system

Abstract

The system according to the embodiment aims to efficiently collect and analyze customer requests and improve services. According to an embodiment, the system includes a collection unit, an analysis unit, and an improvement unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The improvement unit improves the service based on the analysis results obtained by the analysis unit.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background 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 has had the problem of not being able to efficiently collect and analyze customer requests and reflect them in service improvements.

[0005] The system according to the embodiment aims to efficiently collect and analyze customer requests and improve services. [Means for solving the problem]

[0006] A system according to an embodiment includes a collection unit, an analysis unit, and an improvement unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The improvement unit improves the service based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect and analyze customer requests and improve services. [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 service improvement system according to an embodiment of the present invention collects and analyzes customer requests regarding a company's services from social media, word-of-mouth, and other sources to improve the service. This service improvement system first collects customer opinions and requests from social media and review sites. Next, it uses AI to analyze the collected data and identify trends in requests and areas for improvement. Finally, it improves the service based on the identified areas for improvement. This system enables rapid response to customer needs and improves the quality of the service. For example, customer opinions and requests are collected from social media and review sites. Related posts are extracted using specific keywords or hashtags. For example, keywords such as "#companyservicename" and "#request" are searched on social media to collect related posts. Next, the collected data is analyzed using AI. The AI ​​analyzes the collected data and identifies trends in requests and areas for improvement. For example, natural language processing technology is used to classify the content of the posts and extract frequently occurring requests and issues. This makes it easier to understand customer needs. Finally, the service is improved based on the identified areas for improvement. For example, new features are added to address frequently occurring requests or measures are taken to resolve issues. In this way, by reflecting customer feedback, service quality can be improved. For example, by improving a function that many users find difficult to use on social media, user satisfaction can be increased. Also, by further strengthening a function that has received high praise on review sites, competitiveness can be increased. In this way, the service improvement system can quickly respond to customer needs and improve service quality.

[0029] A service improvement system according to an embodiment includes a collection unit, an analysis unit, and an improvement unit. The collection unit collects data. For example, the collection unit collects customer opinions and requests from social media sites and review sites. The collection unit can extract related posts using specific keywords or hashtags. For example, the collection unit searches for keywords such as "#companyservicename" or "#request" on social media sites and collects related posts. The collection unit can also update the collected data in real time. For example, the collection unit instantly updates the collected data to always maintain the latest information. The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to analyze the data and identify trends in requests and areas for improvement. For example, the analysis unit uses natural language processing technology to classify the content of posts and extract frequently occurring requests and issues. Natural language processing technology can be realized using, for example, morphological analysis, grammatical analysis, semantic analysis, and other technologies. The analysis unit can visualize the analysis results. For example, the analysis unit displays the analysis results in the form of graphs, charts, dashboards, and the like to make the data easier to understand. The improvement unit improves the service based on the analysis results obtained by the analysis unit. The improvement unit improves the service based on the identified improvements. For example, the improvement unit adds new functions in response to frequently requested features or takes measures to resolve problems. The improvement unit can automatically reflect the improvements. For example, the improvement unit automatically reflects the improvements by methods such as automatic updates using algorithms or script execution. This allows the service improvement system according to the embodiment to efficiently collect, analyze, and improve data. Some or all of the above-described processing in the collection unit, analysis unit, and improvement unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may input data collected from social media or review sites into AI and cause the AI ​​to collect the data. The analysis unit may input the data collected by the collection unit into AI and cause the AI ​​to analyze the data. The improvement unit may input the analysis results obtained by the analysis unit into AI and cause the AI ​​to improve the service. This allows the service improvement system to efficiently collect, analyze, and improve data.

[0030] The collection unit can collect data using specific keywords or hashtags. The collection unit, for example, extracts related posts using specific keywords or hashtags. For example, the collection unit searches for keywords such as "#companyservicename" or "#request" on social media and collects related posts. The collection unit can also collect data using trending keywords or industry-specific hashtags. For example, the collection unit searches for trending keywords on social media and collects related posts. The collection unit can also collect data using industry-specific hashtags. For example, the collection unit searches for industry-specific hashtags and collects related posts. In this way, the collection unit can efficiently collect related data by using specific keywords or hashtags. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input specific keywords or hashtags into AI and have the AI ​​collect data.

[0031] The analysis unit can analyze data using natural language processing technology and identify trends in requests and areas for improvement. The analysis unit analyzes collected data using, for example, natural language processing technology. For example, the analysis unit can use morphological analysis to break down posted content into words and extract frequently occurring keywords. The analysis unit can also use grammatical analysis to analyze the grammatical structure of the posted content and identify trends in requests. The analysis unit can also use semantic analysis to analyze the meaning of the posted content and identify areas for improvement. For example, the analysis unit can use morphological analysis to break down posted content into words and extract frequently occurring keywords. Grammatical analysis is used to analyze the grammatical structure of the posted content and identify trends in requests. Semantic analysis is used to analyze the meaning of the posted content and identify areas for improvement. In this way, the analysis unit can accurately identify trends in requests and areas for improvement by using natural language processing technology. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input collected data into an AI and have the AI ​​analyze the data.

[0032] The improvement department can improve the service based on the identified improvements. The improvement department, for example, improves the service based on the identified improvements. For example, the improvement department adds new functions in response to frequently requested features. The improvement department can also take measures to resolve problems. The improvement department can also implement measures to improve the quality of the service based on the identified improvements. For example, the improvement department adds new functions in response to frequently requested features. The measures to resolve problems are taken to improve the quality of the service. The measures to improve the quality of the service based on the identified improvements are implemented with the aim of improving the service. As a result, the improvement department improves the service based on the identified improvements, thereby improving the quality of the service. Some or all of the above-mentioned processing in the improvement department may be performed using, for example, AI, or may be performed without using AI. For example, the improvement department can input the identified improvements into AI and have the AI ​​execute the service improvement.

