System

Abstract

To provide a system for efficiently collecting request data from a customer and determining appropriate priority.SOLUTION: A system includes a collection part, an analysis part, and a determination part. The collection part collects request data, which is a request from a customer. The analysis part analyzes the data collected by the collection part. The determination part determines a priority corresponding to the request in accordance with a source code for realizing a service, which is an object of the request, or usage status data of a service, which is an object of the request, and the analysis result obtained by the analysis part.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to efficiently collect customer request data and determine appropriate priorities.

[0005] The system according to the embodiment aims to efficiently collect request data from customers and determine appropriate priorities. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a determination unit. The collection unit collects request data that is requests from customers. The analysis unit analyzes the data collected by the collection unit. The determination unit determines a priority order corresponding to the requests based on source code that realizes the requested service or usage status data of the requested service and the analysis results obtained by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can efficiently collect request data from customers and determine appropriate priorities. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The system according to the embodiment of the present invention is a system that takes in improvement requests and new function request data from customers who use a service, and lists the items to be repaired and the priority order. This system first includes a collection unit that collects the request data from customers. This collection unit collects the request data from questionnaires, feedback forms, and customer support records. Next, the system includes an analysis unit that analyzes the collected data. This analysis unit analyzes the data using an algorithm that evaluates the frequency, importance, and impact of the requests. Furthermore, it is also possible to estimate the user's emotions and analyze the contents of the request data based on the estimated emotions. Next, the system includes a determination unit that determines the priority order corresponding to the requests based on the source code that realizes the service that is the subject of the request, the service usage status data, and the analysis results. This determination unit determines the priority order of the items to be repaired and new functions based on the analysis results. It is also possible to determine the priority order by taking into account the difficulty and impact of processing. Furthermore, it is also possible to analyze the past feedback history of users and select the optimal collection method. For example, frequently requested functions and requests with high importance are given priority, so that the needs of users can be met quickly. Furthermore, by taking into account the difficulty and impact of processing, optimal resource allocation is possible, and efficient development is realized. This enables the system to efficiently improve services and develop new functions, thereby improving user satisfaction.

[0029] The system according to the embodiment includes a collection unit, an analysis unit, and a determination unit. The collection unit collects request data, which is a request from a customer. The request data includes, for example, a text format, a voice format, an image format, and the like, but is not limited to such examples. The collection unit collects the request data, for example, from a questionnaire, a feedback form, or a record of customer support. For example, the collection unit can collect the request data through an online questionnaire. The collection unit can also collect the request data from a user using a feedback form. Furthermore, the collection unit can analyze the record of customer support and collect the request data. For example, the collection unit analyzes the call record or chat log of the customer support and extracts the request data. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, an algorithm that evaluates the frequency, importance, and impact of the request. For example, the analysis unit analyzes the frequency of the request data using a statistical method. The analysis unit can also evaluate the importance of the request data using a machine learning algorithm. Furthermore, the analysis unit can analyze the impact on the system to evaluate the impact of the request data. For example, the analysis unit analyzes the impact on the number of users to evaluate the impact of the requested data. The determination unit determines the priority order corresponding to the request based on the source code that realizes the requested service or the usage status data of the requested service and the analysis result obtained by the analysis unit. The determination unit determines the priority order of the repair items and new functions based on the analysis result, for example. For example, the determination unit determines the priority order based on the urgency and impact of the requested data. The determination unit can also determine the priority order taking into account the difficulty and impact of the processing. For example, the determination unit determines the priority order based on the technical complexity and the need for resources. This enables the system according to the embodiment to efficiently collect, analyze, and prioritize requested data from customers. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can determine the priority order using an AI model that inputs the urgency and impact of the requested data and outputs the priority order.

[0030] The collection unit collects request data, which is requests from customers. The request data includes, for example, text format, audio format, image format, and the like, but is not limited to such examples. The collection unit collects the request data, for example, from questionnaires, feedback forms, and customer support records. For example, the collection unit can collect the request data through an online questionnaire. The collection unit can also collect the request data from users using a feedback form. Furthermore, the collection unit can analyze customer support records to collect the request data. For example, the collection unit analyzes customer support call records and chat logs to extract the request data. The collection unit makes full use of various technologies to efficiently collect these data. For example, voice recognition technology is used to collect voice data, and the contents of the call are converted into text data. Image data is collected by using image recognition technology to extract text and objects within the image. This allows the collection unit to centrally manage and efficiently collect request data in various formats. The collection unit also enables real-time data collection by adjusting the frequency and timing of data collection. For example, customer support chat logs are automatically collected every time a conversation ends and immediately sent for analysis. This allows the collection department to always have the latest requested data and respond quickly. Furthermore, to ensure the privacy and security of the data, the collection department implements encryption technology and access control to thoroughly protect the data. This allows the collection department to collect requested data efficiently and safely without compromising the trust of users.

[0031] The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, an algorithm that evaluates the frequency, importance, and impact of the request. For example, the analysis unit analyzes the frequency of the request data using a statistical method. The analysis unit can also evaluate the importance of the request data using a machine learning algorithm. Furthermore, the analysis unit can analyze the impact on the system to evaluate the impact of the request data. For example, the analysis unit analyzes the impact on the number of users to evaluate the impact of the request data. Specifically, the analysis unit classifies the collected request data using natural language processing technology and understands the content of the request. For example, text data is classified into major themes using topic modeling, and voice data is converted into text using voice recognition technology and then analyzed in the same manner. Image data is extracted from related requests using image recognition technology. This allows the analysis unit to analyze data in a unified manner in various formats and grasp the overall picture of the request. Furthermore, the analysis unit evaluates the importance and impact of the request data using a machine learning algorithm. For example, a model is constructed to predict the urgency and impact of the request based on the frequency of the request data and past response history. This model takes the contents of the request data and related metadata (e.g., user attributes and past request history) as input, and outputs the importance and impact of the request. This enables the analysis unit to efficiently evaluate the priority of requests and respond quickly. Furthermore, the analysis unit simulates the impact on the system to evaluate the impact of the request data. For example, it predicts the impact that adding a new function or modifying an existing function will have on the number of users and system performance. This allows the analysis unit to evaluate in advance the impact that realizing a request will have on the entire system and devise optimal countermeasures.

[0032] The determination unit determines the priority order corresponding to the request based on the source code that realizes the service that is the subject of the request, or the usage status data of the service that is the subject of the request, and the analysis result obtained by the analysis unit. The determination unit, for example, determines the priority order of the repair items and new functions based on the analysis result. For example, the determination unit determines the priority order based on the urgency and impact of the request data. The determination unit can also determine the priority order taking into account the difficulty and impact of the processing. For example, the determination unit determines the priority order based on the technical complexity and the need for resources. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can determine the priority order using an AI model that inputs the urgency and impact of the request data and outputs the priority order. Specifically, the determination unit executes an algorithm for determining the priority order based on the urgency and impact of the request data provided by the analysis unit. This algorithm calculates the optimal priority order by considering multiple factors such as the content and impact of the request, urgency, and technical feasibility. For example, if the urgency of the request is high and the impact is also large, the request is given a high priority order. On the other hand, if a request is technically difficult to achieve and requires a lot of resources, the priority of that request may be set low. Furthermore, the decision unit can use AI to determine the priority. For example, an AI model that has learned from past request data and the corresponding results is used to predict the priority of a new request. This AI model inputs the contents of the request data and related metadata, and outputs the priority. This enables the decision unit to determine the priority with high accuracy based on past data. Furthermore, the decision unit makes the priority determination process transparent and provides it in an explainable form to the relevant parties. For example, the reasons for determining the priority and the factors considered are recorded in detail and reported to the relevant parties. This allows the decision unit to ensure the transparency and reliability of the request priority determination process, and to gain the understanding and consent of the relevant parties.

