System

Abstract

An object of a system according to an embodiment is to improve the efficiency of a complaint handling process and propose an improvement.SOLUTION: A system includes a reception part, an analysis part, a provision part, a recording part, and an improvement proposal part. The reception part inputs the content of a claim. The analysis part analyzes the claim content input by the reception part. The providing unit provides the user with the countermeasure generated by the analysis unit. The recording unit records the result of the complaint handling based on the countermeasure provided by the providing unit. The improvement proposal unit proposes an improvement based on the data recorded by the recording unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not efficiently handle complaints, and there is room for improvement.

[0005] The system according to the embodiment aims to streamline the complaint handling process and propose improvements. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, a recording unit, and an improvement suggestion unit. The reception unit inputs the content of the complaint. The analysis unit analyzes the content of the complaint input by the reception unit. The provision unit provides the user with the countermeasures generated by the analysis unit. The recording unit records the results of the complaint handling based on the countermeasures provided by the provision unit. The improvement suggestion unit proposes improvements based on the data recorded by the recording unit. [Effects of the Invention]

[0007] The system according to the embodiment can streamline the complaint handling process and suggest improvements. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple 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), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A complaint handling system according to an embodiment of the present invention automatically analyzes the content of a complaint, generates an optimal countermeasure, and provides it to the user. The complaint handling system inputs the content of the complaint, and a generation AI analyzes the content and generates an optimal countermeasure. The generated countermeasure is provided to the user, who can then respond to the complaint based on the countermeasure. For example, the complaint handling system inputs detailed information about the complaint, such as the cause of the complaint, specific details, and related evidence. This information is then input to the generation AI. The complaint handling system then uses the generation AI to analyze the input complaint content. The generation AI then understands the content of the complaint and generates an optimal countermeasure. For example, if the complaint is caused by a product defect, the generation AI generates specific steps to correct the defect and how to explain the defect to the customer. The complaint handling system then provides the generated countermeasure to the user. The user can respond to the complaint based on the countermeasure provided by the generation AI. For example, the user can resolve the complaint by correcting the product defect according to the steps proposed by the generation AI and providing an appropriate explanation to the customer. This improves the efficiency of complaint handling and reduces the burden on the user. Furthermore, the generation AI can generate more accurate countermeasures by learning from past complaint data. This also improves the quality of complaint handling. Furthermore, the complaint handling system can record the results of complaint handling and use them for future complaint handling. For example, if there are many complaints about a particular product, it can suggest improvements to that product. This can prevent complaints from occurring in the first place. This allows the complaint handling system to improve the efficiency and quality of complaint handling. For example, users can respond to complaints quickly and appropriately simply by following the specific response measures provided by the generation AI.

[0029] A complaint handling system according to an embodiment includes a reception unit, an analysis unit, a provision unit, a recording unit, and an improvement proposal unit. The reception unit inputs the content of the complaint. The content of the complaint may include, but is not limited to, a product defect or dissatisfaction with the service. The reception unit accepts various input methods, such as text input, voice input, and image attachment. The reception unit is also required to input detailed information about the complaint. For example, the reception unit inputs the cause of the complaint, specific details, and related evidence. This information is input to a generation AI. The analysis unit uses the generation AI to analyze the content of the complaint input by the reception unit. The analysis is performed using, for example, text analysis and sentiment analysis, but is not limited to, examples. For example, the generation AI understands the content of the complaint and generates an optimal countermeasure. The generation AI includes an algorithm for learning past complaint data and generating an optimal countermeasure. The provision unit provides the countermeasure generated by the analysis unit to a user. For example, the method of providing the countermeasure includes, but is not limited to, sending an email or checking on a dedicated dashboard. For example, the provision unit can resolve a complaint by correcting a product defect according to the procedure proposed by the generation AI and providing an appropriate explanation to the customer. The recording unit records the results of the complaint handling based on the countermeasures provided by the provision unit. Recording methods include, but are not limited to, database or cloud management. For example, the recording unit can store the results of the complaint handling in a database and use them for future complaint handling. The improvement suggestion unit proposes improvements based on the data recorded by the recording unit. For example, when proposing improvements to a specific product, the improvement suggestions include the data on which the suggestions are based and the method used to identify the improvements. This allows the complaint handling system according to the embodiment to improve the efficiency and quality of complaint handling. For example, a user can respond to a complaint quickly and appropriately by simply following the specific countermeasures provided by the generation AI.

[0030] The reception unit can accept multiple input methods, such as text input, voice input, and image attachment. The reception unit accepts various input methods, such as text input, voice input, and image attachment. Examples of text input include, but are not limited to, free text and multiple choice format. Examples of voice input include, but are not limited to, voice recognition technology and uploading a recorded file. Examples of image attachment include, but are not limited to, image files in JPEG format and PNG format. This allows users to enter claims in a variety of ways. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can convert voice input into text data using voice recognition technology.

[0031] The analysis unit may include an algorithm for learning from past claim data and generating a countermeasure. The analysis unit uses the generation AI to analyze the claim content input by the reception unit. The analysis is performed using, for example, text analysis, sentiment analysis, etc., but is not limited to these examples. The generation AI includes an algorithm for learning from past claim data and generating an optimal countermeasure. For example, the generation AI understands the content of the claim based on the past claim data and generates an optimal countermeasure. The generation AI can analyze the content of the claim and generate a countermeasure using a machine learning algorithm or a rule-based algorithm. This allows for the generation of more accurate countermeasures by utilizing past data. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input past claim data into the generation AI and have the generation AI generate a countermeasure.

[0032] The providing unit may include methods such as sending an email or checking on a dedicated dashboard. The providing unit provides the countermeasures generated by the analysis unit to the user. Examples of providing methods include, but are not limited to, sending an email or checking on a dedicated dashboard. For example, the providing unit may resolve a complaint by correcting a product defect according to the procedure proposed by the generation AI and providing an appropriate explanation to the customer. Examples of sending an email include, but are not limited to, HTML email, text email, etc. The dedicated dashboard may include, but are not limited to, a web-based dashboard, a mobile app, etc. This allows the user to receive the countermeasures in various ways. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without AI. For example, the providing unit may send the countermeasures generated by the generation AI by email.

[0033] The recording unit may include management in a database or on a cloud. The recording unit records the results of the complaint handling based on the countermeasures provided by the providing unit. Examples of recording methods include, but are not limited to, management in a database or on a cloud. For example, the recording unit may store the results of the complaint handling in a database and use them for subsequent complaint handling. Examples of databases include, but are not limited to, SQL databases and NoSQL databases. Examples of clouds include, but are not limited to, cloud services such as AWS (registered trademark) and Google (registered trademark) Cloud. This allows for efficient management of the results of the complaint handling. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit may automatically store the results of the complaint handling in a database on a cloud.

[0034] When proposing improvements to a specific product, the improvement suggestion unit can make the suggestions based on specific data and identify the improvements using a specific method. The improvement suggestion unit proposes improvements based on data recorded by the recording unit. The improvement suggestion includes, for example, what data the suggestion is based on and what method is used to identify the improvements when proposing improvements to a specific product. Examples of specific data include, but are not limited to, user feedback and product usage data. Examples of specific methods include, but are not limited to, data mining and statistical analysis. This clarifies the basis for the improvement suggestions. Some or all of the above-described processing in the improvement suggestion unit may be performed using, or without, AI. For example, the improvement suggestion unit may input user feedback to a generation AI and have the generation AI execute improvement suggestions.

[0035] The reception unit can add a function to automatically complete detailed information about a claim, thereby reducing the effort required for input. For example, when a user inputs a summary of a claim, the reception unit automatically completes related detailed information. The reception unit can also automatically complete related evidence and data when a user inputs the cause of the claim. The reception unit can also automatically complete information about past similar claims when a user inputs the content of the claim. This reduces the effort required for user input. Automatic completion of detailed information is performed, for example, by using past input data or by prediction using a generation AI. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past claim data into the generation AI and have the generation AI automatically complete the detailed information.

[0036] The reception unit can analyze the content of the claim in real time and provide appropriate guidance while the user is entering the content. For example, the reception unit can analyze the content of the claim in real time and display appropriate input guidance while the user is entering the content of the claim. The reception unit can also display guidance encouraging the user to enter relevant evidence and data while the user is entering the cause of the claim. The reception unit can also display guidance encouraging the user to refer to information on past similar claims while the user is entering the details of the claim. This allows the user to enter the claim while receiving appropriate guidance. Real-time analysis can be performed, for example, by streaming data analysis or real-time feedback. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the content of the claim to a generation AI in real time and have the generation AI provide appropriate guidance.

