a particular product, it may suggest a review of the design or improvement of the manufacturing process, as there may be problems with the product's design or manufacturing process. Similarly, if there are many complaints about a particular service, it may suggest improvements to the service delivery method or staff training, as there may be problems with the service delivery method or response process. Furthermore, the generation unit can build predictive models based on complaint data to predict the risk of future complaints. This allows companies to take proactive measures and prevent complaints from occurring in the first place. The generation unit provides these insights to companies and provides a foundation for implementing specific improvement measures.

[0033] The Recommendation Department proposes action plans based on the insights generated by the Generation Department. For example, the Recommendation Department identifies specific points and process defects that cause complaints and proposes action plans to prevent future complaints. Specifically, the Recommendation Department can propose concrete action plans such as reviewing product design or improving service delivery processes. For example, if there are many complaints about a particular product, there may be problems with the product's design or manufacturing process, so the Recommendation Department would propose reviewing the design or improving the manufacturing process. Similarly, if there are many complaints about a particular service, there may be problems with the service delivery method or response process, so the Recommendation Department would propose improving the service delivery method or training staff. Furthermore, the Recommendation Department can build predictive models based on complaint data to predict the risk of future complaints. This allows companies to take preventative measures and prevent complaints from occurring in the first place. The Recommendation Department provides these action plans to companies and provides a foundation for implementing specific improvement measures.

[0034] The Provider Department provides the action plan proposed by the Recommendation Department. For example, the Provider Department provides the proposed action plan to the company. Specifically, the Provider Department notifies each department of the company of the proposed action plan and provides support for its implementation. For example, it provides specific procedures for reviewing product designs and training programs to improve service delivery processes. The Provider Department also monitors the implementation status of the action plan and provides feedback as needed. This enables the company to effectively implement the proposed action plan and prevent complaints from occurring. Furthermore, the Provider Department can also evaluate the results of the action plan implementation and provide feedback for improvement. For example, it evaluates whether the number of complaints has decreased after the implementation of the action plan and proposes additional improvement measures as needed. In this way, the Provider Department can support the company in effectively preventing complaints and improving customer satisfaction.

[0035] The data collection unit can collect detailed data such as the content of the complaint, the date and time of occurrence, and customer attributes. For example, the data collection unit can record the details of the complaint and accurately record the date and time of occurrence. The data collection unit can also collect customer attribute information, such as the customer's age, gender, and place of residence. This allows for more accurate analysis by collecting detailed complaint data. 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 attribute information into AI, and the AI ​​can automatically collect the data.

[0036] The analysis unit can analyze the text data of claims and extract the frequency, trends, and relevance of the issues. For example, the analysis unit can analyze the text data of claims using natural language processing techniques. For example, the analysis unit can extract keywords from the text data of claims and measure their frequency. The analysis unit can also analyze the text data of claims over time to grasp trends. Furthermore, the analysis unit can perform correlation analysis on the text data of claims to evaluate their relevance. In this way, by analyzing the text data of claims, the frequency, trends, and relevance of the issues can be grasped. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input the text data of claims into a generative AI, and the generative AI can perform the analysis automatically.

[0037] The generation unit can generate insights based on the frequency, trends, and relevance of the extracted problems. For example, the generation unit can generate insights using data mining techniques. For instance, based on the extracted data, the generation unit can identify the root causes of problems and propose improvements. The generation unit can also use machine learning algorithms to predict future complaints and suggest preventative measures. This helps companies develop improvement strategies by generating insights based on the extracted data. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can input the extracted data into a generation AI, which can then automatically generate insights.

[0038] The recommendation department can identify specific points and process defects that cause complaints and propose action plans to prevent future complaints. For example, the recommendation department can identify process defects using flowchart analysis. For example, the recommendation department can identify the source of complaints and evaluate their scope of impact. The recommendation department can also identify bottlenecks and propose solutions. This allows for the identification of specific problems and the proposal of action plans to prevent future complaints, thereby improving the quality of the company's services and products. Some or all of the above processes in the recommendation department may be performed using AI, or not. For example, the recommendation department can input the points that cause complaints into AI, and the AI ​​can automatically propose an action plan.

[0039] The service provider can provide the proposed action plan to the company. For example, the service provider can provide the proposed action plan in report format. For example, the service provider can create a report detailing the action plan and submit it to the company. Alternatively, the service provider can provide the proposed action plan in presentation format. For example, the service provider can create a slide presentation outlining the action plan and give it to the company. By providing the company with the proposed action plan, the company can quickly implement the improvement measures. Some or all of the above processes performed by the service provider may be carried out using AI, for example, or not. For example, the service provider can input the proposed action plan into an AI, which can then automatically generate a report.

[0040] The data collection unit can analyze a customer's past claim history and select the optimal collection method when collecting claim data. For example, if a customer has filed claims frequently in the past, the data collection unit can provide a simple collection method. Alternatively, if a customer has filed detailed claims in the past, the data collection unit can provide a detailed collection method. Furthermore, the data collection unit can customize the collection method for specific issues based on the customer's past claim history. This allows for the selection of the optimal collection method by analyzing the customer's past claim history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the customer's past claim history into AI, which can then automatically select the optimal collection method.

[0041] The data collection unit can filter the collected claim data based on the customer's current purchasing habits and areas of interest. For example, the data collection unit can prioritize collecting claims related to products recently purchased by the customer. The data collection unit can also filter and collect claims related to the customer's areas of interest. Furthermore, the data collection unit can collect highly relevant claim data based on the customer's purchase history. This allows for the collection of highly relevant claim data by filtering based on the customer's purchasing habits and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the customer's purchase history into AI, which can then automatically perform the filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the customer's geographical location when collecting claim data. For example, if a customer lives in a specific region, the data collection unit will prioritize the collection of claim data related to that region. The data collection unit can also collect claim data related to region-specific issues based on the customer's geographical location. Furthermore, the data collection unit can acquire the customer's location information in real time and collect highly relevant claim data. This allows for the priority collection of claim data related to region-specific issues by considering the customer's geographical location. 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 the customer's geographical location information into AI, which can then automatically prioritize the collection of highly relevant data.

