system

Abstract

The system according to the embodiment aims to understand the emotions and psychological states of employees and customers and propose appropriate countermeasures. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects text data from employees or customers. The analysis unit analyzes the text data collected by the collection unit to determine emotions and psychological states. The proposal unit proposes countermeasures based on the analysis results obtained by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to accurately grasp the emotions and psychological state of employees and customers and quickly propose appropriate countermeasures.

[0005] The system according to the embodiment aims to understand the emotions and psychological states of employees and customers and propose appropriate countermeasures. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects text data from employees or customers. The analysis unit analyzes the text data collected by the collection unit to determine emotions and psychological states. The proposal unit proposes countermeasures based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the emotions and psychological state of employees and customers and propose appropriate countermeasures. [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 pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example 1) An emotion analysis system according to an embodiment of the present invention is a system for understanding the emotions and psychological states of employees and customers and providing effective responses. This emotion analysis system collects text data from employees and customers, analyzes it using AI to determine their emotions and psychological states, and proposes appropriate countermeasures. For example, it detects stress and dissatisfaction from the content of employees' emails and chats and understands customer satisfaction and needs from customer feedback. Based on the analysis results, it proposes counseling and training to employees to reduce stress and provides personalized services and promotions to customers. This system enables companies and organizations to understand the emotions and psychological states of employees in real time and respond quickly. It also accurately understands customer emotions and needs and can develop more effective marketing strategies. This improves organizational productivity and employee happiness, contributing to improved customer satisfaction and strengthened competitiveness. This allows the emotion analysis system to understand the emotions and psychological states of employees and customers in real time and respond quickly.

[0029] A sentiment analysis system according to an embodiment includes a collection unit, an analysis unit, and a suggestion unit. The collection unit collects text data from employees or customers. Examples of text data from employees or customers include, but are not limited to, emails, chats, reviews, and surveys. The collection unit can collect, for example, the content of employees' emails and chats. The collection unit can also collect customer feedback. The analysis unit analyzes the text data collected by the collection unit to determine emotions and psychological states. The analysis unit can, for example, determine employee stress and dissatisfaction using an emotion analysis algorithm. The analysis unit can also determine customer satisfaction and needs. The suggestion unit proposes countermeasures based on the analysis results obtained by the analysis unit. For example, the suggestion unit can propose counseling or training to employees to reduce stress. The suggestion unit can also propose personalized services and promotions to customers. As a result, the sentiment analysis system according to an embodiment can grasp the emotions and psychological states of employees and customers and propose appropriate countermeasures.

[0030] The collection unit can collect the contents of employees' emails or chats. For example, the collection unit can collect employees' work emails. For example, the collection unit can automatically filter employees' work emails and extract data necessary for emotion analysis. The collection unit can also collect the contents of employees' personal chats. For example, the collection unit can analyze the contents of employees' personal chats to detect signs of stress or dissatisfaction. Furthermore, the collection unit can collect the contents of employees' group chats. For example, the collection unit can analyze the contents of employees' group chats to understand the emotional state of the entire team. In this way, by collecting the contents of employees' emails and chats, it is possible to understand their emotions and psychological states.

[0031] The analysis unit can analyze the collected text data and determine the employee's stress or dissatisfaction. The analysis unit analyzes the collected text data using, for example, an emotion analysis algorithm. For example, the analysis unit analyzes the frequency of occurrence of keywords indicating stress or dissatisfaction from the content of the employee's emails or chats. The analysis unit can also quantify the employee's emotional state using emotion scoring. For example, the analysis unit analyzes the content of the employee's emails or chats and calculates an emotion score. Furthermore, the analysis unit can determine the employee's psychological state using natural language processing technology. For example, the analysis unit analyzes the content of the employee's emails or chats to detect signs of stress or dissatisfaction. This can determine the employee's stress or dissatisfaction and suggest appropriate countermeasures.

[0032] The suggestion unit can suggest counseling or training to the employee for stress reduction based on the analysis results. The suggestion unit can, for example, suggest online counseling. For example, the suggestion unit can suggest making an appointment for online counseling to reduce the employee's stress. The suggestion unit can also suggest stress management training. For example, the suggestion unit can suggest participating in stress management training to reduce the employee's stress. Furthermore, the suggestion unit can also suggest a relaxation program. For example, the suggestion unit can suggest using a relaxation program to reduce the employee's stress. In this way, by suggesting counseling or training to reduce stress to the employee, the employee's stress can be reduced.