[0033] The collection unit can instantly update the collected data. For example, the collection unit updates the collected data in real time. For example, the collection unit instantly updates the collected data and always maintains the latest information. The collection unit can also update the data using periodic batch processing. For example, the collection unit updates the data at regular time intervals and maintains the latest information. Furthermore, the collection unit can update the data by combining real-time updates and periodic batch processing. For example, the collection unit updates the data by combining real-time updates and periodic batch processing and always maintains the latest information. In this way, the collection unit can always maintain the latest information by updating the collected data in real time. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the collected data into AI and have the AI ​​perform the data update.

[0034] The analysis unit can visualize the analysis results. For example, the analysis unit visualizes the analysis results. For example, the analysis unit displays the analysis results in the form of graphs, charts, dashboards, etc., to facilitate understanding of the data. The analysis unit can also display the analysis results as infographics. For example, the analysis unit displays the analysis results in a visually easy-to-understand format to facilitate understanding of the data. Furthermore, the analysis unit can update the analysis results in real time to display the latest information. For example, the analysis unit updates the analysis results in real time to display the latest information. In this way, the analysis unit visualizes the analysis results, making it easier to understand the data. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit may input the analysis results into AI and have the AI ​​perform the visualization.

[0035] The improvement unit can automatically reflect improvements. The improvement unit, for example, automatically reflects improvements. For example, the improvement unit automatically reflects improvements by methods such as automatic updates using an algorithm or execution of a script. The improvement unit can also build a system for automatically reflecting improvements. For example, the improvement unit builds a system for automatically reflecting improvements and quickly improves the service. Furthermore, the improvement unit can also build a feedback loop for automatically reflecting improvements. For example, the improvement unit builds a feedback loop for automatically reflecting improvements and improves the quality of the service. In this way, the improvement unit can quickly improve the service by automatically reflecting improvements. Some or all of the above-mentioned processing in the improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit can input improvements into AI and have the AI ​​execute service improvements.

[0036] The collection unit can analyze the user's past posting history and select an appropriate collection method when collecting data. For example, the collection unit can analyze the user's past posting history and select an appropriate collection method when collecting data. For example, the collection unit collects related posts based on keywords that the user frequently posted in the past. The collection unit can also prioritize collecting data posted during a specific time period from the user's past posting history. Furthermore, the collection unit can analyze the user's past posting history and collect posts related to a specific topic. For example, the collection unit collects related posts based on keywords that the user frequently posted in the past. The collection unit prioritizes collecting data posted during a specific time period from the user's past posting history. The collection unit analyzes the user's past posting history and collects posts related to a specific topic. This allows the collection unit to select an optimal collection method by analyzing the user's past posting history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past posting history into AI and cause the AI ​​to execute a process of selecting an appropriate collection method.

[0037] The collection unit may perform filtering based on the user's current areas of interest at the time of collection. The collection unit may, for example, perform filtering based on the user's current areas of interest at the time of collection. For example, the collection unit may preferentially collect posts related to topics in which the user is currently interested. The collection unit may also filter posts containing specific keywords based on the user's current areas of interest. The collection unit may also filter posts containing related hashtags based on the user's current areas of interest. For example, the collection unit may preferentially collect posts related to topics in which the user is currently interested. The collection unit may filter posts containing specific keywords based on the user's current areas of interest. The collection unit may filter posts containing related hashtags based on the user's current areas of interest. This allows the collection unit to collect highly relevant data by filtering based on the user's current areas of interest. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit may input the user's current areas of interest into AI and have the AI ​​perform the filtering.

[0038] The collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information during collection. For example, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information during collection. For example, if the user is in a specific area, the collection unit prioritizes collecting posts related to that area. The collection unit can also prioritize collecting posts related to a specific event based on the user's geographical location information. Furthermore, the collection unit can prioritize collecting posts related to issues specific to the area by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting posts related to that area. Based on the user's geographical location information, the collection unit prioritizes collecting posts related to a specific event. Based on the user's geographical location information, the collection unit prioritizes collecting posts related to issues specific to the area. In this way, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into the AI ​​and have the AI ​​perform a process to prioritize collecting highly relevant data.

[0039] The collection unit can analyze the user's social media activity and collect related data at the time of collection. For example, the collection unit analyzes the user's social media activity and collects related data at the time of collection. For example, the collection unit collects data from social media platforms frequently used by the user. The collection unit can also collect posts related to a specific topic from the user's social media activity. Furthermore, the collection unit can analyze the user's social media activity and collect posts including a specific hashtag. For example, the collection unit collects data from social media platforms frequently used by the user. The collection unit collects posts related to a specific topic from the user's social media activity. The user's social media activity is analyzed and posts including a specific hashtag are collected. This allows the collection unit to efficiently collect related data by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's social media activity into AI and cause the AI ​​to perform a process of collecting related data.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit performs a simplified analysis on data with low importance. The analysis unit determines the priority of the analysis based on the importance of the data. In this way, the analysis unit adjusts the level of detail of the analysis based on the importance of the data, thereby enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit may input the importance of the data to AI and cause the AI ​​to execute processing to adjust the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply an image recognition algorithm to image data. The analysis unit can also apply a voice recognition algorithm to voice data. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit applies an image recognition algorithm to image data. The analysis unit applies a voice recognition algorithm to voice data. In this way, the analysis unit can apply different analysis algorithms depending on the category of data, thereby enabling accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the category of data into AI and cause the AI ​​to execute processing to apply different analysis algorithms.

[0042] The analysis unit can determine the analysis priority based on the time of data collection during analysis. The analysis unit, for example, determines the analysis priority based on the time of data collection during analysis. For example, the analysis unit prioritizes analyzing the most recent data. The analysis unit can also emphasize the most recent data while referring to past data. Furthermore, the analysis unit can adjust the analysis priority based on the time of data collection. For example, the analysis unit prioritizes analyzing the most recent data. Emphasizes the most recent data while referring to past data. The analysis unit adjusts the analysis priority based on the time of data collection. In this way, the analysis unit can prioritize analyzing the most recent data by determining the analysis priority based on the time of data collection. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data collection into AI and cause AI to execute processing to determine the analysis priority.