[0043] The data collection unit can analyze customers' social media activity and collect relevant data when collecting complaint data. For example, the data collection unit can collect complaint content posted by customers on social media. The data collection unit can also extract relevant complaint data from customers' social media activity. Furthermore, the data collection unit can analyze customers' responses on social media and collect complaint data. In this way, relevant complaint data can be collected by analyzing customers' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input customer social media activity data into AI, and the AI ​​can automatically collect relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the claims during text analysis. For example, the analysis unit will perform a detailed analysis on high-importance claims. Conversely, it can perform a simplified analysis on low-importance claims. Furthermore, the analysis unit can dynamically adjust the depth of the analysis according to the importance of the claims. This allows for detailed analysis of important claims by adjusting the level of detail based on the importance of the claims. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input claim importance data into AI, and the AI ​​can automatically adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the claim category during text analysis. For example, it can apply a product-specific analysis algorithm to product-related claims, and a service-specific analysis algorithm to service-related claims. Furthermore, the analysis unit can select the optimal analysis algorithm based on the claim category. This allows for more accurate analysis results by applying the most suitable analysis algorithm for each claim category. 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 claim category data into an AI, which can then automatically apply the optimal analysis algorithm.

[0046] The analysis department can prioritize the analysis based on the claim filing date during text analysis. For example, the analysis department can prioritize the analysis of recently filed claims. It can also postpone the analysis of older claims. Furthermore, the analysis department can dynamically adjust the analysis priority based on the claim filing date. This allows for prioritizing the analysis of the most recent claims by determining the analysis priority based on the claim filing date. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input claim filing date data into AI, which can then automatically determine the priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of claims during text analysis. For example, the analysis unit can prioritize the analysis of highly relevant claims. It can also postpone the analysis of less relevant claims. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of claims. This allows for the prioritization of highly relevant claims by adjusting the order of analysis based on the relevance of claims. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input claim relevance data into AI, which can then automatically adjust the order.

[0048] The generation unit can adjust the level of detail generated based on the importance of the extracted problems when generating insights. For example, the generation unit generates detailed insights for high-importance problems. It can also generate simplified insights for low-importance problems. Furthermore, the generation unit can dynamically adjust the level of detail of the insights according to the importance of the problems. This allows for the provision of detailed insights for important problems by adjusting the level of detail of the insights based on the importance of the extracted problems. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input problem importance data into the AI, and the AI ​​can automatically adjust the level of detail.

[0049] The generation unit can apply different generation algorithms depending on the problem category when generating insights. For example, the generation unit can apply a product-specific generation algorithm to product-related problems. It can also apply a service-specific generation algorithm to service-related problems. Furthermore, the generation unit can select the optimal generation algorithm depending on the problem category. By applying the optimal generation algorithm according to the problem category, more accurate insights can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input problem category data into the AI, which can then automatically apply the optimal generation algorithm.

[0050] The generation unit can prioritize insights based on when the problem occurred. For example, it can prioritize generating insights for recently occurring problems. It can also postpone generating insights for older problems. Furthermore, the generation unit can dynamically adjust the priority of insights based on when the problem occurred. This allows for priority provision of insights to the most recent problems by prioritizing insights based on when the problem occurred. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input problem occurrence data into the AI, which can then automatically determine the priority.

[0051] The generation unit can adjust the order of insights based on the relevance of the problems when generating insights. For example, the generation unit will prioritize generating insights for highly relevant problems. It can also postpone generating insights for less relevant problems. Furthermore, the generation unit can dynamically adjust the order of insights based on the relevance of the problems. This allows for prioritizing the provision of insights to highly relevant problems by adjusting the order of insights based on the relevance of the problems. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input problem relevance data into the AI, and the AI ​​can automatically adjust the order.

[0052] The recommendation department can select the optimal recommendation method by referring to past claim data when proposing action plans. For example, the recommendation department can propose the optimal action plan based on past claim data. The recommendation department can also propose action plans for similar problems based on past claim data. Furthermore, the recommendation department can analyze past claim data and propose the most effective action plan. In this way, the optimal action plan can be proposed by referring to past claim data. Some or all of the above processing in the recommendation department may be performed using AI, for example, or not using AI. For example, the recommendation department can input past claim data into AI, and the AI ​​can automatically select the optimal recommendation method.

[0053] The recommendation unit can customize the recommendations based on customer attribute information when proposing action plans. For example, the recommendation unit can propose an appropriate action plan based on the customer's age. It can also propose a customized action plan based on the customer's purchase history. Furthermore, the recommendation unit can propose a region-specific action plan based on the customer's regional information. This allows for the provision of more appropriate action plans by customizing the recommendations based on customer attribute information. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input customer attribute information into AI, and the AI ​​can automatically customize the recommendations.

[0054] The recommendation unit can select the optimal recommendation method when proposing action plans, taking into account the customer's geographical location information. For example, if a customer lives in a specific region, the recommendation unit will propose an action plan relevant to that region. The recommendation unit can also propose action plans for region-specific issues based on the customer's geographical location information. Furthermore, the recommendation unit can acquire the customer's location information in real time and propose the optimal action plan. This allows for the proposal of optimal action plans for region-specific issues by considering the customer's geographical location information. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not. For example, the recommendation unit can input the customer's geographical location information into AI, which can then automatically select the optimal recommendation method.

[0055] The recommendation department can analyze the customer's social media activity and adjust the recommendations when proposing action plans. For example, the recommendation department can propose action plans based on what the customer has posted on social media. It can also propose relevant action plans based on the customer's social media activity. Furthermore, the recommendation department can analyze the customer's social media reactions and adjust the action plans. This allows the recommendation department to propose relevant action plans by analyzing the customer's social media activity. Some or all of the above processes in the recommendation department may be performed using AI, for example, or not. For example, the recommendation department can input customer social media activity data into AI, and the AI ​​can automatically adjust the recommendations.

[0056] The service provider can select the optimal service method by referring to past service history when providing an action plan. For example, the service provider can provide the optimal action plan based on past service history. The service provider can also provide action plans for similar problems based on past service history. Furthermore, the service provider can analyze past service history and provide the most effective action plan. In this way, the optimal action plan can be provided by referring to past service history. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past service history into AI, and the AI ​​can automatically select the optimal service method.