[0037] The reception unit can be added with a function to categorize the content of complaints and assign them to the appropriate analysis unit. For example, the reception unit automatically analyzes the content of complaints entered by a user and classifies them by category. Furthermore, if the content of a complaint is related to a product defect, the reception unit can categorize it into a product defect category and assign it to the appropriate analysis unit. Furthermore, if the content of a complaint is related to a service problem, the reception unit can categorize it into a service problem category and assign it to the appropriate analysis unit. This allows for appropriate analysis based on the content of the complaint. Categorization can be performed using, for example, a text classification algorithm or rule-based classification. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the content of a complaint into a generation AI and have the generation AI perform categorization and assignment to an analysis unit.

[0038] The reception unit can be added with a function to accept not only voice input and image attachment of the content of the complaint, but also video input. For example, the reception unit can allow the user to not only input the content of the complaint by voice, but also to explain it using video. The reception unit can also allow the user to record and attach a video of the circumstances under which the complaint occurred. The reception unit can also allow the user to explain the details of the complaint using video and provide it to the analysis unit. This allows the user to input the complaint using video. Video input is performed, for example, in MP4 format or real-time streaming. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input video data into a generation AI and have the generation AI analyze the video data.

[0039] The reception unit can be added with a function for receiving the content of a claim in multiple languages ​​and automatically translating it in the analysis unit. For example, the reception unit allows a user to input the content of a claim in multiple languages, which is then automatically translated by the analysis unit. The reception unit can also allow a user to input details of the claim in different languages, which is then automatically translated by the analysis unit. The reception unit can also allow a user to input the cause of the claim in multiple languages, which is then automatically translated by the analysis unit. This enables claims to be input in multiple languages. Multilingual support is achieved, for example, by using a translation algorithm or a language selection option. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the content of the claim to a generation AI, which can then perform multilingual translation.

[0040] The reception unit can be added with a function to automatically collect complaint content from social media and import it into the reception unit. For example, the reception unit automatically collects complaint content posted by users on social media and imports it into the reception unit. The reception unit can also automatically collect complaint information shared by users on social media and import it into the reception unit. The reception unit can also automatically collect complaint content commented by users on social media and import it into the reception unit. This allows complaint information to be automatically collected from social media. Social media information is collected from social media platforms such as Twitter and Facebook. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input social media data into a generation AI and have the generation AI collect complaint information.

[0041] The analysis unit may add functionality based on relevant laws and regulations or industry standards when analyzing the content of a claim. For example, the analysis unit may consider relevant laws and regulations when analyzing the content of a claim and generate appropriate countermeasures. The analysis unit may also consider industry standards when analyzing the content of a claim and generate appropriate countermeasures. The analysis unit may also consider both laws and industry standards when analyzing the content of a claim and generate optimal countermeasures. This allows countermeasures to be generated that take laws and industry standards into account. Examples of laws and regulations include, but are not limited to, provisions of specific laws and regulations. Examples of industry standards include, but are not limited to, ISO standards and industry guidelines. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input data on relevant laws and regulations or industry standards into the generation AI and cause the generation AI to generate countermeasures.

[0042] The analysis unit may add a function to reference solutions to similar claims in the past when analyzing the content of a claim. For example, when analyzing the content of a claim, the analysis unit may reference solutions to similar claims in the past to generate an optimal solution. Furthermore, when analyzing the content of a claim, the analysis unit may reference a database of similar claims in the past to generate an appropriate solution. Furthermore, when analyzing the content of a claim, the analysis unit may reference solutions to similar claims in the past to generate an optimal solution. This allows for the generation of solutions that reference solutions to similar claims in the past. Examples of similar claims in the past include, but are not limited to, data referenced by methods such as database search and text mining. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit may input data on similar claims in the past into the generation AI and have the generation AI reference solutions.

[0043] When analyzing the content of a claim, the analysis unit can add functions based on the product's usage environment or conditions. For example, when analyzing the content of a claim, the analysis unit can consider the product's usage environment and generate appropriate countermeasures. Furthermore, when analyzing the content of a claim, the analysis unit can also consider the product's usage conditions and generate optimal countermeasures. Furthermore, when analyzing the content of a claim, the analysis unit can consider both the product's usage environment and conditions and generate optimal countermeasures. This allows countermeasures to be generated that take into account the product's usage environment and conditions. Examples of usage environments include, but are not limited to, temperature, humidity, and location of use. Examples of conditions include, but are not limited to, frequency of use and load conditions. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input data on the product's usage environment and conditions into the generation AI and have the generation AI generate countermeasures.

[0044] The analysis unit may be added with a function to refer to related patent information when analyzing the content of a claim. For example, the analysis unit may refer to related patent information when analyzing the content of a claim and generate appropriate countermeasures. The analysis unit may also refer to a patent database when analyzing the content of a claim and generate optimal countermeasures. The analysis unit may also refer to related patent information when analyzing the content of a claim and generate appropriate countermeasures. This allows countermeasures to be generated with reference to patent information. Patent information includes, but is not limited to, patent databases and patent numbers. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input patent information data into a generation AI and have the generation AI generate countermeasures.

[0045] The analysis unit may be added with a function to reference competitors' product information when analyzing the content of a claim. For example, the analysis unit may reference competitors' product information when analyzing the content of a claim and generate appropriate countermeasures. Furthermore, the analysis unit may reference a competitors' product database when analyzing the content of a claim and generate optimal countermeasures. Furthermore, the analysis unit may refer to competitors' product information when analyzing the content of a claim and generate appropriate countermeasures. This allows countermeasures to be generated that reference competitors' product information. Competitor product information includes, but is not limited to, product catalogs and website information. Some or all of the above-described processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit may input competitors' product information data into the generation AI and have the generation AI generate countermeasures.

[0046] The analysis unit can add a function based on product life cycle data when analyzing the content of a complaint. For example, when analyzing the content of a complaint, the analysis unit takes into account the product life cycle data and generates an appropriate countermeasure. Furthermore, when analyzing the content of a complaint, the analysis unit can also refer to a product life cycle database and generate an optimal countermeasure. Furthermore, when analyzing the content of a complaint, the analysis unit can also refer to the product life cycle data and generate an appropriate countermeasure. This makes it possible to generate a countermeasure that takes into account the product life cycle data. Life cycle data includes, for example, the product's lifespan and maintenance history, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the product life cycle data into the generation AI and have the generation AI generate a countermeasure.

[0047] The providing unit may add a function of including specific procedures and reference materials in the solutions it provides. For example, the providing unit may include specific procedures in the solutions it provides. The providing unit may also include related reference materials in the solutions it provides. The providing unit may also include solutions to past similar claims in the solutions it provides. This allows the provision of solutions that include specific procedures and reference materials. Examples of specific procedures include, but are not limited to, step-by-step guides and video tutorials. Examples of reference materials include, but are not limited to, manuals and FAQs. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit may provide a user with specific procedures and reference materials generated by the generation AI.

[0048] The providing unit can add a function that reflects the user's past complaint handling history in the countermeasures to be provided. For example, the providing unit reflects the user's past complaint handling history in the countermeasures to be provided and proposes the optimal countermeasure. The providing unit can also refer to solutions that the user has used in the past when proposing the countermeasures to be provided. The providing unit can also propose the most effective countermeasure based on the user's past complaint handling history when proposing the countermeasures to be provided. This makes it possible to provide countermeasures that reflect the user's past complaint handling history. Past complaint handling history includes, for example, database searches and history logs, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past complaint handling history into a generation AI and cause the generation AI to generate countermeasures.

[0049] The providing unit can add a function to improve the countermeasures to be provided by reflecting user feedback. For example, the providing unit can reflect user feedback in the countermeasures to be provided and improve the countermeasures from the next time onwards. The providing unit can also propose more effective countermeasures to be provided based on user feedback. The providing unit can also improve the accuracy of the countermeasures to be provided by referring to user feedback. This makes it possible to provide countermeasures that reflect user feedback. Feedback includes, for example, surveys and reviews, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user feedback data into the generating AI and cause the generating AI to improve the countermeasures.

[0050] The providing unit may add a function of providing the countermeasure to be provided in a format optimized for the user's device. For example, the providing unit may provide the countermeasure to be provided in a format optimized for a smartphone. The providing unit may also provide the countermeasure to be provided in a format optimized for a tablet. The providing unit may also provide the countermeasure to be provided in a format optimized for a desktop. This allows the countermeasure to be provided in a format optimized for the user's device. Examples of a format optimized for a device include, but are not limited to, a mobile-friendly layout and a responsive design. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may provide the countermeasure generated by the generation AI in a format optimized for the user's device.

[0051] The providing unit can add a function to make the countermeasures to be provided multilingual in accordance with the user's language setting. For example, the providing unit automatically translates the countermeasures to be provided based on the user's language setting. The providing unit can also provide the countermeasures to be provided in multiple languages. The providing unit can also provide the countermeasures to be provided in a language selected by the user. This makes it possible to provide countermeasures in multiple languages. Multilingual support includes, for example, a translation algorithm and a language selection option, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide the countermeasures generated by the generation AI in multiple languages.