[0033] The collection unit can collect the request data from a questionnaire or a feedback form or a customer support record. The collection unit can collect the request data through, for example, an online questionnaire. For example, the collection unit collects the request data from the user using a web form. The collection unit can also collect the request data from the user using a feedback form. For example, the collection unit collects the request data using an in-app form. Furthermore, the collection unit can analyze the customer support record and collect the request data. For example, the collection unit analyzes the customer support call record or chat log to extract the request data. This makes it possible to collect the request data in various ways. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the customer support record into AI and cause the AI ​​to extract the request data.

[0034] The analysis unit can analyze the data using an algorithm that evaluates the frequency, priority, and impact of the request. The analysis unit, for example, uses a statistical method to evaluate the frequency of the request. For example, the analysis unit measures the monthly occurrence count and the weekly occurrence count. The analysis unit can also use a machine learning algorithm to evaluate the priority of the request. For example, the analysis unit calculates an importance score and an impact score. Furthermore, the analysis unit can analyze the impact on the system to evaluate the impact of the request. For example, the analysis unit evaluates the impact on the number of users and the impact on the system. This enables detailed analysis of the request data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the request data into AI and have the AI ​​execute the evaluation of the frequency, priority, and impact.

[0035] The determination unit can determine the priority of the repair items and new functions based on the analysis results. The determination unit, for example, determines the priority of the repair items and new functions based on the analysis results. For example, the determination unit determines the priority based on the urgency and impact of the requested data. The determination unit can also determine the priority taking into account the difficulty and impact of the processing. For example, the determination unit determines the priority based on the technical complexity and the need for resources. As a result, efficient repairs and new functions can be developed by determining the priority based on the analysis results. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can determine the priority using an AI model that inputs the urgency and impact of the requested data and outputs the priority.

[0036] The determination unit can determine the priority order based on the difficulty and impact of the processing. The determination unit, for example, considers technical complexity and resource need to evaluate the difficulty of the processing. For example, the determination unit determines the priority order based on technical complexity. The determination unit can also determine the priority order based on resource need. Furthermore, the determination unit can also consider the impact on users and the impact on the system to evaluate the impact. For example, the determination unit determines the priority order based on the impact on the number of users and the impact on the system. This enables optimal allocation of resources by considering the difficulty and impact of the processing. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can determine the priority order using an AI model that inputs technical complexity and resource need and outputs a priority order.

[0037] The collection unit can analyze the user's past feedback history and select the optimal collection method. The collection unit, for example, analyzes past questionnaire results and support history in order to analyze the user's past feedback history. For example, the collection unit identifies the collection method that the user preferred based on the past questionnaire results. The collection unit can also identify the most effective collection timing based on the support history. Furthermore, the collection unit can analyze the past feedback content and collect requests regarding a specific topic. For example, the collection unit analyzes the past feedback content and extracts request data regarding a specific topic. In this way, the optimal collection method can be selected by analyzing the user's past feedback history. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the past feedback history into AI and cause the AI ​​to select the optimal collection method.

[0038] The collection unit can analyze the user's past feedback history and select the optimal collection method. The collection unit, for example, analyzes past questionnaire results and support history in order to analyze the user's past feedback history. For example, the collection unit identifies the collection method that the user preferred based on the past questionnaire results. The collection unit can also identify the most effective collection timing based on the support history. Furthermore, the collection unit can analyze the past feedback content and collect requests regarding a specific topic. For example, the collection unit analyzes the past feedback content and extracts request data regarding a specific topic. In this way, the optimal collection method can be selected by analyzing the user's past feedback history. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the past feedback history into AI and cause the AI ​​to select the optimal collection method.

[0039] The collection unit can perform filtering based on the user's current project and field of interest when collecting the desired data. For example, the collection unit preferentially collects desired data related to the user's current project. For example, the collection unit collects desired data related to a project the user is currently working on. The collection unit can also filter and collect related desired data based on the user's field of interest. For example, the collection unit collects related desired data based on the user's field of interest. Furthermore, the collection unit can collect desired data at an appropriate timing according to the user's project progress. For example, the collection unit adjusts the timing of collecting the desired data based on the user's project progress. This makes it possible to collect highly relevant desired data by filtering based on the user's current project and field of interest. Part 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 data related to the user's project and field of interest into AI and have the AI ​​perform filtering.

[0040] When collecting the desired data, the collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user. The collection unit, for example, preferentially collects desired data related to a specific region based on the geographical location information of the user. For example, when the user is in a specific region, the collection unit collects desired data related to the region. The collection unit can also consider the geographical location information in order to collect problems and requests specific to the region. For example, the collection unit collects problems and requests specific to the region based on the geographical location information of the user. Furthermore, when the user is moving, the collection unit can also collect the most relevant desired data based on the current location. For example, when the user is moving, the collection unit collects desired data based on the current location. In this way, by considering the geographical location information of the user, it is possible to preferentially collect desired data specific to the region. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI and cause the AI ​​to execute collection of highly relevant data.

[0041] The collection unit can analyze the user's social media activities and collect related request data when collecting the request data. The collection unit, for example, analyzes the user's social media posts and collects related request data. For example, the collection unit analyzes the user's social media posts and extracts the request data. The collection unit can also determine the optimal collection timing based on the user's social media activity frequency. For example, the collection unit analyzes the user's social media activity frequency and adjusts the collection timing. Furthermore, the collection unit can also collect request data of the user's followers and friends on social media and perform a comprehensive analysis. For example, the collection unit collects request data of the user's followers and friends on social media and performs a comprehensive analysis. In this way, the user's social media activity can be analyzed to collect related request data. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity to AI and cause the AI ​​to execute collection of related request data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the request when analyzing the request data. The analysis unit, for example, considers the impact on the number of users and the impact on the system in order to evaluate the importance of the request. For example, the analysis unit evaluates the importance of the request based on the impact on the number of users. The analysis unit can also evaluate the importance of the request based on the impact on the system. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the importance of the request. For example, the analysis unit analyzes the request data with high importance in detail to identify specific improvement points. The analysis unit can also analyze the request data with low importance simply to grasp the overall trend. Furthermore, the analysis unit can appropriately analyze the request data with medium importance to propose a balanced improvement measure. As a result, an efficient analysis can be performed by adjusting the level of detail of the analysis based on the importance of the request. A part 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 importance of the request data to AI and cause the AI ​​to adjust the level of detail of the analysis.

[0043] When analyzing the request data, the analysis unit can apply different analysis algorithms according to the category of the request. The analysis unit applies different analysis algorithms according to, for example, the category of the request data. For example, the analysis unit applies a technical analysis algorithm to technical request data. The analysis unit can also apply an analysis algorithm specialized for user experience to request data related to user experience. Furthermore, the analysis unit can apply a business analysis algorithm to business-related request data. For example, the analysis unit applies a business analysis algorithm to business-related request data. This enables more appropriate analysis by applying different analysis algorithms according to the category of the request. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the category of the request data to AI and cause AI to apply an appropriate analysis algorithm.