[0033] The collection unit can collect customer reviews and survey feedback. The collection unit can, for example, collect customer product reviews. For example, the collection unit automatically collects customer product reviews on an online platform. The collection unit can also collect customer satisfaction survey feedback. For example, the collection unit automatically collects responses to customer satisfaction surveys and uses them for analysis. The collection unit can also collect customer feedback on social media. For example, the collection unit collects customers' posts and comments on social media and uses them for analysis. In this way, by collecting customer feedback, it is possible to understand customer sentiment and needs.

[0034] The analysis unit can analyze the collected feedback and determine customer satisfaction or needs. The analysis unit analyzes the collected feedback using, for example, a sentiment analysis algorithm. For example, the analysis unit analyzes the frequency of occurrence of keywords indicating satisfaction or needs from the content of customer reviews and questionnaires. The analysis unit can also quantify the emotional state of the customer using sentiment scoring. For example, the analysis unit analyzes the content of customer reviews and questionnaires and calculates a sentiment score. Furthermore, the analysis unit can determine customer satisfaction or needs using natural language processing technology. For example, the analysis unit analyzes the content of customer reviews and questionnaires to detect signs of satisfaction or needs. This can determine customer satisfaction or needs and suggest appropriate countermeasures.

[0035] The suggestion unit can suggest personalized services or promotions to the customer based on the analysis results. The suggestion unit can, for example, suggest individual discount offers. For example, the suggestion unit suggests individual discount offers based on the customer's satisfaction level and needs. The suggestion unit can also make customized service suggestions. For example, the suggestion unit suggests customized services based on the customer's satisfaction level and needs. Furthermore, the suggestion unit can also suggest specific promotions. For example, the suggestion unit suggests specific promotions based on the customer's satisfaction level and needs. This makes it possible to improve customer satisfaction by suggesting personalized services and promotions to the customer.

[0036] The sentiment analysis system further includes a collection unit that analyzes past communication histories of employees and customers and selects the optimal collection method. The collection unit can, for example, analyze the frequency and content of past emails and chats to select the most effective collection method. The collection unit can also prioritize the collection of messages containing specific keywords based on the content of past feedback. The collection unit can also analyze past communication patterns and determine the optimal collection timing. This allows the optimal collection method to be selected by analyzing the past communication history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past communication histories into a generation AI and have the generation AI select the optimal collection method.

[0037] The sentiment analysis system further includes a collection unit that, when collecting text data, filters the data based on the employee's or customer's current projects or areas of interest. For example, the collection unit can prioritize the collection of messages related to ongoing projects. The collection unit can also filter and collect messages containing keywords related to the employee's or customer's areas of interest. Furthermore, the collection unit can collect messages related to specific projects and understand the progress of the projects. By filtering based on the current projects or areas of interest, highly relevant data can be collected. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data related to projects or areas of interest into a generation AI and have the generation AI perform the filtering.

[0038] The sentiment analysis system further includes a collection unit that, when collecting text data, prioritizes the collection of highly relevant data based on the geographical location information of employees or customers. For example, if an employee or customer is in a specific area, the collection unit can prioritize the collection of messages related to that area. The collection unit can also collect messages related to specific events or situations based on the geographical location information. The collection unit can also collect data for understanding emotions and needs by area, taking the geographical location information into account. This allows the collection of highly relevant data to be prioritized by taking the geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input geographical location information into a generation AI and cause the generation AI to collect highly relevant data.

[0039] The sentiment analysis system further includes a collection unit that analyzes the social media activities of employees or customers and collects related data when collecting text data. The collection unit can, for example, analyze the content of posts on social media to collect data related to emotions and needs. The collection unit can also analyze the activity patterns of employees and customers on social media and determine the optimal collection timing. The collection unit can also collect related data based on the interests of employees and customers on social media. In this way, related data can be collected by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media activities into a generation AI and cause the generation AI to collect related data.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the text data. For example, the analysis unit can perform a detailed analysis on messages with high importance. The analysis unit can also perform a simplified analysis on messages with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance. In this way, by adjusting the level of detail of the analysis based on the importance of the text data, more important data can be analyzed in detail. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the text data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the type of text data. For example, the analysis unit can apply a stress analysis algorithm to messages related to stress. The analysis unit can also apply a dissatisfaction analysis algorithm to messages related to dissatisfaction. Furthermore, the analysis unit can apply a motivation analysis algorithm to messages related to motivation. In this way, by applying different analysis algorithms depending on the type of text data, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the type of text data to the generation AI and cause the generation AI to apply different analysis algorithms.