[0052] The provision unit can add a function to customize the countermeasures to be provided according to the user's industry or occupation. For example, the provision unit customizes the countermeasures to be provided according to the user's industry. The provision unit can also customize the countermeasures to be provided according to the user's occupation. The provision unit can also optimize the countermeasures to be provided according to the user's industry and occupation. This makes it possible to provide countermeasures according to the user's industry and occupation. Customization according to the industry or occupation includes, for example, industry-specific terminology and occupation-specific needs, but is not limited to such examples. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can customize the countermeasures generated by the generation AI according to the user's industry and occupation.

[0053] The recording unit may be added with a function to record the results of complaint handling in chronological order and analyze trends. For example, the recording unit may record the results of complaint handling in chronological order and analyze trends. The recording unit may also record the results of complaint handling in chronological order and analyze trends over a specific period of time. The recording unit may also record the results of complaint handling in chronological order and analyze long-term trends. This allows the results of complaint handling to be recorded in chronological order and analyzed for trends. Examples of chronological recording include, but are not limited to, adding timestamps and using a time-series database. Examples of trend analysis include, but are not limited to, time-series analysis and creating trend lines. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or without AI. For example, the recording unit may input the results of complaint handling into a generation AI and have the generation AI perform trend analysis.

[0054] The recording unit may be added with a function for recording the results of complaint handling based on relevant laws and regulations and industry standards. For example, the recording unit records the results of complaint handling based on relevant laws and regulations. The recording unit may also record the results of complaint handling based on industry standards. The recording unit may also record the results of complaint handling based on both laws and industry standards. This allows recording based on laws and industry standards. Laws and regulations include, but are not limited to, the provisions of specific laws and regulations. Industry standards include, but are not limited to, ISO standards and industry guidelines. Some or all of the above-mentioned processing in the recording unit may be performed using, or without, AI. For example, the recording unit may input data on laws and regulations and industry standards into a generation AI and cause the generation AI to generate records.

[0055] The recording unit may be added with a function to record the results of the complaint handling together with user feedback. For example, the recording unit records the results of the complaint handling together with user feedback. The recording unit may also record the results of the complaint handling by reflecting user feedback. The recording unit may also record the results of the complaint handling with improvements based on user feedback. This allows for a record that includes user feedback. Feedback includes, for example, questionnaires and reviews, but is not limited to such examples. Some or all of the above-described processing in the recording unit may be performed using, or without, AI. For example, the recording unit may input user feedback data into a generation AI and have the generation AI generate a record.

[0056] The recording unit may add a function to share the results of the complaint handling process on the cloud, allowing multiple users to access them. For example, the recording unit may share the results of the complaint handling process on the cloud, allowing multiple users to access them. The recording unit may also share the results of the complaint handling process on the cloud, allowing them to be accessed in real time. The recording unit may also share the results of the complaint handling process on the cloud, allowing them to be accessed from different devices. This allows the results of the complaint handling process to be shared on the cloud. Sharing on the cloud may include, but is not limited to, cloud storage services and setting access permissions. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit may input the results of the complaint handling process to a generation AI and have the generation AI share the results on the cloud.

[0057] The recording unit can be added with a function to automatically back up the results of complaint handling to a database. For example, the recording unit automatically backs up the results of complaint handling to a database. The recording unit can also periodically back up the results of complaint handling to a database. The recording unit can also back up the results of complaint handling to a database in real time. This allows the results of complaint handling to be automatically backed up. Examples of automatic backup include, but are not limited to, a regular backup schedule and specifying a backup destination. Some or all of the above-mentioned processing in the recording unit may be performed using, or without, AI. For example, the recording unit can input the results of complaint handling to a generation AI and have the generation AI perform the backup.

[0058] The recording unit can add a function to customize and record the results of a complaint handling process according to the user's industry and occupation. For example, the recording unit customizes and records the results of a complaint handling process according to the user's industry. The recording unit can also customize and record the results of a complaint handling process according to the user's occupation. The recording unit can also optimize and record the results of a complaint handling process according to the user's industry and occupation. This allows recording according to the user's industry and occupation. Customization according to the industry or occupation includes, for example, industry-specific terminology and occupation-specific needs, but is not limited to such examples. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the results of a complaint handling process into a generation AI and have the generation AI execute the customized record.

[0059] The improvement suggestion unit may add a function to refer to past complaint data and its solutions when making an improvement suggestion. For example, when making an improvement suggestion, the improvement suggestion unit may refer to past complaint data and its solutions to propose optimal improvements. The improvement suggestion unit may also refer to a past complaint database to propose appropriate improvements when making an improvement suggestion. The improvement suggestion unit may also refer to past complaint data and its solutions to propose optimal improvements when making an improvement suggestion. This allows improvement suggestions to be made that refer to past complaint data and its solutions. Examples of past complaint data include, but are not limited to, database searches and history logs. Some or all of the above-described processing in the improvement suggestion unit may be performed using, or without, AI. For example, the improvement suggestion unit may input past complaint data into a generation AI and have the generation AI generate an improvement suggestion.

[0060] The improvement suggestion unit may be added with a function that considers the product's usage environment and conditions when making an improvement suggestion. For example, when making an improvement suggestion, the improvement suggestion unit may consider the product's usage environment and propose appropriate improvements. Furthermore, when making an improvement suggestion, the improvement suggestion unit may also consider the product's usage conditions and propose optimal improvements. Furthermore, when making an improvement suggestion, the improvement suggestion unit may consider both the product's usage environment and conditions and propose optimal improvements. This allows improvement suggestions to be made that take into account the product's usage environment and conditions. Examples of usage environments include, but are not limited to, temperature, humidity, and usage location. Examples of conditions include, but are not limited to, frequency of use and load conditions. Some or all of the above-described processing in the improvement suggestion unit may be performed using, or without, AI. For example, the improvement suggestion unit may input data on the product's usage environment and conditions into a generation AI and cause the generation AI to generate an improvement suggestion.

[0061] The improvement suggestion unit may be added with a function that takes into account relevant laws and regulations and industry standards when making an improvement suggestion. For example, when making an improvement suggestion, the improvement suggestion unit may consider relevant laws and regulations and propose appropriate improvements. The improvement suggestion unit may also consider industry standards and propose appropriate improvements when making an improvement suggestion. The improvement suggestion unit may also consider both laws and industry standards and propose optimal improvements when making an improvement suggestion. This enables improvement suggestions that take into account laws and industry standards. Examples of laws and regulations include, but are not limited to, provisions of specific laws and regulations. Examples of industry standards include, but are not limited to, ISO standards and industry guidelines. Some or all of the above-described processing in the improvement suggestion unit may be performed using, or without, AI. For example, the improvement suggestion unit may input data on laws and regulations and industry standards into the generation AI and cause the generation AI to generate improvement suggestions.

[0062] The improvement suggestion unit may add a function to refer to competitors' product information when making an improvement suggestion. For example, when making an improvement suggestion, the improvement suggestion unit may refer to competitors' product information and propose appropriate improvements. Furthermore, when making an improvement suggestion, the improvement suggestion unit may refer to a competitors' product database and propose optimal improvements. Furthermore, when making an improvement suggestion, the improvement suggestion unit may refer to competitors' product information and propose appropriate improvements. This allows improvement suggestions to be made with reference to competitors' product information. Competitor product information includes, for example, product catalogs and website information, but is not limited to such examples. Some or all of the above-described processing in the improvement suggestion unit may be performed using, or without, AI. For example, the improvement suggestion unit may input competitors' product information data into the generation AI and cause the generation AI to generate an improvement suggestion.

[0063] When making an improvement proposal, the improvement proposal unit can add functions based on the product life cycle data. For example, when making an improvement proposal, the improvement proposal unit can consider the product life cycle data and propose appropriate improvements. Furthermore, when making an improvement proposal, the improvement proposal unit can refer to a product life cycle database and propose optimal improvements. Furthermore, when making an improvement proposal, the improvement proposal unit can refer to the product life cycle data and propose appropriate improvements. This allows improvement proposals to be made taking the product life cycle data into consideration. Life cycle data includes, for example, the product's lifespan and maintenance history, but is not limited to such examples. Some or all of the above-mentioned processing in the improvement proposal unit may be performed using, or without, AI. For example, the improvement proposal unit can input the product life cycle data into a generation AI and cause the generation AI to generate an improvement proposal.