[0043] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit postpones analysis of less relevant data. The analysis unit adjusts the order of analysis based on the relevance of the data. In this way, the analysis unit can adjust the order of analysis based on the relevance of the data, thereby enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI and cause the AI ​​to execute processing to adjust the order of analysis.

[0044] The improvement unit can analyze the user's past feedback and select the optimal improvement method when making an improvement. For example, the improvement unit analyzes the user's past feedback and selects the optimal improvement method when making an improvement. For example, the improvement unit selects the optimal improvement method based on the user's past feedback. The improvement unit can also select an improvement method for resolving frequently occurring problems from the user's past feedback. Furthermore, the improvement unit can analyze the user's past feedback and select the most effective improvement method. For example, the improvement unit selects the optimal improvement method based on the user's past feedback. The improvement unit selects an improvement method for resolving frequently occurring problems from the user's past feedback. The improvement unit analyzes the user's past feedback and selects the most effective improvement method. In this way, the improvement unit can select the optimal improvement method by analyzing the user's past feedback. Some or all of the above-mentioned processing in the improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit can input the user's past feedback into AI and cause the AI ​​to execute a process of selecting the optimal improvement method.

[0045] The improvement unit can customize the means for improvement based on the user's current needs during improvement. The improvement unit, for example, customizes the means for improvement based on the user's current needs during improvement. For example, the improvement unit adds a specific function based on the user's current needs. The improvement unit can also improve an existing function based on the user's current needs. Furthermore, the improvement unit can customize the entire service based on the user's current needs. For example, the improvement unit adds a specific function based on the user's current needs. Improves an existing function. Customizes the entire service. This enables the improvement unit to customize the means for improvement based on the user's current needs, thereby enabling more appropriate improvement. Some or all of the above-described processing in the improvement unit may be performed using, or without, AI. For example, the improvement unit may input the user's current needs into AI and cause the AI ​​to execute processing to customize the means for improvement.

[0046] The improvement unit can select an appropriate improvement method by taking into account the user's geographical location information when making an improvement. For example, the improvement unit selects an appropriate improvement method by taking into account the user's geographical location information when making an improvement. For example, if the user is in a specific area, the improvement unit selects an improvement method related to that area. The improvement unit can also select an improvement method for solving problems specific to that area based on the user's geographical location information. Furthermore, the improvement unit can select an optimal improvement method by taking into account the user's geographical location information. For example, if the user is in a specific area, the improvement unit selects an improvement method related to that area. The improvement unit selects an improvement method for solving problems specific to that area based on the user's geographical location information. The optimal improvement method is selected by taking into account the user's geographical location information. This enables the improvement unit to make improvements that address problems specific to that area by taking into account the user's geographical location information. Some or all of the above-described processing by the improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit can input the user's geographical location information into AI and cause the AI ​​to execute a process of selecting an appropriate improvement method.

[0047] The improvement unit can analyze the user's social media activity and suggest improvement measures during improvement. For example, the improvement unit analyzes the user's social media activity and suggests improvement measures during improvement. For example, the improvement unit suggests improvement methods related to a specific topic from the user's social media activity. The improvement unit can also analyze the user's social media activity and suggest optimal improvement methods. Furthermore, the improvement unit can suggest improvement methods related to a specific hashtag based on the user's social media activity. For example, the improvement unit suggests improvement methods related to a specific topic from the user's social media activity. The improvement unit analyzes the user's social media activity and suggests optimal improvement methods. The improvement unit suggests improvement methods related to a specific hashtag based on the user's social media activity. In this way, the improvement unit can suggest more effective improvement measures by analyzing the user's social media activity. Some or all of the above-mentioned processing in the improvement unit may be performed using, or without, AI. For example, the improvement unit can input the user's social media activity into AI and cause the AI ​​to execute processing to suggest improvement measures.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The collection unit can collect user device information and adjust the data collection method based on the type of device and usage. For example, the collection unit can prioritize collection of posts from smartphones. The collection unit can also collect posts from desktops using a different method. Furthermore, the collection unit can filter data based on the type of application the user is using. This allows the collection unit to select the optimal collection method based on the user's device information, enabling efficient data collection.

[0050] The analysis unit can analyze the user's purchase history and identify trends in demand based on purchasing behavior. For example, the analysis unit prioritizes analysis of demands related to products frequently purchased by the user. The analysis unit can also extract demands related to specific seasons or events from the user's purchase history. Furthermore, the analysis unit can predict future purchasing behavior based on the user's purchase history and identify demands based on that prediction. This allows the analysis unit to accurately identify trends in demand based on the user's purchase history.

[0051] The improvement department can personalize the service based on user feedback. For example, the improvement department can enhance features that the user prefers and remove features that the user does not need. The improvement department can also automatically adjust individual settings based on the user's usage history. Furthermore, the improvement department can customize the interface of the service based on user feedback. In this way, the improvement department can improve the user experience by personalizing the service based on user feedback.

