system

Abstract

The system according to the embodiment aims to allow employees to express their honest opinions while ensuring anonymity. [Solution] A system according to an embodiment includes a reception unit, a conversion unit, a storage unit, and a provision unit. The reception unit receives free comments from employees. The conversion unit analyzes the free comments received by the reception unit and converts them into standardized wording. The storage unit stores the free comments converted by the conversion unit. The provision unit allows managers to access the free comments stored by the storage unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was possible to identify people from the content and expression of free comments, making it difficult for employees to express their true opinions.

[0005] The system according to the embodiment aims to allow employees to express their honest opinions while ensuring anonymity. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a conversion unit, a storage unit, and a provision unit. The reception unit receives free comments from employees. The conversion unit analyzes the free comments received by the reception unit and converts them into standardized phrases. The storage unit stores the free comments converted by the conversion unit. The provision unit allows managers to access the free comments stored by the storage unit. [Effects of the Invention]

[0007] The system according to the embodiment allows employees to express their honest opinions while maintaining anonymity. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An anonymization tool according to an embodiment of the present invention is a system that automatically analyzes free comments written by employees and converts them into uniform phrases. This anonymization tool analyzes free comments written by employees, extracts specific phrases and expressions, and converts them into uniform phrases, allowing employees to express their true opinions while maintaining anonymity. For example, when employees respond to a survey, the anonymization tool inputs free comments. These free comments are then input into the anonymization tool. The anonymization tool then analyzes the free comments using a natural language processing (NLP) algorithm and extracts specific phrases and expressions. For example, if a comment such as "This project was very difficult" is input, the anonymization tool converts it into a uniform phrase such as "This project was difficult." The anonymization tool then saves the converted free comments and makes them accessible to managers through a dedicated dashboard. This allows managers to analyze the free comments expressed in uniform phrases and identify organizational challenges and areas for improvement. For example, if multiple employees comment "This project was difficult," problems with the project can be identified and improvements can be considered. This allows the anonymization tool to anonymize employees' free comments and collect honest opinions. For example, free comments written by employees can be analyzed quickly and accurately, allowing managers to analyze the comments without preconceptions. Employees can also feel confident that their opinions are anonymous and express their honest opinions with peace of mind.

[0029] The anonymization tool according to the embodiment includes a receiving unit, a conversion unit, a storage unit, and a providing unit. The receiving unit receives free comments from employees. The free comments from employees may be in, for example, text, audio, or image format, but are not limited to these examples. The receiving unit directly inputs free comments in text format, for example. The receiving unit can also convert free comments in audio format into text data using audio recognition technology. The receiving unit can also convert free comments in image format into text data using image recognition technology. For example, the receiving unit converts audio data into text data using audio recognition technology. Image recognition technology analyzes image data and converts it into text data. The conversion unit uses a natural language processing (NLP) algorithm to analyze the free comments received by the receiving unit and convert them into a unified phrase. The conversion unit analyzes the free comments using techniques such as morphological analysis, grammatical analysis, and semantic analysis. For example, the conversion unit analyzes the words in the free comments using morphological analysis and grammatical analysis to analyze the sentence structure. The conversion unit analyzes the meaning of the free comments using semantic analysis and converts them into unified phrases. The storage unit stores the free comments converted by the conversion unit. The storage unit stores the free comments in a database, such as a relational database or a NoSQL database. For example, the storage unit stores the free comments in a table format using a relational database. The storage unit can also store the free comments in a document format using a NoSQL database. The provision unit allows managers to access the free comments stored by the storage unit. The provision unit displays the free comments through, for example, a dedicated dashboard. For example, the provision unit provides a web-based dashboard so that managers can access the free comments through a browser. The provision unit also provides a mobile application so that managers can access the free comments through a smartphone or tablet. As a result, the anonymization tool according to the embodiment can anonymize employees' free comments and collect honest opinions.For example, free comments written by employees can be analyzed quickly and accurately, allowing managers to analyze the comments without prejudice. Employees can also feel confident that their opinions are anonymous and can express their true feelings without worry.

[0030] The conversion unit can analyze the free comments using a natural language processing (NLP) algorithm and extract specific phrases or expressions. Natural language processing (NLP) algorithms include, but are not limited to, morphological analysis, grammatical analysis, and semantic analysis. The conversion unit can analyze the words of the free comments using morphological analysis. For example, the conversion unit can use a morphological analysis algorithm to divide the words of the free comments and identify the part of speech of each word. The conversion unit can also analyze the sentence structure of the free comments using grammatical analysis. For example, the conversion unit can use a grammatical analysis algorithm to analyze the sentence structure of the free comments and identify sentence elements such as a subject, predicate, and object. The conversion unit can also analyze the meaning of the free comments using semantic analysis. For example, the conversion unit can use a semantic analysis algorithm to analyze the meaning of the sentences of the free comments and extract specific phrases or expressions. This improves the accuracy of analyzing the free comments by using natural language processing. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit may analyze free comments using an AI model that takes free comments as input and outputs specific phrases or ways of expression.

[0031] The providing unit can enable the managers to access the free comments through a dedicated dashboard. The dedicated dashboard includes, for example, display items and operation methods, but is not limited to these examples. The providing unit, for example, provides a web-based dashboard, allowing the managers to access the free comments through a browser. For example, the providing unit may display a list of free comments on the dashboard, allowing the managers to click on each comment to view details. The providing unit may also provide a mobile application, allowing the managers to access the free comments through a smartphone or tablet. For example, the providing unit may provide a free comment notification function in the mobile application, notifying the managers when a new comment is added. This makes it easier for the managers to access the free comments. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit may display the free comments using an AI model that receives the free comments as input and outputs data to be displayed on the dashboard.

[0032] The storage unit can store the free comments in a database. Examples of databases include, but are not limited to, relational databases and NoSQL databases. The storage unit, for example, uses a relational database to store the free comments in a table format. For example, the storage unit stores the free comments in each row of a table and assigns a unique identifier to each comment. The storage unit can also store the free comments in a document format using a NoSQL database. For example, the storage unit stores the free comments as documents and assigns metadata to each document. This improves the efficiency of storing the free comments. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can store the free comments using an AI model that receives the free comments as input and outputs data to be stored in a database.

[0033] The providing unit may enable managers to analyze free comments expressed in a unified manner. Examples of free comments expressed in a unified manner include, but are not limited to, standard expressions and specific templates. For example, the providing unit may display free comments expressed in a unified manner on a dashboard, allowing managers to click on each comment to view details. For example, the providing unit may provide a filtering function on the dashboard, allowing managers to search for comments based on specific keywords or categories. The providing unit may also display free comments expressed in a unified manner as graphs or charts, allowing managers to visually analyze the comments. For example, the providing unit may display graphs showing the frequency and trends of comments, allowing managers to understand comment patterns. This allows managers to analyze comments without preconceptions. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit may analyze comments using an AI model that receives free comments expressed in a unified manner and outputs analysis results.

[0034] When converting free comments into unified phrases, the conversion unit can analyze the entire sentence and convert it into unified phrases. Examples of analyzing the entire sentence include, but are not limited to, contextual analysis and semantic analysis. For example, the conversion unit analyzes the sentences before and after the free comment using contextual analysis. For example, the conversion unit analyzes the sentences before and after the free comment using a contextual analysis algorithm to understand the meaning of the entire sentence. The conversion unit can also analyze the meaning of the sentence of the free comment using semantic analysis. For example, the conversion unit analyzes the meaning of the sentence of the free comment using a semantic analysis algorithm and converts it into unified phrases. This improves the accuracy of the conversion by analyzing the entire sentence. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can analyze the free comments using an AI model that receives free comments as input and outputs unified phrases.

[0035] The reception unit can analyze the user's past comment submission history and select an appropriate reception method. For example, the reception unit retrieves the user's past comment submission history from a database and analyzes it. For example, the reception unit analyzes the date, time, and content of comments submitted by the user in the past and selects the optimal reception method. The reception unit can also encourage submission during a specific time period based on the user's past submission history. For example, the reception unit identifies a time period in which the user frequently submitted comments in the past and encourages submission during that time period. The reception unit can also provide an optimal interface based on the user's past submission history. For example, the reception unit preferentially suggests a reception method (such as voice or text) that the user has previously preferred. This makes it possible to provide the optimal reception method based on the user's past history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can select a reception method using an AI model that inputs the user's past comment submission history and outputs the optimal reception method.

[0036] When receiving free comments, the reception unit can filter the comments based on the user's current project or areas of interest. The reception unit, for example, obtains the user's current project or areas of interest from a project management tool or survey results and performs filtering. For example, the reception unit prioritizes receiving questions related to projects in which the user is currently involved. The reception unit can also receive free comments on topics related to the user's areas of interest. For example, the reception unit filters based on keywords related to the user's areas of interest and receives related comments. The reception unit can also receive free comments at an appropriate time depending on the progress of the user's project. For example, the reception unit monitors the progress of the project and receives comments at an appropriate time. This allows appropriate comments to be received based on the user's interests. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can filter comments using an AI model that inputs data on the user's projects and areas of interest and outputs filtering results.

[0037] When accepting a free comment, the acceptance unit can select an appropriate acceptance means depending on the user's input method. The acceptance unit, for example, detects the user's input method (voice, text, image, etc.) and selects an appropriate acceptance means. For example, if the user prefers voice input, the acceptance unit can preferentially accept voice input. Also, if the user prefers text input, the acceptance unit can preferentially accept text input. For example, if the user selects text input, the acceptance unit provides an interface for text input. Also, if the user inputs a comment using an image, the acceptance unit can perform image analysis and provide appropriate feedback. For example, the acceptance unit analyzes image data and converts it into text data for acceptance. This makes it possible to provide an optimal acceptance means depending on the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without AI. For example, the acceptance unit can select the acceptance means using an AI model that inputs the user's input method and outputs an appropriate acceptance means.

[0038] When accepting free comments, the acceptance unit can prioritize accepting highly relevant comments in consideration of the user's geographical location information. The acceptance unit, for example, acquires the user's geographical location information from GPS data or an IP address and prioritizes accepting highly relevant comments. For example, if the user is in a specific area, the acceptance unit can prioritize accepting comments related to that area. Furthermore, if the user is on a business trip, the acceptance unit can also prioritize accepting comments related to the business trip destination. For example, the acceptance unit can accept comments based on the user's experiences while on a business trip. Furthermore, if the user is at home, the acceptance unit can also prioritize accepting comments related to the user's home. For example, the acceptance unit can accept comments based on the user's experiences at home. In this way, highly relevant comments can be accepted based on the user's geographical location information. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can accept comments using an AI model that inputs the user's geographical location information and outputs highly relevant comments.

