System

Abstract

An object of a system according to an embodiment is to efficiently aggregate opinions of employees and propose an appropriate improvement measure.SOLUTION: A system includes a question execution unit, an opinion box setting unit, an opinion aggregation unit, and a report creation unit. A question execution part executes a descriptive question monthly. The opinion box setting part sets an opinion box in which an employee can always write an opinion. The opinion aggregation unit aggregates the opinions collected by the question execution unit and the opinion box installation unit. The report creation part creates a report on the basis of the opinions tabulated by the opinion tabulation part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to efficiently collect employee opinions and derive appropriate improvement measures.

[0005] The system according to the embodiment aims to efficiently collect employee opinions and propose appropriate improvement measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a question implementation unit, a suggestion box installation unit, an opinion counting unit, and a report creation unit. The question implementation unit implements descriptive questions monthly. The suggestion box installation unit installs a suggestion box where employees can write their opinions at any time. The opinion counting unit counts the opinions collected by the question implementation unit and the suggestion box installation unit. The report creation unit creates a report based on the opinions counted by the opinion counting unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect employee opinions and propose appropriate improvement measures. [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) The survey system according to the embodiment of the present invention is a system that can acquire descriptive questions (real voices) by utilizing the text summarization and editing functions of generative AI. This allows the survey system to efficiently collect employee opinions and quickly implement improvement measures based on them.

[0029] A survey system according to an embodiment includes a question-asking unit, a suggestion box installation unit, an opinion tallying unit, and a report creation unit. The question-asking unit asks employees descriptive questions monthly. For example, the question may be set monthly, such as "Please freely describe your thoughts about your work this month." The suggestion box installation unit installs a suggestion box where employees can write their opinions at any time. For example, the suggestion box may be provided not only as a physical box but also as an online form or application. The opinion tallying unit tallys the opinions collected by the question-asking unit and the suggestion box installation unit. For example, a generation AI analyzes the responses collected from employees and extracts common themes and important opinions. The report creation unit creates a report based on the opinions tallying the opinions by the opinion tallying unit. For example, the generation AI generates a report summarizing important opinions and common themes in a format such as "The main opinions this month are as follows." This allows the survey system according to an embodiment to efficiently collect employee opinions and quickly implement improvement measures based on them. For example, if employees can report problems they encounter while working in real time and then quickly implement improvement measures based on that information, it is expected that work efficiency will improve and employee satisfaction will increase.

[0030] The question implementation unit allows the generation AI to automatically classify answers to descriptive questions by theme and generate a detailed analysis report for each theme. For example, the question implementation unit allows the generation AI to automatically classify answers to descriptive questions by theme and generate a detailed analysis report for each theme. For example, it analyzes the answers and extracts common themes to create a report. The generation AI also automatically classifies answers to descriptive questions by theme and generates a detailed analysis report for each theme. For example, it analyzes the answers and extracts important themes to create a report. The question implementation unit allows the generation AI to automatically classify answers to descriptive questions by theme and generate a detailed analysis report for each theme. For example, it analyzes the answers and compiles opinions by theme to create a report. This makes it possible to classify employee opinions by theme and perform detailed analysis.

[0031] In the question implementation unit, the generation AI automatically translates the answers to the written questions and can also collect opinions from international employees. In the question implementation unit, for example, the generation AI automatically translates the answers to the written questions and also collects opinions from international employees. For example, the answers are translated into multiple languages ​​such as English and French and then collected. In addition, the generation AI automatically translates the answers to the written questions and also collects opinions from international employees. For example, a report is created based on the translated opinions. In addition, the generation AI automatically translates the answers to the written questions and also collects opinions from international employees. For example, improvement measures are proposed based on the translated opinions. This makes it possible to collect opinions from international employees and support multiple languages.