[0042] During analysis, the analysis unit can determine the priority of analysis based on the submission date and time of the text data. For example, the analysis unit can prioritize analysis of recently submitted text data. The analysis unit can also prioritize analysis of data with high urgency based on the time of submission. Furthermore, the analysis unit can adjust the order of analysis according to the time of submission. In this way, by determining the priority of analysis based on the time of submission of the text data, data with high urgency can be analyzed with priority. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission date and time to the generation AI and have the generation AI determine the analysis priority.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the text data. For example, the analysis unit can prioritize analyzing highly relevant messages. The analysis unit can also postpone analyzing less relevant messages. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance. In this way, by adjusting the order of analysis based on the relevance of the text data, more relevant data can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the text data to the generation AI and cause the generation AI to adjust the order of analysis.

[0044] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the analysis result. For example, the proposal unit can make a detailed proposal for an analysis result with high importance. The proposal unit can also make a simplified proposal for an analysis result with low importance. Furthermore, the proposal unit can determine the priority of the proposal according to the importance. As a result, by adjusting the level of detail of the proposal based on the importance of the analysis result, more important proposals can be given priority. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the importance of the analysis result to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0045] When making a proposal, the proposal unit can apply different proposal algorithms depending on the type of analysis result. For example, the proposal unit can apply a proposal algorithm for stress reduction to analysis results related to stress. The proposal unit can also apply a proposal algorithm for dissatisfaction relief to analysis results related to dissatisfaction. The proposal unit can also apply a proposal algorithm for motivation improvement to analysis results related to motivation. In this way, by applying different proposal algorithms depending on the type of analysis result, more appropriate proposals can be provided. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the type of analysis result to the generation AI and cause the generation AI to apply different proposal algorithms.

[0046] When making a proposal, the suggestion unit can determine the priority of the proposal based on the submission date and time of the analysis result. The suggestion unit can, for example, prioritize proposals based on the most recently submitted analysis result. The suggestion unit can also prioritize proposals with a high urgency based on the submission date and time. Furthermore, the suggestion unit can adjust the order of proposals according to the submission date and time. In this way, by determining the priority of proposals based on the submission date and time of the analysis result, it is possible to prioritize proposals with a high urgency. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the submission date and time to the generation AI and have the generation AI determine the priority of the proposals.

[0047] The suggestion unit can adjust the order of suggestions based on the relevance of the analysis results when making suggestions. For example, the suggestion unit can prioritize suggestions based on highly relevant analysis results. The suggestion unit can also postpone suggestions based on less relevant analysis results. Furthermore, the suggestion unit can dynamically adjust the order of suggestions according to the relevance. As a result, by adjusting the order of suggestions based on the relevance of the analysis results, more relevant suggestions can be prioritized. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the relevance of the analysis results to the generation AI and cause the generation AI to adjust the order of suggestions.

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

[0049] The emotion analysis system may further include a voice analysis unit that collects and analyzes voice data. The voice analysis unit can, for example, collect telephone conversations and voice messages between employees and customers and analyze their emotions and psychological states. For example, the voice analysis unit can detect stress and dissatisfaction from employee telephone conversations and understand customer satisfaction and needs from their voice messages. The voice analysis unit can also analyze parameters such as tone, speed, and volume of the voice to more accurately determine the emotional state. This makes it possible to understand emotions and psychological states from a more multifaceted perspective by utilizing not only text data but also voice data.

[0050] The emotion analysis system may further include an image analysis unit that collects and analyzes image data. The image analysis unit may analyze, for example, the facial expressions and gestures of employees and customers to determine their emotions and psychological states. For example, the image analysis unit may detect stress or dissatisfaction from employees' facial expressions and grasp customer satisfaction and needs from their facial expressions. The image analysis unit may also analyze gestures and changes in posture to more accurately determine emotional states. This allows for a more multifaceted understanding of emotions and psychological states by utilizing image data in addition to text data and voice data.

[0051] The emotion analysis system may further include a biometric analysis unit that collects and analyzes biometric data. The biometric analysis unit may collect biometric data, such as the heart rate and electrodermal activity of employees and customers, and analyze their emotions and psychological states. For example, the biometric analysis unit may detect stress and dissatisfaction from fluctuations in an employee's heart rate, and may understand customer satisfaction and needs from their electrodermal activity. The biometric analysis unit may also combine and analyze multiple pieces of biometric data to more accurately determine emotional states. This allows for a more multifaceted understanding of emotions and psychological states by utilizing biometric data in addition to text data, voice data, and image data.