[0057] The service provider can select the optimal delivery method when providing an action plan, taking into account the customer's device information. For example, if the customer is using a smartphone, the service provider can provide an action plan adapted to the screen size. Furthermore, if the customer is using a tablet, the service provider can provide an action plan optimized for a larger screen. Additionally, if the customer is using a smartwatch, the service provider can provide a concise and highly visible action plan. This allows the service provider to deliver the action plan in the most optimal format by considering the customer's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the customer's device information into AI, which can then automatically select the optimal delivery method.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The data collection unit, when collecting customer complaint data, can analyze the customer's past purchase history and collect additional information to identify the root cause of the complaint. For example, it can collect information about products and services that the customer has purchased in the past to understand the frequency and trends of complaints. The data collection unit can also identify patterns of complaints for specific products or services from the customer's purchase history. Furthermore, the data collection unit can collect additional information based on the customer's purchase history to identify the root cause of the complaint. This allows for a more accurate identification of the root cause of complaints by analyzing the customer's purchase history.

[0060] The generation unit can customize the content of insights by considering customer attribute information when generating them. For example, it can provide optimal insights for specific customer segments based on attribute information such as customer age, gender, and place of residence. The generation unit can also provide customized insights for individual customers based on their purchase history and complaint history. Furthermore, the generation unit can adjust how insights are delivered based on customer attribute information. This allows for the provision of more appropriate insights by considering customer attribute information.

[0061] The recommendations department can analyze customers' social media activity when proposing action plans and propose relevant action plans. For example, it can propose specific action plans based on the content of complaints posted by customers on social media. The recommendations department can also identify the causes of complaints from customers' social media activity and propose action plans based on that. Furthermore, the recommendations department can analyze customers' responses on social media and adjust the priorities of action plans. In this way, by analyzing customers' social media activity, it is possible to propose more appropriate action plans.

[0062] The service provider can select the optimal delivery method when providing action plans, taking into account the customer's device information. For example, if the customer is using a smartphone, an action plan tailored to the screen size can be provided. If the customer is using a tablet, an action plan optimized for a larger screen can be provided. Furthermore, if the customer is using a smartwatch, a concise and highly visible action plan can be provided. In this way, by considering the customer's device information, action plans can be delivered in the most optimal format.

[0063] The analytics department can prioritize the analysis of claim data based on when the claims were filed. For example, it can prioritize the analysis of recently filed claims and respond quickly, while delaying older claims. Furthermore, it can dynamically adjust the analysis priority based on the claim filing date. This allows for prioritizing the analysis of the most recent claims by determining the analysis priority based on the filing date.

[0064] The generation unit can prioritize insights based on when the problem occurred. For example, it can prioritize generating insights for recently occurring problems, while prioritizing insights for older problems. Furthermore, it can dynamically adjust the priority of insights based on when the problem occurred. This allows for priority provision of insights to the most recent problems by prioritizing insights based on when the problem occurred.

[0065] The recommendation department can customize the recommendations based on customer attribute information when proposing action plans. For example, it can propose appropriate action plans based on the customer's age. It can also propose customized action plans based on the customer's purchase history. Furthermore, it can propose region-specific action plans based on the customer's location information. In this way, by customizing the recommendations based on customer attribute information, it is possible to provide more appropriate action plans.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The collection unit collects customer complaint data. The collection unit collects detailed data such as the content of the complaint, the date and time it occurred, and the customer's attributes. The collection unit can collect various types of complaint data, such as complaints about product defects and complaints about service responses. Step 2: The analysis department performs text analysis on the claim data collected by the data collection department. The analysis department analyzes the text data of the claims to extract the frequency, trends, and relevance of the issues. For example, if there are many complaints about a particular product, it is possible to identify common problems with that product. In addition, by analyzing data such as the frequency and timing of complaints and customer attributes, it is possible to understand the trends in the complaints. Step 3: The generation unit generates insights based on the frequency, trends, and relevance of the problems extracted by the analysis unit. Based on the extracted data, the generation unit generates insights that help companies develop improvement strategies. Step 4: The recommendations team proposes an action plan based on the insights generated by the generation team. The recommendations team identifies specific points and process defects that cause complaints and proposes an action plan to prevent future complaints. For example, they may propose specific action plans such as reviewing the product design or improving the service delivery process. Step 5: The Providing Department provides the action plan proposed by the Advocating Department. The Providing Department provides the proposed action plan to the company.

[0068] (Example of form 2) An AI system according to an embodiment of the present invention is a system that analyzes customer complaint data and identifies the problems causing those complaints. The AI ​​system collects customer complaint data, performs text analysis, extracts the frequency, trends, and relevance of problems, and generates insights. This helps companies develop measures to improve their services and products. The AI ​​system also clarifies specific points and process defects that cause complaints and proposes action plans to prevent future complaints. For example, the AI ​​system collects customer complaint data. In this process, it collects detailed data such as the content of the complaint, the date and time of occurrence, and the customer's attributes. For example, it can collect various types of complaint data, such as complaints about product defects or complaints about service responses. Next, the AI ​​performs text analysis on the collected complaint data. The AI ​​analyzes the text data of the complaints and extracts the frequency, trends, and relevance of problems. For example, if there are many complaints about a particular product, it can identify common problems with that product. Furthermore, by analyzing data such as the frequency and timing of complaints and customer attributes, it can grasp the trends of complaints. In addition, the AI ​​clarifies specific points and process defects that cause complaints. For example, it can identify specific problems, such as flaws in product design or defects in the service delivery process. This allows companies to understand the root cause of the problem and take appropriate corrective actions. Finally, AI can suggest action plans to prevent future complaints. For example, it can suggest specific action plans such as reviewing product design or improving the service delivery process. This allows companies to prevent complaints from occurring and improve customer satisfaction. In this way, AI systems can efficiently collect, analyze, generate insights from customer complaint data, suggest action plans, and provide them to customers.