[0039] The reception unit may analyze the user's social media activity and receive related comments when receiving a free comment. The reception unit, for example, acquires and analyzes the user's social media activity from a social media platform. For example, the reception unit may preferentially receive comments related to topics mentioned by the user on social media. The reception unit may also receive related comments based on the user's social media activity history. For example, the reception unit may analyze the content of the user's social media posts and receive related comments. The reception unit may also receive related comments by referring to the activities of the user's friends on social media. For example, the reception unit may receive comments related to topics mentioned by the user's friends. This allows related comments to be received based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may receive comments using an AI model that inputs the user's social media activity data and outputs related comments.

[0040] The reception unit can customize the reception method by reflecting the user's past feedback when receiving free comments. The reception unit, for example, acquires and analyzes the user's past feedback from a database. For example, the reception unit preferentially provides the user's previously preferred reception method. The reception unit can also customize the reception interface based on the user's past feedback. For example, the reception unit can adjust the design and functions of the reception interface by reflecting the user's past feedback. The reception unit can also adjust the reception timing by reflecting the user's past feedback. For example, the reception unit can prompt the user to receive comments during a time period that the user previously preferred. This allows the reception method to be customized based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can customize the reception method by using an AI model that inputs the user's past feedback data and outputs a customized reception method.

[0041] During conversion, the conversion unit can adjust the accuracy of the conversion based on the importance of the comment. The conversion unit evaluates the importance based on, for example, the content and influence of the comment. For example, the conversion unit analyzes the content of the comment and converts important comments into detailed comments, accurately expressing every detail. The conversion unit can also convert general comments into concise comments to focus on the main points. For example, the conversion unit converts general comments into concise comments to focus on the main points. The conversion unit can also simplify and convert comments with low importance. For example, the conversion unit simplifies and converts comments with low importance. This allows conversion with an appropriate level of detail depending on the importance of the comment. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can adjust the accuracy of the conversion using an AI model that inputs comment importance data and outputs the accuracy of the conversion.

[0042] During conversion, the conversion unit can apply different conversion algorithms depending on the category of the comment. The conversion unit, for example, identifies the category of the comment and selects an appropriate conversion algorithm. For example, the conversion unit applies a conversion algorithm including technical terms to technical comments. The conversion unit can also apply a conversion algorithm that suppresses emotions to emotional comments. For example, the conversion unit converts emotional comments using a suppressed expression method. The conversion unit can also apply a standard conversion algorithm to general comments. For example, the conversion unit converts general comments using a standard expression method. This makes it possible to apply an appropriate conversion algorithm depending on the category of the comment. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can apply a conversion algorithm using an AI model that inputs comment category data and outputs an appropriate conversion algorithm.

[0043] During conversion, the conversion unit can improve the accuracy of the conversion by referring to the user's past conversion results. The conversion unit, for example, retrieves and analyzes the user's past conversion results from a database. For example, the conversion unit preferentially applies the conversion method that the user has previously preferred. The conversion unit can also adjust the conversion algorithm based on the user's past conversion results. For example, the conversion unit analyzes the user's past conversion results and suggests the optimal conversion method. This improves the accuracy of the conversion by referring to the user's past conversion results. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can improve the accuracy of the conversion by using an AI model that inputs the user's past conversion result data and outputs the conversion accuracy.

[0044] During conversion, the conversion unit can determine the order of conversion based on the submission time of the comments. The conversion unit, for example, obtains the submission date and time of the comments from a database and determines the order of conversion. For example, the conversion unit prioritizes converting the most recent comments. The conversion unit can also postpone comments that were submitted earlier. For example, the conversion unit postpones comments that were submitted earlier. The conversion unit can also dynamically adjust the order of conversion based on the submission time. For example, the conversion unit dynamically adjusts the order of conversion based on the submission time. This allows conversion to be performed with appropriate priority based on the submission time of the comments. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can determine the order of conversion using an AI model that receives comment submission time data as input and outputs the order of conversion.

[0045] The conversion unit can adjust the order of conversion based on the relevance of the comments during conversion. The conversion unit, for example, evaluates the similarity of the content of the comments and related topics and adjusts the order of conversion. For example, the conversion unit prioritizes conversion of comments related to important topics. The conversion unit can also postpone comments related to general topics. For example, the conversion unit postpones comments related to general topics. The conversion unit can also dynamically adjust the order of conversion based on the relevance of the comments. For example, the conversion unit dynamically adjusts the order of conversion based on the relevance of the comments. This allows conversion in an appropriate order based on the relevance of the comments. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can adjust the order of conversion using an AI model that inputs comment relevance data and outputs a conversion order.

[0046] During conversion, the conversion unit can adjust the use of conversion terminology according to the user's level of expertise. The conversion unit, for example, evaluates the user's level of expertise based on the presence or absence of qualifications and years of experience and selects appropriate terminology. For example, the conversion unit performs conversion using a lot of technical terminology for users with high technical expertise. The conversion unit can also perform conversion using simpler language for users with low technical expertise. For example, the conversion unit converts using simpler language for users with low technical expertise. The conversion unit can also dynamically adjust the use of technical terminology for conversion according to the user's level of expertise. For example, the conversion unit dynamically adjusts the use of technical terminology for conversion according to the user's level of expertise. This allows conversion using appropriate technical terminology according to the user's level of expertise. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without AI. For example, the conversion unit can adjust the conversion terminology using an AI model that inputs the user's level of expertise data and outputs conversion terminology.

[0047] The storage unit can optimize the storage algorithm by referring to past storage data when storing data. The storage unit, for example, retrieves and analyzes past storage data from a database. For example, the storage unit selects an optimal storage algorithm based on the past storage data. The storage unit can also analyze the past storage data and adjust the storage algorithm. For example, the storage unit improves storage efficiency by referring to the past storage data. In this way, the storage algorithm is optimized by referring to the past storage data. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can optimize the storage algorithm using an AI model that inputs past storage data and outputs a storage algorithm.

[0048] The storage unit can update the stored data by reflecting user feedback when storing the data. The storage unit, for example, obtains and analyzes user feedback from a database. For example, the storage unit periodically updates the stored data based on the user feedback. The storage unit can also improve the quality of the stored data by reflecting the user feedback. For example, the storage unit optimizes the structure of the stored data by referring to the user feedback. This allows the stored data to be updated based on the user feedback. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can update the stored data using an AI model that receives user feedback data as input and outputs stored data.

[0049] The storage unit can set a priority of the stored data based on the submission time of the comment when storing the data. The storage unit, for example, obtains the submission date and time of the comment from a database and sets the priority of the stored data. For example, the storage unit prioritizes and stores the most recent comments. The storage unit can also prioritize and store comments that were submitted earlier. For example, the storage unit can prioritize and store comments that were submitted earlier. The storage unit can also dynamically adjust the weighting of the stored data based on the submission time. For example, the storage unit dynamically adjusts the weighting of the stored data based on the submission time. This allows data to be stored with an appropriate weighting based on the submission time of the comment. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can set the priority of the stored data using an AI model that inputs comment submission time data and outputs the priority of the stored data.

[0050] The storage unit can integrate information from different data sources to increase the amount of stored data when storing the data. For example, the storage unit obtains and integrates information from different data sources from an external API or an internal database. For example, the storage unit integrates and stores other survey data in addition to users' free comments. The storage unit can also integrate users' social media activity data to enrich the stored data. For example, the storage unit integrates users' social media activity data to enrich the stored data. The storage unit can also integrate users' work history data to enrich the stored data. For example, the storage unit integrates users' work history data to enrich the stored data. In this way, the stored data is enriched by integrating information from different data sources. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can enrich the stored data using an AI model that inputs data from different data sources and outputs integrated stored data.

[0051] When providing the display, the providing unit can select the optimal display method by referring to the user's past operation history. The providing unit, for example, acquires and analyzes the user's past operation history from a database. For example, the providing unit preferentially provides display methods that the user has previously preferred. The providing unit can also customize the display interface based on the user's past operation history. For example, the providing unit adjusts the display timing by reflecting the user's past operation history. This makes it possible to provide the optimal display method based on the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can select the display method using an AI model that inputs the user's past operation history data and outputs the optimal display method.

[0052] The providing unit can customize the display content according to the user's current task when providing the display content. The providing unit, for example, obtains the user's current task from a task management tool or a project management tool and customizes the display content. For example, the providing unit prioritizes displaying comments related to the task the user is currently working on. The providing unit can also display appropriate comments according to the user's current task progress. For example, the providing unit customizes the display content based on the importance of the user's task. This makes it possible to provide appropriate display content according to the user's current task. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can customize the display content using an AI model that inputs the user's current task data and outputs customized display content.

[0053] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display. The providing unit, for example, acquires the user's device information based on the device type and OS version and selects the optimal display method. For example, if the user is using a smartphone, the providing unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. For example, if the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is concise and highly visible. For example, if the user is using a smartwatch, the providing unit provides a display method that is concise and highly visible. This makes it possible to provide the optimal display method based on the user's device information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can select the display method using an AI model that inputs the user's device information and outputs the optimal display method.

[0054] The providing unit can make the display content multilingual according to the user's language setting when providing the content. The providing unit, for example, acquires the user's language setting from a user profile or browser settings and makes the display content multilingual. For example, the providing unit automatically sets the display content based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. For example, when the user selects a specific language, the providing unit provides the display content in that language. This makes it possible to provide multilingual display content based on the user's language setting. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can make the display content multilingual using an AI model that receives the user's language setting data as input and outputs multilingual display content.

[0055] The providing unit can analyze the user's social media activity and provide related information at the time of providing. For example, the providing unit obtains and analyzes the user's social media activity from a social media platform. For example, the providing unit provides information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. For example, the providing unit provides information about related places and events based on the activity of the user's friends on social media. This makes it possible to provide related information based on the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that inputs the user's social media activity data and outputs related information.