[0052] The emotion analysis system may further include a behavioral analysis unit that collects and analyzes user behavioral data. The behavioral analysis unit may, for example, collect website browsing history and app usage history of employees and customers and analyze their emotions and psychological states. For example, the behavioral analysis unit may detect stress and dissatisfaction from employees' website browsing history and understand customer satisfaction and needs from their app usage history. The behavioral analysis unit may also analyze users' behavioral patterns to more accurately determine their emotional states. This allows for a more multifaceted understanding of emotions and psychological states by utilizing behavioral data in addition to text data, voice data, image data, and biometric data.

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

[0054] Step 1: The collection unit collects text data from employees or customers. Text data from employees or customers includes emails, chats, reviews, surveys, etc. The collection unit can collect the contents of employee emails and chats, as well as customer feedback. Step 2: The analysis unit analyzes the text data collected by the collection unit and determines emotions and psychological states. The analysis unit can use emotion analysis algorithms to determine employee stress and dissatisfaction, as well as customer satisfaction and needs. Step 3: The proposal unit proposes countermeasures based on the analysis results obtained by the analysis unit. The proposal unit can propose counseling and training to employees to reduce stress, and can propose personalized services and promotions to customers.

[0055] (Example 2) An emotion analysis system according to an embodiment of the present invention is a system for understanding the emotions and psychological states of employees and customers and providing effective responses. This emotion analysis system collects text data from employees and customers, analyzes it using AI to determine their emotions and psychological states, and proposes appropriate countermeasures. For example, it detects stress and dissatisfaction from the content of employees' emails and chats and understands customer satisfaction and needs from customer feedback. Based on the analysis results, it proposes counseling and training to employees to reduce stress and provides personalized services and promotions to customers. This system enables companies and organizations to understand the emotions and psychological states of employees in real time and respond quickly. It also accurately understands customer emotions and needs and can develop more effective marketing strategies. This improves organizational productivity and employee happiness, contributing to improved customer satisfaction and strengthened competitiveness. This allows the emotion analysis system to understand the emotions and psychological states of employees and customers in real time and respond quickly.

[0056] A sentiment analysis system according to an embodiment includes a collection unit, an analysis unit, and a suggestion unit. The collection unit collects text data from employees or customers. Examples of text data from employees or customers include, but are not limited to, emails, chats, reviews, and surveys. The collection unit can collect, for example, the content of employees' emails and chats. The collection unit can also collect customer feedback. The analysis unit analyzes the text data collected by the collection unit to determine emotions and psychological states. The analysis unit can, for example, determine employee stress and dissatisfaction using an emotion analysis algorithm. The analysis unit can also determine customer satisfaction and needs. The suggestion unit proposes countermeasures based on the analysis results obtained by the analysis unit. For example, the suggestion unit can propose counseling or training to employees to reduce stress. The suggestion unit can also propose personalized services and promotions to customers. As a result, the sentiment analysis system according to an embodiment can grasp the emotions and psychological states of employees and customers and propose appropriate countermeasures.

[0057] The collection unit can collect the contents of employees' emails or chats. For example, the collection unit can collect employees' work emails. For example, the collection unit can automatically filter employees' work emails and extract data necessary for emotion analysis. The collection unit can also collect the contents of employees' personal chats. For example, the collection unit can analyze the contents of employees' personal chats to detect signs of stress or dissatisfaction. Furthermore, the collection unit can collect the contents of employees' group chats. For example, the collection unit can analyze the contents of employees' group chats to understand the emotional state of the entire team. In this way, by collecting the contents of employees' emails and chats, it is possible to understand their emotions and psychological states.

[0058] The analysis unit can analyze the collected text data and determine the employee's stress or dissatisfaction. The analysis unit analyzes the collected text data using, for example, an emotion analysis algorithm. For example, the analysis unit analyzes the frequency of occurrence of keywords indicating stress or dissatisfaction from the content of the employee's emails or chats. The analysis unit can also quantify the employee's emotional state using emotion scoring. For example, the analysis unit analyzes the content of the employee's emails or chats and calculates an emotion score. Furthermore, the analysis unit can determine the employee's psychological state using natural language processing technology. For example, the analysis unit analyzes the content of the employee's emails or chats to detect signs of stress or dissatisfaction. This can determine the employee's stress or dissatisfaction and suggest appropriate countermeasures.