[0069] The AI ​​system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a recommendation unit, and a provision unit. The collection unit collects customer complaint data. The collection unit collects detailed data such as the content of the complaint, the date and time of occurrence, and customer attributes. The collection unit can collect various types of complaint data, such as complaints about product defects and complaints about service responses. The analysis unit performs text analysis on the complaint data collected by the collection unit. The analysis unit analyzes the text data of the complaints and extracts the frequency, trends, and relevance of the problems. For example, if there are many complaints about a particular product, the analysis unit can identify common problems with that product. The analysis unit can also grasp the trends of complaints by analyzing data such as the frequency and timing of complaint occurrences and customer attributes. The generation unit generates insights based on the frequency, trends, and relevance of the problems extracted by the analysis unit. For example, the generation unit generates insights based on the extracted data that can help companies develop improvement measures. The recommendation unit proposes an action plan based on the insights generated by the generation unit. The recommendation department, for example, identifies specific points and process defects that cause complaints and proposes action plans to prevent future complaints. The recommendation department may propose specific action plans, such as reviewing product design or improving service delivery processes. The delivery department provides the action plans proposed by the recommendation department. The delivery department provides the proposed action plans to companies, for example. As a result, the AI ​​system according to the embodiment can efficiently collect, analyze, generate insights from customer complaint data, propose action plans, and provide them.

[0070] The data collection unit collects customer complaint data. This includes detailed data such as the nature of the complaint, the date and time of occurrence, and customer attributes. Specifically, the unit collects data from customer complaint forms, customer support call records, emails, and chat logs. This data includes not only text data but also audio and image data. For example, photos and videos sent by customers when reporting product defects are also collected. The data collection unit centrally manages this data and stores it in a database. Furthermore, the data collection unit allows for flexible configuration of data collection frequency and methods. For example, it can be configured to collect data in real time or periodically. The data collection unit also has features to detect data duplication and omissions and clean the data as needed to ensure data quality. This enables the data collection unit to efficiently and accurately collect customer complaint data, providing high-quality data necessary for subsequent analysis and insight generation.

[0071] The analysis department performs text analysis on the complaint data collected by the collection department. For example, the analysis department analyzes the text data of complaints to extract the frequency, trends, and relevance of problems. Specifically, it uses natural language processing (NLP) techniques to analyze the content of complaints, extract keywords, and perform sentiment analysis. For example, it extracts keywords such as "defect," "delay," and "poor service" from the complaint text and calculates the frequency of each keyword. It also uses sentiment analysis to determine whether a complaint is classified as positive, negative, or neutral. Furthermore, the analysis department grasps the trends of complaints by analyzing data such as the frequency and timing of complaint occurrences and customer attributes. For example, if complaints about a particular product are concentrated at a specific time, there may be a problem with the manufacturing lot or shipping timing of that product. Also, if complaints are frequent in a particular customer segment (e.g., age or region), there may be a problem specific to that customer segment. In this way, the analysis department can analyze the collected complaint data from multiple angles and clarify the root causes and trends of problems.

[0072] The generation unit generates insights based on the frequency, trends, and relevance of problems extracted by the analysis unit. For example, the generation unit generates insights that help companies develop improvement measures based on the extracted data. Specifically, the generation unit uses AI to analyze data and propose root causes of problems and specific actions for improvement. For example, if there are many complaints about a particular product, it may suggest a review of the design or improvement of the manufacturing process, as there may be problems with the product's design or manufacturing process. Similarly, if there are many complaints about a particular service, it may suggest improvements to the service delivery method or staff training, as there may be problems with the service delivery method or response process. Furthermore, the generation unit can build predictive models based on complaint data to predict the risk of future complaints. This allows companies to take proactive measures and prevent complaints from occurring in the first place. The generation unit provides these insights to companies and provides a foundation for implementing specific improvement measures.

[0073] The Recommendation Department proposes action plans based on the insights generated by the Generation Department. For example, the Recommendation Department identifies specific points and process defects that cause complaints and proposes action plans to prevent future complaints. Specifically, the Recommendation Department can propose concrete action plans such as reviewing product design or improving service delivery processes. For example, if there are many complaints about a particular product, there may be problems with the product's design or manufacturing process, so the Recommendation Department would propose reviewing the design or improving the manufacturing process. Similarly, if there are many complaints about a particular service, there may be problems with the service delivery method or response process, so the Recommendation Department would propose improving the service delivery method or training staff. Furthermore, the Recommendation Department can build predictive models based on complaint data to predict the risk of future complaints. This allows companies to take preventative measures and prevent complaints from occurring in the first place. The Recommendation Department provides these action plans to companies and provides a foundation for implementing specific improvement measures.

[0074] The Provider Department provides the action plan proposed by the Recommendation Department. For example, the Provider Department provides the proposed action plan to the company. Specifically, the Provider Department notifies each department of the company of the proposed action plan and provides support for its implementation. For example, it provides specific procedures for reviewing product designs and training programs to improve service delivery processes. The Provider Department also monitors the implementation status of the action plan and provides feedback as needed. This enables the company to effectively implement the proposed action plan and prevent complaints from occurring. Furthermore, the Provider Department can also evaluate the results of the action plan implementation and provide feedback for improvement. For example, it evaluates whether the number of complaints has decreased after the implementation of the action plan and proposes additional improvement measures as needed. In this way, the Provider Department can support the company in effectively preventing complaints and improving customer satisfaction.

[0075] The data collection unit can collect detailed data such as the content of the complaint, the date and time of occurrence, and customer attributes. For example, the data collection unit can record the details of the complaint and accurately record the date and time of occurrence. The data collection unit can also collect customer attribute information, such as the customer's age, gender, and place of residence. This allows for more accurate analysis by collecting detailed complaint data. 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 attribute information into AI, and the AI ​​can automatically collect the data.

[0076] The analysis unit can analyze the text data of claims and extract the frequency, trends, and relevance of the issues. For example, the analysis unit can analyze the text data of claims using natural language processing techniques. For example, the analysis unit can extract keywords from the text data of claims and measure their frequency. The analysis unit can also analyze the text data of claims over time to grasp trends. Furthermore, the analysis unit can perform correlation analysis on the text data of claims to evaluate their relevance. In this way, by analyzing the text data of claims, the frequency, trends, and relevance of the issues can be grasped. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input the text data of claims into a generative AI, and the generative AI can perform the analysis automatically.