[0056] The providing unit can customize the display content by reflecting the user's past feedback when providing the display content. The providing unit, for example, acquires and analyzes the user's past feedback from a database. For example, the providing unit periodically updates the display content based on the user's past feedback. The providing unit can also improve the quality of the display content by reflecting the user's past feedback. For example, the providing unit optimizes the structure of the display content by referring to the user's past feedback. This makes it possible to provide appropriate display content based on the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can customize the display content using an AI model that inputs the user's past feedback data and outputs customized display content.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The reception unit can analyze the user's past comment submission history and select an appropriate reception method. For example, the reception unit retrieves the user's past comment submission history from a database and analyzes it. For example, the reception unit analyzes the date, time, and content of comments submitted by the user in the past and selects the optimal reception method. The reception unit can also encourage submission during a specific time period based on the user's past submission history. For example, the reception unit identifies a time period in which the user frequently submitted comments in the past and encourages submission during that time period. The reception unit can also provide an optimal interface based on the user's past submission history. For example, the reception unit preferentially suggests a reception method (such as voice or text) that the user has previously preferred. This makes it possible to provide the optimal reception method based on the user's past history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can select a reception method using an AI model that inputs the user's past comment submission history and outputs the optimal reception method.

[0059] When receiving free comments, the reception unit may filter the comments based on the user's current project or areas of interest. For example, the reception unit may obtain the user's current project or areas of interest from a project management tool or survey results and perform filtering. For example, the reception unit may preferentially receive questions related to projects in which the user is currently involved. The reception unit may also receive free comments on topics related to the user's areas of interest. For example, the reception unit may filter based on keywords related to the user's areas of interest and receive related comments. The reception unit may also receive free comments at an appropriate time depending on the progress of the user's project. For example, the reception unit may monitor the progress of the project and receive comments at an appropriate time. This allows appropriate comments to be received based on the user's interests. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may filter comments using an AI model that inputs data on the user's projects and areas of interest and outputs filtering results.

[0060] When accepting a free comment, the acceptance unit can select an appropriate acceptance means depending on the user's input method. For example, the acceptance unit detects the user's input method (voice, text, image, etc.) and selects an appropriate acceptance means. For example, if the user prefers voice input, the acceptance unit can preferentially accept voice input. Also, if the user prefers text input, the acceptance unit can preferentially accept text input. For example, if the user selects text input, the acceptance unit provides an interface for text input. Also, if the user enters a comment using an image, the acceptance unit can perform image analysis and provide appropriate feedback. For example, the acceptance unit analyzes image data and converts it into text data for acceptance. This makes it possible to provide an optimal acceptance means depending on the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without AI. For example, the acceptance unit can select the acceptance means using an AI model that inputs the user's input method and outputs an appropriate acceptance means.

[0061] When accepting free comments, the acceptance unit can prioritize accepting highly relevant comments by taking into account the user's geographical location information. For example, the acceptance unit acquires the user's geographical location information from GPS data or an IP address, and prioritizes accepting highly relevant comments. For example, if the user is in a specific area, the acceptance unit can prioritize accepting comments related to that area. Furthermore, if the user is on a business trip, the acceptance unit can also prioritize accepting comments related to the business trip destination. For example, the acceptance unit can accept comments based on the user's experiences while on a business trip. Furthermore, if the user is at home, the acceptance unit can also prioritize accepting comments related to the user's home. For example, the acceptance unit can accept comments based on the user's experiences at home. In this way, highly relevant comments can be accepted based on the user's geographical location information. Some or all of the above-described processing by the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can accept comments using an AI model that inputs the user's geographical location information and outputs highly relevant comments.

[0062] The reception unit may analyze the user's social media activity and receive related comments when receiving a free comment. For example, the reception unit may acquire and analyze the user's social media activity from a social media platform. For example, the reception unit may preferentially receive comments related to topics mentioned by the user on social media. The reception unit may also receive related comments based on the user's social media activity history. For example, the reception unit may analyze the content of the user's social media posts and receive related comments. The reception unit may also receive related comments by referring to the activities of the user's friends on social media. For example, the reception unit may receive comments related to topics mentioned by the user's friends. This allows related comments to be received based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may receive comments using an AI model that receives the user's social media activity data as input and outputs related comments.

[0063] When accepting free comments, the reception unit can customize the reception method by reflecting the user's past feedback. For example, the reception unit retrieves and analyzes the user's past feedback from a database. For example, the reception unit preferentially provides the user's previously preferred reception method. The reception unit can also customize the reception interface based on the user's past feedback. For example, the reception unit adjusts the design and functionality of the reception interface by reflecting the user's past feedback. The reception unit can also adjust the reception timing by reflecting the user's past feedback. For example, the reception unit prompts the user to accept comments during a time period that the user previously preferred. This allows the reception method to be customized based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can customize the reception method by using an AI model that inputs the user's past feedback data and outputs a customized reception method.

[0064] During conversion, the conversion unit can adjust the accuracy of the conversion based on the importance of the comment. For example, the conversion unit evaluates the importance based on the content and impact of the comment. For example, the conversion unit analyzes the content of the comment and converts important comments into detailed comments, accurately expressing every detail. The conversion unit can also convert general comments into concise comments to focus on the main points. For example, the conversion unit converts general comments into concise comments to focus on the main points. The conversion unit can also simplify and convert comments with low importance. For example, the conversion unit simplifies and converts comments with low importance. This allows conversion with an appropriate level of detail depending on the importance of the comment. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can adjust the accuracy of the conversion using an AI model that inputs comment importance data and outputs the accuracy of the conversion.

[0065] During conversion, the conversion unit can apply different conversion algorithms depending on the category of the comment. For example, the conversion unit identifies the category of the comment and selects an appropriate conversion algorithm. For example, the conversion unit applies a conversion algorithm including technical terms to technical comments. The conversion unit can also apply a conversion algorithm that suppresses emotions to emotional comments. For example, the conversion unit converts emotional comments using a suppressed expression method. The conversion unit can also apply a standard conversion algorithm to general comments. For example, the conversion unit converts general comments using a standard expression method. This makes it possible to apply an appropriate conversion algorithm depending on the category of the comment. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can apply a conversion algorithm using an AI model that inputs comment category data and outputs an appropriate conversion algorithm.

[0066] During conversion, the conversion unit can improve the accuracy of the conversion by referring to the user's past conversion results. For example, the conversion unit retrieves and analyzes the user's past conversion results from a database. For example, the conversion unit preferentially applies the conversion method that the user has previously preferred. The conversion unit can also adjust the conversion algorithm based on the user's past conversion results. For example, the conversion unit analyzes the user's past conversion results and suggests the optimal conversion method. This improves the accuracy of the conversion by referring to the user's past conversion results. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can improve the accuracy of the conversion by using an AI model that inputs the user's past conversion result data and outputs the conversion accuracy.

[0067] During conversion, the conversion unit can determine the order of conversion based on the submission time of the comments. For example, the conversion unit obtains the submission date and time of the comments from a database and determines the order of conversion. For example, the conversion unit prioritizes converting the most recent comments. The conversion unit can also postpone comments that were submitted earlier. For example, the conversion unit postpones comments that were submitted earlier. The conversion unit can also dynamically adjust the order of conversion based on the submission time. For example, the conversion unit dynamically adjusts the order of conversion based on the submission time. This allows conversion to be performed with appropriate priority based on the submission time of the comments. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can determine the order of conversion using an AI model that inputs comment submission time data and outputs the order of conversion.

[0068] The conversion unit can adjust the order of conversion based on the relevance of the comments during conversion. For example, the conversion unit evaluates the similarity of the content of the comments and related topics and adjusts the order of conversion. For example, the conversion unit prioritizes conversion of comments related to important topics. The conversion unit can also postpone comments related to general topics. For example, the conversion unit postpones comments related to general topics. The conversion unit can also dynamically adjust the order of conversion based on the relevance of the comments. For example, the conversion unit dynamically adjusts the order of conversion based on the relevance of the comments. This allows conversion in an appropriate order based on the relevance of the comments. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can adjust the order of conversion using an AI model that receives comment relevance data as input and outputs the order of conversion.

[0069] During conversion, the conversion unit can adjust the use of conversion terminology according to the user's level of expertise. For example, the conversion unit evaluates the user's level of expertise based on the presence or absence of qualifications and years of experience and selects appropriate terminology. For example, the conversion unit performs conversion using a lot of technical terminology for users with high technical expertise. The conversion unit can also perform conversion using simpler language for users with low technical expertise. For example, the conversion unit converts using simpler language for users with low technical expertise. The conversion unit can also dynamically adjust the use of technical terminology for conversion according to the user's level of expertise. For example, the conversion unit dynamically adjusts the use of technical terminology for conversion according to the user's level of expertise. This allows conversion using appropriate technical terminology according to the user's level of expertise. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can adjust the conversion terminology using an AI model that inputs the user's level of expertise data and outputs conversion terminology.

[0070] The storage unit can optimize the storage algorithm by referring to past storage data when storing data. For example, the storage unit retrieves and analyzes past storage data from a database. For example, the storage unit selects an optimal storage algorithm based on the past storage data. The storage unit can also analyze the past storage data and adjust the storage algorithm. For example, the storage unit improves storage efficiency by referring to the past storage data. In this way, the storage algorithm is optimized by referring to the past storage data. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can optimize the storage algorithm using an AI model that inputs past storage data and outputs a storage algorithm.

[0071] The storage unit can update the stored data by reflecting user feedback when storing the data. For example, the storage unit obtains and analyzes user feedback from a database. For example, the storage unit periodically updates the stored data based on the user feedback. The storage unit can also improve the quality of the stored data by reflecting the user feedback. For example, the storage unit optimizes the structure of the stored data by referring to the user feedback. This allows the stored data to be updated based on the user feedback. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can update the stored data using an AI model that receives user feedback data as input and outputs stored data.

[0072] The storage unit can set a priority of the stored data based on the time of submission of the comment when storing the data. For example, the storage unit retrieves the submission date and time of the comment from a database and sets the priority of the stored data. For example, the storage unit prioritizes and stores the most recent comment. The storage unit can also prioritize and store comments that were submitted earlier. For example, the storage unit can prioritize and store comments that were submitted earlier. The storage unit can also dynamically adjust the weighting of the stored data based on the time of submission. For example, the storage unit dynamically adjusts the weighting of the stored data based on the time of submission. This allows data to be stored with an appropriate weighting based on the time of submission of the comment. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can set the priority of the stored data using an AI model that inputs comment submission date data and outputs the priority of the stored data.

[0073] The storage unit can integrate information from different data sources to increase the amount of stored data when storing the data. For example, the storage unit obtains and integrates information from different data sources from an external API or an internal database. For example, the storage unit integrates and stores other survey data in addition to users' free comments. The storage unit can also integrate users' social media activity data to enrich the stored data. For example, the storage unit integrates users' social media activity data to enrich the stored data. The storage unit can also integrate users' work history data to enrich the stored data. For example, the storage unit integrates users' work history data to enrich the stored data. In this way, the stored data is enriched by integrating information from different data sources. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can enrich the stored data using an AI model that inputs data from different data sources and outputs integrated stored data.