[0064] The improvement suggestion unit can add a function for customizing the improvement suggestion according to the user's industry or occupation when making an improvement suggestion. For example, the improvement suggestion unit customizes the improvement suggestion according to the user's industry when making an improvement suggestion. Furthermore, the improvement suggestion unit can also customize the improvement suggestion according to the user's occupation when making an improvement suggestion. Furthermore, the improvement suggestion unit can optimize the improvement suggestion according to the user's industry and occupation when making an improvement suggestion. This allows improvement suggestions to be made according to the user's industry and occupation. Customization according to the industry or occupation includes, for example, industry-specific terminology and occupation-specific needs, but is not limited to such examples. Some or all of the above-described processing in the improvement suggestion unit may be performed using, or without, AI. For example, the improvement suggestion unit can input data related to the user's industry and occupation into the generation AI and cause the generation AI to generate improvement suggestions.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The complaint handling system can further add a function that takes into account relevant laws, regulations, and industry standards when analyzing the content of a complaint. For example, if the content of a complaint violates a specific law, the system generates a countermeasure based on that law. It can also propose a countermeasure based on industry standards. This makes it possible to provide an appropriate countermeasure that takes into account laws, regulations, and industry standards. Laws and regulations include, but are not limited to, the provisions of specific laws and regulations. Industry standards include, but are not limited to, ISO standards and industry guidelines. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input data on relevant laws, regulations, and industry standards into the generation AI and have the generation AI generate a countermeasure.

[0067] The complaint handling system can further add a function to reference solutions to similar complaints in the past when analyzing the content of a complaint. For example, when analyzing the content of a complaint, the system can reference a database of similar complaints in the past to generate an optimal solution. It can also refer to solutions to similar complaints in the past to generate an appropriate solution. This allows for the provision of solutions that reference solutions to similar complaints in the past. Similar complaints in the past include, but are not limited to, data referenced by methods such as database search and text mining. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input data on similar complaints in the past into the generation AI and have the generation AI refer to solutions.

[0068] The complaint handling system can further add a function that takes into account the product's usage environment and conditions when analyzing the content of a complaint. For example, when analyzing the content of a complaint, the system can take the product's usage environment into account and generate an appropriate countermeasure. It can also take the product's usage conditions into account to generate an optimal countermeasure. Furthermore, it can take both the product's usage environment and conditions into account to generate an optimal countermeasure. This makes it possible to provide a countermeasure that takes into account the product's usage environment and conditions. The usage environment includes, but is not limited to, temperature, humidity, and location of use. The conditions include, but are not limited to, frequency of use and load conditions. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input data on the product's usage environment and conditions into the generation AI and have the generation AI generate a countermeasure.

[0069] The claim handling system can further add a function to reference related patent information when analyzing the content of a claim. For example, when analyzing the content of a claim, it can reference related patent information and generate appropriate countermeasures. It can also reference a patent database to generate optimal countermeasures. It can also refer to related patent information to generate appropriate countermeasures. This makes it possible to provide countermeasures that reference patent information. Patent information includes, but is not limited to, patent databases and patent numbers. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input patent information data into the generation AI and have the generation AI generate countermeasures.

[0070] The complaint handling system can further add a function to refer to competitors' product information when analyzing the content of a complaint. For example, when analyzing the content of a complaint, the system can refer to competitors' product information and generate appropriate countermeasures. It can also refer to a competitors' product database to generate optimal countermeasures. Furthermore, it can refer to competitors' product information to generate appropriate countermeasures. This makes it possible to provide countermeasures that refer to competitors' product information. Competitor product information includes, but is not limited to, product catalogs and website information. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input competitors' product information data into the generation AI and have the generation AI generate countermeasures.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The reception department inputs the details of the complaint. The details of the complaint may include product defects or dissatisfaction with the service. The reception department accepts various input methods, such as text input, voice input, and image attachments. Detailed information about the complaint, such as the cause of the complaint, specific details, and related evidence, is also required to be entered. Step 2: The analysis unit uses the generation AI to analyze the complaint content entered by the reception unit. The analysis is performed using methods such as text analysis and sentiment analysis. The generation AI learns from past complaint data in order to understand the content of the complaint and generate the optimal response. Step 3: The provision unit provides the countermeasures generated by the analysis unit to the user. Methods of provision include sending emails or checking on a dedicated dashboard. For example, the provision unit fixes the product defect according to the procedure proposed by the generation AI and provides an appropriate explanation to the customer. Step 4: The Recording Department records the results of the complaint handling based on the countermeasures provided by the Providing Department. Recording methods include database management and cloud management. For example, the Recording Department saves the results of the complaint handling in a database and uses them for future complaint handling. Step 5: The improvement suggestion unit proposes improvements based on the data recorded by the recording unit. The improvement suggestion includes the data on which the proposal is based and the method used to identify the improvements when proposing improvements to a specific product.

[0073] (Example 2) A complaint handling system according to an embodiment of the present invention automatically analyzes the content of a complaint, generates an optimal countermeasure, and provides it to the user. The complaint handling system inputs the content of the complaint, and a generation AI analyzes the content and generates an optimal countermeasure. The generated countermeasure is provided to the user, who can then respond to the complaint based on the countermeasure. For example, the complaint handling system inputs detailed information about the complaint, such as the cause of the complaint, specific details, and related evidence. This information is then input to the generation AI. The complaint handling system then uses the generation AI to analyze the input complaint content. The generation AI then understands the content of the complaint and generates an optimal countermeasure. For example, if the complaint is caused by a product defect, the generation AI generates specific steps to correct the defect and how to explain the defect to the customer. The complaint handling system then provides the generated countermeasure to the user. The user can respond to the complaint based on the countermeasure provided by the generation AI. For example, the user can resolve the complaint by correcting the product defect according to the steps proposed by the generation AI and providing an appropriate explanation to the customer. This improves the efficiency of complaint handling and reduces the burden on the user. Furthermore, the generation AI can generate more accurate countermeasures by learning from past complaint data. This also improves the quality of complaint handling. Furthermore, the complaint handling system can record the results of complaint handling and use them for future complaint handling. For example, if there are many complaints about a particular product, it can suggest improvements to that product. This can prevent complaints from occurring in the first place. This allows the complaint handling system to improve the efficiency and quality of complaint handling. For example, users can respond to complaints quickly and appropriately simply by following the specific response measures provided by the generation AI.

[0074] A complaint handling system according to an embodiment includes a reception unit, an analysis unit, a provision unit, a recording unit, and an improvement proposal unit. The reception unit inputs the content of the complaint. The content of the complaint may include, but is not limited to, a product defect or dissatisfaction with the service. The reception unit accepts various input methods, such as text input, voice input, and image attachment. The reception unit is also required to input detailed information about the complaint. For example, the reception unit inputs the cause of the complaint, specific details, and related evidence. This information is input to a generation AI. The analysis unit uses the generation AI to analyze the content of the complaint input by the reception unit. The analysis is performed using, for example, text analysis and sentiment analysis, but is not limited to, examples. For example, the generation AI understands the content of the complaint and generates an optimal countermeasure. The generation AI includes an algorithm for learning past complaint data and generating an optimal countermeasure. The provision unit provides the countermeasure generated by the analysis unit to a user. For example, the method of providing the countermeasure includes, but is not limited to, sending an email or checking on a dedicated dashboard. For example, the provision unit can resolve a complaint by correcting a product defect according to the procedure proposed by the generation AI and providing an appropriate explanation to the customer. The recording unit records the results of the complaint handling based on the countermeasures provided by the provision unit. Recording methods include, but are not limited to, database or cloud management. For example, the recording unit can store the results of the complaint handling in a database and use them for future complaint handling. The improvement suggestion unit proposes improvements based on the data recorded by the recording unit. For example, when proposing improvements to a specific product, the improvement suggestions include the data on which the suggestions are based and the method used to identify the improvements. This allows the complaint handling system according to the embodiment to improve the efficiency and quality of complaint handling. For example, a user can respond to a complaint quickly and appropriately by simply following the specific countermeasures provided by the generation AI.

[0075] The reception unit can accept multiple input methods, such as text input, voice input, and image attachment. The reception unit accepts various input methods, such as text input, voice input, and image attachment. Examples of text input include, but are not limited to, free text and multiple choice format. Examples of voice input include, but are not limited to, voice recognition technology and uploading a recorded file. Examples of image attachment include, but are not limited to, image files in JPEG format and PNG format. This allows users to enter claims in a variety of ways. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can convert voice input into text data using voice recognition technology.

[0076] The analysis unit may include an algorithm for learning from past claim data and generating a countermeasure. The analysis unit uses the generation AI to analyze the claim content input by the reception unit. The analysis is performed using, for example, text analysis, sentiment analysis, etc., but is not limited to these examples. The generation AI includes an algorithm for learning from past claim data and generating an optimal countermeasure. For example, the generation AI understands the content of the claim based on the past claim data and generates an optimal countermeasure. The generation AI can analyze the content of the claim and generate a countermeasure using a machine learning algorithm or a rule-based algorithm. This allows for the generation of more accurate countermeasures by utilizing past data. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input past claim data into the generation AI and have the generation AI generate a countermeasure.