[0077] The generation unit can generate insights based on the frequency, trends, and relevance of the extracted problems. For example, the generation unit can generate insights using data mining techniques. For instance, based on the extracted data, the generation unit can identify the root causes of problems and propose improvements. The generation unit can also use machine learning algorithms to predict future complaints and suggest preventative measures. This helps companies develop improvement strategies by generating insights based on the extracted data. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can input the extracted data into a generation AI, which can then automatically generate insights.

[0078] The recommendation department can identify specific points and process defects that cause complaints and propose action plans to prevent future complaints. For example, the recommendation department can identify process defects using flowchart analysis. For example, the recommendation department can identify the source of complaints and evaluate their scope of impact. The recommendation department can also identify bottlenecks and propose solutions. This allows for the identification of specific problems and the proposal of action plans to prevent future complaints, thereby improving the quality of the company's services and products. Some or all of the above processes in the recommendation department may be performed using AI, or not. For example, the recommendation department can input the points that cause complaints into AI, and the AI ​​can automatically propose an action plan.

[0079] The service provider can provide the proposed action plan to the company. For example, the service provider can provide the proposed action plan in report format. For example, the service provider can create a report detailing the action plan and submit it to the company. Alternatively, the service provider can provide the proposed action plan in presentation format. For example, the service provider can create a slide presentation outlining the action plan and give it to the company. By providing the company with the proposed action plan, the company can quickly implement the improvement measures. Some or all of the above processes performed by the service provider may be carried out using AI, for example, or not. For example, the service provider can input the proposed action plan into an AI, which can then automatically generate a report.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of complaint data collection based on the estimated emotions. For example, if the user is angry, the data collection unit can immediately collect complaint data to encourage a quick response. If the user is calm, the data collection unit can take more time to collect detailed complaint data. Furthermore, if the user is feeling anxious, the data collection unit can adjust the collection timing to provide reassurance. By adjusting the collection timing according to the user's emotions, complaint data can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into the generative AI, which can automatically adjust the collection timing.

[0081] The data collection unit can analyze a customer's past claim history and select the optimal collection method when collecting claim data. For example, if a customer has filed claims frequently in the past, the data collection unit can provide a simple collection method. Alternatively, if a customer has filed detailed claims in the past, the data collection unit can provide a detailed collection method. Furthermore, the data collection unit can customize the collection method for specific issues based on the customer's past claim history. This allows for the selection of the optimal collection method by analyzing the customer's past claim history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the customer's past claim history into AI, which can then automatically select the optimal collection method.

[0082] The data collection unit can filter the collected claim data based on the customer's current purchasing habits and areas of interest. For example, the data collection unit can prioritize collecting claims related to products recently purchased by the customer. The data collection unit can also filter and collect claims related to the customer's areas of interest. Furthermore, the data collection unit can collect highly relevant claim data based on the customer's purchase history. This allows for the collection of highly relevant claim data by filtering based on the customer's purchasing habits and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the customer's purchase history into AI, which can then automatically perform the filtering.

[0083] The data collection unit can estimate the user's emotions and determine the priority of the complaint data to collect based on the estimated emotions. For example, if a user expresses strong dissatisfaction, the data collection unit will prioritize collecting that complaint data. Conversely, if a user expresses minor dissatisfaction, the data collection unit may postpone collecting that complaint data. Furthermore, the data collection unit can dynamically adjust the order in which the complaint data is collected based on the user's emotions. This allows for the priority collection of important complaint data by prioritizing the complaint data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can then automatically determine the priority.

[0084] The data collection unit can prioritize the collection of highly relevant data by considering the customer's geographical location when collecting claim data. For example, if a customer lives in a specific region, the data collection unit will prioritize the collection of claim data related to that region. The data collection unit can also collect claim data related to region-specific issues based on the customer's geographical location. Furthermore, the data collection unit can acquire the customer's location information in real time and collect highly relevant claim data. This allows for the priority collection of claim data related to region-specific issues by considering the customer's geographical location. 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 the customer's geographical location information into AI, which can then automatically prioritize the collection of highly relevant data.

[0085] The data collection unit can analyze customers' social media activity and collect relevant data when collecting complaint data. For example, the data collection unit can collect complaint content posted by customers on social media. The data collection unit can also extract relevant complaint data from customers' social media activity. Furthermore, the data collection unit can analyze customers' responses on social media and collect complaint data. In this way, relevant complaint data can be collected by analyzing customers' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input customer social media activity data into AI, and the AI ​​can automatically collect relevant data.

[0086] The analysis unit can estimate the user's emotions and adjust the text analysis's presentation based on the estimated emotions. For example, if the user is angry, the analysis unit can use softer language to alleviate the emotion. If the user is calm, the analysis unit can also provide detailed analysis results. Furthermore, if the user is feeling anxious, the analysis unit can use reassuring language. By adjusting the text analysis's presentation based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI, which can then automatically adjust the presentation.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the claims during text analysis. For example, the analysis unit will perform a detailed analysis on high-importance claims. Conversely, it can perform a simplified analysis on low-importance claims. Furthermore, the analysis unit can dynamically adjust the depth of the analysis according to the importance of the claims. This allows for detailed analysis of important claims by adjusting the level of detail based on the importance of the claims. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input claim importance data into AI, and the AI ​​can automatically adjust the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the claim category during text analysis. For example, it can apply a product-specific analysis algorithm to product-related claims, and a service-specific analysis algorithm to service-related claims. Furthermore, the analysis unit can select the optimal analysis algorithm based on the claim category. This allows for more accurate analysis results by applying the most suitable analysis algorithm for each claim category. 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 claim category data into an AI, which can then automatically apply the optimal analysis algorithm.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the text analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. Conversely, if the user is relaxed, the analysis unit can provide a detailed analysis. Furthermore, the analysis unit can dynamically adjust the length of the analysis results based on the user's emotions. This allows for the provision of analysis results tailored to the user's needs by adjusting the length of the text analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into the generative AI, which can then automatically adjust the length of the analysis.