[0074] When providing the display method, the providing unit can select the optimal display method by referring to the user's past operation history. For example, the providing unit retrieves the user's past operation history from a database and analyzes it. For example, the providing unit preferentially provides display methods that the user has preferred in the past. The providing unit can also customize the display interface based on the user's past operation history. For example, the providing unit adjusts the display timing by reflecting the user's past operation history. This makes it possible to provide the optimal display method based on the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can select the display method using an AI model that inputs the user's past operation history data and outputs the optimal display method.

[0075] The providing unit can customize the display content according to the user's current task when providing the display content. For example, the providing unit obtains the user's current task from a task management tool or a project management tool and customizes the display content. For example, the providing unit prioritizes displaying comments related to the task the user is currently working on. The providing unit can also display appropriate comments according to the user's current task progress. For example, the providing unit customizes the display content based on the importance of the user's task. This makes it possible to provide appropriate display content according to the user's current task. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can customize the display content using an AI model that inputs the user's current task data and outputs customized display content.

[0076] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display. For example, the providing unit acquires the user's device information based on the device type and OS version and selects the optimal display method. For example, if the user is using a smartphone, the providing unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. For example, if the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is concise and highly visible. For example, if the user is using a smartwatch, the providing unit provides a display method that is concise and highly visible. This makes it possible to provide the optimal display method based on the user's device information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can select the display method using an AI model that inputs the user's device information and outputs the optimal display method.

[0077] The providing unit can make the display content multilingual according to the user's language setting when providing the content. For example, the providing unit acquires the user's language setting from a user profile or browser settings and makes the display content multilingual. For example, the providing unit automatically sets the display content based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. For example, when the user selects a specific language, the providing unit provides the display content in that language. This makes it possible to provide multilingual display content based on the user's language setting. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can make the display content multilingual using an AI model that receives the user's language setting data as input and outputs multilingual display content.

[0078] The providing unit can analyze the user's social media activity and provide related information at the time of providing. For example, the providing unit obtains and analyzes the user's social media activity from a social media platform. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. For example, the providing unit can provide information about related places and events based on the activity of the user's friends on social media. This makes it possible to provide related information based on the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that inputs the user's social media activity data and outputs related information.

[0079] The providing unit can customize the display content by reflecting the user's past feedback when providing the display content. For example, the providing unit retrieves and analyzes the user's past feedback from a database. For example, the providing unit periodically updates the display content based on the user's past feedback. The providing unit can also improve the quality of the display content by reflecting the user's past feedback. For example, the providing unit optimizes the structure of the display content by referring to the user's past feedback. This makes it possible to provide appropriate display content based on the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can customize the display content using an AI model that inputs the user's past feedback data and outputs customized display content.

[0080] The processing flow of the first embodiment will be briefly explained below.

[0081] Step 1: The reception unit receives free comments from employees. Employee free comments can be in text, audio, image, or other formats. The reception unit directly inputs free comments in text format. It also converts free comments in audio format into text data using voice recognition technology, and converts free comments in image format into text data using image recognition technology. Step 2: The conversion unit uses natural language processing (NLP) algorithms to analyze the free comments received by the reception unit and convert them into unified phrases. The conversion unit analyzes the free comments using techniques such as morphological analysis, grammatical analysis, and semantic analysis, and converts them into unified phrases. Step 3: The storage unit stores the free comments converted by the conversion unit in a database such as a relational database or a NoSQL database. Step 4: The provider makes the free comments stored by the storage unit accessible to managers. The provider displays the free comments through a dedicated dashboard or mobile application, allowing managers to access the free comments via a browser, smartphone, or tablet.

[0082] (Example 2) An anonymization tool according to an embodiment of the present invention is a system that automatically analyzes free comments written by employees and converts them into uniform phrases. This anonymization tool analyzes free comments written by employees, extracts specific phrases and expressions, and converts them into uniform phrases, allowing employees to express their true opinions while maintaining anonymity. For example, when employees respond to a survey, the anonymization tool inputs free comments. These free comments are then input into the anonymization tool. The anonymization tool then analyzes the free comments using a natural language processing (NLP) algorithm and extracts specific phrases and expressions. For example, if a comment such as "This project was very difficult" is input, the anonymization tool converts it into a uniform phrase such as "This project was difficult." The anonymization tool then saves the converted free comments and makes them accessible to managers through a dedicated dashboard. This allows managers to analyze the free comments expressed in uniform phrases and identify organizational challenges and areas for improvement. For example, if multiple employees comment "This project was difficult," problems with the project can be identified and improvements can be considered. This allows the anonymization tool to anonymize employees' free comments and collect honest opinions. For example, free comments written by employees can be analyzed quickly and accurately, allowing managers to analyze the comments without preconceptions. Employees can also feel confident that their opinions are anonymous and express their honest opinions with peace of mind.

[0083] The anonymization tool according to the embodiment includes a receiving unit, a conversion unit, a storage unit, and a providing unit. The receiving unit receives free comments from employees. The free comments from employees may be in, for example, text, audio, or image format, but are not limited to these examples. The receiving unit directly inputs free comments in text format, for example. The receiving unit can also convert free comments in audio format into text data using audio recognition technology. The receiving unit can also convert free comments in image format into text data using image recognition technology. For example, the receiving unit converts audio data into text data using audio recognition technology. Image recognition technology analyzes image data and converts it into text data. The conversion unit uses a natural language processing (NLP) algorithm to analyze the free comments received by the receiving unit and convert them into a unified phrase. The conversion unit analyzes the free comments using techniques such as morphological analysis, grammatical analysis, and semantic analysis. For example, the conversion unit analyzes the words in the free comments using morphological analysis and grammatical analysis to analyze the sentence structure. The conversion unit analyzes the meaning of the free comments using semantic analysis and converts them into unified phrases. The storage unit stores the free comments converted by the conversion unit. The storage unit stores the free comments in a database, such as a relational database or a NoSQL database. For example, the storage unit stores the free comments in a table format using a relational database. The storage unit can also store the free comments in a document format using a NoSQL database. The provision unit allows managers to access the free comments stored by the storage unit. The provision unit displays the free comments through, for example, a dedicated dashboard. For example, the provision unit provides a web-based dashboard so that managers can access the free comments through a browser. The provision unit also provides a mobile application so that managers can access the free comments through a smartphone or tablet. As a result, the anonymization tool according to the embodiment can anonymize employees' free comments and collect honest opinions.For example, free comments written by employees can be analyzed quickly and accurately, allowing managers to analyze the comments without prejudice. Employees can also feel confident that their opinions are anonymous and can express their true feelings without worry.

[0084] The conversion unit can analyze the free comments using a natural language processing (NLP) algorithm and extract specific phrases or expressions. Natural language processing (NLP) algorithms include, but are not limited to, morphological analysis, grammatical analysis, and semantic analysis. The conversion unit can analyze the words of the free comments using morphological analysis. For example, the conversion unit can use a morphological analysis algorithm to divide the words of the free comments and identify the part of speech of each word. The conversion unit can also analyze the sentence structure of the free comments using grammatical analysis. For example, the conversion unit can use a grammatical analysis algorithm to analyze the sentence structure of the free comments and identify sentence elements such as a subject, predicate, and object. The conversion unit can also analyze the meaning of the free comments using semantic analysis. For example, the conversion unit can use a semantic analysis algorithm to analyze the meaning of the sentences of the free comments and extract specific phrases or expressions. This improves the accuracy of analyzing the free comments by using natural language processing. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit may analyze free comments using an AI model that takes free comments as input and outputs specific phrases or ways of expression.

[0085] The providing unit can enable the managers to access the free comments through a dedicated dashboard. The dedicated dashboard includes, for example, display items and operation methods, but is not limited to these examples. The providing unit, for example, provides a web-based dashboard, allowing the managers to access the free comments through a browser. For example, the providing unit may display a list of free comments on the dashboard, allowing the managers to click on each comment to view details. The providing unit may also provide a mobile application, allowing the managers to access the free comments through a smartphone or tablet. For example, the providing unit may provide a free comment notification function in the mobile application, notifying the managers when a new comment is added. This makes it easier for the managers to access the free comments. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit may display the free comments using an AI model that receives the free comments as input and outputs data to be displayed on the dashboard.

[0086] The storage unit can store the free comments in a database. Examples of databases include, but are not limited to, relational databases and NoSQL databases. The storage unit, for example, uses a relational database to store the free comments in a table format. For example, the storage unit stores the free comments in each row of a table and assigns a unique identifier to each comment. The storage unit can also store the free comments in a document format using a NoSQL database. For example, the storage unit stores the free comments as documents and assigns metadata to each document. This improves the efficiency of storing the free comments. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can store the free comments using an AI model that receives the free comments as input and outputs data to be stored in a database.

[0087] The providing unit may enable managers to analyze free comments expressed in a unified manner. Examples of free comments expressed in a unified manner include, but are not limited to, standard expressions and specific templates. For example, the providing unit may display free comments expressed in a unified manner on a dashboard, allowing managers to click on each comment to view details. For example, the providing unit may provide a filtering function on the dashboard, allowing managers to search for comments based on specific keywords or categories. The providing unit may also display free comments expressed in a unified manner as graphs or charts, allowing managers to visually analyze the comments. For example, the providing unit may display graphs showing the frequency and trends of comments, allowing managers to understand comment patterns. This allows managers to analyze comments without preconceptions. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit may analyze comments using an AI model that receives free comments expressed in a unified manner and outputs analysis results.

[0088] When converting free comments into unified phrases, the conversion unit can analyze the entire sentence and convert it into unified phrases. Examples of analyzing the entire sentence include, but are not limited to, contextual analysis and semantic analysis. For example, the conversion unit analyzes the sentences before and after the free comment using contextual analysis. For example, the conversion unit analyzes the sentences before and after the free comment using a contextual analysis algorithm to understand the meaning of the entire sentence. The conversion unit can also analyze the meaning of the sentence of the free comment using semantic analysis. For example, the conversion unit analyzes the meaning of the sentence of the free comment using a semantic analysis algorithm and converts it into unified phrases. This improves the accuracy of the conversion by analyzing the entire sentence. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can analyze the free comments using an AI model that receives free comments as input and outputs unified phrases.