[0052] The collection unit can analyze the user's browsing history and preferentially collect related data. For example, the collection unit can preferentially collect data from websites frequently visited by the user. The collection unit can also extract data related to a specific topic from the user's browsing history. Furthermore, the collection unit can predict future areas of interest based on the user's browsing history and collect data based on that prediction. This allows the collection unit to efficiently collect data based on the user's browsing history.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The collection unit collects data. For example, the collection unit collects customer opinions and requests from social media and review sites. The collection unit can extract related posts using specific keywords or hashtags. For example, the collection unit can search for keywords such as "#companyservicename" or "#request" on social media and collect related posts. The collection unit can also update the collected data in real time. For example, the collection unit can instantly update the collected data and always maintain the latest information. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to analyze the data and identify trends in requests and areas for improvement. For example, the analysis unit uses natural language processing technology to classify the content of posts and extract frequently occurring requests and problems. Natural language processing technology is realized using techniques such as morphological analysis, grammatical analysis, and semantic analysis. The analysis unit can visualize the analysis results. For example, the analysis unit displays the analysis results in the form of graphs, charts, dashboards, etc., making the data easier to understand. Step 3: The Improvement Department improves the service based on the analysis results obtained by the Analysis Department. The Improvement Department improves the service based on the identified areas for improvement. For example, the Improvement Department adds new functions in response to frequently requested features or takes measures to resolve problems. The Improvement Department can automatically reflect the areas for improvement. For example, the Improvement Department can automatically reflect the areas for improvement by using automatic updates via algorithms or by executing scripts.

[0055] (Example 2) A service improvement system according to an embodiment of the present invention collects and analyzes customer requests regarding a company's services from social media, word-of-mouth, and other sources to improve the service. This service improvement system first collects customer opinions and requests from social media and review sites. Next, it uses AI to analyze the collected data and identify trends in requests and areas for improvement. Finally, it improves the service based on the identified areas for improvement. This system enables rapid response to customer needs and improves the quality of the service. For example, customer opinions and requests are collected from social media and review sites. Related posts are extracted using specific keywords or hashtags. For example, keywords such as "#companyservicename" and "#request" are searched on social media to collect related posts. Next, the collected data is analyzed using AI. The AI ​​analyzes the collected data and identifies trends in requests and areas for improvement. For example, natural language processing technology is used to classify the content of the posts and extract frequently occurring requests and issues. This makes it easier to understand customer needs. Finally, the service is improved based on the identified areas for improvement. For example, new features are added to address frequently occurring requests or measures are taken to resolve issues. In this way, by reflecting customer feedback, service quality can be improved. For example, by improving a function that many users find difficult to use on social media, user satisfaction can be increased. Also, by further strengthening a function that has received high praise on review sites, competitiveness can be increased. In this way, the service improvement system can quickly respond to customer needs and improve service quality.

[0056] A service improvement system according to an embodiment includes a collection unit, an analysis unit, and an improvement unit. The collection unit collects data. For example, the collection unit collects customer opinions and requests from social media sites and review sites. The collection unit can extract related posts using specific keywords or hashtags. For example, the collection unit searches for keywords such as "#companyservicename" or "#request" on social media sites and collects related posts. The collection unit can also update the collected data in real time. For example, the collection unit instantly updates the collected data to always maintain the latest information. The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to analyze the data and identify trends in requests and areas for improvement. For example, the analysis unit uses natural language processing technology to classify the content of posts and extract frequently occurring requests and issues. Natural language processing technology can be realized using, for example, morphological analysis, grammatical analysis, semantic analysis, and other technologies. The analysis unit can visualize the analysis results. For example, the analysis unit displays the analysis results in the form of graphs, charts, dashboards, and the like to make the data easier to understand. The improvement unit improves the service based on the analysis results obtained by the analysis unit. The improvement unit improves the service based on the identified improvements. For example, the improvement unit adds new functions in response to frequently requested features or takes measures to resolve problems. The improvement unit can automatically reflect the improvements. For example, the improvement unit automatically reflects the improvements by methods such as automatic updates using algorithms or script execution. This allows the service improvement system according to the embodiment to efficiently collect, analyze, and improve data. Some or all of the above-described processing in the collection unit, analysis unit, and improvement unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may input data collected from social media or review sites into AI and cause the AI ​​to collect the data. The analysis unit may input the data collected by the collection unit into AI and cause the AI ​​to analyze the data. The improvement unit may input the analysis results obtained by the analysis unit into AI and cause the AI ​​to improve the service. This allows the service improvement system to efficiently collect, analyze, and improve data.

[0057] The collection unit can collect data using specific keywords or hashtags. The collection unit, for example, extracts related posts using specific keywords or hashtags. For example, the collection unit searches for keywords such as "#companyservicename" or "#request" on social media and collects related posts. The collection unit can also collect data using trending keywords or industry-specific hashtags. For example, the collection unit searches for trending keywords on social media and collects related posts. The collection unit can also collect data using industry-specific hashtags. For example, the collection unit searches for industry-specific hashtags and collects related posts. In this way, the collection unit can efficiently collect related data by using specific keywords or hashtags. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input specific keywords or hashtags into AI and have the AI ​​collect data.

[0058] The analysis unit can analyze data using natural language processing technology and identify trends in requests and areas for improvement. The analysis unit analyzes collected data using, for example, natural language processing technology. For example, the analysis unit can use morphological analysis to break down posted content into words and extract frequently occurring keywords. The analysis unit can also use grammatical analysis to analyze the grammatical structure of the posted content and identify trends in requests. The analysis unit can also use semantic analysis to analyze the meaning of the posted content and identify areas for improvement. For example, the analysis unit can use morphological analysis to break down posted content into words and extract frequently occurring keywords. Grammatical analysis is used to analyze the grammatical structure of the posted content and identify trends in requests. Semantic analysis is used to analyze the meaning of the posted content and identify areas for improvement. In this way, the analysis unit can accurately identify trends in requests and areas for improvement by using natural language processing technology. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input collected data into an AI and have the AI ​​analyze the data.

[0059] The improvement department can improve the service based on the identified improvements. The improvement department, for example, improves the service based on the identified improvements. For example, the improvement department adds new functions in response to frequently requested features. The improvement department can also take measures to resolve problems. The improvement department can also implement measures to improve the quality of the service based on the identified improvements. For example, the improvement department adds new functions in response to frequently requested features. The measures to resolve problems are taken to improve the quality of the service. The measures to improve the quality of the service based on the identified improvements are implemented with the aim of improving the service. As a result, the improvement department improves the service based on the identified improvements, thereby improving the quality of the service. Some or all of the above-mentioned processing in the improvement department may be performed using, for example, AI, or may be performed without using AI. For example, the improvement department can input the identified improvements into AI and have the AI ​​execute the service improvement.