[0090] The analysis department can prioritize the analysis based on the claim filing date during text analysis. For example, the analysis department can prioritize the analysis of recently filed claims. It can also postpone the analysis of older claims. Furthermore, the analysis department can dynamically adjust the analysis priority based on the claim filing date. This allows for prioritizing the analysis of the most recent claims by determining the analysis priority based on the claim filing date. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input claim filing date data into AI, which can then automatically determine the priority.

[0091] The analysis unit can adjust the order of analysis based on the relevance of claims during text analysis. For example, the analysis unit can prioritize the analysis of highly relevant claims. It can also postpone the analysis of less relevant claims. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of claims. This allows for the prioritization of highly relevant claims by adjusting the order of analysis based on the relevance of claims. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input claim relevance data into AI, which can then automatically adjust the order.

[0092] The generation unit can estimate the user's emotions and adjust the method of generating insights based on the estimated user emotions. For example, if the user is relaxed, the generation unit can provide detailed insights. If the user is in a hurry, the generation unit can also provide concise insights. Furthermore, the generation unit can dynamically adjust the method of generating insights based on the user's emotions. This allows for the provision of more appropriate insights by adjusting the method of generating insights based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generative AI, and the generative AI can automatically adjust the generation method.

[0093] The generation unit can adjust the level of detail generated based on the importance of the extracted problems when generating insights. For example, the generation unit generates detailed insights for high-importance problems. It can also generate simplified insights for low-importance problems. Furthermore, the generation unit can dynamically adjust the level of detail of the insights according to the importance of the problems. This allows for the provision of detailed insights for important problems by adjusting the level of detail of the insights based on the importance of the extracted problems. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input problem importance data into the AI, and the AI ​​can automatically adjust the level of detail.

[0094] The generation unit can apply different generation algorithms depending on the problem category when generating insights. For example, the generation unit can apply a product-specific generation algorithm to product-related problems. It can also apply a service-specific generation algorithm to service-related problems. Furthermore, the generation unit can select the optimal generation algorithm depending on the problem category. By applying the optimal generation algorithm according to the problem category, more accurate insights can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input problem category data into the AI, which can then automatically apply the optimal generation algorithm.

[0095] The generation unit can estimate the user's emotions and adjust the length of the insights based on the estimated emotions. For example, if the user is in a hurry, the generation unit can provide short, concise insights. Conversely, if the user is relaxed, the generation unit can provide detailed insights. Furthermore, the generation unit can dynamically adjust the length of the insights based on the user's emotions. This allows for the provision of insights tailored to the user's needs by adjusting the length of the insights based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generative AI, which can then automatically adjust the length of the insights.

[0096] The generation unit can prioritize insights based on when the problem occurred. For example, it can prioritize generating insights for recently occurring problems. It can also postpone generating insights for older problems. Furthermore, the generation unit can dynamically adjust the priority of insights based on when the problem occurred. This allows for priority provision of insights to the most recent problems by prioritizing insights based on when the problem occurred. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input problem occurrence data into the AI, which can then automatically determine the priority.

[0097] The generation unit can adjust the order of insights based on the relevance of the problems when generating insights. For example, the generation unit will prioritize generating insights for highly relevant problems. It can also postpone generating insights for less relevant problems. Furthermore, the generation unit can dynamically adjust the order of insights based on the relevance of the problems. This allows for prioritizing the provision of insights to highly relevant problems by adjusting the order of insights based on the relevance of the problems. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input problem relevance data into the AI, and the AI ​​can automatically adjust the order.

[0098] The recommendation unit can estimate the user's emotions and adjust the method of recommending action plans based on the estimated emotions. For example, if the user is relaxed, the recommendation unit may recommend a detailed action plan. If the user is in a hurry, the recommendation unit may also recommend a concise action plan. Furthermore, the recommendation unit can dynamically adjust the method of recommending action plans based on the user's emotions. This allows for the provision of more appropriate action plans by adjusting the method of recommending action plans based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI, or not using AI. For example, the recommendation unit can input user emotion data into a generative AI, which can then automatically adjust the recommendation method.

[0099] The recommendation department can select the optimal recommendation method by referring to past claim data when proposing action plans. For example, the recommendation department can propose the optimal action plan based on past claim data. The recommendation department can also propose action plans for similar problems based on past claim data. Furthermore, the recommendation department can analyze past claim data and propose the most effective action plan. In this way, the optimal action plan can be proposed by referring to past claim data. Some or all of the above processing in the recommendation department may be performed using AI, for example, or not using AI. For example, the recommendation department can input past claim data into AI, and the AI ​​can automatically select the optimal recommendation method.

[0100] The recommendation unit can customize the recommendations based on customer attribute information when proposing action plans. For example, the recommendation unit can propose an appropriate action plan based on the customer's age. It can also propose a customized action plan based on the customer's purchase history. Furthermore, the recommendation unit can propose a region-specific action plan based on the customer's regional information. This allows for the provision of more appropriate action plans by customizing the recommendations based on customer attribute information. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input customer attribute information into AI, and the AI ​​can automatically customize the recommendations.

[0101] The recommendation unit can estimate the user's emotions and determine the priority of action plans based on the estimated emotions. For example, if the user expresses strong dissatisfaction, the recommendation unit will recommend that action plan as the highest priority. Conversely, if the user expresses minor dissatisfaction, the recommendation unit may postpone that action plan. Furthermore, the recommendation unit can dynamically adjust the priority of action plans based on the user's emotions. This allows important action plans to be recommended preferentially by determining the priority of action plans based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI, or not using AI. For example, the recommendation unit can input user emotion data into a generative AI, which can then automatically determine the priority.

[0102] The recommendation unit can select the optimal recommendation method when proposing action plans, taking into account the customer's geographical location information. For example, if a customer lives in a specific region, the recommendation unit will propose an action plan relevant to that region. The recommendation unit can also propose action plans for region-specific issues based on the customer's geographical location information. Furthermore, the recommendation unit can acquire the customer's location information in real time and propose the optimal action plan. This allows for the proposal of optimal action plans for region-specific issues by considering the customer's geographical location information. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not. For example, the recommendation unit can input the customer's geographical location information into AI, which can then automatically select the optimal recommendation method.