[0044] When analyzing the request data, the analysis unit can determine the priority of the analysis based on the time of submission of the request. The analysis unit, for example, considers the submission date and time or the submission month in order to evaluate the time of submission of the request. For example, the analysis unit prioritizes the analysis of recently submitted request data and responds promptly. The analysis unit can also reevaluate request data submitted in the past and respond as necessary. Furthermore, the analysis unit can adjust the analysis order of the request data based on the submission time. For example, the analysis unit optimizes the analysis order of the request data based on the submission time. This enables a prompt response by determining the priority of the analysis based on the submission time of the request. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the submission time of the request data to AI and have the AI ​​execute the determination of the priority of the analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the requests when analyzing the request data. The analysis unit, for example, considers requests related to the same topic or requests from the same user in order to evaluate the relevance of the request data. For example, the analysis unit prioritizes analysis of highly relevant request data and responds quickly. The analysis unit can also postpone less relevant request data and grasp the overall trend. Furthermore, the analysis unit can optimize the order of analysis based on the relevance of the request data. For example, the analysis unit adjusts the order of analysis based on the relevance of the request data. As a result, efficient analysis is possible by adjusting the order of analysis based on the relevance of the request. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the request data to AI and cause AI to adjust the order of analysis.

[0046] The determination unit can improve the accuracy of the determination by considering the interrelationships of requests when determining the priority order. The determination unit, for example, considers the strength of dependencies and associations to evaluate the interrelationships of requests. For example, the determination unit groups related requests and determines the priority order. The determination unit can also analyze the interrelationships of requests and prioritize the most influential requests. Furthermore, the determination unit can optimize the overall priority order based on the interrelationships of requests. For example, the determination unit adjusts the priority order based on the interrelationships of requests. This makes it possible to determine the priority order with higher accuracy by considering the interrelationships of requests. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI or may be performed without using AI. For example, the determination unit can input the interrelationships of requests to AI and cause the AI ​​to execute the determination of the priority order.

[0047] The determination unit can make a decision by considering attribute information of the request submitter when determining the priority. The determination unit, for example, considers age, sex, occupation, etc. to evaluate the attribute information of the request submitter. For example, the determination unit determines the priority of the request based on the submitter's job title and influence. The determination unit can also determine the priority by considering the submitter's past feedback history. Furthermore, the determination unit can determine the optimal priority based on the submitter's attribute information. For example, the determination unit adjusts the priority based on the submitter's attribute information. As a result, by considering the attribute information of the request submitter, it is possible to determine a more appropriate priority. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the submitter's attribute information to AI and cause AI to execute the priority determination.

[0048] The determination unit can make a decision taking into consideration the geographic distribution of requests when determining the priority order. The determination unit, for example, considers data by region or data by country in order to evaluate the geographic distribution of requests. For example, the determination unit prioritizes requests from a specific region. The determination unit can also prioritize issues specific to a region based on the geographic distribution. Furthermore, the determination unit can optimize the overall priority order by considering the geographic distribution. For example, the determination unit adjusts the priority order based on the geographic distribution. This makes it possible to determine the priority order to address issues specific to a region by considering the geographic distribution of requests. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the geographic distribution of requests to AI and cause the AI ​​to execute the priority order determination.

[0049] The determination unit can improve the accuracy of the determination when determining the priority order by referring to related literature of the request. The determination unit, for example, refers to academic papers and technical reports to evaluate related literature of the request. For example, the determination unit refers to related literature and evaluates the feasibility of the request. The determination unit can also evaluate the impact of the request based on the related literature. Furthermore, the determination unit can determine the optimal priority order by referring to related literature. For example, the determination unit adjusts the priority order based on the related literature. As a result, by referring to related literature of the request, it is possible to determine the priority order with higher accuracy. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input related literature to AI and cause AI to execute the priority order determination.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The collection unit can analyze the user's social media activities and collect related request data. For example, the collection unit analyzes the user's social media posts and extracts the request data. The collection unit can also determine the optimal collection timing based on the user's frequency of social media activity. Furthermore, the collection unit can also collect request data of the user's followers and friends on social media and perform a comprehensive analysis. In this way, the related request data can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's social media activities into AI and cause the AI ​​to collect related request data.

[0052] When analyzing the request data, the analysis unit can determine the priority of the analysis based on the time of submission of the request. For example, the analysis unit can give priority to analyzing recently submitted request data and respond quickly. The analysis unit can also reevaluate previously submitted request data and respond as necessary. Furthermore, the analysis unit can adjust the analysis order of the request data based on the time of submission. This allows for a quick response by determining the priority of the analysis based on the time of submission of the request. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the time of submission of the request data to AI and have the AI ​​execute the determination of the priority of the analysis.

[0053] The determination unit can improve the accuracy of the determination when determining the priority order by considering the interrelationships of the requests. For example, the determination unit groups related requests and determines the priority order. The determination unit can also analyze the interrelationships of the requests and prioritize the most influential requests. Furthermore, the determination unit can optimize the overall priority order based on the interrelationships of the requests. This allows for more accurate determination of the priority order by considering the interrelationships of the requests. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI or may be performed without using AI. For example, the determination unit can input the interrelationships of the requests to AI and cause the AI ​​to execute the determination of the priority order.

[0054] The collection unit can preferentially collect highly relevant data by taking into account the geographical location information of the user. For example, when the user is in a specific area, the collection unit collects request data related to the area. The collection unit can also consider the geographical location information to collect problems and requests specific to the area. Furthermore, when the user is moving, the collection unit can also collect the most relevant request data based on the current location. In this way, by taking into account the geographical location information of the user, it is possible to preferentially collect region-specific request data. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI and cause the AI ​​to collect highly relevant data.

[0055] The determination unit can make a decision by considering attribute information of the request submitter when determining the priority order. For example, the determination unit determines the priority order of the request based on the job title and influence of the submitter. The determination unit can also determine the priority order by considering the submitter's past feedback history. Furthermore, the determination unit can determine the optimal priority order based on the attribute information of the submitter. This makes it possible to determine a more appropriate priority order by considering the attribute information of the request submitter. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the attribute information of the submitter to AI and cause the AI ​​to execute the priority order determination.

[0056] The process flow of the first embodiment will be briefly described below.

[0057] Step 1: The collection unit collects request data, which is requests from customers. The request data may be in text, audio, image, or other formats. The collection unit collects the request data from surveys, feedback forms, and customer support records. For example, the request data can be collected through online surveys and feedback forms. In addition, the request data can be extracted by analyzing customer support call records and chat logs. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using algorithms that evaluate the frequency, importance, and impact of requests. For example, the analysis unit analyzes the frequency of request data using statistical methods and evaluates the importance of the request data using machine learning algorithms. The analysis unit can also evaluate the impact of the request data by analyzing its impact on the system. Step 3: The determination unit determines the priority of the requests based on the source code that realizes the requested service or the usage data of the requested service and the analysis results obtained by the analysis unit. The determination unit determines the priority of repair items and new functions based on the analysis results. For example, the priority can be determined based on the urgency and impact of the requested data, technical complexity, and resource needs. This enables the system to efficiently collect, analyze, and prioritize request data from customers.