[0032] The question implementation unit allows the generation AI to collect answers to descriptive questions as voice data and convert them into text using voice recognition technology. For example, the question implementation unit allows the generation AI to collect answers to descriptive questions as voice data and convert them into text using voice recognition technology. For example, an employee records their answer verbally and converts it into text. The generation AI also collects answers to descriptive questions as voice data and converts it into text using voice recognition technology. For example, the voice data is analyzed and converted into text data. The generation AI also collects answers to descriptive questions as voice data and converts it into text using voice recognition technology. For example, a report is created based on the voice data. This allows the voice data to be converted into text and employee opinions to be collected in a variety of formats.

[0033] The suggestion box installation unit allows the generation AI to automatically classify opinions posted to the suggestion box and prioritize them according to importance. In the suggestion box installation unit, for example, the generation AI automatically classifies opinions posted to the suggestion box and prioritizes them according to importance. For example, it analyzes the content of the opinions and determines the priority based on the importance score. The generation AI also automatically classifies opinions posted to the suggestion box and prioritizes them according to importance. For example, it displays important opinions preferentially. The generation AI also automatically classifies opinions posted to the suggestion box and prioritizes them according to importance. For example, it classifies opinions based on the importance score and determines the priority. In this way, by prioritizing opinions according to their importance, it is possible to respond quickly to important opinions.

[0034] The suggestion box installation unit allows the generation AI to analyze opinions posted to the suggestion box and automatically generate solutions to specific problems. In the suggestion box installation unit, for example, the generation AI analyzes opinions posted to the suggestion box and automatically generates solutions to specific problems. For example, it analyzes the content of the opinions and proposes solutions. The generation AI also analyzes opinions posted to the suggestion box and automatically generates solutions to specific problems. For example, it identifies problems and proposes solutions. The generation AI also analyzes opinions posted to the suggestion box and automatically generates solutions to specific problems. For example, it proposes solutions based on the content of the opinions. This enables rapid response by automatically generating solutions to specific problems.

[0035] The suggestion box installation unit can enable the suggestion box to collect opinions not only through online forms or applications but also through chatbots. The suggestion box installation unit, for example, enables the suggestion box to collect opinions not only through online forms or applications but also through chatbots. For example, opinions are collected using a chatbot. The generation AI also enables the suggestion box to collect opinions not only through online forms or applications but also through chatbots. For example, opinions are collected using a chatbot and summarized. The suggestion box also enables the suggestion box to collect opinions not only through online forms or applications but also through chatbots. For example, opinions are collected using a chatbot and a report is created. This allows opinions to be collected in a variety of ways, making it possible to aggregate employee opinions more widely.

[0036] The suggestion box installation unit allows the generation AI to automatically translate opinions posted to the suggestion box and generate multilingual reports. The suggestion box installation unit, for example, allows the generation AI to automatically translate opinions posted to the suggestion box and generate multilingual reports. For example, a report is created by translating into multiple languages ​​such as English and French. The generation AI also automatically translates opinions posted to the suggestion box and generates multilingual reports. For example, a report is created based on the translated opinions. The generation AI also automatically translates opinions posted to the suggestion box and generates multilingual reports. For example, improvement measures are proposed based on the translated opinions. In this way, by generating multilingual reports, the opinions of international employees can also be reflected.

[0037] In the opinion aggregation section, the generation AI automatically visualizes the aggregated opinions and incorporates them into the report as graphs and charts. In the opinion aggregation section, for example, the generation AI automatically visualizes the aggregated opinions and incorporates them into the report as graphs and charts. For example, the frequency of opinions and distribution by theme are displayed in graphs. The generation AI also automatically visualizes the aggregated opinions and incorporates them into the report as graphs and charts. For example, the ratio of positive opinions to negative opinions is shown in a pie chart. The generation AI also automatically visualizes the aggregated opinions and incorporates them into the report as graphs and charts. For example, monthly fluctuations in opinions are displayed in a line graph. In this way, visualizing opinions makes the report easier to understand.