[0060] The collection unit can instantly update the collected data. For example, the collection unit updates the collected data in real time. For example, the collection unit instantly updates the collected data and always maintains the latest information. The collection unit can also update the data using periodic batch processing. For example, the collection unit updates the data at regular time intervals and maintains the latest information. Furthermore, the collection unit can update the data by combining real-time updates and periodic batch processing. For example, the collection unit updates the data by combining real-time updates and periodic batch processing and always maintains the latest information. In this way, the collection unit can always maintain the latest information by updating the collected data in real time. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the collected data into AI and have the AI ​​perform the data update.

[0061] The analysis unit can visualize the analysis results. For example, the analysis unit visualizes the analysis results. For example, the analysis unit displays the analysis results in the form of graphs, charts, dashboards, etc., to facilitate understanding of the data. The analysis unit can also display the analysis results as infographics. For example, the analysis unit displays the analysis results in a visually easy-to-understand format to facilitate understanding of the data. Furthermore, the analysis unit can update the analysis results in real time to display the latest information. For example, the analysis unit updates the analysis results in real time to display the latest information. In this way, the analysis unit visualizes the analysis results, making it easier to understand the data. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit may input the analysis results into AI and have the AI ​​perform the visualization.

[0062] The improvement unit can automatically reflect improvements. The improvement unit, for example, automatically reflects improvements. For example, the improvement unit automatically reflects improvements by methods such as automatic updates using an algorithm or execution of a script. The improvement unit can also build a system for automatically reflecting improvements. For example, the improvement unit builds a system for automatically reflecting improvements and quickly improves the service. Furthermore, the improvement unit can also build a feedback loop for automatically reflecting improvements. For example, the improvement unit builds a feedback loop for automatically reflecting improvements and improves the quality of the service. In this way, the improvement unit can quickly improve the service by automatically reflecting improvements. Some or all of the above-mentioned processing in the improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit can input improvements into AI and have the AI ​​execute service improvements.

[0063] The collection unit can use a method for estimating a user's emotions to determine the priority of data to be collected based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions and determines the priority of data to be collected based on the estimated user's emotions. For example, if the user is dissatisfied, the collection unit prioritizes collecting posts related to the dissatisfaction. Furthermore, if the user is satisfied, the collection unit can also prioritize collecting posts related to the satisfaction. Furthermore, if the user is excited, the collection unit can also prioritize collecting posts related to the excitement. For example, if the user is dissatisfied, the collection unit prioritizes collecting posts related to the dissatisfaction. If the user is satisfied, the collection unit prioritizes collecting posts related to the satisfaction. If the user is excited, the collection unit prioritizes collecting posts related to the excitement. In this way, the collection unit can prioritize collecting important data by determining the priority of data based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative 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 collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's emotions into AI and have the AI ​​perform a process of determining the priority of the data.

[0064] The collection unit can analyze the user's past posting history and select an appropriate collection method when collecting data. For example, the collection unit can analyze the user's past posting history and select an appropriate collection method when collecting data. For example, the collection unit collects related posts based on keywords that the user frequently posted in the past. The collection unit can also prioritize collecting data posted during a specific time period from the user's past posting history. Furthermore, the collection unit can analyze the user's past posting history and collect posts related to a specific topic. For example, the collection unit collects related posts based on keywords that the user frequently posted in the past. The collection unit prioritizes collecting data posted during a specific time period from the user's past posting history. The collection unit analyzes the user's past posting history and collects posts related to a specific topic. This allows the collection unit to select an optimal collection method by analyzing the user's past posting history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past posting history into AI and cause the AI ​​to execute a process of selecting an appropriate collection method.

[0065] The collection unit may perform filtering based on the user's current areas of interest at the time of collection. The collection unit may, for example, perform filtering based on the user's current areas of interest at the time of collection. For example, the collection unit may preferentially collect posts related to topics in which the user is currently interested. The collection unit may also filter posts containing specific keywords based on the user's current areas of interest. The collection unit may also filter posts containing related hashtags based on the user's current areas of interest. For example, the collection unit may preferentially collect posts related to topics in which the user is currently interested. The collection unit may filter posts containing specific keywords based on the user's current areas of interest. The collection unit may filter posts containing related hashtags based on the user's current areas of interest. This allows the collection unit to collect highly relevant data by filtering based on the user's current areas of interest. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit may input the user's current areas of interest into AI and have the AI ​​perform the filtering.

[0066] The collection unit can adjust the timing of data collection based on the estimated user emotion using a method for estimating the user emotion. The collection unit, for example, estimates the user emotion and adjusts the timing of data collection based on the estimated user emotion. For example, if the user is dissatisfied, the collection unit predicts the timing when the dissatisfaction will be posted and collects the data. Furthermore, if the user is satisfied, the collection unit can predict the timing when the satisfaction will be posted and collect the data. Furthermore, if the user is excited, the collection unit can predict the timing when the excitement will be posted and collect the data. For example, if the user is dissatisfied, the collection unit predicts the timing when the dissatisfaction will be posted and collects the data. If the user is satisfied, the collection unit predicts the timing when the satisfaction will be posted and collects the data. If the user is excited, the collection unit predicts the timing when the excitement will be posted and collects the data. In this way, the collection unit can collect data at an appropriate timing by adjusting the timing of data collection based on the user emotion. Emotion estimation is realized, for example, using an emotion estimation function using an emotion engine or a generative 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 collection unit may be performed using, or without, an AI. For example, the collection unit may input the user's emotions into the AI ​​and cause the AI ​​to execute a process of adjusting the timing of data collection.