[0058] (Example 2) The system according to the embodiment of the present invention is a system that takes in improvement requests and new function request data from customers who use a service, and lists the items to be repaired and the priority order. This system first includes a collection unit that collects the request data from customers. This collection unit collects the request data from questionnaires, feedback forms, and customer support records. Next, the system includes an analysis unit that analyzes the collected data. This analysis unit analyzes the data using an algorithm that evaluates the frequency, importance, and impact of the requests. Furthermore, it is also possible to estimate the user's emotions and analyze the contents of the request data based on the estimated emotions. Next, the system includes a determination unit that determines the priority order corresponding to the requests based on the source code that realizes the service that is the subject of the request, the service usage status data, and the analysis results. This determination unit determines the priority order of the items to be repaired and new functions based on the analysis results. It is also possible to determine the priority order by taking into account the difficulty and impact of processing. Furthermore, it is also possible to analyze the past feedback history of users and select the optimal collection method. For example, frequently requested functions and requests with high importance are given priority, so that the needs of users can be met quickly. Furthermore, by taking into account the difficulty and impact of processing, optimal resource allocation is possible, and efficient development is realized. This enables the system to efficiently improve services and develop new functions, thereby improving user satisfaction.

[0059] The system according to the embodiment includes a collection unit, an analysis unit, and a determination unit. The collection unit collects request data, which is a request from a customer. The request data includes, for example, a text format, a voice format, an image format, and the like, but is not limited to such examples. The collection unit collects the request data, for example, from a questionnaire, a feedback form, or a record of customer support. For example, the collection unit can collect the request data through an online questionnaire. The collection unit can also collect the request data from a user using a feedback form. Furthermore, the collection unit can analyze the record of customer support and collect the request data. For example, the collection unit analyzes the call record or chat log of the customer support and extracts the request data. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, an algorithm that evaluates the frequency, importance, and impact of the request. For example, the analysis unit analyzes the frequency of the request data using a statistical method. The analysis unit can also evaluate the importance of the request data using a machine learning algorithm. Furthermore, the analysis unit can analyze the impact on the system to evaluate the impact of the request data. For example, the analysis unit analyzes the impact on the number of users to evaluate the impact of the requested data. The determination unit determines the priority order corresponding to the request based on the source code that realizes the requested service or the usage status data of the requested service and the analysis result obtained by the analysis unit. The determination unit determines the priority order of the repair items and new functions based on the analysis result, for example. For example, the determination unit determines the priority order based on the urgency and impact of the requested data. The determination unit can also determine the priority order taking into account the difficulty and impact of the processing. For example, the determination unit determines the priority order based on the technical complexity and the need for resources. This enables the system according to the embodiment to efficiently collect, analyze, and prioritize requested data from customers. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can determine the priority order using an AI model that inputs the urgency and impact of the requested data and outputs the priority order.

[0060] The collection unit collects request data, which is requests from customers. The request data includes, for example, text format, audio format, image format, and the like, but is not limited to such examples. The collection unit collects the request data, for example, from questionnaires, feedback forms, and customer support records. For example, the collection unit can collect the request data through an online questionnaire. The collection unit can also collect the request data from users using a feedback form. Furthermore, the collection unit can analyze customer support records to collect the request data. For example, the collection unit analyzes customer support call records and chat logs to extract the request data. The collection unit makes full use of various technologies to efficiently collect these data. For example, voice recognition technology is used to collect voice data, and the contents of the call are converted into text data. Image data is collected by using image recognition technology to extract text and objects within the image. This allows the collection unit to centrally manage and efficiently collect request data in various formats. The collection unit also enables real-time data collection by adjusting the frequency and timing of data collection. For example, customer support chat logs are automatically collected every time a conversation ends and immediately sent for analysis. This allows the collection department to always have the latest requested data and respond quickly. Furthermore, to ensure the privacy and security of the data, the collection department implements encryption technology and access control to thoroughly protect the data. This allows the collection department to collect requested data efficiently and safely without compromising the trust of users.

[0061] The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, an algorithm that evaluates the frequency, importance, and impact of the request. For example, the analysis unit analyzes the frequency of the request data using a statistical method. The analysis unit can also evaluate the importance of the request data using a machine learning algorithm. Furthermore, the analysis unit can analyze the impact on the system to evaluate the impact of the request data. For example, the analysis unit analyzes the impact on the number of users to evaluate the impact of the request data. Specifically, the analysis unit classifies the collected request data using natural language processing technology and understands the content of the request. For example, text data is classified into major themes using topic modeling, and voice data is converted into text using voice recognition technology and then analyzed in the same manner. Image data is extracted from related requests using image recognition technology. This allows the analysis unit to analyze data in a unified manner in various formats and grasp the overall picture of the request. Furthermore, the analysis unit evaluates the importance and impact of the request data using a machine learning algorithm. For example, a model is constructed to predict the urgency and impact of the request based on the frequency of the request data and past response history. This model takes the contents of the request data and related metadata (e.g., user attributes and past request history) as input, and outputs the importance and impact of the request. This enables the analysis unit to efficiently evaluate the priority of requests and respond quickly. Furthermore, the analysis unit simulates the impact on the system to evaluate the impact of the request data. For example, it predicts the impact that adding a new function or modifying an existing function will have on the number of users and system performance. This allows the analysis unit to evaluate in advance the impact that realizing a request will have on the entire system and devise optimal countermeasures.

[0062] The determination unit determines the priority order corresponding to the request based on the source code that realizes the service that is the subject of the request, or the usage status data of the service that is the subject of the request, and the analysis result obtained by the analysis unit. The determination unit, for example, determines the priority order of the repair items and new functions based on the analysis result. For example, the determination unit determines the priority order based on the urgency and impact of the request data. The determination unit can also determine the priority order taking into account the difficulty and impact of the processing. For example, the determination unit determines the priority order based on the technical complexity and the need for resources. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can determine the priority order using an AI model that inputs the urgency and impact of the request data and outputs the priority order. Specifically, the determination unit executes an algorithm for determining the priority order based on the urgency and impact of the request data provided by the analysis unit. This algorithm calculates the optimal priority order by considering multiple factors such as the content and impact of the request, urgency, and technical feasibility. For example, if the urgency of the request is high and the impact is also large, the request is given a high priority order. On the other hand, if a request is technically difficult to achieve and requires a lot of resources, the priority of that request may be set low. Furthermore, the decision unit can use AI to determine the priority. For example, an AI model that has learned from past request data and the corresponding results is used to predict the priority of a new request. This AI model inputs the contents of the request data and related metadata, and outputs the priority. This enables the decision unit to determine the priority with high accuracy based on past data. Furthermore, the decision unit makes the priority determination process transparent and provides it in an explainable form to the relevant parties. For example, the reasons for determining the priority and the factors considered are recorded in detail and reported to the relevant parties. This allows the decision unit to ensure the transparency and reliability of the request priority determination process, and to gain the understanding and consent of the relevant parties.