[0038] The opinion aggregation unit allows the generation AI to analyze the aggregated opinions and perform a detailed analysis of a specific theme, thereby providing in-depth insights. The opinion aggregation unit, for example, allows the generation AI to analyze the aggregated opinions and perform a detailed analysis of a specific theme, thereby providing in-depth insights. For example, it analyzes the trends in opinions on a specific theme. The generation AI also analyzes the aggregated opinions and performs a detailed analysis of a specific theme, thereby providing in-depth insights. For example, it analyzes the fluctuations in opinions for each theme. The generation AI also analyzes the aggregated opinions and performs a detailed analysis of a specific theme, thereby providing in-depth insights. For example, it analyzes the correlations between opinions for each theme. In this way, in-depth insights can be obtained by performing a detailed analysis of a specific theme.

[0039] The opinion aggregation unit allows the AI ​​that generates the aggregated opinions to automatically generate reports in different formats. For example, the AI ​​that generates the aggregated opinions may automatically generate reports in different formats. For example, it may summarize opinions as presentation slides. In addition, the generation AI may automatically generate reports of the aggregated opinions in different formats. For example, it may summarize opinions in video format. In addition, the AI ​​that generates the aggregated opinions may automatically generate reports in different formats. For example, it may visualize opinions as infographics. This allows for diversifying reporting methods by generating reports in different formats.

[0040] The opinion aggregation unit allows the generation AI to analyze the aggregated opinions and generate reports customized for different departments or teams. In the opinion aggregation unit, for example, the generation AI analyzes the aggregated opinions and generates reports customized for different departments or teams. For example, a report summarizing opinions for each department is created. The generation AI also analyzes the aggregated opinions and generates reports customized for different departments or teams. For example, a report summarizing opinions for each team is created. The generation AI also analyzes the aggregated opinions and generates reports customized for different departments or teams. For example, a report summarizing opinion trends for each department is created. In this way, by generating reports customized for each department or team, it becomes possible to provide reports that meet the needs of each department or team.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] When collecting employee opinions, the Questionnaire Implementation Department can provide customized questions based on the employee's skills and experience. For example, it can set questions about basic work for new employees and questions about specialized work for experienced employees. It can also customize questions according to the employee's position or department. This allows it to obtain appropriate feedback according to each employee's situation.

[0043] The questionnaire implementation unit can provide a function to ensure anonymity when collecting employee opinions. For example, it can allow employees to submit their opinions anonymously. It can also process data to ensure that the content of opinions is not linked to specific individuals. It can also provide guidelines to ensure anonymity and create an environment where employees can submit their opinions with peace of mind. This allows employees to express their opinions freely.

[0044] The questionnaire implementation department can incorporate game elements when collecting employee opinions. For example, they can introduce a system where employees earn points by submitting opinions and receive rewards when they reach a certain number of points. They can also award badges and titles based on the frequency and quality of opinions submitted. They can also display a ranking of opinion submissions to encourage competition among employees. This can increase employees' motivation to submit their opinions.

[0045] The suggestion box installation unit can allow employees to use video messages when submitting their opinions. For example, employees can record video messages using their smartphones or computers and post them to the suggestion box. It can also provide a function to convert video messages into text. It can also analyze the content of video messages and extract important opinions. This allows employees to submit their opinions in a wider variety of ways.

[0046] When an employee submits a suggestion, the suggestion box installation unit can automatically suggest related past suggestions and solutions based on the content of the suggestion. For example, if a similar suggestion has been made in the past in response to the suggestion submitted by an employee, it will display that suggestion and its solution. It can also provide related resources and documents. It can also compare past and current suggestions to show progress on improvement. This allows for effective use of employee suggestions.

[0047] When an employee submits a suggestion box, the suggestion box can automatically suggest relevant training and education programs based on the content of the suggestion box. For example, if an employee submits a suggestion about a specific skill, training programs related to that skill will be displayed. It can also provide resources to strengthen the necessary skills and knowledge based on the employee's suggestion. It can also suggest customized training plans based on the employee's suggestion. This can support the improvement of employees' skills.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The Questioning Department will ask employees to write descriptive questions monthly. For example, they will be asked to write freely about their impressions of their work this month. Step 2: The suggestion box installation department will install a suggestion box where employees can leave their opinions at any time. For example, this can be provided as a physical box, or as an online form or application. Step 3: The opinion aggregation unit aggregates the opinions collected by the questioning unit and the suggestion box installation unit. For example, the generation AI analyzes the responses collected from employees and extracts common themes and important opinions. Step 4: The report creation unit creates a report based on the opinions collected by the opinion collection unit. For example, the generation AI generates a report summarizing important opinions and common themes in a format such as, "The main opinions this month are as follows."