[0059] The suggestion unit can suggest counseling or training to the employee for stress reduction based on the analysis results. The suggestion unit can, for example, suggest online counseling. For example, the suggestion unit can suggest making an appointment for online counseling to reduce the employee's stress. The suggestion unit can also suggest stress management training. For example, the suggestion unit can suggest participating in stress management training to reduce the employee's stress. Furthermore, the suggestion unit can also suggest a relaxation program. For example, the suggestion unit can suggest using a relaxation program to reduce the employee's stress. In this way, by suggesting counseling or training to reduce stress to the employee, the employee's stress can be reduced.

[0060] The collection unit can collect customer reviews and survey feedback. The collection unit can, for example, collect customer product reviews. For example, the collection unit automatically collects customer product reviews on an online platform. The collection unit can also collect customer satisfaction survey feedback. For example, the collection unit automatically collects responses to customer satisfaction surveys and uses them for analysis. The collection unit can also collect customer feedback on social media. For example, the collection unit collects customers' posts and comments on social media and uses them for analysis. In this way, by collecting customer feedback, it is possible to understand customer sentiment and needs.

[0061] The analysis unit can analyze the collected feedback and determine customer satisfaction or needs. The analysis unit analyzes the collected feedback using, for example, a sentiment analysis algorithm. For example, the analysis unit analyzes the frequency of occurrence of keywords indicating satisfaction or needs from the content of customer reviews and questionnaires. The analysis unit can also quantify the emotional state of the customer using sentiment scoring. For example, the analysis unit analyzes the content of customer reviews and questionnaires and calculates a sentiment score. Furthermore, the analysis unit can determine customer satisfaction or needs using natural language processing technology. For example, the analysis unit analyzes the content of customer reviews and questionnaires to detect signs of satisfaction or needs. This can determine customer satisfaction or needs and suggest appropriate countermeasures.

[0062] The suggestion unit can suggest personalized services or promotions to the customer based on the analysis results. The suggestion unit can, for example, suggest individual discount offers. For example, the suggestion unit suggests individual discount offers based on the customer's satisfaction level and needs. The suggestion unit can also make customized service suggestions. For example, the suggestion unit suggests customized services based on the customer's satisfaction level and needs. Furthermore, the suggestion unit can also suggest specific promotions. For example, the suggestion unit suggests specific promotions based on the customer's satisfaction level and needs. This makes it possible to improve customer satisfaction by suggesting personalized services and promotions to the customer.

[0063] The emotion analysis system further includes a collection unit that estimates the user's emotion and adjusts the timing of text data collection based on the estimated user emotion. For example, if the user is feeling stressed, the collection unit can delay the collection timing and collect data when the user is relaxed. Furthermore, if the user is relaxed, the collection unit can collect data immediately and grasp the user's emotion in real time. Furthermore, if the user is in a hurry, the collection unit can collect data in a short time and analyze it quickly. This allows data to be collected at a more appropriate time by adjusting the collection timing according to the user's emotion. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0064] The sentiment analysis system further includes a collection unit that analyzes past communication histories of employees and customers and selects the optimal collection method. The collection unit can, for example, analyze the frequency and content of past emails and chats to select the most effective collection method. The collection unit can also prioritize the collection of messages containing specific keywords based on the content of past feedback. The collection unit can also analyze past communication patterns and determine the optimal collection timing. This allows the optimal collection method to be selected by analyzing the past communication history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past communication histories into a generation AI and have the generation AI select the optimal collection method.

[0065] The sentiment analysis system further includes a collection unit that, when collecting text data, filters the data based on the employee's or customer's current projects or areas of interest. For example, the collection unit can prioritize the collection of messages related to ongoing projects. The collection unit can also filter and collect messages containing keywords related to the employee's or customer's areas of interest. Furthermore, the collection unit can collect messages related to specific projects and understand the progress of the projects. By filtering based on the current projects or areas of interest, highly relevant data can be collected. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data related to projects or areas of interest into a generation AI and have the generation AI perform the filtering.

[0066] The emotion analysis system further includes a collection unit that estimates the user's emotion and prioritizes the text data to be collected based on the estimated user emotion. For example, if the user is feeling stressed, the collection unit can prioritize collecting stress-related messages. Furthermore, if the user is relaxed, the collection unit can prioritize collecting everyday messages. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting urgent messages. By prioritizing the text data to be collected according to the user's emotion, more important data can be collected preferentially. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0067] The sentiment analysis system further includes a collection unit that, when collecting text data, prioritizes the collection of highly relevant data based on the geographical location information of employees or customers. For example, if an employee or customer is in a specific area, the collection unit can prioritize the collection of messages related to that area. The collection unit can also collect messages related to specific events or situations based on the geographical location information. The collection unit can also collect data for understanding emotions and needs by area, taking the geographical location information into account. This allows the collection of highly relevant data to be prioritized by taking the geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input geographical location information into a generation AI and cause the generation AI to collect highly relevant data.