[0103] The recommendation department can analyze the customer's social media activity and adjust the recommendations when proposing action plans. For example, the recommendation department can propose action plans based on what the customer has posted on social media. It can also propose relevant action plans based on the customer's social media activity. Furthermore, the recommendation department can analyze the customer's social media reactions and adjust the action plans. This allows the recommendation department to propose relevant action plans by analyzing the customer's social media activity. Some or all of the above processes in the recommendation department may be performed using AI, for example, or not. For example, the recommendation department can input customer social media activity data into AI, and the AI ​​can automatically adjust the recommendations.

[0104] The service provider can estimate the user's emotions and adjust the method of providing the action plan based on the estimated emotions. For example, if the user is relaxed, the service provider can provide a detailed action plan. If the user is in a hurry, the service provider can also provide a concise action plan. Furthermore, the service provider can dynamically adjust the method of providing the action plan based on the user's emotions. This allows for the provision of a more appropriate action plan by adjusting the method of providing the action plan based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into the generative AI, and the generative AI can automatically adjust the method of provision.

[0105] The service provider can select the optimal service method by referring to past service history when providing an action plan. For example, the service provider can provide the optimal action plan based on past service history. The service provider can also provide action plans for similar problems based on past service history. Furthermore, the service provider can analyze past service history and provide the most effective action plan. In this way, the optimal action plan can be provided by referring to past service history. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past service history into AI, and the AI ​​can automatically select the optimal service method.

[0106] The service provider can estimate the user's emotions and adjust the frequency of providing action plans based on the estimated emotions. For example, if the user expresses strong dissatisfaction, the service provider will provide action plans more frequently. Conversely, if the user expresses minor dissatisfaction, the service provider can reduce the frequency of provision. Furthermore, the service provider can dynamically adjust the frequency of provision of action plans based on the user's emotions. This allows for the provision of action plans at a more appropriate frequency by adjusting the frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into the generative AI, which can then automatically adjust the frequency of provision.

[0107] The service provider can select the optimal delivery method when providing an action plan, taking into account the customer's device information. For example, if the customer is using a smartphone, the service provider can provide an action plan adapted to the screen size. Furthermore, if the customer is using a tablet, the service provider can provide an action plan optimized for a larger screen. Additionally, if the customer is using a smartwatch, the service provider can provide a concise and highly visible action plan. This allows the service provider to deliver the action plan in the most optimal format by considering the customer's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the customer's device information into AI, which can then automatically select the optimal delivery method.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The data collection unit, when collecting customer complaint data, can analyze the customer's past purchase history and collect additional information to identify the root cause of the complaint. For example, it can collect information about products and services that the customer has purchased in the past to understand the frequency and trends of complaints. The data collection unit can also identify patterns of complaints for specific products or services from the customer's purchase history. Furthermore, the data collection unit can collect additional information based on the customer's purchase history to identify the root cause of the complaint. This allows for a more accurate identification of the root cause of complaints by analyzing the customer's purchase history.

[0110] The analytics department can estimate customer sentiment when performing text analysis of complaint data and adjust the level of detail of the analysis based on that estimated sentiment. For example, if a customer expresses strong dissatisfaction, a detailed analysis can be performed to identify the root cause of the problem. Conversely, if a customer expresses minor dissatisfaction, a simpler analysis can be performed. Furthermore, the analytics department can dynamically adjust the priority of the analysis based on customer sentiment. This allows for more appropriate analysis results by adjusting the level of detail of the analysis based on customer sentiment.

[0111] The generation unit can customize the content of insights by considering customer attribute information when generating them. For example, it can provide optimal insights for specific customer segments based on attribute information such as customer age, gender, and place of residence. The generation unit can also provide customized insights for individual customers based on their purchase history and complaint history. Furthermore, the generation unit can adjust how insights are delivered based on customer attribute information. This allows for the provision of more appropriate insights by considering customer attribute information.

[0112] The recommendations department can analyze customers' social media activity when proposing action plans and propose relevant action plans. For example, it can propose specific action plans based on the content of complaints posted by customers on social media. The recommendations department can also identify the causes of complaints from customers' social media activity and propose action plans based on that. Furthermore, the recommendations department can analyze customers' responses on social media and adjust the priorities of action plans. In this way, by analyzing customers' social media activity, it is possible to propose more appropriate action plans.

[0113] The service provider can select the optimal delivery method when providing action plans, taking into account the customer's device information. For example, if the customer is using a smartphone, an action plan tailored to the screen size can be provided. If the customer is using a tablet, an action plan optimized for a larger screen can be provided. Furthermore, if the customer is using a smartwatch, a concise and highly visible action plan can be provided. In this way, by considering the customer's device information, action plans can be delivered in the most optimal format.

[0114] The data collection unit can estimate the user's emotions and adjust the method of collecting complaint data based on those emotions. For example, if the user is angry, it can quickly collect complaint data and respond immediately. If the user is calm, it can take more time to collect detailed complaint data. Furthermore, if the user is feeling anxious, it can adjust the collection method to provide reassurance. By adjusting the collection method according to the user's emotions, complaint data can be collected at a more appropriate time.

[0115] The analytics department can prioritize the analysis of claim data based on when the claims were filed. For example, it can prioritize the analysis of recently filed claims and respond quickly, while delaying older claims. Furthermore, it can dynamically adjust the analysis priority based on the claim filing date. This allows for prioritizing the analysis of the most recent claims by determining the analysis priority based on the filing date.

[0116] The generation unit can prioritize insights based on when the problem occurred. For example, it can prioritize generating insights for recently occurring problems, while prioritizing insights for older problems. Furthermore, it can dynamically adjust the priority of insights based on when the problem occurred. This allows for priority provision of insights to the most recent problems by prioritizing insights based on when the problem occurred.

[0117] The recommendation department can customize the recommendations based on customer attribute information when proposing action plans. For example, it can propose appropriate action plans based on the customer's age. It can also propose customized action plans based on the customer's purchase history. Furthermore, it can propose region-specific action plans based on the customer's location information. In this way, by customizing the recommendations based on customer attribute information, it is possible to provide more appropriate action plans.