[0050] (Example 2) The survey system according to the embodiment of the present invention is a system that can acquire descriptive questions (real voices) by utilizing the text summarization and editing functions of generative AI. This allows the survey system to efficiently collect employee opinions and quickly implement improvement measures based on them.

[0051] A survey system according to an embodiment includes a question-asking unit, a suggestion box installation unit, an opinion tallying unit, and a report creation unit. The question-asking unit asks employees descriptive questions monthly. For example, the question may be set monthly, such as "Please freely describe your thoughts about your work this month." The suggestion box installation unit installs a suggestion box where employees can write their opinions at any time. For example, the suggestion box may be provided not only as a physical box but also as an online form or application. The opinion tallying unit tallys the opinions collected by the question-asking unit and the suggestion box installation unit. For example, a generation AI analyzes the responses collected from employees and extracts common themes and important opinions. The report creation unit creates a report based on the opinions tallying the opinions by the opinion tallying unit. For example, the generation AI generates a report summarizing important opinions and common themes in a format such as "The main opinions this month are as follows." This allows the survey system according to an embodiment to efficiently collect employee opinions and quickly implement improvement measures based on them. For example, if employees can report problems they encounter while working in real time and then quickly implement improvement measures based on that information, it is expected that work efficiency will improve and employee satisfaction will increase.

[0052] The question implementation unit allows the generation AI to perform sentiment analysis on answers to written questions and automatically classify opinions into positive and negative. For example, the question implementation unit allows the generation AI to perform sentiment analysis on answers to written questions and automatically classify opinions into positive and negative. For example, it analyzes employee answers and classifies opinions based on sentiment scores. The generation AI also performs sentiment analysis on answers to written questions and automatically classifies opinions into positive and negative. For example, it classifies opinions based on sentiment scores and displays positive opinions preferentially. The generation AI also performs sentiment analysis on answers to written questions and automatically classifies opinions into positive and negative. For example, it classifies opinions based on sentiment scores and suggests countermeasures for negative opinions. This makes it possible to classify employee opinions based on their emotions and respond appropriately.

[0053] The question implementation unit allows the generation AI to automatically classify answers to descriptive questions by theme and generate a detailed analysis report for each theme. For example, the question implementation unit allows the generation AI to automatically classify answers to descriptive questions by theme and generate a detailed analysis report for each theme. For example, it analyzes the answers and extracts common themes to create a report. The generation AI also automatically classifies answers to descriptive questions by theme and generates a detailed analysis report for each theme. For example, it analyzes the answers and extracts important themes to create a report. The question implementation unit allows the generation AI to automatically classify answers to descriptive questions by theme and generate a detailed analysis report for each theme. For example, it analyzes the answers and compiles opinions by theme to create a report. This makes it possible to classify employee opinions by theme and perform detailed analysis.

[0054] The question implementation unit can use the emotion estimation function to analyze employee emotions in real time and track emotional fluctuations on a monthly basis. The question implementation unit, for example, uses the emotion estimation function to analyze employee emotions in real time and track emotional fluctuations on a monthly basis. For example, it analyzes employee responses and tracks emotional fluctuations based on an emotion score. The generation AI also uses the emotion estimation function to analyze employee emotions in real time and track emotional fluctuations on a monthly basis. For example, it displays emotional fluctuations in a graph based on the emotion score. The emotion estimation function also analyzes employee emotions in real time and tracks emotional fluctuations on a monthly basis. For example, it compiles emotional fluctuations into a report based on the emotion score. This makes it possible to track employee emotional fluctuations in real time and take appropriate action.