[0089] The reception unit can estimate the user's emotions and adjust the timing of accepting free comments based on the estimated user emotions. The reception unit, for example, uses an emotion analysis algorithm to estimate the user's emotions. For example, the reception unit can analyze the user's facial expressions using facial expression recognition technology to estimate emotions. The reception unit can also analyze the tone and speed of the user's voice using voice analysis technology to estimate emotions. For example, the reception unit can analyze the tone and speed of the user's voice to estimate stress or relaxation levels. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor to estimate emotions. For example, the reception unit can estimate emotions based on heart rate fluctuations. This allows free comments to be accepted at appropriate times depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0090] The reception unit can analyze the user's past comment submission history and select an appropriate reception method. For example, the reception unit retrieves the user's past comment submission history from a database and analyzes it. For example, the reception unit analyzes the date, time, and content of comments submitted by the user in the past and selects the optimal reception method. The reception unit can also encourage submission during a specific time period based on the user's past submission history. For example, the reception unit identifies a time period in which the user frequently submitted comments in the past and encourages submission during that time period. The reception unit can also provide an optimal interface based on the user's past submission history. For example, the reception unit preferentially suggests a reception method (such as voice or text) that the user has previously preferred. This makes it possible to provide the optimal reception method based on the user's past history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can select a reception method using an AI model that inputs the user's past comment submission history and outputs the optimal reception method.

[0091] When receiving free comments, the reception unit can filter the comments based on the user's current project or areas of interest. The reception unit, for example, obtains the user's current project or areas of interest from a project management tool or survey results and performs filtering. For example, the reception unit prioritizes receiving questions related to projects in which the user is currently involved. The reception unit can also receive free comments on topics related to the user's areas of interest. For example, the reception unit filters based on keywords related to the user's areas of interest and receives related comments. The reception unit can also receive free comments at an appropriate time depending on the progress of the user's project. For example, the reception unit monitors the progress of the project and receives comments at an appropriate time. This allows appropriate comments to be received based on the user's interests. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can filter comments using an AI model that inputs data on the user's projects and areas of interest and outputs filtering results.

[0092] When accepting a free comment, the acceptance unit can select an appropriate acceptance means depending on the user's input method. The acceptance unit, for example, detects the user's input method (voice, text, image, etc.) and selects an appropriate acceptance means. For example, if the user prefers voice input, the acceptance unit can preferentially accept voice input. Also, if the user prefers text input, the acceptance unit can preferentially accept text input. For example, if the user selects text input, the acceptance unit provides an interface for text input. Also, if the user inputs a comment using an image, the acceptance unit can perform image analysis and provide appropriate feedback. For example, the acceptance unit analyzes image data and converts it into text data for acceptance. This makes it possible to provide an optimal acceptance means depending on the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without AI. For example, the acceptance unit can select the acceptance means using an AI model that inputs the user's input method and outputs an appropriate acceptance means.

[0093] The reception unit can estimate the user's emotions and determine the priority of comments to be received based on the estimated user emotions. The reception unit, for example, uses an emotion analysis algorithm to estimate the user's emotions. For example, the reception unit can analyze the user's facial expressions using facial expression recognition technology to estimate emotions. The reception unit can also use voice analysis technology to analyze the tone and speed of the user's voice to estimate emotions. For example, the reception unit can analyze the tone and speed of the user's voice and, if the user expresses strong emotions, prioritize receiving the comments. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor to estimate emotions. For example, the reception unit can estimate emotions based on heart rate fluctuations and, if the user is relaxed, prioritize receiving comments. This allows the priority of comments to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0094] When accepting free comments, the acceptance unit can prioritize accepting highly relevant comments in consideration of the user's geographical location information. The acceptance unit, for example, acquires the user's geographical location information from GPS data or an IP address and prioritizes accepting highly relevant comments. For example, if the user is in a specific area, the acceptance unit can prioritize accepting comments related to that area. Furthermore, if the user is on a business trip, the acceptance unit can also prioritize accepting comments related to the business trip destination. For example, the acceptance unit can accept comments based on the user's experiences while on a business trip. Furthermore, if the user is at home, the acceptance unit can also prioritize accepting comments related to the user's home. For example, the acceptance unit can accept comments based on the user's experiences at home. In this way, highly relevant comments can be accepted based on the user's geographical location information. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can accept comments using an AI model that inputs the user's geographical location information and outputs highly relevant comments.

[0095] The reception unit may analyze the user's social media activity and receive related comments when receiving a free comment. The reception unit, for example, acquires and analyzes the user's social media activity from a social media platform. For example, the reception unit may preferentially receive comments related to topics mentioned by the user on social media. The reception unit may also receive related comments based on the user's social media activity history. For example, the reception unit may analyze the content of the user's social media posts and receive related comments. The reception unit may also receive related comments by referring to the activities of the user's friends on social media. For example, the reception unit may receive comments related to topics mentioned by the user's friends. This allows related comments to be received based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may receive comments using an AI model that inputs the user's social media activity data and outputs related comments.

[0096] The reception unit can customize the reception method by reflecting the user's past feedback when receiving free comments. The reception unit, for example, acquires and analyzes the user's past feedback from a database. For example, the reception unit preferentially provides the user's previously preferred reception method. The reception unit can also customize the reception interface based on the user's past feedback. For example, the reception unit can adjust the design and functions of the reception interface by reflecting the user's past feedback. The reception unit can also adjust the reception timing by reflecting the user's past feedback. For example, the reception unit can prompt the user to receive comments during a time period that the user previously preferred. This allows the reception method to be customized based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can customize the reception method by using an AI model that inputs the user's past feedback data and outputs a customized reception method.

[0097] The conversion unit can estimate the user's emotion and adjust the expression method of the conversion based on the estimated user's emotion. The conversion unit, for example, estimates the user's emotion using an emotion analysis algorithm. For example, the conversion unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The conversion unit can also analyze the tone and speed of the user's voice using voice analysis technology to estimate the emotion. For example, the conversion unit can analyze the tone and speed of the user's voice and convert the user's voice to a calm expression if the user is relaxed. The conversion unit can also convert the user's voice to a concise and clear expression if the user is stressed. For example, the conversion unit can convert the user's voice to a concise and clear expression if the user is stressed. The conversion unit can also convert the user's voice to a subdued expression if the user is excited. For example, the conversion unit can convert the user's voice to a subdued expression if the user is excited. This allows the conversion to an appropriate expression depending on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the conversion unit may be performed using AI, or may be performed without using AI. For example, the conversion unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0098] During conversion, the conversion unit can adjust the accuracy of the conversion based on the importance of the comment. The conversion unit evaluates the importance based on, for example, the content and influence of the comment. For example, the conversion unit analyzes the content of the comment and converts important comments into detailed comments, accurately expressing every detail. The conversion unit can also convert general comments into concise comments to focus on the main points. For example, the conversion unit converts general comments into concise comments to focus on the main points. The conversion unit can also simplify and convert comments with low importance. For example, the conversion unit simplifies and converts comments with low importance. This allows conversion with an appropriate level of detail depending on the importance of the comment. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can adjust the accuracy of the conversion using an AI model that inputs comment importance data and outputs the accuracy of the conversion.

[0099] During conversion, the conversion unit can apply different conversion algorithms depending on the category of the comment. The conversion unit, for example, identifies the category of the comment and selects an appropriate conversion algorithm. For example, the conversion unit applies a conversion algorithm including technical terms to technical comments. The conversion unit can also apply a conversion algorithm that suppresses emotions to emotional comments. For example, the conversion unit converts emotional comments using a suppressed expression method. The conversion unit can also apply a standard conversion algorithm to general comments. For example, the conversion unit converts general comments using a standard expression method. This makes it possible to apply an appropriate conversion algorithm depending on the category of the comment. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can apply a conversion algorithm using an AI model that inputs comment category data and outputs an appropriate conversion algorithm.

[0100] During conversion, the conversion unit can improve the accuracy of the conversion by referring to the user's past conversion results. The conversion unit, for example, retrieves and analyzes the user's past conversion results from a database. For example, the conversion unit preferentially applies the conversion method that the user has previously preferred. The conversion unit can also adjust the conversion algorithm based on the user's past conversion results. For example, the conversion unit analyzes the user's past conversion results and suggests the optimal conversion method. This improves the accuracy of the conversion by referring to the user's past conversion results. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can improve the accuracy of the conversion by using an AI model that inputs the user's past conversion result data and outputs the conversion accuracy.

[0101] The conversion unit can estimate the user's emotion and adjust the length of the conversion based on the estimated user emotion. The conversion unit, for example, uses an emotion analysis algorithm to estimate the user's emotion. For example, the conversion unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The conversion unit can also use voice analysis technology to analyze the tone and speed of the user's voice to estimate the emotion. For example, if the user is in a hurry, the conversion unit can perform a short, to-the-point conversion. If the user is relaxed, the conversion unit can perform a longer conversion with detailed explanations. For example, if the user is relaxed, the conversion unit can perform a longer conversion with detailed explanations. If the user is excited, the conversion unit can perform a conversion with visually stimulating effects. For example, if the user is excited, the conversion unit can perform a conversion with visually stimulating effects. This allows the conversion to be of an appropriate length depending on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the conversion unit may be performed using AI, or may be performed without using AI. For example, the conversion unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0102] During conversion, the conversion unit can determine the order of conversion based on the submission time of the comments. The conversion unit, for example, obtains the submission date and time of the comments from a database and determines the order of conversion. For example, the conversion unit prioritizes converting the most recent comments. The conversion unit can also postpone comments that were submitted earlier. For example, the conversion unit postpones comments that were submitted earlier. The conversion unit can also dynamically adjust the order of conversion based on the submission time. For example, the conversion unit dynamically adjusts the order of conversion based on the submission time. This allows conversion to be performed with appropriate priority based on the submission time of the comments. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can determine the order of conversion using an AI model that receives comment submission time data as input and outputs the order of conversion.

[0103] The conversion unit can adjust the order of conversion based on the relevance of the comments during conversion. The conversion unit, for example, evaluates the similarity of the content of the comments and related topics and adjusts the order of conversion. For example, the conversion unit prioritizes conversion of comments related to important topics. The conversion unit can also postpone comments related to general topics. For example, the conversion unit postpones comments related to general topics. The conversion unit can also dynamically adjust the order of conversion based on the relevance of the comments. For example, the conversion unit dynamically adjusts the order of conversion based on the relevance of the comments. This allows conversion in an appropriate order based on the relevance of the comments. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can adjust the order of conversion using an AI model that inputs comment relevance data and outputs a conversion order.