[0067] The collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information during collection. For example, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information during collection. For example, if the user is in a specific area, the collection unit prioritizes collecting posts related to that area. The collection unit can also prioritize collecting posts related to a specific event based on the user's geographical location information. Furthermore, the collection unit can prioritize collecting posts related to issues specific to the area by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting posts related to that area. Based on the user's geographical location information, the collection unit prioritizes collecting posts related to a specific event. Based on the user's geographical location information, the collection unit prioritizes collecting posts related to issues specific to the area. In this way, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into the AI ​​and have the AI ​​perform a process to prioritize collecting highly relevant data.

[0068] The collection unit can analyze the user's social media activity and collect related data at the time of collection. For example, the collection unit analyzes the user's social media activity and collects related data at the time of collection. For example, the collection unit collects data from social media platforms frequently used by the user. The collection unit can also collect posts related to a specific topic from the user's social media activity. Furthermore, the collection unit can analyze the user's social media activity and collect posts including a specific hashtag. For example, the collection unit collects data from social media platforms frequently used by the user. The collection unit collects posts related to a specific topic from the user's social media activity. The user's social media activity is analyzed and posts including a specific hashtag are collected. This allows the collection unit to efficiently collect related data by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's social media activity into AI and cause the AI ​​to perform a process of collecting related data.

[0069] The analysis unit can use a method for estimating a user's emotion to adjust the way the analysis is presented based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the way the analysis is presented based on the estimated user's emotion. For example, if the user is dissatisfied, the analysis unit provides an analysis result that emphasizes the dissatisfaction. Furthermore, if the user is satisfied, the analysis unit can provide an analysis result that emphasizes the satisfaction. Furthermore, if the user is excited, the analysis unit can provide an analysis result that emphasizes the excitement. For example, if the user is dissatisfied, the analysis unit provides an analysis result that emphasizes the dissatisfaction. If the user is satisfied, the analysis unit provides an analysis result that emphasizes the satisfaction. If the user is excited, the analysis unit provides an analysis result that emphasizes the excitement. In this way, the analysis unit can adjust the way the analysis is presented based on the user's emotion and provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative 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 may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's emotions into AI and have the AI ​​execute a process to adjust the way the analysis is presented.

[0070] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit performs a simplified analysis on data with low importance. The analysis unit determines the priority of the analysis based on the importance of the data. In this way, the analysis unit adjusts the level of detail of the analysis based on the importance of the data, thereby enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit may input the importance of the data to AI and cause the AI ​​to execute processing to adjust the level of detail of the analysis.

[0071] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply an image recognition algorithm to image data. The analysis unit can also apply a voice recognition algorithm to voice data. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit applies an image recognition algorithm to image data. The analysis unit applies a voice recognition algorithm to voice data. In this way, the analysis unit can apply different analysis algorithms depending on the category of data, thereby enabling accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the category of data into AI and cause the AI ​​to execute processing to apply different analysis algorithms.

[0072] The analysis unit can adjust the length of the analysis based on the estimated user emotion using a method for estimating the user emotion. The analysis unit, for example, estimates the user emotion and adjusts the length of the analysis based on the estimated user emotion. For example, the analysis unit provides a detailed analysis when the user is dissatisfied. The analysis unit can also provide a concise analysis when the user is satisfied. The analysis unit can also provide a visually stimulating analysis when the user is excited. For example, the analysis unit provides a detailed analysis when the user is dissatisfied. The analysis unit provides a concise analysis when the user is satisfied. The analysis unit provides a visually stimulating analysis when the user is excited. This allows the analysis unit to adjust the length of the analysis based on the user emotion and provide an analysis result of an appropriate length. Emotion estimation is achieved using an emotion estimation function, for example, 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-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotions into the AI ​​and have the AI ​​perform a process to adjust the length of the analysis.

[0073] The analysis unit can determine the analysis priority based on the time of data collection during analysis. The analysis unit, for example, determines the analysis priority based on the time of data collection during analysis. For example, the analysis unit prioritizes analyzing the most recent data. The analysis unit can also emphasize the most recent data while referring to past data. Furthermore, the analysis unit can adjust the analysis priority based on the time of data collection. For example, the analysis unit prioritizes analyzing the most recent data. Emphasizes the most recent data while referring to past data. The analysis unit adjusts the analysis priority based on the time of data collection. In this way, the analysis unit can prioritize analyzing the most recent data by determining the analysis priority based on the time of data collection. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data collection into AI and cause AI to execute processing to determine the analysis priority.

[0074] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit postpones analysis of less relevant data. The analysis unit adjusts the order of analysis based on the relevance of the data. In this way, the analysis unit can adjust the order of analysis based on the relevance of the data, thereby enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI and cause the AI ​​to execute processing to adjust the order of analysis.

[0075] The improvement unit can use a method for estimating a user's emotion to adjust the improvement method based on the estimated user's emotion. The improvement unit, for example, estimates the user's emotion and adjusts the improvement method based on the estimated user's emotion. For example, if the user is dissatisfied, the improvement unit proposes an improvement method to resolve the dissatisfaction. Furthermore, if the user is satisfied, the improvement unit can also propose an improvement method to maintain the satisfaction. Furthermore, if the user is excited, the improvement unit can also propose an improvement method to maintain the excitement. For example, if the user is dissatisfied, the improvement unit proposes an improvement method to resolve the dissatisfaction. If the user is satisfied, the improvement unit proposes an improvement method to maintain the satisfaction. If the user is excited, the improvement unit proposes an improvement method to maintain the excitement. This allows the improvement unit to adjust the improvement method based on the user's emotion, thereby enabling more effective improvement. Emotion estimation is realized using an emotion estimation function, for example, using 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 improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit may input the user's emotions into AI and have the AI ​​execute processing to adjust the improvement method.