[0055] In the question implementation unit, the generation AI automatically translates the answers to the written questions and can also collect opinions from international employees. In the question implementation unit, for example, the generation AI automatically translates the answers to the written questions and also collects opinions from international employees. For example, the answers are translated into multiple languages ​​such as English and French and then collected. In addition, the generation AI automatically translates the answers to the written questions and also collects opinions from international employees. For example, a report is created based on the translated opinions. In addition, the generation AI automatically translates the answers to the written questions and also collects opinions from international employees. For example, improvement measures are proposed based on the translated opinions. This makes it possible to collect opinions from international employees and support multiple languages.

[0056] The question implementation unit allows the generation AI to collect answers to descriptive questions as voice data and convert them into text using voice recognition technology. For example, the question implementation unit allows the generation AI to collect answers to descriptive questions as voice data and convert them into text using voice recognition technology. For example, an employee records their answer verbally and converts it into text. The generation AI also collects answers to descriptive questions as voice data and converts it into text using voice recognition technology. For example, the voice data is analyzed and converted into text data. The generation AI also collects answers to descriptive questions as voice data and converts it into text using voice recognition technology. For example, a report is created based on the voice data. This allows the voice data to be converted into text and employee opinions to be collected in a variety of formats.

[0057] The question implementation unit uses the emotion estimation function to provide real-time feedback on the emotions expressed by employees when they answer essay questions, thereby eliciting positive opinions. The question implementation unit, for example, uses the emotion estimation function to provide real-time feedback on the emotions expressed by employees when they answer essay questions, thereby eliciting positive opinions. For example, feedback is provided based on an emotion score. Furthermore, the generation AI uses the emotion estimation function to provide real-time feedback on the emotions expressed by employees when they answer essay questions, thereby eliciting positive opinions. For example, a message emphasizing positive emotions is displayed. Furthermore, the emotion estimation function is used to provide real-time feedback on the emotions expressed by employees when they answer essay questions, thereby eliciting positive opinions. For example, suggestions are made to elicit positive opinions based on the emotion score. In this way, it is possible to provide real-time feedback on employees' emotions and elicit positive opinions.

[0058] The suggestion box installation unit allows the generation AI to automatically classify opinions posted to the suggestion box and prioritize them according to importance. In the suggestion box installation unit, for example, the generation AI automatically classifies opinions posted to the suggestion box and prioritizes them according to importance. For example, it analyzes the content of the opinions and determines the priority based on the importance score. The generation AI also automatically classifies opinions posted to the suggestion box and prioritizes them according to importance. For example, it displays important opinions preferentially. The generation AI also automatically classifies opinions posted to the suggestion box and prioritizes them according to importance. For example, it classifies opinions based on the importance score and determines the priority. In this way, by prioritizing opinions according to their importance, it is possible to respond quickly to important opinions.

[0059] The suggestion box installation unit allows the generation AI to analyze opinions posted to the suggestion box and automatically generate solutions to specific problems. In the suggestion box installation unit, for example, the generation AI analyzes opinions posted to the suggestion box and automatically generates solutions to specific problems. For example, it analyzes the content of the opinions and proposes solutions. The generation AI also analyzes opinions posted to the suggestion box and automatically generates solutions to specific problems. For example, it identifies problems and proposes solutions. The generation AI also analyzes opinions posted to the suggestion box and automatically generates solutions to specific problems. For example, it proposes solutions based on the content of the opinions. This enables rapid response by automatically generating solutions to specific problems.

[0060] The suggestion box installation unit can use the emotion estimation function to analyze the emotional tone of opinions posted to the suggestion box and encourage a quick response to negative opinions. The suggestion box installation unit, for example, uses the emotion estimation function to analyze the emotional tone of opinions posted to the suggestion box and encourage a quick response to negative opinions. For example, it identifies negative opinions based on an emotion score. Furthermore, the generation AI uses the emotion estimation function to analyze the emotional tone of opinions posted to the suggestion box and encourages a quick response to negative opinions. For example, it suggests a response to negative opinions. Furthermore, the emotion estimation function can be used to analyze the emotional tone of opinions posted to the suggestion box and encourage a quick response to negative opinions. For example, it processes negative opinions preferentially based on the emotion score. This makes it possible to quickly resolve employee dissatisfaction by responding quickly to negative opinions.