[0063] The collection unit can collect the request data from a questionnaire or a feedback form or a customer support record. The collection unit can collect the request data through, for example, an online questionnaire. For example, the collection unit collects the request data from the user using a web form. The collection unit can also collect the request data from the user using a feedback form. For example, the collection unit collects the request data using an in-app form. Furthermore, the collection unit can analyze the customer support record and collect the request data. For example, the collection unit analyzes the customer support call record or chat log to extract the request data. This makes it possible to collect the request data in various ways. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the customer support record into AI and cause the AI ​​to extract the request data.

[0064] The analysis unit can analyze the data using an algorithm that evaluates the frequency, priority, and impact of the request. The analysis unit, for example, uses a statistical method to evaluate the frequency of the request. For example, the analysis unit measures the monthly occurrence count and the weekly occurrence count. The analysis unit can also use a machine learning algorithm to evaluate the priority of the request. For example, the analysis unit calculates an importance score and an impact score. Furthermore, the analysis unit can analyze the impact on the system to evaluate the impact of the request. For example, the analysis unit evaluates the impact on the number of users and the impact on the system. This enables detailed analysis of the request data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the request data into AI and have the AI ​​execute the evaluation of the frequency, priority, and impact.

[0065] The determination unit can determine the priority of the repair items and new functions based on the analysis results. The determination unit, for example, determines the priority of the repair items and new functions based on the analysis results. For example, the determination unit determines the priority based on the urgency and impact of the requested data. The determination unit can also determine the priority taking into account the difficulty and impact of the processing. For example, the determination unit determines the priority based on the technical complexity and the need for resources. As a result, efficient repairs and new functions can be developed by determining the priority based on the analysis results. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can determine the priority using an AI model that inputs the urgency and impact of the requested data and outputs the priority.

[0066] The determination unit can determine the priority order based on the difficulty and impact of the processing. The determination unit, for example, considers technical complexity and resource need to evaluate the difficulty of the processing. For example, the determination unit determines the priority order based on technical complexity. The determination unit can also determine the priority order based on resource need. Furthermore, the determination unit can also consider the impact on users and the impact on the system to evaluate the impact. For example, the determination unit determines the priority order based on the impact on the number of users and the impact on the system. This enables optimal allocation of resources by considering the difficulty and impact of the processing. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can determine the priority order using an AI model that inputs technical complexity and resource need and outputs a priority order.

[0067] The analysis unit can estimate the user's emotion and analyze the content of the request indicated by the request data based on the estimated user's emotion. The analysis unit uses, for example, text analysis or voice analysis to estimate the user's emotion. For example, the analysis unit estimates the user's emotion using text analysis. The analysis unit can also estimate the user's emotion using voice analysis. Furthermore, the analysis unit can analyze the content of the request indicated by the request data based on the estimated user's emotion. For example, the analysis unit analyzes the content of the request data using text mining. The analysis unit can also analyze the content of the request data using semantic analysis. This makes it possible to respond more appropriately by analyzing the content of the request data based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input text data to a generation AI to estimate a user's emotions and have the generation AI perform emotion estimation.

[0068] The collection unit can analyze the user's past feedback history and select the optimal collection method. The collection unit, for example, analyzes past questionnaire results and support history in order to analyze the user's past feedback history. For example, the collection unit identifies the collection method that the user preferred based on the past questionnaire results. The collection unit can also identify the most effective collection timing based on the support history. Furthermore, the collection unit can analyze the past feedback content and collect requests regarding a specific topic. For example, the collection unit analyzes the past feedback content and extracts request data regarding a specific topic. In this way, the optimal collection method can be selected by analyzing the user's past feedback history. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the past feedback history into AI and cause the AI ​​to select the optimal collection method.

[0069] The collection unit can estimate the user's emotion and adjust the collection timing of the request data based on the estimated user's emotion. The collection unit uses, for example, text analysis or voice analysis to estimate the user's emotion. For example, the collection unit estimates the user's emotion using text analysis. The collection unit can also estimate the user's emotion using voice analysis. Furthermore, the collection unit can adjust the collection timing of the request data based on the estimated user's emotion. For example, when the user is stressed, the collection unit delays the collection timing to collect the request in a relaxed state. Furthermore, when the user is satisfied, the collection unit can instantly collect the request data and obtain positive feedback. Furthermore, when the user is dissatisfied, the collection unit can quickly collect the request data and obtain information for problem solving. As a result, by adjusting the collection timing based on the user's emotion, the request data can be collected at a more appropriate timing. The emotion estimation is realized using, for example, an emotion engine or a generation AI using an emotion estimation function. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part 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 may input text data to a generation AI to estimate a user's emotion, and cause the generation AI to estimate the emotion.

[0070] The collection unit can analyze the user's past feedback history and select the optimal collection method. The collection unit, for example, analyzes past questionnaire results and support history in order to analyze the user's past feedback history. For example, the collection unit identifies the collection method that the user preferred based on the past questionnaire results. The collection unit can also identify the most effective collection timing based on the support history. Furthermore, the collection unit can analyze the past feedback content and collect requests regarding a specific topic. For example, the collection unit analyzes the past feedback content and extracts request data regarding a specific topic. In this way, the optimal collection method can be selected by analyzing the user's past feedback history. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the past feedback history into AI and cause the AI ​​to select the optimal collection method.

[0071] The collection unit can perform filtering based on the user's current project and field of interest when collecting the desired data. For example, the collection unit preferentially collects desired data related to the user's current project. For example, the collection unit collects desired data related to a project the user is currently working on. The collection unit can also filter and collect related desired data based on the user's field of interest. For example, the collection unit collects related desired data based on the user's field of interest. Furthermore, the collection unit can collect desired data at an appropriate timing according to the user's project progress. For example, the collection unit adjusts the timing of collecting the desired data based on the user's project progress. This makes it possible to collect highly relevant desired data by filtering based on the user's current project and field of interest. Part 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 data related to the user's project and field of interest into AI and have the AI ​​perform filtering.

[0072] The collection unit can estimate the user's emotion and determine the priority of the desired data to be collected based on the estimated user's emotion. The collection unit uses, for example, text analysis or voice analysis to estimate the user's emotion. For example, the collection unit estimates the user's emotion using text analysis. The collection unit can also estimate the user's emotion using voice analysis. Furthermore, the collection unit can determine the priority of the desired data to be collected based on the estimated user's emotion. For example, when the user is dissatisfied, the collection unit preferentially collects the desired data and responds quickly. Furthermore, when the user is satisfied, the collection unit preferentially collects positive feedback and can grasp the strengths of the service. Furthermore, when the user has neutral emotions, the collection unit can collect overall desired data in a balanced manner. As a result, by determining the priority of the desired data to be collected based on the user's emotion, more important desired data can be preferentially collected. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part 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 may input text data to a generation AI to estimate a user's emotion, and cause the generation AI to estimate the emotion.

[0073] When collecting the desired data, the collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user. The collection unit, for example, preferentially collects desired data related to a specific region based on the geographical location information of the user. For example, when the user is in a specific region, the collection unit collects desired data related to the region. The collection unit can also consider the geographical location information in order to collect problems and requests specific to the region. For example, the collection unit collects problems and requests specific to the region based on the geographical location information of the user. Furthermore, when the user is moving, the collection unit can also collect the most relevant desired data based on the current location. For example, when the user is moving, the collection unit collects desired data based on the current location. In this way, by considering the geographical location information of the user, it is possible to preferentially collect desired data specific to the region. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI and cause the AI ​​to execute collection of highly relevant data.