[0076] The improvement unit can analyze the user's past feedback and select the optimal improvement method when making an improvement. For example, the improvement unit analyzes the user's past feedback and selects the optimal improvement method when making an improvement. For example, the improvement unit selects the optimal improvement method based on the user's past feedback. The improvement unit can also select an improvement method for resolving frequently occurring problems from the user's past feedback. Furthermore, the improvement unit can analyze the user's past feedback and select the most effective improvement method. For example, the improvement unit selects the optimal improvement method based on the user's past feedback. The improvement unit selects an improvement method for resolving frequently occurring problems from the user's past feedback. The improvement unit analyzes the user's past feedback and selects the most effective improvement method. In this way, the improvement unit can select the optimal improvement method by analyzing the user's past feedback. Some or all of the above-mentioned processing in the improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit can input the user's past feedback into AI and cause the AI ​​to execute a process of selecting the optimal improvement method.

[0077] The improvement unit can customize the means for improvement based on the user's current needs during improvement. The improvement unit, for example, customizes the means for improvement based on the user's current needs during improvement. For example, the improvement unit adds a specific function based on the user's current needs. The improvement unit can also improve an existing function based on the user's current needs. Furthermore, the improvement unit can customize the entire service based on the user's current needs. For example, the improvement unit adds a specific function based on the user's current needs. Improves an existing function. Customizes the entire service. This enables the improvement unit to customize the means for improvement based on the user's current needs, thereby enabling more appropriate improvement. Some or all of the above-described processing in the improvement unit may be performed using, or without, AI. For example, the improvement unit may input the user's current needs into AI and cause the AI ​​to execute processing to customize the means for improvement.

[0078] The improvement unit can determine the priority of improvements based on the estimated user emotions using a method for estimating user emotions. The improvement unit, for example, estimates the user emotions and determines the priority of improvements based on the estimated user emotions. For example, if the user is dissatisfied, the improvement unit prioritizes improvements to resolve the dissatisfaction. Furthermore, if the user is satisfied, the improvement unit can prioritize improvements to maintain the satisfaction. Furthermore, if the user is excited, the improvement unit can prioritize improvements to maintain the excitement. For example, if the user is dissatisfied, the improvement unit prioritizes improvements to resolve the dissatisfaction. If the user is satisfied, the improvement unit prioritizes improvements to maintain the satisfaction. If the user is excited, the improvement unit prioritizes improvements to maintain the excitement. In this way, the improvement unit can prioritize important improvements by determining the priority of improvements based on the user emotions. Emotion estimation is realized using an emotion estimation function, for example, using 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 improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit may input the user's emotions into AI and have the AI ​​execute a process to determine the priority of improvements.

[0079] The improvement unit can select an appropriate improvement method by taking into account the user's geographical location information when making an improvement. For example, the improvement unit selects an appropriate improvement method by taking into account the user's geographical location information when making an improvement. For example, if the user is in a specific area, the improvement unit selects an improvement method related to that area. The improvement unit can also select an improvement method for solving problems specific to that area based on the user's geographical location information. Furthermore, the improvement unit can select an optimal improvement method by taking into account the user's geographical location information. For example, if the user is in a specific area, the improvement unit selects an improvement method related to that area. The improvement unit selects an improvement method for solving problems specific to that area based on the user's geographical location information. The optimal improvement method is selected by taking into account the user's geographical location information. This enables the improvement unit to make improvements that address problems specific to that area by taking into account the user's geographical location information. Some or all of the above-described processing by the improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit can input the user's geographical location information into AI and cause the AI ​​to execute a process of selecting an appropriate improvement method.

[0080] The improvement unit can analyze the user's social media activity and suggest improvement measures during improvement. For example, the improvement unit analyzes the user's social media activity and suggests improvement measures during improvement. For example, the improvement unit suggests improvement methods related to a specific topic from the user's social media activity. The improvement unit can also analyze the user's social media activity and suggest optimal improvement methods. Furthermore, the improvement unit can suggest improvement methods related to a specific hashtag based on the user's social media activity. For example, the improvement unit suggests improvement methods related to a specific topic from the user's social media activity. The improvement unit analyzes the user's social media activity and suggests optimal improvement methods. The improvement unit suggests improvement methods related to a specific hashtag based on the user's social media activity. In this way, the improvement unit can suggest more effective improvement measures by analyzing the user's social media activity. Some or all of the above-mentioned processing in the improvement unit may be performed using, or without, AI. For example, the improvement unit can input the user's social media activity into AI and cause the AI ​​to execute processing to suggest improvement measures. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and improvement 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 collection unit collects customer opinions and requests from social media and review sites using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using AI to identify trends in requests and areas for improvement. The improvement unit is realized by the specific processing unit 290 of the data processing device 12 and improves the service based on the identified areas for improvement. Each of the elements of the collection unit, analysis unit, and improvement unit is also realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and improvement unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects customer opinions and requests from social media and review sites using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using AI to identify trends in requests and areas for improvement. The improvement unit is realized by the specific processing unit 290 of the data processing device 12 and improves services based on the identified areas for improvement. Each of the elements of the collection unit, analysis unit, and improvement unit is also realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and improvement unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects customer opinions and requests from social media and review sites using the camera 42 and microphone 238 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using AI to identify trends in requests and areas for improvement. The improvement unit is realized by the specific processing unit 290 of the data processing device 12 and improves the service based on the identified areas for improvement. Each of the elements of the collection unit, analysis unit, and improvement unit is also realized, for example, by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and improvement unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects customer opinions and requests from social media and review sites using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using AI to identify trends in requests and areas for improvement. The improvement unit is realized by the specific processing unit 290 of the data processing device 12 and improves the service based on the identified areas for improvement. Each of the elements of the collection unit, analysis unit, and improvement unit is also realized, for example, by the control unit 46A of the robot 414.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The collection unit can collect user device information and adjust the data collection method based on the type of device and usage. For example, the collection unit can prioritize collection of posts from smartphones. The collection unit can also collect posts from desktops using a different method. Furthermore, the collection unit can filter data based on the type of application the user is using. This allows the collection unit to select the optimal collection method based on the user's device information, enabling efficient data collection.