[0061] The suggestion box installation unit can enable the suggestion box to collect opinions not only through online forms or applications but also through chatbots. The suggestion box installation unit, for example, enables the suggestion box to collect opinions not only through online forms or applications but also through chatbots. For example, opinions are collected using a chatbot. The generation AI also enables the suggestion box to collect opinions not only through online forms or applications but also through chatbots. For example, opinions are collected using a chatbot and summarized. The suggestion box also enables the suggestion box to collect opinions not only through online forms or applications but also through chatbots. For example, opinions are collected using a chatbot and a report is created. This allows opinions to be collected in a variety of ways, making it possible to aggregate employee opinions more widely.

[0062] The suggestion box installation unit allows the generation AI to automatically translate opinions posted to the suggestion box and generate multilingual reports. The suggestion box installation unit, for example, allows the generation AI to automatically translate opinions posted to the suggestion box and generate multilingual reports. For example, a report is created by translating into multiple languages ​​such as English and French. The generation AI also automatically translates opinions posted to the suggestion box and generates multilingual reports. For example, a report is created based on the translated opinions. The generation AI also automatically translates opinions posted to the suggestion box and generates multilingual reports. For example, improvement measures are proposed based on the translated opinions. In this way, by generating multilingual reports, the opinions of international employees can also be reflected.

[0063] The suggestion box installation unit can use the emotion estimation function to analyze the emotional tone of the opinions posted to the suggestion box and provide feedback that emphasizes positive opinions. The suggestion box installation unit, for example, uses the emotion estimation function to analyze the emotional tone of the opinions posted to the suggestion box and provide feedback that emphasizes positive opinions. For example, it emphasizes positive opinions based on the emotion score. Furthermore, the generation AI uses the emotion estimation function to analyze the emotional tone of the opinions posted to the suggestion box and provide feedback that emphasizes positive opinions. For example, it preferentially displays positive opinions. Furthermore, it uses the emotion estimation function to analyze the emotional tone of the opinions posted to the suggestion box and provide feedback that emphasizes positive opinions. For example, it makes a suggestion to emphasize positive opinions based on the emotion score. In this way, it is possible to improve employee motivation by emphasizing positive opinions.

[0064] In the opinion aggregation section, the generation AI automatically visualizes the aggregated opinions and incorporates them into the report as graphs and charts. In the opinion aggregation section, for example, the generation AI automatically visualizes the aggregated opinions and incorporates them into the report as graphs and charts. For example, the frequency of opinions and distribution by theme are displayed in graphs. The generation AI also automatically visualizes the aggregated opinions and incorporates them into the report as graphs and charts. For example, the ratio of positive opinions to negative opinions is shown in a pie chart. The generation AI also automatically visualizes the aggregated opinions and incorporates them into the report as graphs and charts. For example, monthly fluctuations in opinions are displayed in a line graph. In this way, visualizing opinions makes the report easier to understand.

[0065] The opinion aggregation unit allows the generation AI to analyze the aggregated opinions and perform a detailed analysis of a specific theme, thereby providing in-depth insights. The opinion aggregation unit, for example, allows the generation AI to analyze the aggregated opinions and perform a detailed analysis of a specific theme, thereby providing in-depth insights. For example, it analyzes the trends in opinions on a specific theme. The generation AI also analyzes the aggregated opinions and performs a detailed analysis of a specific theme, thereby providing in-depth insights. For example, it analyzes the fluctuations in opinions for each theme. The generation AI also analyzes the aggregated opinions and performs a detailed analysis of a specific theme, thereby providing in-depth insights. For example, it analyzes the correlations between opinions for each theme. In this way, in-depth insights can be obtained by performing a detailed analysis of a specific theme.