[0074] The collection unit can analyze the user's social media activities and collect related request data when collecting the request data. The collection unit, for example, analyzes the user's social media posts and collects related request data. For example, the collection unit analyzes the user's social media posts and extracts the request data. The collection unit can also determine the optimal collection timing based on the user's social media activity frequency. For example, the collection unit analyzes the user's social media activity frequency and adjusts the collection timing. Furthermore, the collection unit can also collect request data of the user's followers and friends on social media and perform a comprehensive analysis. For example, the collection unit collects request data of the user's followers and friends on social media and performs a comprehensive analysis. In this way, the user's social media activity can be analyzed to collect related request data. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity to AI and cause the AI ​​to execute collection of related request data.

[0075] The analysis unit can estimate the user's emotion and analyze the content of the request data based on the estimated user's emotion. The analysis unit uses, for example, text analysis or voice analysis to estimate the user's emotion. For example, the analysis unit estimates the user's emotion using text analysis. The analysis unit can also estimate the user's emotion using voice analysis. Furthermore, the analysis unit can analyze the content of the request indicated by the request data based on the estimated user's emotion. For example, the analysis unit analyzes the content of the request data using text mining. The analysis unit can also analyze the content of the request data using semantic analysis. This makes it possible to analyze the content of the request data based on the user's emotion, thereby enabling a more appropriate response. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input text data to a generation AI to estimate a user's emotions and have the generation AI perform emotion estimation.

[0076] The analysis unit can adjust the level of detail of the analysis based on the importance of the request when analyzing the request data. The analysis unit, for example, considers the impact on the number of users and the impact on the system in order to evaluate the importance of the request. For example, the analysis unit evaluates the importance of the request based on the impact on the number of users. The analysis unit can also evaluate the importance of the request based on the impact on the system. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the importance of the request. For example, the analysis unit analyzes the request data with high importance in detail to identify specific improvement points. The analysis unit can also analyze the request data with low importance simply to grasp the overall trend. Furthermore, the analysis unit can appropriately analyze the request data with medium importance to propose a balanced improvement measure. As a result, an efficient analysis can be performed by adjusting the level of detail of the analysis based on the importance of the request. A part 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 importance of the request data to AI and cause the AI ​​to adjust the level of detail of the analysis.

[0077] When analyzing the request data, the analysis unit can apply different analysis algorithms according to the category of the request. The analysis unit applies different analysis algorithms according to, for example, the category of the request data. For example, the analysis unit applies a technical analysis algorithm to technical request data. The analysis unit can also apply an analysis algorithm specialized for user experience to request data related to user experience. Furthermore, the analysis unit can apply a business analysis algorithm to business-related request data. For example, the analysis unit applies a business analysis algorithm to business-related request data. This enables more appropriate analysis by applying different analysis algorithms according to the category of the request. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the category of the request data to AI and cause AI to apply an appropriate analysis algorithm.

[0078] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit uses, for example, text analysis or voice analysis to estimate the user's emotion. For example, the analysis unit estimates the user's emotion using text analysis. The analysis unit can also estimate the user's emotion using voice analysis. Furthermore, the analysis unit can adjust the display method of the analysis result based on the estimated user's emotion. For example, the analysis unit provides a display method that emphasizes the problem when the user is dissatisfied. Furthermore, the analysis unit can provide a display method that emphasizes positive request data when the user is satisfied. Furthermore, the analysis unit can display the overall request data in a balanced manner when the user has neutral emotions. This allows for more appropriate display by adjusting the display method of the analysis result based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part 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 may input text data to a generation AI to estimate a user's emotion, and have the generation AI execute emotion estimation.

[0079] When analyzing the request data, the analysis unit can determine the priority of the analysis based on the time of submission of the request. The analysis unit, for example, considers the submission date and time or the submission month in order to evaluate the time of submission of the request. For example, the analysis unit prioritizes the analysis of recently submitted request data and responds promptly. The analysis unit can also reevaluate request data submitted in the past and respond as necessary. Furthermore, the analysis unit can adjust the analysis order of the request data based on the submission time. For example, the analysis unit optimizes the analysis order of the request data based on the submission time. This enables a prompt response by determining the priority of the analysis based on the submission time of the request. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the submission time of the request data to AI and have the AI ​​execute the determination of the priority of the analysis.

[0080] The analysis unit can adjust the order of analysis based on the relevance of the requests when analyzing the request data. The analysis unit, for example, considers requests related to the same topic or requests from the same user in order to evaluate the relevance of the request data. For example, the analysis unit prioritizes analysis of highly relevant request data and responds quickly. The analysis unit can also postpone less relevant request data and grasp the overall trend. Furthermore, the analysis unit can optimize the order of analysis based on the relevance of the request data. For example, the analysis unit adjusts the order of analysis based on the relevance of the request data. As a result, efficient analysis is possible by adjusting the order of analysis based on the relevance of the request. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the request data to AI and cause AI to adjust the order of analysis.

[0081] The determination unit can estimate the user's emotion and adjust the priority determination method based on the estimated user's emotion. The determination unit uses, for example, text analysis or voice analysis to estimate the user's emotion. For example, the determination unit estimates the user's emotion using text analysis. The determination unit can also estimate the user's emotion using voice analysis. Furthermore, the determination unit can adjust the priority determination method based on the estimated user's emotion. For example, the determination unit prioritizes the user's request when the user is dissatisfied. Furthermore, the determination unit can prioritize the user's request when the user is satisfied. Furthermore, the determination unit can also prioritize the user's request when the user has a neutral emotion. In this way, by adjusting the priority determination method based on the user's emotion, it becomes possible to determine a more appropriate priority. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit may input text data to a generation AI to estimate a user's emotion, and cause the generation AI to estimate the emotion.

[0082] The determination unit can improve the accuracy of the determination by considering the interrelationships of requests when determining the priority order. The determination unit, for example, considers the strength of dependencies and associations to evaluate the interrelationships of requests. For example, the determination unit groups related requests and determines the priority order. The determination unit can also analyze the interrelationships of requests and prioritize the most influential requests. Furthermore, the determination unit can optimize the overall priority order based on the interrelationships of requests. For example, the determination unit adjusts the priority order based on the interrelationships of requests. This makes it possible to determine the priority order with higher accuracy by considering the interrelationships of requests. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI or may be performed without using AI. For example, the determination unit can input the interrelationships of requests to AI and cause the AI ​​to execute the determination of the priority order.

[0083] The determination unit can make a decision by considering attribute information of the request submitter when determining the priority. The determination unit, for example, considers age, sex, occupation, etc. to evaluate the attribute information of the request submitter. For example, the determination unit determines the priority of the request based on the submitter's job title and influence. The determination unit can also determine the priority by considering the submitter's past feedback history. Furthermore, the determination unit can determine the optimal priority based on the submitter's attribute information. For example, the determination unit adjusts the priority based on the submitter's attribute information. As a result, by considering the attribute information of the request submitter, it is possible to determine a more appropriate priority. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the submitter's attribute information to AI and cause AI to execute the priority determination.