[0068] The sentiment analysis system further includes a collection unit that analyzes the social media activities of employees or customers and collects related data when collecting text data. The collection unit can, for example, analyze the content of posts on social media to collect data related to emotions and needs. The collection unit can also analyze the activity patterns of employees and customers on social media and determine the optimal collection timing. The collection unit can also collect related data based on the interests of employees and customers on social media. In this way, related data can be collected by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media activities into a generation AI and cause the generation AI to collect related data.

[0069] The emotion analysis system further includes an analysis unit that estimates the user's emotion and adjusts the presentation method of the analysis based on the estimated user emotion. For example, if the user is feeling stressed, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result that focuses on the main points. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0070] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the text data. For example, the analysis unit can perform a detailed analysis on messages with high importance. The analysis unit can also perform a simplified analysis on messages with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance. In this way, by adjusting the level of detail of the analysis based on the importance of the text data, more important data can be analyzed in detail. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the text data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0071] During analysis, the analysis unit can apply different analysis algorithms depending on the type of text data. For example, the analysis unit can apply a stress analysis algorithm to messages related to stress. The analysis unit can also apply a dissatisfaction analysis algorithm to messages related to dissatisfaction. Furthermore, the analysis unit can apply a motivation analysis algorithm to messages related to motivation. In this way, by applying different analysis algorithms depending on the type of text data, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the type of text data to the generation AI and cause the generation AI to apply different analysis algorithms.

[0072] The emotion analysis system further includes an analysis unit that estimates the user's emotion and adjusts the length of the analysis based on the estimated user emotion. For example, if the user is feeling stressed, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is in a hurry, the analysis unit can also provide a concise analysis result. This allows for adjusting the length of the analysis according to the user's emotion, thereby providing a more appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] During analysis, the analysis unit can determine the priority of analysis based on the submission date and time of the text data. For example, the analysis unit can prioritize analysis of recently submitted text data. The analysis unit can also prioritize analysis of data with high urgency based on the time of submission. Furthermore, the analysis unit can adjust the order of analysis according to the time of submission. In this way, by determining the priority of analysis based on the time of submission of the text data, data with high urgency can be analyzed with priority. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission date and time to the generation AI and have the generation AI determine the analysis priority.

[0074] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the text data. For example, the analysis unit can prioritize analyzing highly relevant messages. The analysis unit can also postpone analyzing less relevant messages. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance. In this way, by adjusting the order of analysis based on the relevance of the text data, more relevant data can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the text data to the generation AI and cause the generation AI to adjust the order of analysis.

[0075] The emotion analysis system further includes a suggestion unit that estimates the user's emotion and adjusts the way suggestions are expressed based on the estimated user emotion. For example, if the user is feeling stressed, the suggestion unit can provide simple, highly visible suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. If the user is in a hurry, the suggestion unit can also provide concise suggestions that focus on the main points. This allows for adjusting the way suggestions are expressed according to the user's emotion, thereby providing more appropriate suggestions. The emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0076] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the analysis result. For example, the proposal unit can make a detailed proposal for an analysis result with high importance. The proposal unit can also make a simplified proposal for an analysis result with low importance. Furthermore, the proposal unit can determine the priority of the proposal according to the importance. As a result, by adjusting the level of detail of the proposal based on the importance of the analysis result, more important proposals can be given priority. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the importance of the analysis result to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0077] When making a proposal, the proposal unit can apply different proposal algorithms depending on the type of analysis result. For example, the proposal unit can apply a proposal algorithm for stress reduction to analysis results related to stress. The proposal unit can also apply a proposal algorithm for dissatisfaction relief to analysis results related to dissatisfaction. The proposal unit can also apply a proposal algorithm for motivation improvement to analysis results related to motivation. In this way, by applying different proposal algorithms depending on the type of analysis result, more appropriate proposals can be provided. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the type of analysis result to the generation AI and cause the generation AI to apply different proposal algorithms.