[0066] The opinion aggregation unit can use the emotion estimation function to analyze the emotional trends of the aggregated opinions and reflect the emotional fluctuations in the report. The opinion aggregation unit, for example, uses the emotion estimation function to analyze the emotional trends of the aggregated opinions and reflect the emotional fluctuations in the report. For example, the fluctuations in positive and negative opinions are displayed in a graph. The generation AI also uses the emotion estimation function to analyze the emotional trends of the aggregated opinions and reflect the emotional fluctuations in the report. For example, the fluctuations in emotion scores are displayed in a line graph. The emotion estimation function also analyzes the emotional trends of the aggregated opinions and reflects the emotional fluctuations in the report. For example, monthly emotional fluctuations are compiled in a report. In this way, by analyzing emotional trends and reflecting them in the report, it is possible to understand the emotional fluctuations of employees.

[0067] The opinion aggregation unit allows the AI ​​that generates the aggregated opinions to automatically generate reports in different formats. For example, the AI ​​that generates the aggregated opinions may automatically generate reports in different formats. For example, it may summarize opinions as presentation slides. In addition, the generation AI may automatically generate reports of the aggregated opinions in different formats. For example, it may summarize opinions in video format. In addition, the AI ​​that generates the aggregated opinions may automatically generate reports in different formats. For example, it may visualize opinions as infographics. This allows for diversifying reporting methods by generating reports in different formats.

[0068] The opinion aggregation unit allows the generation AI to analyze the aggregated opinions and generate reports customized for different departments or teams. In the opinion aggregation unit, for example, the generation AI analyzes the aggregated opinions and generates reports customized for different departments or teams. For example, a report summarizing opinions for each department is created. The generation AI also analyzes the aggregated opinions and generates reports customized for different departments or teams. For example, a report summarizing opinions for each team is created. The generation AI also analyzes the aggregated opinions and generates reports customized for different departments or teams. For example, a report summarizing opinion trends for each department is created. In this way, by generating reports customized for each department or team, it becomes possible to provide reports that meet the needs of each department or team.

[0069] The opinion aggregation unit can use the emotion estimation function to analyze the emotional trends of the aggregated opinions and generate a report that emphasizes positive opinions. The opinion aggregation unit, for example, uses the emotion estimation function to analyze the emotional trends of the aggregated opinions and generate a report that emphasizes positive opinions. For example, a report that emphasizes positive opinions is created. The generation AI also uses the emotion estimation function to analyze the emotional trends of the aggregated opinions and generate a report that emphasizes positive opinions. For example, positive opinions are preferentially displayed. The emotion estimation function also analyzes the emotional trends of the aggregated opinions and generates a report that emphasizes positive opinions. For example, positive opinions are emphasized based on the emotion score. In this way, by emphasizing positive opinions, the content of the report becomes more positive.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] When collecting employee opinions, the Questionnaire Implementation Department can provide customized questions based on the employee's skills and experience. For example, it can set questions about basic work for new employees and questions about specialized work for experienced employees. It can also customize questions according to the employee's position or department. This allows it to obtain appropriate feedback according to each employee's situation.

[0072] The questionnaire implementation unit can provide individualized feedback based on employees' responses. For example, the generation AI can make specific improvement suggestions in response to opinions submitted by employees. It can also automatically generate messages of thanks for employees' opinions. Furthermore, it can suggest related training and resources based on employees' opinions. This ensures that employees' opinions are respected and encourages concrete action.

[0073] The questionnaire implementation unit can provide a function to ensure anonymity when collecting employee opinions. For example, it can allow employees to submit their opinions anonymously. It can also process data to ensure that the content of opinions is not linked to specific individuals. It can also provide guidelines to ensure anonymity and create an environment where employees can submit their opinions with peace of mind. This allows employees to express their opinions freely.

[0074] The questionnaire implementation department can incorporate game elements when collecting employee opinions. For example, they can introduce a system where employees earn points by submitting opinions and receive rewards when they reach a certain number of points. They can also award badges and titles based on the frequency and quality of opinions submitted. They can also display a ranking of opinion submissions to encourage competition among employees. This can increase employees' motivation to submit their opinions.

[0075] When collecting employee opinions, the questionnaire implementation department can use the emotion estimation function to analyze employees' stress levels and provide appropriate support. For example, it can detect signs of stress from employees' responses and suggest counseling or mental health support as needed. It can also provide relaxation techniques and stress management resources to employees with high stress levels. This supports employees' mental health and creates a comfortable working environment.