[0084] The determination unit can estimate the user's emotion and adjust the display method of the priority order based on the estimated user's emotion. The determination unit uses, for example, text analysis or voice analysis to estimate the user's emotion. For example, the determination unit estimates the user's emotion using text analysis. The determination unit can also estimate the user's emotion using voice analysis. Furthermore, the determination unit can adjust the display method of the priority order based on the estimated user's emotion. For example, the determination unit provides a display method that emphasizes problems when the user is dissatisfied. Furthermore, the determination unit can provide a display method that emphasizes positive requests when the user is satisfied. Furthermore, the determination unit can display the overall requests in a balanced manner when the user has neutral emotions. This enables a more appropriate display by adjusting the display method of the priority order based on the user's emotion. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit may input text data to a generation AI to estimate a user's emotion, and cause the generation AI to estimate the emotion.

[0085] The determination unit can make a decision taking into consideration the geographic distribution of requests when determining the priority order. The determination unit, for example, considers data by region or data by country in order to evaluate the geographic distribution of requests. For example, the determination unit prioritizes requests from a specific region. The determination unit can also prioritize issues specific to a region based on the geographic distribution. Furthermore, the determination unit can optimize the overall priority order by considering the geographic distribution. For example, the determination unit adjusts the priority order based on the geographic distribution. This makes it possible to determine the priority order to address issues specific to a region by considering the geographic distribution of requests. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the geographic distribution of requests to AI and cause the AI ​​to execute the priority order determination.

[0086] The determination unit can improve the accuracy of the determination when determining the priority order by referring to related literature of the request. The determination unit, for example, refers to academic papers and technical reports to evaluate related literature of the request. For example, the determination unit refers to related literature and evaluates the feasibility of the request. The determination unit can also evaluate the impact of the request based on the related literature. Furthermore, the determination unit can determine the optimal priority order by referring to related literature. For example, the determination unit adjusts the priority order based on the related literature. As a result, by referring to related literature of the request, it is possible to determine the priority order with higher accuracy. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input related literature to AI and cause AI to execute the priority order determination.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The collection unit can analyze the user's social media activities and collect related request data. For example, the collection unit analyzes the user's social media posts and extracts the request data. The collection unit can also determine the optimal collection timing based on the user's frequency of social media activity. Furthermore, the collection unit can also collect request data of the user's followers and friends on social media and perform a comprehensive analysis. In this way, the related request data can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's social media activities into AI and cause the AI ​​to collect related request data.

[0089] When analyzing the request data, the analysis unit can determine the priority of the analysis based on the time of submission of the request. For example, the analysis unit can give priority to analyzing recently submitted request data and respond quickly. The analysis unit can also reevaluate previously submitted request data and respond as necessary. Furthermore, the analysis unit can adjust the analysis order of the request data based on the time of submission. This allows for a quick response by determining the priority of the analysis based on the time of submission of the request. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the time of submission of the request data to AI and have the AI ​​execute the determination of the priority of the analysis.

[0090] The determination unit can improve the accuracy of the determination when determining the priority order by considering the interrelationships of the requests. For example, the determination unit groups related requests and determines the priority order. The determination unit can also analyze the interrelationships of the requests and prioritize the most influential requests. Furthermore, the determination unit can optimize the overall priority order based on the interrelationships of the requests. This allows for more accurate determination of the priority order by considering the interrelationships of the requests. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI or may be performed without using AI. For example, the determination unit can input the interrelationships of the requests to AI and cause the AI ​​to execute the determination of the priority order.

[0091] The collection unit can preferentially collect highly relevant data by taking into account the geographical location information of the user. For example, when the user is in a specific area, the collection unit collects request data related to the area. The collection unit can also consider the geographical location information to collect problems and requests specific to the area. Furthermore, when the user is moving, the collection unit can also collect the most relevant request data based on the current location. In this way, by taking into account the geographical location information of the user, it is possible to preferentially collect region-specific request data. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI and cause the AI ​​to collect highly relevant data.

[0092] The determination unit can make a decision by considering attribute information of the request submitter when determining the priority order. For example, the determination unit determines the priority order of the request based on the job title and influence of the submitter. The determination unit can also determine the priority order by considering the submitter's past feedback history. Furthermore, the determination unit can determine the optimal priority order based on the attribute information of the submitter. This makes it possible to determine a more appropriate priority order by considering the attribute information of the request submitter. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the attribute information of the submitter to AI and cause the AI ​​to execute the priority order determination.

[0093] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, when the user is dissatisfied, the analysis unit provides a display method that emphasizes the problem. In addition, when the user is satisfied, the analysis unit can also provide a display method that emphasizes positive request data. Furthermore, when the user has neutral emotions, the analysis unit can also display the overall request data in a balanced manner. This allows for more appropriate display by adjusting the display method of the analysis results based on the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the analysis unit can input text data to the generation AI in order to estimate the user's emotions, and cause the generation AI to execute emotion estimation.

[0094] The collection unit can estimate the user's emotion and adjust the timing of collecting the request data based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit delays the collection timing and collects the request in a relaxed state. Also, when the user is satisfied, the collection unit can immediately collect the request data and obtain positive feedback. Furthermore, when the user is dissatisfied, the collection unit can quickly collect the request data and obtain information for solving the problem. In this way, the collection timing can be adjusted based on the user's emotion, so that the request data can be collected at a more appropriate timing. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the collection unit can input text data to the generation AI in order to estimate the user's emotion, and cause the generation AI to execute the emotion estimation.

[0095] The determination unit can estimate the user's emotion and adjust the priority determination method based on the estimated user's emotion. For example, when the user is dissatisfied, the determination unit can prioritize the request. In addition, when the user is satisfied, the determination unit can also prioritize positive requests. Furthermore, when the user has neutral emotions, the determination unit can also handle the overall requests in a balanced manner. This allows more appropriate priority determination by adjusting the priority determination method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the determination unit can input text data to the generation AI to estimate the user's emotion, and cause the generation AI to execute emotion estimation.

[0096] The analysis unit can estimate the user's emotion and analyze the content of the request data based on the estimated user's emotion. For example, the analysis unit estimates the user's emotion using text analysis. The analysis unit can also estimate the user's emotion using voice analysis. Furthermore, the analysis unit can analyze the content of the request indicated by the request data based on the estimated user's emotion. This allows for a more appropriate response by analyzing the content of the request data based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input text data to the generation AI in order to estimate the user's emotion, and cause the generation AI to execute emotion estimation.

[0097] The collection unit can estimate the user's emotions and determine the priority of the requested data to be collected based on the estimated user's emotions. For example, when the user is dissatisfied, the collection unit preferentially collects the requested data and responds quickly. Also, when the user is satisfied, the collection unit can preferentially collect positive feedback and grasp the strengths of the service. Furthermore, when the user has neutral emotions, the collection unit can collect overall requested data in a balanced manner. As a result, by determining the priority of the requested data to be collected based on the user's emotions, more important requested data can be preferentially collected. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input text data to the generation AI in order to estimate the user's emotions, and cause the generation AI to execute emotion estimation.

[0098] The process flow of the second embodiment will be briefly described below.