[0078] The emotion analysis system further includes a suggestion unit that estimates the user's emotion and adjusts the length of the suggestion based on the estimated user's emotion. For example, if the user is feeling stressed, the suggestion unit can provide a short, to-the-point suggestion. Furthermore, if the user is relaxed, the suggestion unit can provide a detailed suggestion. Furthermore, if the user is in a hurry, the suggestion unit can provide a concise suggestion. This allows for adjusting the length of the suggestion according to the user's emotion, thereby providing more appropriate suggestions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0079] When making a proposal, the suggestion unit can determine the priority of the proposal based on the submission date and time of the analysis result. The suggestion unit can, for example, prioritize proposals based on the most recently submitted analysis result. The suggestion unit can also prioritize proposals with a high urgency based on the submission date and time. Furthermore, the suggestion unit can adjust the order of proposals according to the submission date and time. In this way, by determining the priority of proposals based on the submission date and time of the analysis result, it is possible to prioritize proposals with a high urgency. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the submission date and time to the generation AI and have the generation AI determine the priority of the proposals.

[0080] The suggestion unit can adjust the order of suggestions based on the relevance of the analysis results when making suggestions. For example, the suggestion unit can prioritize suggestions based on highly relevant analysis results. The suggestion unit can also postpone suggestions based on less relevant analysis results. Furthermore, the suggestion unit can dynamically adjust the order of suggestions according to the relevance. As a result, by adjusting the order of suggestions based on the relevance of the analysis results, more relevant suggestions can be prioritized. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the relevance of the analysis results to the generation AI and cause the generation AI to adjust the order of suggestions. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the smart device 14 and collects text data from employees and customers. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected text data to determine emotions and psychological states. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests appropriate countermeasures based on the analysis results. Some or all of the collection unit, analysis unit, and suggestion unit may be realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the smart glasses 214 and collects text data from employees and customers. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected text data to determine emotions and psychological states. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests appropriate countermeasures based on the analysis results. Some or all of the collection unit, analysis unit, and suggestion unit may be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and suggestion unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the headset type terminal 314 and collects text data from employees and customers. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected text data to determine emotions and psychological states. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests appropriate countermeasures based on the analysis results. Some or all of the collection unit, analysis unit, and suggestion unit may be realized, for example, by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and suggestion unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the robot 414 and collects text data from employees and customers. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected text data to determine emotions and psychological states. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests appropriate countermeasures based on the analysis results. Some or all of the collection unit, analysis unit, and suggestion unit may be realized, for example, by the control unit 46A of the robot 414.

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

[0082] The emotion analysis system may further include a voice analysis unit that collects and analyzes voice data. The voice analysis unit can, for example, collect telephone conversations and voice messages between employees and customers and analyze their emotions and psychological states. For example, the voice analysis unit can detect stress and dissatisfaction from employee telephone conversations and understand customer satisfaction and needs from their voice messages. The voice analysis unit can also analyze parameters such as tone, speed, and volume of the voice to more accurately determine the emotional state. This makes it possible to understand emotions and psychological states from a more multifaceted perspective by utilizing not only text data but also voice data.

[0083] The emotion analysis system may further include an image analysis unit that collects and analyzes image data. The image analysis unit may analyze, for example, the facial expressions and gestures of employees and customers to determine their emotions and psychological states. For example, the image analysis unit may detect stress or dissatisfaction from employees' facial expressions and grasp customer satisfaction and needs from their facial expressions. The image analysis unit may also analyze gestures and changes in posture to more accurately determine emotional states. This allows for a more multifaceted understanding of emotions and psychological states by utilizing image data in addition to text data and voice data.

[0084] The emotion analysis system may further include a biometric analysis unit that collects and analyzes biometric data. The biometric analysis unit may collect biometric data, such as the heart rate and electrodermal activity of employees and customers, and analyze their emotions and psychological states. For example, the biometric analysis unit may detect stress and dissatisfaction from fluctuations in an employee's heart rate, and may understand customer satisfaction and needs from their electrodermal activity. The biometric analysis unit may also combine and analyze multiple pieces of biometric data to more accurately determine emotional states. This allows for a more multifaceted understanding of emotions and psychological states by utilizing biometric data in addition to text data, voice data, and image data.

[0085] The emotion analysis system may further include a behavioral analysis unit that collects and analyzes user behavioral data. The behavioral analysis unit may, for example, collect website browsing history and app usage history of employees and customers and analyze their emotions and psychological states. For example, the behavioral analysis unit may detect stress and dissatisfaction from employees' website browsing history and understand customer satisfaction and needs from their app usage history. The behavioral analysis unit may also analyze users' behavioral patterns to more accurately determine their emotional states. This allows for a more multifaceted understanding of emotions and psychological states by utilizing behavioral data in addition to text data, voice data, image data, and biometric data.