[0077] The providing unit may include methods such as sending an email or checking on a dedicated dashboard. The providing unit provides the countermeasures generated by the analysis unit to the user. Examples of providing methods include, but are not limited to, sending an email or checking on a dedicated dashboard. For example, the providing unit may resolve a complaint by correcting a product defect according to the procedure proposed by the generation AI and providing an appropriate explanation to the customer. Examples of sending an email include, but are not limited to, HTML email, text email, etc. The dedicated dashboard may include, but are not limited to, a web-based dashboard, a mobile app, etc. This allows the user to receive the countermeasures in various ways. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without AI. For example, the providing unit may send the countermeasures generated by the generation AI by email.

[0078] The recording unit may include management in a database or on a cloud. The recording unit records the results of the complaint handling based on the countermeasures provided by the providing unit. Examples of recording methods include, but are not limited to, management in a database or on a cloud. For example, the recording unit may store the results of the complaint handling in a database and use them for subsequent complaint handling. Examples of databases include, but are not limited to, SQL databases and NoSQL databases. Examples of clouds include, but are not limited to, cloud services such as AWS and Google Cloud. This allows for efficient management of the results of the complaint handling. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit may automatically store the results of the complaint handling in a database on a cloud.

[0079] When proposing improvements to a specific product, the improvement suggestion unit can make the suggestions based on specific data and identify the improvements using a specific method. The improvement suggestion unit proposes improvements based on data recorded by the recording unit. The improvement suggestion includes, for example, what data the suggestion is based on and what method is used to identify the improvements when proposing improvements to a specific product. Examples of specific data include, but are not limited to, user feedback and product usage data. Examples of specific methods include, but are not limited to, data mining and statistical analysis. This clarifies the basis for the improvement suggestions. Some or all of the above-described processing in the improvement suggestion unit may be performed using, or without, AI. For example, the improvement suggestion unit may input user feedback to a generation AI and have the generation AI execute improvement suggestions.

[0080] The reception unit can estimate the user's emotions and adjust the complaint content input method based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable the user to quickly input the complaint content. This allows an input method to be provided that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI adjust the input method based on the emotion.

[0081] The reception unit can add a function to automatically complete detailed information about a claim, thereby reducing the effort required for input. For example, when a user inputs a summary of a claim, the reception unit automatically completes related detailed information. The reception unit can also automatically complete related evidence and data when a user inputs the cause of the claim. The reception unit can also automatically complete information about past similar claims when a user inputs the content of the claim. This reduces the effort required for user input. Automatic completion of detailed information is performed, for example, by using past input data or by prediction using a generation AI. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past claim data into the generation AI and have the generation AI automatically complete the detailed information.

[0082] The reception unit can analyze the content of the claim in real time and provide appropriate guidance while the user is entering the content. For example, the reception unit can analyze the content of the claim in real time and display appropriate input guidance while the user is entering the content of the claim. The reception unit can also display guidance encouraging the user to enter relevant evidence and data while the user is entering the cause of the claim. The reception unit can also display guidance encouraging the user to refer to information on past similar claims while the user is entering the details of the claim. This allows the user to enter the claim while receiving appropriate guidance. Real-time analysis can be performed, for example, by streaming data analysis or real-time feedback. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the content of the claim to a generation AI in real time and have the generation AI provide appropriate guidance.

[0083] The reception unit can be added with a function to categorize the content of complaints and assign them to the appropriate analysis unit. For example, the reception unit automatically analyzes the content of complaints entered by a user and classifies them by category. Furthermore, if the content of a complaint is related to a product defect, the reception unit can categorize it into a product defect category and assign it to the appropriate analysis unit. Furthermore, if the content of a complaint is related to a service problem, the reception unit can categorize it into a service problem category and assign it to the appropriate analysis unit. This allows for appropriate analysis based on the content of the complaint. Categorization can be performed using, for example, a text classification algorithm or rule-based classification. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the content of a complaint into a generation AI and have the generation AI perform categorization and assignment to an analysis unit.

[0084] The reception unit can estimate the user's emotions and prioritize input content based on the estimated user emotions. For example, when the user is stressed, the reception unit can prioritize displaying important input items to allow the user to complete input quickly. Furthermore, when the user is relaxed, the reception unit can display detailed input items to allow the user to carefully complete input. Furthermore, when the user is in a hurry, the reception unit can prioritize displaying the most important input items to allow the user to complete input quickly. This allows the priority of input content to be determined according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the input content.

[0085] The reception unit can be added with a function to accept not only voice input and image attachment of the content of the complaint, but also video input. For example, the reception unit can allow the user to not only input the content of the complaint by voice, but also to explain it using video. The reception unit can also allow the user to record and attach a video of the circumstances under which the complaint occurred. The reception unit can also allow the user to explain the details of the complaint using video and provide it to the analysis unit. This allows the user to input the complaint using video. Video input is performed, for example, in MP4 format or real-time streaming. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input video data into a generation AI and have the generation AI analyze the video data.

[0086] The reception unit can be added with a function for receiving the content of a claim in multiple languages ​​and automatically translating it in the analysis unit. For example, the reception unit allows a user to input the content of a claim in multiple languages, which is then automatically translated by the analysis unit. The reception unit can also allow a user to input details of the claim in different languages, which is then automatically translated by the analysis unit. The reception unit can also allow a user to input the cause of the claim in multiple languages, which is then automatically translated by the analysis unit. This enables claims to be input in multiple languages. Multilingual support is achieved, for example, by using a translation algorithm or a language selection option. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the content of the claim to a generation AI, which can then perform multilingual translation.

[0087] The reception unit can be added with a function to automatically collect complaint content from social media and import it into the reception unit. For example, the reception unit automatically collects complaint content posted by users on social media and imports it into the reception unit. The reception unit can also automatically collect complaint information shared by users on social media and import it into the reception unit. The reception unit can also automatically collect complaint content commented by users on social media and import it into the reception unit. This allows complaint information to be automatically collected from social media. Social media information is collected from social media platforms such as Twitter and Facebook. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input social media data into a generation AI and have the generation AI collect complaint information.

[0088] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can quickly analyze and generate concise countermeasures. Furthermore, if the user is relaxed, the analysis unit can also perform a detailed analysis and generate thorough countermeasures. Furthermore, if the user is in a hurry, the analysis unit can quickly analyze and prioritize the most important countermeasures. This allows analysis to be performed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis algorithm.

[0089] The analysis unit may add functionality based on relevant laws and regulations or industry standards when analyzing the content of a claim. For example, the analysis unit may consider relevant laws and regulations when analyzing the content of a claim and generate appropriate countermeasures. The analysis unit may also consider industry standards when analyzing the content of a claim and generate appropriate countermeasures. The analysis unit may also consider both laws and industry standards when analyzing the content of a claim and generate optimal countermeasures. This allows countermeasures to be generated that take laws and industry standards into account. Examples of laws and regulations include, but are not limited to, provisions of specific laws and regulations. Examples of industry standards include, but are not limited to, ISO standards and industry guidelines. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input data on relevant laws and regulations or industry standards into the generation AI and cause the generation AI to generate countermeasures.

[0090] The analysis unit may add a function to reference solutions to similar claims in the past when analyzing the content of a claim. For example, when analyzing the content of a claim, the analysis unit may reference solutions to similar claims in the past to generate an optimal solution. Furthermore, when analyzing the content of a claim, the analysis unit may reference a database of similar claims in the past to generate an appropriate solution. Furthermore, when analyzing the content of a claim, the analysis unit may reference solutions to similar claims in the past to generate an optimal solution. This allows for the generation of solutions that reference solutions to similar claims in the past. Examples of similar claims in the past include, but are not limited to, data referenced by methods such as database search and text mining. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit may input data on similar claims in the past into the generation AI and have the generation AI reference solutions.

[0091] When analyzing the content of a claim, the analysis unit can add functions based on the product's usage environment or conditions. For example, when analyzing the content of a claim, the analysis unit can consider the product's usage environment and generate appropriate countermeasures. Furthermore, when analyzing the content of a claim, the analysis unit can also consider the product's usage conditions and generate optimal countermeasures. Furthermore, when analyzing the content of a claim, the analysis unit can consider both the product's usage environment and conditions and generate optimal countermeasures. This allows countermeasures to be generated that take into account the product's usage environment and conditions. Examples of usage environments include, but are not limited to, temperature, humidity, and location of use. Examples of conditions include, but are not limited to, frequency of use and load conditions. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input data on the product's usage environment and conditions into the generation AI and have the generation AI generate countermeasures.