[0104] During conversion, the conversion unit can adjust the use of conversion terminology according to the user's level of expertise. The conversion unit, for example, evaluates the user's level of expertise based on the presence or absence of qualifications and years of experience and selects appropriate terminology. For example, the conversion unit performs conversion using a lot of technical terminology for users with high technical expertise. The conversion unit can also perform conversion using simpler language for users with low technical expertise. For example, the conversion unit converts using simpler language for users with low technical expertise. The conversion unit can also dynamically adjust the use of technical terminology for conversion according to the user's level of expertise. For example, the conversion unit dynamically adjusts the use of technical terminology for conversion according to the user's level of expertise. This allows conversion using appropriate technical terminology according to the user's level of expertise. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without AI. For example, the conversion unit can adjust the conversion terminology using an AI model that inputs the user's level of expertise data and outputs conversion terminology.

[0105] The storage unit can estimate the user's emotions and select data to be saved based on the estimated user emotions. The storage unit estimates the user's emotions using, for example, an emotion analysis algorithm. For example, the storage unit can analyze the user's facial expressions using facial expression recognition technology to estimate emotions. The storage unit can also estimate emotions by analyzing the tone and speed of the user's voice using voice analysis technology. For example, the storage unit can analyze the tone and speed of the user's voice and prioritize saving comments that express strong emotions. The storage unit can also estimate emotions by collecting the user's biometric data (heart rate and electrodermal activity) using a sensor. For example, the storage unit can estimate emotions based on heart rate fluctuations and save relaxed comments with normal priority. This allows appropriate data to be saved depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0106] The storage unit can optimize the storage algorithm by referring to past storage data when storing data. The storage unit, for example, retrieves and analyzes past storage data from a database. For example, the storage unit selects an optimal storage algorithm based on the past storage data. The storage unit can also analyze the past storage data and adjust the storage algorithm. For example, the storage unit improves storage efficiency by referring to the past storage data. In this way, the storage algorithm is optimized by referring to the past storage data. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can optimize the storage algorithm using an AI model that inputs past storage data and outputs a storage algorithm.

[0107] The storage unit can update the stored data by reflecting user feedback when storing the data. The storage unit, for example, obtains and analyzes user feedback from a database. For example, the storage unit periodically updates the stored data based on the user feedback. The storage unit can also improve the quality of the stored data by reflecting the user feedback. For example, the storage unit optimizes the structure of the stored data by referring to the user feedback. This allows the stored data to be updated based on the user feedback. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can update the stored data using an AI model that receives user feedback data as input and outputs stored data.

[0108] The storage unit can estimate the user's emotions and adjust the frequency of saving based on the estimated user emotions. The storage unit estimates the user's emotions using, for example, an emotion analysis algorithm. For example, the storage unit can analyze the user's facial expressions using facial expression recognition technology to estimate emotions. The storage unit can also estimate emotions by analyzing the tone and speed of the user's voice using voice analysis technology. For example, the storage unit can analyze the tone and speed of the user's voice and save data frequently if the user has strong emotions. The storage unit can also estimate emotions by collecting the user's biometric data (heart rate and electrodermal activity) using a sensor. For example, the storage unit can estimate emotions based on heart rate fluctuations and save data at a normal frequency if the user is relaxed. This allows data to be saved at an appropriate frequency depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0109] The storage unit can set a priority of the stored data based on the submission time of the comment when storing the data. The storage unit, for example, obtains the submission date and time of the comment from a database and sets the priority of the stored data. For example, the storage unit prioritizes and stores the most recent comments. The storage unit can also prioritize and store comments that were submitted earlier. For example, the storage unit can prioritize and store comments that were submitted earlier. The storage unit can also dynamically adjust the weighting of the stored data based on the submission time. For example, the storage unit dynamically adjusts the weighting of the stored data based on the submission time. This allows data to be stored with an appropriate weighting based on the submission time of the comment. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can set the priority of the stored data using an AI model that inputs comment submission time data and outputs the priority of the stored data.

[0110] The storage unit can integrate information from different data sources to increase the amount of stored data when storing the data. For example, the storage unit obtains and integrates information from different data sources from an external API or an internal database. For example, the storage unit integrates and stores other survey data in addition to users' free comments. The storage unit can also integrate users' social media activity data to enrich the stored data. For example, the storage unit integrates users' social media activity data to enrich the stored data. The storage unit can also integrate users' work history data to enrich the stored data. For example, the storage unit integrates users' work history data to enrich the stored data. In this way, the stored data is enriched by integrating information from different data sources. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can enrich the stored data using an AI model that inputs data from different data sources and outputs integrated stored data.

[0111] The providing unit can estimate the user's emotions and adjust the display method of the comment to be provided based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions using an emotion analysis algorithm. For example, the providing unit can analyze the user's facial expressions using facial expression recognition technology to estimate emotions. The providing unit can also estimate emotions by analyzing the tone and speed of the user's voice using voice analysis technology. For example, the providing unit can analyze the tone and speed of the user's voice and, if the user is nervous, provide a simple, highly visible display method. The providing unit can also provide a display method including detailed information if the user is relaxed. For example, the providing unit can provide a display method that includes detailed information if the user is relaxed. The providing unit can also provide a display method that focuses on the main points if the user is in a hurry. For example, the providing unit can provide a display method that focuses on the main points if the user is in a hurry. This allows comments to be provided in an appropriate display method depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0112] When providing the display, the providing unit can select the optimal display method by referring to the user's past operation history. The providing unit, for example, acquires and analyzes the user's past operation history from a database. For example, the providing unit preferentially provides display methods that the user has previously preferred. The providing unit can also customize the display interface based on the user's past operation history. For example, the providing unit adjusts the display timing by reflecting the user's past operation history. This makes it possible to provide the optimal display method based on the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can select the display method using an AI model that inputs the user's past operation history data and outputs the optimal display method.

[0113] The providing unit can customize the display content according to the user's current task when providing the display content. The providing unit, for example, obtains the user's current task from a task management tool or a project management tool and customizes the display content. For example, the providing unit prioritizes displaying comments related to the task the user is currently working on. The providing unit can also display appropriate comments according to the user's current task progress. For example, the providing unit customizes the display content based on the importance of the user's task. This makes it possible to provide appropriate display content according to the user's current task. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can customize the display content using an AI model that inputs the user's current task data and outputs customized display content.

[0114] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display. The providing unit, for example, acquires the user's device information based on the device type and OS version and selects the optimal display method. For example, if the user is using a smartphone, the providing unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. For example, if the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is concise and highly visible. For example, if the user is using a smartwatch, the providing unit provides a display method that is concise and highly visible. This makes it possible to provide the optimal display method based on the user's device information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can select the display method using an AI model that inputs the user's device information and outputs the optimal display method.

[0115] The providing unit can estimate the user's emotions and adjust the operation procedure for the comment to be provided based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions using an emotion analysis algorithm. For example, the providing unit can analyze the user's facial expressions using facial expression recognition technology to estimate the user's emotions. The providing unit can also estimate the user's emotions by analyzing the tone and speed of the user's voice using voice analysis technology. For example, the providing unit can analyze the tone and speed of the user's voice and provide simple and intuitive operation procedures if the user is nervous. The providing unit can also provide detailed operation procedures if the user is relaxed. For example, the providing unit can provide detailed operation procedures if the user is relaxed. The providing unit can also provide quick operation procedures if the user is in a hurry. For example, the providing unit can provide quick operation procedures if the user is in a hurry. This allows the comment to be provided with appropriate operation procedures depending on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input user emotion data to the generating AI and cause the generating AI to estimate the emotion.

[0116] The providing unit can make the display content multilingual according to the user's language setting when providing the content. The providing unit, for example, acquires the user's language setting from a user profile or browser settings and makes the display content multilingual. For example, the providing unit automatically sets the display content based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. For example, when the user selects a specific language, the providing unit provides the display content in that language. This makes it possible to provide multilingual display content based on the user's language setting. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can make the display content multilingual using an AI model that receives the user's language setting data as input and outputs multilingual display content.

[0117] The providing unit can analyze the user's social media activity and provide related information at the time of providing. For example, the providing unit obtains and analyzes the user's social media activity from a social media platform. For example, the providing unit provides information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. For example, the providing unit provides information about related places and events based on the activity of the user's friends on social media. This makes it possible to provide related information based on the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that inputs the user's social media activity data and outputs related information.

[0118] The providing unit can customize the display content by reflecting the user's past feedback when providing the display content. The providing unit, for example, acquires and analyzes the user's past feedback from a database. For example, the providing unit periodically updates the display content based on the user's past feedback. The providing unit can also improve the quality of the display content by reflecting the user's past feedback. For example, the providing unit optimizes the structure of the display content by referring to the user's past feedback. This makes it possible to provide appropriate display content based on the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can customize the display content using an AI model that inputs the user's past feedback data and outputs customized display content. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, conversion unit, storage unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives free comments from employees. The conversion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the free comments using a natural language processing (NLP) algorithm and converts them into unified phrases. The storage unit, for example, stores the free comments in the database 24 of the data processing device 12. The provision unit, for example, is realized by the control unit 46A of the smart device 14 and displays the stored free comments via a dedicated dashboard. The reception unit, for example, estimates the user's emotions using a sentiment analysis algorithm and adjusts the timing of receiving free comments based on the estimated emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, conversion unit, storage unit, and providing unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives free comments from employees. The conversion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the free comments using a natural language processing (NLP) algorithm and converts them into unified phrases. The storage unit, for example, stores the free comments in the database 24 of the data processing device 12. The providing unit, for example, is realized by the control unit 46A of the smart glasses 214 and displays the stored free comments via a dedicated dashboard. The reception unit, for example, estimates the user's emotions using a sentiment analysis algorithm and adjusts the timing of receiving free comments based on the estimated emotions. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, conversion unit, storage unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and receives free comments from employees. The conversion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the free comments using a natural language processing (NLP) algorithm and converts them into unified phrases. The storage unit, for example, stores the free comments in the database 24 of the data processing device 12. The provision unit, for example, is realized by the control unit 46A of the headset-type terminal 314 and displays the stored free comments via a dedicated dashboard. The reception unit, for example, estimates the user's emotions using a sentiment analysis algorithm and adjusts the timing of receiving free comments based on the estimated emotions. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, conversion unit, storage unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives free comments from employees. The conversion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the free comments using a natural language processing (NLP) algorithm and converts them into unified phrases. The storage unit, for example, stores the free comments in the database 24 of the data processing device 12. The provision unit, for example, is realized by the control unit 46A of the robot 414 and displays the stored free comments via a dedicated dashboard. The reception unit, for example, estimates the user's emotions using a sentiment analysis algorithm and adjusts the timing of receiving free comments based on the estimated emotions.