[0086] The emotion analysis system may further include a notification unit that estimates the user's emotion and adjusts the timing of notification based on the estimated user emotion. For example, if the user is feeling stressed, the notification unit may delay the timing of notification and notify the user when the user is relaxed. Also, if the user is relaxed, the notification unit may notify the user immediately and provide information in real time. Furthermore, if the user is in a hurry, the notification unit may notify the user in a short time and provide information quickly. In this way, by adjusting the timing of notification according to the user's emotion, information can be provided at a more appropriate time.

[0087] The emotion analysis system may further include a feedback unit that estimates the user's emotion and adjusts the content of the feedback based on the estimated user's emotion. For example, the feedback unit may provide positive feedback preferentially when the user is feeling stressed. The feedback unit may also provide detailed feedback when the user is relaxed. The feedback unit may also provide concise feedback when the user is in a hurry. This allows the content of the feedback to be adjusted according to the user's emotion, thereby providing more appropriate feedback.

[0088] The emotion analysis system may further include an interface unit that estimates the user's emotion and adjusts the interface design based on the estimated user emotion. For example, the interface unit may provide a simple, highly visible interface when the user is feeling stressed. The interface unit may also provide an interface that displays detailed information when the user is relaxed. Furthermore, the interface unit may provide a concise interface that focuses on the main points when the user is in a hurry. This allows the interface design to be adjusted according to the user's emotion, thereby providing a more appropriate user experience.

[0089] The emotion analysis system may further include an alert unit that estimates the user's emotion and adjusts the content of the alert based on the estimated user's emotion. For example, the alert unit may refrain from issuing less urgent alerts when the user is feeling stressed. The alert unit may also provide detailed alerts when the user is relaxed. Furthermore, the alert unit may provide concise alerts when the user is in a hurry. This allows the alert content to be adjusted according to the user's emotion, thereby providing more appropriate alerts.

[0090] The emotion analysis system may further include a storage unit that estimates the user's emotion and adjusts the data storage method based on the estimated user emotion. For example, the storage unit may temporarily refrain from saving data when the user is feeling stressed. The storage unit may also save detailed data when the user is relaxed. The storage unit may also save brief data when the user is in a hurry. This allows for more appropriate data management by adjusting the data storage method according to the user's emotion.

[0091] The emotion analysis system may further include a display unit that estimates the user's emotion and adjusts the data display method based on the estimated user emotion. For example, the display unit may provide a simple, highly visible display when the user is feeling stressed. The display unit may also display detailed data when the user is relaxed. Furthermore, the display unit may provide a concise display that focuses on the main points when the user is in a hurry. This allows for more appropriate information to be provided by adjusting the data display method according to the user's emotion.

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

[0093] Step 1: The collection unit collects text data from employees or customers. Text data from employees or customers includes emails, chats, reviews, surveys, etc. The collection unit can collect the contents of employee emails and chats, as well as customer feedback. Step 2: The analysis unit analyzes the text data collected by the collection unit and determines emotions and psychological states. The analysis unit can use emotion analysis algorithms to determine employee stress and dissatisfaction, as well as customer satisfaction and needs. Step 3: The proposal unit proposes countermeasures based on the analysis results obtained by the analysis unit. The proposal unit can propose counseling and training to employees to reduce stress, and can propose personalized services and promotions to customers.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

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

[0100] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0112] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0131] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0137] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0138] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0147] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0149] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0150] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0155] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0156] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0157] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0158] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0159] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0160] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0162] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0163] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0164] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0165] [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects text data from employees or customers; an analysis unit that analyzes the text data collected by the collection unit and determines emotions and psychological states; a proposal unit that proposes a countermeasure based on the analysis result obtained by the analysis unit. A system characterized by:

2. The collecting unit Collect employee email or chat content 2. The system of claim 1.

3. The analysis unit Analyze collected text data to determine employee stress or dissatisfaction 2. The system of claim 1.

4. The proposal unit Based on the analysis results, recommend counseling or training for employees to reduce stress.

2. The system of claim 1.

5. The collecting unit Collect customer reviews and survey feedback 2. The system of claim 1.

6. The analysis unit Analyze collected feedback to determine customer satisfaction or needs 2. The system of claim 1.

7. The proposal unit Offering personalized services or promotions to customers based on the analysis results 2. The system of claim 1.

8. The collecting unit Estimate user emotions and adjust the timing of text data collection based on the estimated user emotions.

2. The system of claim 1.

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