[0092] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This makes it possible to provide a display method that suits the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0093] The analysis unit may be added with a function to refer to related patent information when analyzing the content of a claim. For example, the analysis unit may refer to related patent information when analyzing the content of a claim and generate appropriate countermeasures. The analysis unit may also refer to a patent database when analyzing the content of a claim and generate optimal countermeasures. The analysis unit may also refer to related patent information when analyzing the content of a claim and generate appropriate countermeasures. This allows countermeasures to be generated with reference to patent information. Patent information includes, but is not limited to, patent databases and patent numbers. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input patent information data into a generation AI and have the generation AI generate countermeasures.

[0094] The analysis unit may be added with a function to reference competitors' product information when analyzing the content of a claim. For example, the analysis unit may reference competitors' product information when analyzing the content of a claim and generate appropriate countermeasures. Furthermore, the analysis unit may reference a competitors' product database when analyzing the content of a claim and generate optimal countermeasures. Furthermore, the analysis unit may refer to competitors' product information when analyzing the content of a claim and generate appropriate countermeasures. This allows countermeasures to be generated that reference competitors' product information. Competitor product information includes, but is not limited to, product catalogs and website information. Some or all of the above-described processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit may input competitors' product information data into the generation AI and have the generation AI generate countermeasures.

[0095] The analysis unit can add a function based on product life cycle data when analyzing the content of a complaint. For example, when analyzing the content of a complaint, the analysis unit takes into account the product life cycle data and generates an appropriate countermeasure. Furthermore, when analyzing the content of a complaint, the analysis unit can also refer to a product life cycle database and generate an optimal countermeasure. Furthermore, when analyzing the content of a complaint, the analysis unit can also refer to the product life cycle data and generate an appropriate countermeasure. This makes it possible to generate a countermeasure that takes into account the product life cycle data. Life cycle data includes, for example, the product's lifespan and maintenance history, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the product life cycle data into the generation AI and have the generation AI generate a countermeasure.

[0096] The providing unit can estimate the user's emotions and adjust the method of providing countermeasures based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can provide a concise and highly visible method of providing countermeasures. Furthermore, if the user is relaxed, the providing unit can provide a method of providing detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a method of providing countermeasures that focuses on the main points. This allows a method of providing countermeasures that matches the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the method of providing countermeasures.

[0097] The providing unit may add a function of including specific procedures and reference materials in the solutions it provides. For example, the providing unit may include specific procedures in the solutions it provides. The providing unit may also include related reference materials in the solutions it provides. The providing unit may also include solutions to past similar claims in the solutions it provides. This allows the provision of solutions that include specific procedures and reference materials. Examples of specific procedures include, but are not limited to, step-by-step guides and video tutorials. Examples of reference materials include, but are not limited to, manuals and FAQs. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit may provide a user with specific procedures and reference materials generated by the generation AI.

[0098] The providing unit can add a function that reflects the user's past complaint handling history in the countermeasures to be provided. For example, the providing unit reflects the user's past complaint handling history in the countermeasures to be provided and proposes the optimal countermeasure. The providing unit can also refer to solutions that the user has used in the past when proposing the countermeasures to be provided. The providing unit can also propose the most effective countermeasure based on the user's past complaint handling history when proposing the countermeasures to be provided. This makes it possible to provide countermeasures that reflect the user's past complaint handling history. Past complaint handling history includes, for example, database searches and history logs, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past complaint handling history into a generation AI and cause the generation AI to generate countermeasures.

[0099] The providing unit can add a function to improve the countermeasures to be provided by reflecting user feedback. For example, the providing unit can reflect user feedback in the countermeasures to be provided and improve the countermeasures from the next time onwards. The providing unit can also propose more effective countermeasures to be provided based on user feedback. The providing unit can also improve the accuracy of the countermeasures to be provided by referring to user feedback. This makes it possible to provide countermeasures that reflect user feedback. Feedback includes, for example, surveys and reviews, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user feedback data into the generating AI and cause the generating AI to improve the countermeasures.

[0100] The providing unit can estimate the user's emotions and prioritize countermeasures based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can prioritize the most important countermeasures. Furthermore, if the user is relaxed, the providing unit can provide detailed countermeasures. Furthermore, if the user is in a hurry, the providing unit can quickly provide countermeasures. This allows the prioritization of countermeasures according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of countermeasures.

[0101] The providing unit may add a function of providing the countermeasure to be provided in a format optimized for the user's device. For example, the providing unit may provide the countermeasure to be provided in a format optimized for a smartphone. The providing unit may also provide the countermeasure to be provided in a format optimized for a tablet. The providing unit may also provide the countermeasure to be provided in a format optimized for a desktop. This allows the countermeasure to be provided in a format optimized for the user's device. Examples of a format optimized for a device include, but are not limited to, a mobile-friendly layout and a responsive design. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may provide the countermeasure generated by the generation AI in a format optimized for the user's device.

[0102] The providing unit can add a function to make the countermeasures to be provided multilingual in accordance with the user's language setting. For example, the providing unit automatically translates the countermeasures to be provided based on the user's language setting. The providing unit can also provide the countermeasures to be provided in multiple languages. The providing unit can also provide the countermeasures to be provided in a language selected by the user. This makes it possible to provide countermeasures in multiple languages. Multilingual support includes, for example, a translation algorithm and a language selection option, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide the countermeasures generated by the generation AI in multiple languages.

[0103] The provision unit can add a function to customize the countermeasures to be provided according to the user's industry or occupation. For example, the provision unit customizes the countermeasures to be provided according to the user's industry. The provision unit can also customize the countermeasures to be provided according to the user's occupation. The provision unit can also optimize the countermeasures to be provided according to the user's industry and occupation. This makes it possible to provide countermeasures according to the user's industry and occupation. Customization according to the industry or occupation includes, for example, industry-specific terminology and occupation-specific needs, but is not limited to such examples. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can customize the countermeasures generated by the generation AI according to the user's industry and occupation.

[0104] The recording unit can estimate the user's emotions and adjust the recording method based on the estimated user emotions. For example, if the user is feeling stressed, the recording unit can provide a concise and highly visible recording method. Furthermore, if the user is relaxed, the recording unit can provide a recording method that includes detailed information. Furthermore, if the user is in a hurry, the recording unit can provide a recording method that focuses on the main points. This makes it possible to provide a recording method that suits the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the recording unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the recording unit can input the user's emotion data into the generation AI and have the generation AI adjust the recording method.

[0105] The recording unit may be added with a function to record the results of complaint handling in chronological order and analyze trends. For example, the recording unit may record the results of complaint handling in chronological order and analyze trends. The recording unit may also record the results of complaint handling in chronological order and analyze trends over a specific period of time. The recording unit may also record the results of complaint handling in chronological order and analyze long-term trends. This allows the results of complaint handling to be recorded in chronological order and analyzed for trends. Examples of chronological recording include, but are not limited to, adding timestamps and using a time-series database. Examples of trend analysis include, but are not limited to, time-series analysis and creating trend lines. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or without AI. For example, the recording unit may input the results of complaint handling into a generation AI and have the generation AI perform trend analysis.

[0106] The recording unit may be added with a function for recording the results of complaint handling based on relevant laws and regulations and industry standards. For example, the recording unit records the results of complaint handling based on relevant laws and regulations. The recording unit may also record the results of complaint handling based on industry standards. The recording unit may also record the results of complaint handling based on both laws and industry standards. This allows recording based on laws and industry standards. Laws and regulations include, but are not limited to, the provisions of specific laws and regulations. Industry standards include, but are not limited to, ISO standards and industry guidelines. Some or all of the above-mentioned processing in the recording unit may be performed using, or without, AI. For example, the recording unit may input data on laws and regulations and industry standards into a generation AI and cause the generation AI to generate records.

[0107] The recording unit may be added with a function to record the results of the complaint handling together with user feedback. For example, the recording unit records the results of the complaint handling together with user feedback. The recording unit may also record the results of the complaint handling by reflecting user feedback. The recording unit may also record the results of the complaint handling with improvements based on user feedback. This allows for a record that includes user feedback. Feedback includes, for example, questionnaires and reviews, but is not limited to such examples. Some or all of the above-described processing in the recording unit may be performed using, or without, AI. For example, the recording unit may input user feedback data into a generation AI and have the generation AI generate a record.

[0108] The recording unit can estimate the user's emotions and prioritize the recorded content based on the estimated user emotions. For example, when the user is feeling stressed, the recording unit prioritizes recording the most important recorded content. Furthermore, when the user is relaxed, the recording unit can also record detailed recorded content. Furthermore, when the user is in a hurry, the recording unit can prioritize recording content that highlights the main points. This allows the prioritization of recorded content according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recording unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recording unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the recorded content.