[0119] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0120] The reception unit can estimate the user's emotions and adjust the timing of receiving free comments based on the estimated user emotions. For example, the reception unit can analyze the user's facial expressions using facial expression recognition technology to estimate emotions. The reception unit can also analyze the user's voice tone and speed using voice analysis technology to estimate emotions. For example, the reception unit can analyze the user's voice tone and speed to estimate stress or relaxation levels. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor to estimate emotions. For example, the reception unit can estimate emotions based on heart rate fluctuations. This allows free comments to be received at appropriate times according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0121] The conversion unit can estimate the user's emotion and adjust the expression method of the conversion based on the estimated user's emotion. For example, the conversion unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The conversion unit can also analyze the tone and speed of the user's voice using voice analysis technology to estimate the emotion. For example, the conversion unit can analyze the tone and speed of the user's voice and convert the user's voice to a calm expression if the user is relaxed. The conversion unit can also convert the user's voice to a concise and clear expression if the user is stressed. For example, the conversion unit can convert the user's voice to a concise and clear expression if the user is stressed. The conversion unit can also convert the user's voice to a more subdued expression if the user is excited. For example, the conversion unit can convert the user's voice to a more subdued expression if the user is excited. This allows the conversion to an appropriate expression method depending on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit may input user emotion data to the generation AI and have the generation AI estimate the emotion.

[0122] The providing unit can estimate the user's emotions and adjust the display method of the comment to be provided based on the estimated user's emotions. For example, the providing unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The providing unit can also analyze the user's tone and speed of voice using voice analysis technology to estimate the emotion. For example, the providing unit can analyze the user's tone and speed of voice and, if the user is nervous, provide a simple, highly visible display method. The providing unit can also provide a display method including detailed information if the user is relaxed. For example, the providing unit can provide a display method including detailed information if the user is relaxed. The providing unit can also provide a display method that focuses on the main points if the user is in a hurry. For example, the providing unit can provide a display method that focuses on the main points if the user is in a hurry. This allows the comment to be provided in an appropriate display method depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input user emotion data to the generating AI and cause the generating AI to estimate the emotion.

[0123] The storage unit can estimate a user's emotions and select data to be saved based on the estimated user emotions. For example, the storage unit can analyze a user's facial expression using facial expression recognition technology to estimate emotions. The storage unit can also analyze the tone and speed of a user's voice using voice analysis technology to estimate emotions. For example, the storage unit can analyze the tone and speed of a user's voice and prioritize saving comments that express strong emotions. The storage unit can also collect a user's biometric data (heart rate and electrodermal activity) using a sensor to estimate emotions. For example, the storage unit can estimate emotions based on heart rate fluctuations and save relaxed comments with normal priority. This allows appropriate data to be saved according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the storage unit can be performed using, for example, AI, or without AI. For example, the storage unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0124] The conversion unit can estimate the user's emotion and adjust the length of the conversion based on the estimated user emotion. For example, the conversion unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The conversion unit can also analyze the user's tone and speed of voice using voice analysis technology to estimate the emotion. For example, if the user is in a hurry, the conversion unit can perform a short, to-the-point conversion. If the user is relaxed, the conversion unit can perform a longer conversion including detailed explanations. For example, if the user is relaxed, the conversion unit can perform a longer conversion including detailed explanations. If the user is excited, the conversion unit can perform a conversion that adds visually stimulating effects. For example, if the user is excited, the conversion unit can perform a conversion that adds visually stimulating effects. This allows the conversion to be of an appropriate length depending on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit may input user emotion data to the generation AI and have the generation AI estimate the emotion.

[0125] The reception unit can analyze the user's past comment submission history and select an appropriate reception method. For example, the reception unit retrieves the user's past comment submission history from a database and analyzes it. For example, the reception unit analyzes the date, time, and content of comments submitted by the user in the past and selects the optimal reception method. The reception unit can also encourage submission during a specific time period based on the user's past submission history. For example, the reception unit identifies a time period in which the user frequently submitted comments in the past and encourages submission during that time period. The reception unit can also provide an optimal interface based on the user's past submission history. For example, the reception unit preferentially suggests a reception method (such as voice or text) that the user has previously preferred. This makes it possible to provide the optimal reception method based on the user's past history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can select a reception method using an AI model that inputs the user's past comment submission history and outputs the optimal reception method.

[0126] When receiving free comments, the reception unit may filter the comments based on the user's current project or areas of interest. For example, the reception unit may obtain the user's current project or areas of interest from a project management tool or survey results and perform filtering. For example, the reception unit may preferentially receive questions related to projects in which the user is currently involved. The reception unit may also receive free comments on topics related to the user's areas of interest. For example, the reception unit may filter based on keywords related to the user's areas of interest and receive related comments. The reception unit may also receive free comments at an appropriate time depending on the progress of the user's project. For example, the reception unit may monitor the progress of the project and receive comments at an appropriate time. This allows appropriate comments to be received based on the user's interests. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may filter comments using an AI model that inputs data on the user's projects and areas of interest and outputs filtering results.

[0127] When accepting a free comment, the acceptance unit can select an appropriate acceptance means depending on the user's input method. For example, the acceptance unit detects the user's input method (voice, text, image, etc.) and selects an appropriate acceptance means. For example, if the user prefers voice input, the acceptance unit can preferentially accept voice input. Also, if the user prefers text input, the acceptance unit can preferentially accept text input. For example, if the user selects text input, the acceptance unit provides an interface for text input. Also, if the user enters a comment using an image, the acceptance unit can perform image analysis and provide appropriate feedback. For example, the acceptance unit analyzes image data and converts it into text data for acceptance. This makes it possible to provide an optimal acceptance means depending on the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without AI. For example, the acceptance unit can select the acceptance means using an AI model that inputs the user's input method and outputs an appropriate acceptance means.

[0128] When accepting free comments, the acceptance unit can prioritize accepting highly relevant comments by taking into account the user's geographical location information. For example, the acceptance unit acquires the user's geographical location information from GPS data or an IP address, and prioritizes accepting highly relevant comments. For example, if the user is in a specific area, the acceptance unit can prioritize accepting comments related to that area. Furthermore, if the user is on a business trip, the acceptance unit can also prioritize accepting comments related to the business trip destination. For example, the acceptance unit can accept comments based on the user's experiences while on a business trip. Furthermore, if the user is at home, the acceptance unit can also prioritize accepting comments related to the user's home. For example, the acceptance unit can accept comments based on the user's experiences at home. In this way, highly relevant comments can be accepted based on the user's geographical location information. Some or all of the above-described processing by the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can accept comments using an AI model that inputs the user's geographical location information and outputs highly relevant comments.

[0129] The reception unit may analyze the user's social media activity and receive related comments when receiving a free comment. For example, the reception unit may acquire and analyze the user's social media activity from a social media platform. For example, the reception unit may preferentially receive comments related to topics mentioned by the user on social media. The reception unit may also receive related comments based on the user's social media activity history. For example, the reception unit may analyze the content of the user's social media posts and receive related comments. The reception unit may also receive related comments by referring to the activities of the user's friends on social media. For example, the reception unit may receive comments related to topics mentioned by the user's friends. This allows related comments to be received based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may receive comments using an AI model that receives the user's social media activity data as input and outputs related comments.

[0130] When accepting free comments, the reception unit can customize the reception method by reflecting the user's past feedback. For example, the reception unit retrieves and analyzes the user's past feedback from a database. For example, the reception unit preferentially provides the user's previously preferred reception method. The reception unit can also customize the reception interface based on the user's past feedback. For example, the reception unit adjusts the design and functionality of the reception interface by reflecting the user's past feedback. The reception unit can also adjust the reception timing by reflecting the user's past feedback. For example, the reception unit prompts the user to accept comments during a time period that the user previously preferred. This allows the reception method to be customized based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can customize the reception method by using an AI model that inputs the user's past feedback data and outputs a customized reception method.

[0131] During conversion, the conversion unit can adjust the accuracy of the conversion based on the importance of the comment. For example, the conversion unit evaluates the importance based on the content and impact of the comment. For example, the conversion unit analyzes the content of the comment and converts important comments into detailed comments, accurately expressing every detail. The conversion unit can also convert general comments into concise comments to focus on the main points. For example, the conversion unit converts general comments into concise comments to focus on the main points. The conversion unit can also simplify and convert comments with low importance. For example, the conversion unit simplifies and converts comments with low importance. This allows conversion with an appropriate level of detail depending on the importance of the comment. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can adjust the accuracy of the conversion using an AI model that inputs comment importance data and outputs the accuracy of the conversion.

[0132] During conversion, the conversion unit can apply different conversion algorithms depending on the category of the comment. For example, the conversion unit identifies the category of the comment and selects an appropriate conversion algorithm. For example, the conversion unit applies a conversion algorithm including technical terms to technical comments. The conversion unit can also apply a conversion algorithm that suppresses emotions to emotional comments. For example, the conversion unit converts emotional comments using a suppressed expression method. The conversion unit can also apply a standard conversion algorithm to general comments. For example, the conversion unit converts general comments using a standard expression method. This makes it possible to apply an appropriate conversion algorithm depending on the category of the comment. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can apply a conversion algorithm using an AI model that inputs comment category data and outputs an appropriate conversion algorithm.

[0133] During conversion, the conversion unit can improve the accuracy of the conversion by referring to the user's past conversion results. For example, the conversion unit retrieves and analyzes the user's past conversion results from a database. For example, the conversion unit preferentially applies the conversion method that the user has previously preferred. The conversion unit can also adjust the conversion algorithm based on the user's past conversion results. For example, the conversion unit analyzes the user's past conversion results and suggests the optimal conversion method. This improves the accuracy of the conversion by referring to the user's past conversion results. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can improve the accuracy of the conversion by using an AI model that inputs the user's past conversion result data and outputs the conversion accuracy.

[0134] During conversion, the conversion unit can determine the order of conversion based on the submission time of the comments. For example, the conversion unit obtains the submission date and time of the comments from a database and determines the order of conversion. For example, the conversion unit prioritizes converting the most recent comments. The conversion unit can also postpone comments that were submitted earlier. For example, the conversion unit postpones comments that were submitted earlier. The conversion unit can also dynamically adjust the order of conversion based on the submission time. For example, the conversion unit dynamically adjusts the order of conversion based on the submission time. This allows conversion to be performed with appropriate priority based on the submission time of the comments. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can determine the order of conversion using an AI model that inputs comment submission time data and outputs the order of conversion.