[0076] The suggestion box installation unit can allow employees to use video messages when submitting their opinions. For example, employees can record video messages using their smartphones or computers and post them to the suggestion box. It can also provide a function to convert video messages into text. It can also analyze the content of video messages and extract important opinions. This allows employees to submit their opinions in a wider variety of ways.

[0077] When an employee submits a suggestion, the suggestion box installation unit uses an emotion estimation function to analyze the employee's emotions in real time and provide feedback according to their emotions. For example, if an employee expresses negative emotions, it displays an encouraging message. If an employee expresses positive emotions, it displays a message of gratitude. It is also possible to suggest resources and support according to their emotions. This allows for appropriate responses according to the employee's emotions.

[0078] When an employee submits a suggestion, the suggestion box installation unit can automatically suggest related past suggestions and solutions based on the content of the suggestion. For example, if a similar suggestion has been made in the past in response to the suggestion submitted by an employee, it will display that suggestion and its solution. It can also provide related resources and documents. It can also compare past and current suggestions to show progress on improvement. This allows for effective use of employee suggestions.

[0079] When an employee submits a suggestion box, the suggestion box can automatically suggest relevant training and education programs based on the content of the suggestion box. For example, if an employee submits a suggestion about a specific skill, training programs related to that skill will be displayed. It can also provide resources to strengthen the necessary skills and knowledge based on the employee's suggestion. It can also suggest customized training plans based on the employee's suggestion. This can support the improvement of employees' skills.

[0080] The suggestion box installation unit uses the emotion estimation function to analyze the emotions employees feel when submitting their opinions, and can provide rewards and incentives based on their emotions. For example, employees who submit positive opinions can be offered special rewards and incentives. Employees who submit negative opinions can also be offered resources for improvement along with a message of gratitude. Furthermore, it is possible to provide feedback based on emotions to increase employee motivation. This can increase employees' willingness to submit their opinions.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The Questioning Department will ask employees to write descriptive questions monthly. For example, they will be asked to write freely about their impressions of their work this month. Step 2: The suggestion box installation department will install a suggestion box where employees can leave their opinions at any time. For example, this can be provided as a physical box, or as an online form or application. Step 3: The opinion aggregation unit aggregates the opinions collected by the questioning unit and the suggestion box installation unit. For example, the generation AI analyzes the responses collected from employees and extracts common themes and important opinions. Step 4: The report creation unit creates a report based on the opinions collected by the opinion collection unit. For example, the generation AI generates a report summarizing important opinions and common themes in a format such as, "The main opinions this month are as follows."

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] In the robot 414, 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 robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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. [Explanation of symbols]

[0150] 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 question implementation department that conducts descriptive questions monthly; A suggestion box installation department that installs suggestion boxes where employees can write their opinions at any time; an opinion counting unit that counts the opinions collected by the questioning unit and the suggestion box installation unit; a report creation unit that creates a report based on the opinions collected by the opinion collection unit. A system characterized by:

2. The question implementation unit: Generate answers to written questions. AI analyzes emotions and automatically categorizes positive and negative opinions.

2. The system of claim 1.

3. The question implementation unit: Generate answers to descriptive questions. AI automatically classifies answers by theme and generates a detailed analysis report for each theme.

2. The system of claim 1.

4. The question implementation unit: Analyze the employee's emotions in real time and track fluctuations in those emotions monthly.

2. The system of claim 1.

5. The question implementation unit: Answers to written questions are generated and automatically translated by AI, and opinions from international employees are also collected.

2. The system of claim 1.

6. The question implementation unit: Generate answers to written questions. AI also collects voice data and converts it into text using voice recognition technology.

2. The system of claim 1.

7. The question implementation unit: Real-time feedback on the employee's emotions as they answer written questions is provided to elicit positive feedback.

2. The system of claim 1.

8. The suggestion box installation unit The AI ​​automatically classifies the opinions posted in the suggestion box and prioritizes them according to their importance.

2. The system of claim 1.

Images