[0109] The recording unit may add a function to share the results of the complaint handling process on the cloud, allowing multiple users to access them. For example, the recording unit may share the results of the complaint handling process on the cloud, allowing multiple users to access them. The recording unit may also share the results of the complaint handling process on the cloud, allowing them to be accessed in real time. The recording unit may also share the results of the complaint handling process on the cloud, allowing them to be accessed from different devices. This allows the results of the complaint handling process to be shared on the cloud. Sharing on the cloud may include, but is not limited to, cloud storage services and setting access permissions. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit may input the results of the complaint handling process to a generation AI and have the generation AI share the results on the cloud.

[0110] The recording unit can be added with a function to automatically back up the results of complaint handling to a database. For example, the recording unit automatically backs up the results of complaint handling to a database. The recording unit can also periodically back up the results of complaint handling to a database. The recording unit can also back up the results of complaint handling to a database in real time. This allows the results of complaint handling to be automatically backed up. Examples of automatic backup include, but are not limited to, a regular backup schedule and specifying a backup destination. Some or all of the above-mentioned processing in the recording unit may be performed using, or without, AI. For example, the recording unit can input the results of complaint handling to a generation AI and have the generation AI perform the backup.

[0111] The recording unit can add a function to customize and record the results of a complaint handling process according to the user's industry and occupation. For example, the recording unit customizes and records the results of a complaint handling process according to the user's industry. The recording unit can also customize and record the results of a complaint handling process according to the user's occupation. The recording unit can also optimize and record the results of a complaint handling process according to the user's industry and occupation. This allows recording according to the user's industry and occupation. Customization according to the industry or occupation includes, for example, industry-specific terminology and occupation-specific needs, but is not limited to such examples. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the results of a complaint handling process into a generation AI and have the generation AI execute the customized record.

[0112] The improvement suggestion unit can estimate the user's emotions and adjust the improvement suggestion method based on the estimated user's emotions. For example, if the user is feeling stressed, the improvement suggestion unit can provide concise and highly visible improvement suggestions. Furthermore, if the user is relaxed, the improvement suggestion unit can also provide improvement suggestions that include detailed information. Furthermore, if the user is in a hurry, the improvement suggestion unit can also provide improvement suggestions that focus on the main points. This allows improvement suggestions to be provided according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the improvement suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the improvement suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the improvement suggestion method.

[0113] The improvement suggestion unit may add a function to refer to past complaint data and its solutions when making an improvement suggestion. For example, when making an improvement suggestion, the improvement suggestion unit may refer to past complaint data and its solutions to propose optimal improvements. The improvement suggestion unit may also refer to a past complaint database to propose appropriate improvements when making an improvement suggestion. The improvement suggestion unit may also refer to past complaint data and its solutions to propose optimal improvements when making an improvement suggestion. This allows improvement suggestions to be made that refer to past complaint data and its solutions. Examples of past complaint data include, but are not limited to, database searches and history logs. Some or all of the above-described processing in the improvement suggestion unit may be performed using, or without, AI. For example, the improvement suggestion unit may input past complaint data into a generation AI and have the generation AI generate an improvement suggestion.

[0114] The improvement suggestion unit may be added with a function that considers the product's usage environment and conditions when making an improvement suggestion. For example, when making an improvement suggestion, the improvement suggestion unit may consider the product's usage environment and propose appropriate improvements. Furthermore, when making an improvement suggestion, the improvement suggestion unit may also consider the product's usage conditions and propose optimal improvements. Furthermore, when making an improvement suggestion, the improvement suggestion unit may consider both the product's usage environment and conditions and propose optimal improvements. This allows improvement suggestions to be made that take into account the product's usage environment and conditions. Examples of usage environments include, but are not limited to, temperature, humidity, and usage location. Examples of conditions include, but are not limited to, frequency of use and load conditions. Some or all of the above-described processing in the improvement suggestion unit may be performed using, or without, AI. For example, the improvement suggestion unit may input data on the product's usage environment and conditions into a generation AI and cause the generation AI to generate an improvement suggestion.

[0115] The improvement suggestion unit may be added with a function that takes into account relevant laws and regulations and industry standards when making an improvement suggestion. For example, when making an improvement suggestion, the improvement suggestion unit may consider relevant laws and regulations and propose appropriate improvements. The improvement suggestion unit may also consider industry standards and propose appropriate improvements when making an improvement suggestion. The improvement suggestion unit may also consider both laws and industry standards and propose optimal improvements when making an improvement suggestion. This enables improvement suggestions that take into account laws and industry standards. Examples of laws and regulations include, but are not limited to, provisions of specific laws and regulations. Examples of industry standards include, but are not limited to, ISO standards and industry guidelines. Some or all of the above-described processing in the improvement suggestion unit may be performed using, or without, AI. For example, the improvement suggestion unit may input data on laws and regulations and industry standards into the generation AI and cause the generation AI to generate improvement suggestions.

[0116] The improvement suggestion unit can estimate the user's emotions and prioritize the improvement suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the improvement suggestion unit can prioritize the most important improvement suggestions. Furthermore, if the user is relaxed, the improvement suggestion unit can provide detailed improvement suggestions. Furthermore, if the user is in a hurry, the improvement suggestion unit can quickly provide improvement suggestions. This allows the prioritization of improvement suggestions according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the improvement suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the improvement suggestion unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the improvement suggestions.

[0117] The improvement suggestion unit may add a function to refer to competitors' product information when making an improvement suggestion. For example, when making an improvement suggestion, the improvement suggestion unit may refer to competitors' product information and propose appropriate improvements. Furthermore, when making an improvement suggestion, the improvement suggestion unit may refer to a competitors' product database and propose optimal improvements. Furthermore, when making an improvement suggestion, the improvement suggestion unit may refer to competitors' product information and propose appropriate improvements. This allows improvement suggestions to be made with reference to competitors' product information. Competitor product information includes, for example, product catalogs and website information, but is not limited to such examples. Some or all of the above-described processing in the improvement suggestion unit may be performed using, or without, AI. For example, the improvement suggestion unit may input competitors' product information data into the generation AI and cause the generation AI to generate an improvement suggestion.

[0118] When making an improvement proposal, the improvement proposal unit can add functions based on the product life cycle data. For example, when making an improvement proposal, the improvement proposal unit can consider the product life cycle data and propose appropriate improvements. Furthermore, when making an improvement proposal, the improvement proposal unit can refer to a product life cycle database and propose optimal improvements. Furthermore, when making an improvement proposal, the improvement proposal unit can refer to the product life cycle data and propose appropriate improvements. This allows improvement proposals to be made taking the product life cycle data into consideration. Life cycle data includes, for example, the product's lifespan and maintenance history, but is not limited to such examples. Some or all of the above-mentioned processing in the improvement proposal unit may be performed using, or without, AI. For example, the improvement proposal unit can input the product life cycle data into a generation AI and cause the generation AI to generate an improvement proposal.

[0119] The improvement suggestion unit can add a function for customizing the improvement suggestion according to the user's industry or occupation when making an improvement suggestion. For example, the improvement suggestion unit customizes the improvement suggestion according to the user's industry when making an improvement suggestion. Furthermore, the improvement suggestion unit can also customize the improvement suggestion according to the user's occupation when making an improvement suggestion. Furthermore, the improvement suggestion unit can optimize the improvement suggestion according to the user's industry and occupation when making an improvement suggestion. This allows improvement suggestions to be made according to the user's industry and occupation. Customization according to the industry or occupation includes, for example, industry-specific terminology and occupation-specific needs, but is not limited to such examples. Some or all of the above-described processing in the improvement suggestion unit may be performed using, or without, AI. For example, the improvement suggestion unit can input data related to the user's industry and occupation into the generation AI and cause the generation AI to generate improvement suggestions. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, recording unit, and improvement suggestion unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can input the content of a complaint via the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the content of the complaint using a generation AI. For example, the provision unit provides the generated countermeasure to the user via the output device 40 of the smart device 14 or the communication I / F 26 of the data processing device 12. For example, the recording unit records the results of the complaint handling in the database 24 of the data processing device 12. For example, the improvement suggestion unit proposes improvements based on the data recorded by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, analysis unit, provision unit, recording unit, and improvement suggestion unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can input the content of a complaint via the microphone 238 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the content of the complaint using a generation AI. For example, the provision unit provides the generated countermeasure to the user via the speaker 240 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. For example, the recording unit records the results of the complaint handling in the database 24 of the data processing device 12. For example, the improvement suggestion unit proposes improvements based on the data recorded by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the above-described reception unit, analysis unit, provision unit, recording unit, and improvement suggestion unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can input the content of the complaint via the microphone 238 of the headset-type terminal 314 or the communication I / F 26 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the content of the complaint using a generation AI. For example, the provision unit provides the generated countermeasure to the user via the speaker 240 of the headset-type terminal 314 or the communication I / F 26 of the data processing device 12. For example, the recording unit records the results of the complaint handling in the database 24 of the data processing device 12. For example, the improvement suggestion unit proposes improvements based on the data recorded by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, provision unit, recording unit, and improvement suggestion unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can input the content of the complaint via the microphone 238 of the robot 414 or the communication I / F 26 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the content of the complaint using a generation AI. For example, the provision unit provides the generated countermeasure to the user via the speaker 240 of the robot 414 or the communication I / F 26 of the data processing device 12. For example, the recording unit records the results of the complaint handling in the database 24 of the data processing device 12. For example, the improvement suggestion unit proposes improvements based on the data recorded by the specific processing unit 290 of the data processing device 12.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The complaint handling system can further add a function to estimate a user's emotions and prioritize complaints based on the estimated emotions. For example, if a user is very angry, the system can automatically determine that the complaint should be handled with the highest priority. If the user is feeling anxious, a prompt and courteous response can be prioritized. If the user is relaxed, the system can proceed with the normal response procedure. This enables flexible complaint handling based on the user's emotions. Emotion estimation can be achieved, for example, using an emotion engine or a generation AI. Examples of generation AI include, but are not limited to, text generation AI and multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI or without AI. For example, the analysis unit can input user emotion data into a generation AI and have the generation AI determine the priority of complaints.