[0118] The service provider can estimate the user's emotions and adjust how the action plan is delivered based on those emotions. For example, if the user is relaxed, a detailed action plan can be provided. Conversely, if the user is in a hurry, a concise action plan can be provided. Furthermore, the service provider can dynamically adjust how the action plan is delivered based on the user's emotions. This allows for the delivery of a more appropriate action plan by adjusting how the action plan is delivered based on the user's emotions.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The collection unit collects customer complaint data. The collection unit collects detailed data such as the content of the complaint, the date and time it occurred, and the customer's attributes. The collection unit can collect various types of complaint data, such as complaints about product defects and complaints about service responses. Step 2: The analysis department performs text analysis on the claim data collected by the data collection department. The analysis department analyzes the text data of the claims to extract the frequency, trends, and relevance of the issues. For example, if there are many complaints about a particular product, it is possible to identify common problems with that product. In addition, by analyzing data such as the frequency and timing of complaints and customer attributes, it is possible to understand the trends in the complaints. Step 3: The generation unit generates insights based on the frequency, trends, and relevance of the problems extracted by the analysis unit. Based on the extracted data, the generation unit generates insights that help companies develop improvement strategies. Step 4: The recommendations team proposes an action plan based on the insights generated by the generation team. The recommendations team identifies specific points and process defects that cause complaints and proposes an action plan to prevent future complaints. For example, they may propose specific action plans such as reviewing the product design or improving the service delivery process. Step 5: The Providing Department provides the action plan proposed by the Advocating Department. The Providing Department provides the proposed action plan to the company.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, recommendation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects customer complaint data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs text analysis on the collected complaint data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates insights based on the frequency, trends, and relevance of problems. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an action plan based on the generated insights. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the proposed action plan to the company. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, recommendation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects customer complaint data. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and performs text analysis on the collected complaint data. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates insights based on the frequency, trends, and relevance of the problems. The recommendation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and proposes an action plan based on the generated insights. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides the proposed action plan to the company. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, recommendation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects customer complaint data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs text analysis on the collected complaint data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates insights based on the frequency, trends, and relevance of problems. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an action plan based on the generated insights. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the proposed action plan to the company. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, recommendation unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects customer complaint data. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and performs text analysis on the collected complaint data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates insights based on the frequency, trends, and relevance of problems. The recommendation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes an action plan based on the generated insights. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides the proposed action plan to the company. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] (Note 1) The data collection department collects customer complaint data, An analysis unit performs text analysis on the claim data collected by the aforementioned collection unit, A generation unit that generates insights based on the frequency, trends, and relevance of problems extracted by the analysis unit, A recommendation unit proposes an action plan based on the insights generated by the generation unit, The system includes a provision unit that provides the action plan proposed by the aforementioned recommendation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect detailed data such as the nature of the complaint, the date and time it occurred, and the customer's attributes. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Analyze the text data of the claims to extract the frequency, trends, and relevance of the issues. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate insights based on the frequency, trends, and relevance of the identified problems. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned recommendation section, Identify specific points and process flaws that lead to complaints, and propose action plans to prevent future complaints. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We will provide the proposed action plan to the company. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate user sentiment and adjust the timing of complaint data collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting complaint data, we analyze the customer's past complaint history and select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting complaint data, filtering is performed based on the customer's current purchasing habits and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is We estimate user sentiment and prioritize the complaint data to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting claims data, the collection of highly relevant data is prioritized by considering the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting complaint data, analyze customers' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way text analysis is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During text analysis, adjust the level of detail based on the importance of the claims. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is When performing text analysis, different analytical algorithms are applied depending on the claim category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's sentiment and adjusts the length of the text analysis based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is When performing text analysis, prioritize the analysis based on when the claims were filed. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During text analysis, adjust the order of analysis based on the relevance of the claims. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is We estimate user emotions and adjust how insights are generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating insights, adjust the level of detail based on the importance of the identified issues. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating insights, apply different generation algorithms depending on the problem category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts the length of the insights based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating insights, prioritize them based on when the problem occurred. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating insights, adjust the order of insights based on their relevance to the problem. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned recommendation section, We estimate the user's emotions and adjust the method of recommending action plans based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned recommendation section, When proposing an action plan, refer to past complaint data to select the most appropriate method of recommendation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned recommendation section, When proposing action plans, customize the recommendations based on customer attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned recommendation section, It estimates the user's emotions and prioritizes the action plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned recommendation section, When proposing action plans, we select the most appropriate method of proposal by considering the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned recommendation section, When proposing action plans, we analyze customers' social media activity and adjust the recommendations accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, We estimate the user's emotions and adjust how we deliver action plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing an action plan, the optimal delivery method will be selected by referring to past delivery history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the frequency of providing action plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, When providing an action plan, the optimal delivery method will be selected, taking into account the customer's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0193] 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 data collection department collects customer complaint data, An analysis unit performs text analysis on the claim data collected by the aforementioned collection unit, A generation unit that generates insights based on the frequency, trends, and relevance of problems extracted by the analysis unit, A recommendation unit proposes an action plan based on the insights generated by the generation unit, The system includes a provision unit that provides the action plan proposed by the aforementioned recommendation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect detailed data such as the nature of the complaint, the date and time it occurred, and the customer's attributes. The system according to feature 1.

3. The aforementioned analysis unit is Analyze the text data of the claims to extract the frequency, trends, and relevance of the issues. The system according to feature 1.

4. The generating unit is Generate insights based on the frequency, trends, and relevance of the identified problems. The system according to feature 1.

5. The aforementioned recommendation section, Identify specific points and process flaws that lead to complaints, and propose action plans to prevent future complaints. The system according to feature 1.

6. The aforementioned supply unit is, We will provide the proposed action plan to the company. The system according to feature 1.

7. The aforementioned collection unit is We estimate user sentiment and adjust the timing of complaint data collection based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned collection unit is When collecting complaint data, we analyze the customer's past complaint history and select the most suitable collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting complaint data, filtering is performed based on the customer's current purchasing habits and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is We estimate user sentiment and prioritize the complaint data to collect based on the estimated user sentiment. The system according to feature 1.

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