[0083] The analysis unit can analyze the user's purchase history and identify trends in demand based on purchasing behavior. For example, the analysis unit prioritizes analysis of demands related to products frequently purchased by the user. The analysis unit can also extract demands related to specific seasons or events from the user's purchase history. Furthermore, the analysis unit can predict future purchasing behavior based on the user's purchase history and identify demands based on that prediction. This allows the analysis unit to accurately identify trends in demand based on the user's purchase history.

[0084] The improvement department can personalize the service based on user feedback. For example, the improvement department can enhance features that the user prefers and remove features that the user does not need. The improvement department can also automatically adjust individual settings based on the user's usage history. Furthermore, the improvement department can customize the interface of the service based on user feedback. In this way, the improvement department can improve the user experience by personalizing the service based on user feedback.

[0085] The collection unit can analyze the user's browsing history and preferentially collect related data. For example, the collection unit can preferentially collect data from websites frequently visited by the user. The collection unit can also extract data related to a specific topic from the user's browsing history. Furthermore, the collection unit can predict future areas of interest based on the user's browsing history and collect data based on that prediction. This allows the collection unit to efficiently collect data based on the user's browsing history.

[0086] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is dissatisfied, the analysis unit can perform a detailed analysis to identify the problem. Alternatively, if the user is satisfied, the analysis unit can perform a simplified analysis. Furthermore, if the user is excited, the analysis unit can provide the analysis results in a visually easy-to-understand format. This allows the analysis unit to provide more appropriate analysis results by adjusting the accuracy of the analysis based on the user's emotions.

[0087] The collection unit can estimate the user's emotions and select the type of data to collect based on the estimated emotions. For example, if the user is dissatisfied, the collection unit collects detailed data related to the dissatisfaction. In addition, if the user is satisfied, the collection unit can also collect positive data related to the satisfaction. Furthermore, if the user is excited, the collection unit can also collect data related to the excitement. In this way, the collection unit can collect more relevant data by selecting the type of data to collect based on the user's emotions.

[0088] The improvement unit can estimate the user's emotions and adjust the timing of improvements based on the estimated emotions. For example, if the user is dissatisfied, the improvement unit can make improvements quickly. Also, if the user is satisfied, the improvement unit can make periodic improvements. Furthermore, if the user is excited, the improvement unit can make improvements at a timing that maintains the excitement. In this way, the improvement unit can make more effective improvements by adjusting the timing of improvements based on the user's emotions.

[0089] The analysis unit can estimate the user's emotion and adjust the analysis visualization method based on the estimated emotion. For example, if the user is dissatisfied, the analysis unit can perform visualization that emphasizes the dissatisfaction. Also, if the user is satisfied, the analysis unit can perform visualization that emphasizes the satisfaction. Furthermore, if the user is excited, the analysis unit can perform visualization in a way that visually expresses the excitement. In this way, the analysis unit can provide more appropriate visualization by adjusting the analysis visualization method based on the user's emotion.

[0090] The improvement unit can estimate the user's emotions and adjust the improvement feedback method based on the estimated emotions. For example, if the user is dissatisfied, the improvement unit can provide specific feedback to resolve the dissatisfaction. Furthermore, if the user is satisfied, the improvement unit can provide positive feedback to maintain the satisfaction. Furthermore, if the user is excited, the improvement unit can provide feedback to maintain the excitement. In this way, the improvement unit can provide more effective feedback by adjusting the improvement feedback method based on the user's emotions.

[0091] The collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the user is dissatisfied, the collection unit frequently collects data related to the dissatisfaction. In addition, if the user is satisfied, the collection unit can periodically collect data related to the satisfaction. Furthermore, if the user is excited, the collection unit can collect data related to the excitement in real time. This allows the collection unit to adjust the frequency of data collection based on the user's emotions, thereby enabling more appropriate data collection.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The collection unit collects data. For example, the collection unit collects customer opinions and requests from social media and review sites. The collection unit can extract related posts using specific keywords or hashtags. For example, the collection unit can search for keywords such as "#companyservicename" or "#request" on social media and collect related posts. The collection unit can also update the collected data in real time. For example, the collection unit can instantly update the collected data and always maintain the latest information. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to analyze the data and identify trends in requests and areas for improvement. For example, the analysis unit uses natural language processing technology to classify the content of posts and extract frequently occurring requests and problems. Natural language processing technology is realized using techniques such as morphological analysis, grammatical analysis, and semantic analysis. The analysis unit can visualize the analysis results. For example, the analysis unit displays the analysis results in the form of graphs, charts, dashboards, etc., making the data easier to understand. Step 3: The Improvement Department improves the service based on the analysis results obtained by the Analysis Department. The Improvement Department improves the service based on the identified areas for improvement. For example, the Improvement Department adds new functions in response to frequently requested features or takes measures to resolve problems. The Improvement Department can automatically reflect the areas for improvement. For example, the Improvement Department can automatically reflect the areas for improvement by using automatic updates via algorithms or by executing scripts.

[0094] 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.

[0095] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0096] 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.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] [Explanation of symbols]

[0166] 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 collection unit that collects data; an analysis unit that analyzes the data collected by the collection unit; an improvement unit that improves the service based on the analysis results obtained by the analysis unit; Equipped with A system characterized by:

2. The collecting unit Collect data using specific keywords or hashtags 2. The system of claim 1.

3. The analysis unit Analyze data using natural language processing technology to identify trends and areas for improvement 2. The system of claim 1.

4. The improvement unit Improve our services based on identified areas for improvement 2. The system of claim 1.

5. The collecting unit Instantly update collected data 2. The system of claim 1.

6. The analysis unit Visualizing the analysis results 2. The system of claim 1.

7. The improvement unit Automatically reflect improvements 2. The system of claim 1.

8. The collecting unit Using a method for estimating user emotions, the priority of data to be collected is determined based on the estimated user emotions.

2. The system of claim 1.

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