[0135] The conversion unit can adjust the order of conversion based on the relevance of the comments during conversion. For example, the conversion unit evaluates the similarity of the content of the comments and related topics and adjusts the order of conversion. For example, the conversion unit prioritizes conversion of comments related to important topics. The conversion unit can also postpone comments related to general topics. For example, the conversion unit postpones comments related to general topics. The conversion unit can also dynamically adjust the order of conversion based on the relevance of the comments. For example, the conversion unit dynamically adjusts the order of conversion based on the relevance of the comments. This allows conversion in an appropriate order based on the relevance of the comments. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can adjust the order of conversion using an AI model that receives comment relevance data as input and outputs the order of conversion.

[0136] During conversion, the conversion unit can adjust the use of conversion terminology according to the user's level of expertise. For example, the conversion unit evaluates the user's level of expertise based on the presence or absence of qualifications and years of experience and selects appropriate terminology. For example, the conversion unit performs conversion using a lot of technical terminology for users with high technical expertise. The conversion unit can also perform conversion using simpler language for users with low technical expertise. For example, the conversion unit converts using simpler language for users with low technical expertise. The conversion unit can also dynamically adjust the use of technical terminology for conversion according to the user's level of expertise. For example, the conversion unit dynamically adjusts the use of technical terminology for conversion according to the user's level of expertise. This allows conversion using appropriate technical terminology according to the user's level of expertise. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can adjust the conversion terminology using an AI model that inputs the user's level of expertise data and outputs conversion terminology.

[0137] The storage unit can optimize the storage algorithm by referring to past storage data when storing data. For example, the storage unit retrieves and analyzes past storage data from a database. For example, the storage unit selects an optimal storage algorithm based on the past storage data. The storage unit can also analyze the past storage data and adjust the storage algorithm. For example, the storage unit improves storage efficiency by referring to the past storage data. In this way, the storage algorithm is optimized by referring to the past storage data. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can optimize the storage algorithm using an AI model that inputs past storage data and outputs a storage algorithm.

[0138] The storage unit can update the stored data by reflecting user feedback when storing the data. For example, the storage unit obtains and analyzes user feedback from a database. For example, the storage unit periodically updates the stored data based on the user feedback. The storage unit can also improve the quality of the stored data by reflecting the user feedback. For example, the storage unit optimizes the structure of the stored data by referring to the user feedback. This allows the stored data to be updated based on the user feedback. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can update the stored data using an AI model that receives user feedback data as input and outputs stored data.

[0139] The storage unit can estimate the user's emotions and adjust the frequency of saving based on the estimated user emotions. For example, the storage unit can analyze the user's facial expressions using facial expression recognition technology to estimate emotions. The storage unit can also analyze the user's voice tone and speed using voice analysis technology to estimate emotions. For example, the storage unit can analyze the user's voice tone and speed and save data frequently if the user has strong emotions. The storage unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor to estimate emotions. For example, the storage unit can estimate emotions based on heart rate fluctuations and save data at a normal frequency if the user is relaxed. This allows data to be saved at an appropriate frequency depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the storage unit can be performed using, for example, AI, or without AI. For example, the storage unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0140] The storage unit can set a priority of the stored data based on the time of submission of the comment when storing the data. For example, the storage unit retrieves the submission date and time of the comment from a database and sets the priority of the stored data. For example, the storage unit prioritizes and stores the most recent comment. The storage unit can also prioritize and store comments that were submitted earlier. For example, the storage unit can prioritize and store comments that were submitted earlier. The storage unit can also dynamically adjust the weighting of the stored data based on the time of submission. For example, the storage unit dynamically adjusts the weighting of the stored data based on the time of submission. This allows data to be stored with an appropriate weighting based on the time of submission of the comment. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can set the priority of the stored data using an AI model that inputs comment submission date data and outputs the priority of the stored data.

[0141] The storage unit can integrate information from different data sources to increase the amount of stored data when storing the data. For example, the storage unit obtains and integrates information from different data sources from an external API or an internal database. For example, the storage unit integrates and stores other survey data in addition to users' free comments. The storage unit can also integrate users' social media activity data to enrich the stored data. For example, the storage unit integrates users' social media activity data to enrich the stored data. The storage unit can also integrate users' work history data to enrich the stored data. For example, the storage unit integrates users' work history data to enrich the stored data. In this way, the stored data is enriched by integrating information from different data sources. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can enrich the stored data using an AI model that inputs data from different data sources and outputs integrated stored data.

[0142] When providing the display method, the providing unit can select the optimal display method by referring to the user's past operation history. For example, the providing unit retrieves the user's past operation history from a database and analyzes it. For example, the providing unit preferentially provides display methods that the user has preferred in the past. The providing unit can also customize the display interface based on the user's past operation history. For example, the providing unit adjusts the display timing by reflecting the user's past operation history. This makes it possible to provide the optimal display method based on the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can select the display method using an AI model that inputs the user's past operation history data and outputs the optimal display method.

[0143] The providing unit can customize the display content according to the user's current task when providing the display content. For example, the providing unit obtains the user's current task from a task management tool or a project management tool and customizes the display content. For example, the providing unit prioritizes displaying comments related to the task the user is currently working on. The providing unit can also display appropriate comments according to the user's current task progress. For example, the providing unit customizes the display content based on the importance of the user's task. This makes it possible to provide appropriate display content according to the user's current task. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can customize the display content using an AI model that inputs the user's current task data and outputs customized display content.

[0144] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display. For example, the providing unit acquires the user's device information based on the device type and OS version and selects the optimal display method. For example, if the user is using a smartphone, the providing unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. For example, if the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is concise and highly visible. For example, if the user is using a smartwatch, the providing unit provides a display method that is concise and highly visible. This makes it possible to provide the optimal display method based on the user's device information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can select the display method using an AI model that inputs the user's device information and outputs the optimal display method.

[0145] The providing unit can estimate the user's emotions and adjust the operation procedure for the comment to be provided based on the estimated user's emotions. For example, the providing unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The providing unit can also analyze the user's tone and speed of voice using voice analysis technology to estimate the emotion. For example, the providing unit can analyze the user's tone and speed of voice and provide simple and intuitive operation procedures if the user is nervous. The providing unit can also provide detailed operation procedures if the user is relaxed. For example, the providing unit can provide detailed operation procedures if the user is relaxed. The providing unit can also provide procedures that allow quick operation if the user is in a hurry. For example, the providing unit can provide procedures that allow quick operation if the user is in a hurry. This makes it possible to provide a comment with appropriate operation procedures depending on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input user emotion data to the generating AI and cause the generating AI to estimate the emotion.

[0146] The providing unit can make the display content multilingual according to the user's language setting when providing the content. For example, the providing unit acquires the user's language setting from a user profile or browser settings and makes the display content multilingual. For example, the providing unit automatically sets the display content based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. For example, when the user selects a specific language, the providing unit provides the display content in that language. This makes it possible to provide multilingual display content based on the user's language setting. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can make the display content multilingual using an AI model that receives the user's language setting data as input and outputs multilingual display content.

[0147] The providing unit can analyze the user's social media activity and provide related information at the time of providing. For example, the providing unit obtains and analyzes the user's social media activity from a social media platform. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. For example, the providing unit can provide information about related places and events based on the activity of the user's friends on social media. This makes it possible to provide related information based on the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that inputs the user's social media activity data and outputs related information.

[0148] The providing unit can customize the display content by reflecting the user's past feedback when providing the display content. For example, the providing unit retrieves and analyzes the user's past feedback from a database. For example, the providing unit periodically updates the display content based on the user's past feedback. The providing unit can also improve the quality of the display content by reflecting the user's past feedback. For example, the providing unit optimizes the structure of the display content by referring to the user's past feedback. This makes it possible to provide appropriate display content based on the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can customize the display content using an AI model that inputs the user's past feedback data and outputs customized display content.

[0149] The processing flow of the second embodiment will be briefly explained below.

[0150] Step 1: The reception unit receives free comments from employees. Employee free comments can be in text, audio, image, or other formats. The reception unit directly inputs free comments in text format. It also converts free comments in audio format into text data using voice recognition technology, and converts free comments in image format into text data using image recognition technology. Step 2: The conversion unit uses natural language processing (NLP) algorithms to analyze the free comments received by the reception unit and convert them into unified phrases. The conversion unit analyzes the free comments using techniques such as morphological analysis, grammatical analysis, and semantic analysis, and converts them into unified phrases. Step 3: The storage unit stores the free comments converted by the conversion unit in a database such as a relational database or a NoSQL database. Step 4: The provider makes the free comments stored by the storage unit accessible to managers. The provider displays the free comments through a dedicated dashboard or mobile application, allowing managers to access the free comments via a browser, smartphone, or tablet.

[0151] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0156] 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.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] 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.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] 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.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0172] 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.

[0173] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0174] 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.

[0175] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0176] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0177] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0178] 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.

[0179] 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.

[0180] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0181] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0182] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0183] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0184] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0185] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0186] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0187] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0188] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0189] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0190] 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.

[0191] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0192] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0193] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0194] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0195] 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.

[0196] 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.

[0197] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0198] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0199] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0200] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0201] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0202] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0203] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0204] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0205] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0206] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0207] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0208] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0209] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0210] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0211] 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.

[0212] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0213] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0214] 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.

[0215] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0216] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0217] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0218] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0219] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0220] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0221] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0222] [Explanation of symbols]

[0223] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk that accepts free comments from employees, a conversion unit that analyzes the free comments received by the reception unit and converts them into unified phrases; a storage unit for storing the free comments converted by the conversion unit; a providing unit that allows managers to access the free comments stored by the storage unit; Equipped with A system characterized by:

2. The conversion unit Analyzing free comments using natural language processing algorithms to extract specific phrases or expressions 2. The system of claim 1.

3. The providing unit Provide managerial access through a dedicated dashboard 2. The system of claim 1.

4. The storage unit Save free comments to the database 2. The system of claim 1.

5. The providing unit Enable managers to analyze free comments expressed in a consistent manner 2. The system of claim 1.

6. The conversion unit When converting free comments into unified phrases, the entire sentence is analyzed and converted into unified phrases.

2. The system of claim 1.

7. The reception unit Estimates the user's emotions and adjusts the timing of accepting free comments based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past comment submission history and select the appropriate reception method 2. The system of claim 1.

9. The reception unit Filtering free comments based on users' current projects and areas of interest 2. The system of claim 1.

10. The reception unit When accepting free comments, select the appropriate acceptance method depending on the user's input method.

2. The system of claim 1.

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