[0099] Step 1: The collection unit collects request data, which is requests from customers. The request data may be in text, audio, image, or other formats. The collection unit collects the request data from surveys, feedback forms, and customer support records. For example, the request data can be collected through online surveys and feedback forms. In addition, the request data can be extracted by analyzing customer support call records and chat logs. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using algorithms that evaluate the frequency, importance, and impact of requests. For example, the analysis unit analyzes the frequency of request data using statistical methods and evaluates the importance of the request data using machine learning algorithms. The analysis unit can also evaluate the impact of the request data by analyzing its impact on the system. Step 3: The determination unit determines the priority of the requests based on the source code that realizes the requested service or the usage data of the requested service and the analysis results obtained by the analysis unit. The determination unit determines the priority of repair items and new functions based on the analysis results. For example, the priority can be determined based on the urgency and impact of the requested data, technical complexity, and resource needs. This enables the system to efficiently collect, analyze, and prioritize request data from customers.

[0100] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0101] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0102] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0103] For example, the collection unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the collection unit collects request data from questionnaires, feedback forms, and customer support records. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, for example, and analyzes data using an algorithm that evaluates the frequency, importance, and impact of requests. The determination unit is realized by the specific processing unit 290 of the data processing device 12, for example, and determines the priority of repair items and new functions based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0107] 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.

[0108] 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 the voice according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0110] 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 performed in a secure state.

[0111] Fig. 4 shows an example of 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.

[0112] 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.

[0113] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0115] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0116] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0117] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0118] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0119] For example, the collection unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the collection unit collects request data from questionnaires, feedback forms, and customer support records. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes data using an algorithm that evaluates the frequency, importance, and impact of requests. The determination unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and determines the priority of repair items and new functions based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0123] 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.

[0124] 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 the voice according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0126] 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 performed in a secure state.

[0127] 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.

[0128] 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.

[0129] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0130] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0131] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0132] 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 voice indicating a user input for 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.

[0133] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0134] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0135] For example, the collection unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, request data is collected from questionnaires, feedback forms, and customer support records. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, for example, and analyzes data using an algorithm that evaluates the frequency, importance, and impact of requests. The decision unit is realized by the specific processing unit 290 of the data processing device 12, for example, and decides the priority of repair items and new functions based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0139] 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.

[0140] 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 the voice according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0142] 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 performed in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0144] 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.

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

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0147] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0149] 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 a voice indicating a user input for 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0151] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0152] For example, the collection unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the collection unit collects request data from questionnaires, feedback forms, and customer support records. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, for example, and analyzes data using an algorithm that evaluates the frequency, importance, and impact of requests. The decision unit is realized by the specific processing unit 290 of the data processing device 12, for example, and decides the priority of repair items and new functions based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0153] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0154] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0156] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0158] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0160] 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.

[0161] 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 Universal Serial Bus (USB) 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.

[0162] In addition, 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 upon request from the data processing device 12.

[0163] 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.

[0164] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0165] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0166] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0167] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0169] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0171] (Appendix 1) A collection unit that collects request data, which is requests from customers; an analysis unit that analyzes the data collected by the collection unit; a determination unit that determines a priority order corresponding to the request based on a source code for realizing the requested service or usage status data of the requested service and an analysis result obtained by the analysis unit. A system characterized by: (Appendix 2) The collecting unit includes: Collect requested data from surveys or feedback forms or customer support records 2. The system of claim 1, (Appendix 3) The analysis unit includes: Analyze the data using algorithms that evaluate the frequency, priority, and impact of requests 2. The system of claim 1, (Appendix 4) The determination unit is Prioritize fixes and new features based on analysis results 2. The system of claim 1, (Appendix 5) The determination unit is Prioritize based on the difficulty and impact of the process 2. The system of claim 1, (Appendix 6) The analysis unit includes: A user's emotion is estimated, and the content of the request indicated by the request data is analyzed based on the estimated user's emotion. 2. The system of claim 1, (Appendix 7) The collecting unit includes: Analyze users' past feedback history and choose the appropriate collection method 2. The system of claim 1, (Appendix 8) The collecting unit includes: To estimate a user's emotion and adjust a timing of collecting requested data based on the estimated user's emotion. 2. The system of claim 1, (Appendix 9) The collecting unit includes: Analyze users' past feedback history and choose the best collection method 2. The system of claim 1, (Appendix 10) The collecting unit includes: When collecting request data, filter it based on the user's current projects and areas of interest 2. The system of claim 1, (Appendix 11) The collecting unit includes: To estimate a user's emotion and determine a priority order of desired data to be collected based on the estimated user's emotion. 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting requested data, prioritize collection of highly relevant data by considering the user's geographic location information. 2. The system of claim 1, (Appendix 13) The collecting unit includes: When collecting requested data, analyze your social media activity to collect relevant requested data 2. The system of claim 1, (Appendix 14) The analysis unit includes: To estimate a user's emotion and analyze the content of the request data based on the estimated user's emotion. 2. The system of claim 1, (Appendix 15) The analysis unit includes: When analyzing request data, adjust the level of analysis based on the importance of the request 2. The system of claim 1, (Appendix 16) The analysis unit includes: When analyzing request data, apply different analysis algorithms depending on the request category. 2. The system of claim 1, (Appendix 17) The analysis unit includes: Infer user sentiment and adjust how analysis results are displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 18) The analysis unit includes: When analyzing request data, prioritize analysis based on when the request was submitted 2. The system of claim 1, (Appendix 19) The analysis unit includes: When analyzing request data, adjust the order of analysis based on the relevance of requests 2. The system of claim 1, (Appendix 20) The determination unit is Inferring user emotions and adjusting the prioritization method based on the estimated user emotions 2. The system of claim 1, (Appendix 21) The determination unit is Improve accuracy of prioritization by considering interrelationships of requests 2. The system of claim 1, (Appendix 22) The determination unit is When determining priorities, the attributes of the request submitter are taken into consideration. 2. The system of claim 1, (Appendix 23) The determination unit is Inferring user emotion and adjusting the way priority is displayed based on the estimated user emotion 2. The system of claim 1, (Appendix 24) The determination unit is When determining priorities, consider the geographical distribution of requests. 2. The system of claim 1, (Appendix 25) The determination unit is When determining priorities, refer to relevant literature on requests to improve the accuracy of the decision 2. The system of claim 1, [Explanation of symbols]

[0172] 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 request data, which is requests from customers; an analysis unit that analyzes the data collected by the collection unit; a determination unit that determines a priority order corresponding to the request based on a source code for realizing the requested service or usage status data of the requested service and an analysis result obtained by the analysis unit. A system characterized by:

2. The collecting unit includes: Collect requested data from surveys or feedback forms or customer support records 2. The system of claim 1 .

3. The analysis unit includes: Analyze the data using algorithms that evaluate the frequency, priority, and impact of requests 2. The system of claim 1 .

4. The determination unit is Prioritize based on the difficulty and impact of the process 2. The system of claim 1 .

5. The analysis unit includes: A user's emotion is estimated, and the content of the request indicated by the request data is analyzed based on the estimated user's emotion.

2. The system of claim 1 .

6. The collecting unit includes: Analyze users' past feedback history and choose the appropriate collection method 2. The system of claim 1 .

7. The collecting unit includes: To estimate a user's emotion and adjust a timing of collecting requested data based on the estimated user's emotion.

2. The system of claim 1 .

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