[0122] The complaint handling system can further add a function that takes into account relevant laws, regulations, and industry standards when analyzing the content of a complaint. For example, if the content of a complaint violates a specific law, the system generates a countermeasure based on that law. It can also propose a countermeasure based on industry standards. This makes it possible to provide an appropriate countermeasure that takes into account laws, regulations, and industry standards. Laws and regulations include, but are not limited to, the provisions of specific laws and regulations. Industry standards include, but are not limited to, ISO standards and industry guidelines. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input data on relevant laws, regulations, and industry standards into the generation AI and have the generation AI generate a countermeasure.

[0123] The complaint handling system can further add a function to estimate the user's emotions and adjust the method of providing countermeasures based on the estimated emotions. For example, if the user is stressed, a concise and highly visible method of providing countermeasures can be provided. If the user is relaxed, a method including detailed information can be provided. Furthermore, if the user is in a hurry, a method that focuses on the main points can be provided. This allows the system to provide a method of providing countermeasures that matches the user's emotions. Emotion estimation is achieved using an emotion engine or a generation AI. Examples of the generation AI include, but are not limited to, text generation AI and multimodal generation AI. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the method of providing countermeasures.

[0124] The complaint handling system can further add a function to reference solutions to similar complaints in the past when analyzing the content of a complaint. For example, when analyzing the content of a complaint, the system can reference a database of similar complaints in the past to generate an optimal solution. It can also refer to solutions to similar complaints in the past to generate an appropriate solution. This allows for the provision of solutions that reference solutions to similar complaints in the past. Similar complaints in the past include, but are not limited to, data referenced by methods such as database search and text mining. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input data on similar complaints in the past into the generation AI and have the generation AI refer to solutions.

[0125] The complaint handling system can further add a function to estimate the user's emotions and adjust the complaint content input method based on the estimated emotions. For example, if the user is stressed, a simple interface can be provided to minimize input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow the user to quickly input the complaint content. This allows for an input method tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or a generation AI. Examples of generation AI include, but are not limited to, text generation AI and multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI or without AI. For example, the reception unit can input the user's emotion data into a generation AI and have the generation AI adjust the input method based on the emotion.

[0126] The complaint handling system can further add a function that takes into account the product's usage environment and conditions when analyzing the content of a complaint. For example, when analyzing the content of a complaint, the system can take the product's usage environment into account and generate an appropriate countermeasure. It can also take the product's usage conditions into account to generate an optimal countermeasure. Furthermore, it can take both the product's usage environment and conditions into account to generate an optimal countermeasure. This makes it possible to provide a countermeasure that takes into account the product's usage environment and conditions. The usage environment includes, but is not limited to, temperature, humidity, and location of use. The conditions include, but are not limited to, frequency of use and load conditions. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input data on the product's usage environment and conditions into the generation AI and have the generation AI generate a countermeasure.

[0127] The complaint handling system can further add a function to estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, a quick analysis can be performed to generate a concise response. Alternatively, if the user is relaxed, a detailed analysis can be performed to generate a detailed response. Furthermore, if the user is in a hurry, a quick analysis can be performed to prioritize the most important response. This allows analysis to be performed according to the user's emotions. Emotion estimation is achieved using an emotion engine or a generation AI. Examples of generation AI include, but are not limited to, text generation AI and multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input user emotion data into the generation AI and have the generation AI adjust the analysis algorithm.

[0128] The claim handling system can further add a function to reference related patent information when analyzing the content of a claim. For example, when analyzing the content of a claim, it can reference related patent information and generate appropriate countermeasures. It can also reference a patent database to generate optimal countermeasures. It can also refer to related patent information to generate appropriate countermeasures. This makes it possible to provide countermeasures that reference patent information. Patent information includes, but is not limited to, patent databases and patent numbers. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input patent information data into the generation AI and have the generation AI generate countermeasures.

[0129] The complaint handling system can further add a function to estimate the user's emotions and adjust the recording method based on the estimated emotions. For example, if the user is feeling stressed, a concise and highly visible recording method can be provided. If the user is relaxed, a recording method including detailed information can be provided. Furthermore, if the user is in a hurry, a recording method that focuses on the main points can be provided. This allows for a recording method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion engine or a generation AI. Examples of generation AI include, but are not limited to, text generation AI and multimodal generation AI. Some or all of the above-mentioned processing in the recording unit may be performed using AI or without AI. For example, the recording unit can input the user's emotion data into the generation AI and have the generation AI adjust the recording method.

[0130] The complaint handling system can further add a function to refer to competitors' product information when analyzing the content of a complaint. For example, when analyzing the content of a complaint, the system can refer to competitors' product information and generate appropriate countermeasures. It can also refer to a competitors' product database to generate optimal countermeasures. Furthermore, it can refer to competitors' product information to generate appropriate countermeasures. This makes it possible to provide countermeasures that refer to competitors' product information. Competitor product information includes, but is not limited to, product catalogs and website information. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input competitors' product information data into the generation AI and have the generation AI generate countermeasures.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The reception department inputs the details of the complaint. The details of the complaint may include product defects or dissatisfaction with the service. The reception department accepts various input methods, such as text input, voice input, and image attachments. Detailed information about the complaint, such as the cause of the complaint, specific details, and related evidence, is also required to be entered. Step 2: The analysis unit uses the generation AI to analyze the complaint content entered by the reception unit. The analysis is performed using methods such as text analysis and sentiment analysis. The generation AI learns from past complaint data in order to understand the content of the complaint and generate the optimal response. Step 3: The provision unit provides the countermeasures generated by the analysis unit to the user. Methods of provision include sending emails or checking on a dedicated dashboard. For example, the provision unit fixes the product defect according to the procedure proposed by the generation AI and provides an appropriate explanation to the customer. Step 4: The Recording Department records the results of the complaint handling based on the countermeasures provided by the Providing Department. Recording methods include database management and cloud management. For example, the Recording Department saves the results of the complaint handling in a database and uses them for future complaint handling. Step 5: The improvement suggestion unit proposes improvements based on the data recorded by the recording unit. The improvement suggestion includes the data on which the proposal is based and the method used to identify the improvements when proposing improvements to a specific product.

[0133] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0155] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 7, a 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.

[0171] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0173] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0176] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0177] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0178] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0179] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0181] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0182] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0183] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0188] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0189] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0190] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0194] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0195] 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.

[0196] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0197] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0198] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0199] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0201] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0202] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0203] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0204] [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting the content of a complaint; an analysis unit that analyzes the complaint content input by the reception unit; a providing unit that provides a user with the countermeasure generated by the analysis unit; a recording unit that records the results of the complaint handling based on the countermeasures provided by the providing unit; an improvement suggestion unit that suggests improvements based on the data recorded by the recording unit. A system characterized by:

2. The reception unit Accepts multiple input methods, including text input, voice input, and image attachments The system of claim 1 .

3. The analysis unit Includes algorithms that learn from past claims data and generate countermeasures The system of claim 1 .

4. The providing unit Includes confirmation and provision methods via email or dedicated dashboard The system of claim 1 .

5. The recording unit Including database or cloud management The system of claim 1 .

6. The improvement suggestion unit When proposing improvements to a specific product, the proposal is based on specific data and the improvements are identified using specific methods. The system of claim 1 .

7. The reception unit Estimate the user's emotions and adjust the method of inputting the complaint content based on the estimated user emotions The system of claim 1 .

8. The reception unit Add a function to automatically complete detailed claim information, reducing the amount of input required. The system of claim 1 .

9. The reception unit Analyzes the content of claims in real time and provides appropriate guidance while inputting The system of claim 1 .

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