system

Abstract

The system according to this embodiment aims to enhance the user's motivation for work by identifying the user's strengths and advantages and generating and reading aloud a speech of praise. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a generation unit, and a reading unit. The data collection unit records the user's daily activities and statements in meetings. The analysis unit analyzes the data collected by the data collection unit to identify the user's strengths and advantages. The generation unit generates a speech of praise based on the strengths and advantages identified by the analysis unit. The reading unit reads aloud the speech generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, with the spread of remote work, the opportunity to be directly praised by people decreases, and there is a risk of a decline in self-evaluation.

[0005] The system according to the embodiment aims to enhance the user's vitality in work by identifying the user's strengths and generating and reading aloud praise speeches.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, and a reading unit. The data collection unit records the user's daily activities and statements in meetings. The analysis unit analyzes the data collected by the data collection unit to identify the user's strengths and advantages. The generation unit generates a speech of praise based on the strengths and advantages identified by the analysis unit. The reading unit reads aloud the speech generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can identify the user's strengths and advantages, and generate and read aloud a speech of praise, thereby increasing the user's motivation for work. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The self-evaluation improvement system according to an embodiment of the present invention is a system for improving self-evaluation in a remote work environment. The self-evaluation improvement system records the user's daily actions and statements in meetings, and a generating AI analyzes these records to identify the user's strengths and advantages. The generating AI generates a speech of praise based on the identified strengths and advantages and reads it aloud using speech synthesis technology. This allows the user to reaffirm their strengths and advantages and increase their motivation for work. For example, the self-evaluation improvement system includes a collection unit that records the user's daily actions and statements in meetings. The collection unit records the user's actions and statements and inputs them into the generating AI. For example, it records situations where the user reports on the progress of a project or makes constructive opinions in a meeting. Next, it includes an analysis unit that analyzes the collected data. The analysis unit analyzes the data collected by the collection unit and identifies the user's strengths and advantages. For example, if a user makes a statement that boosts the team's motivation, that statement is identified as the user's strength. The generating AI includes a generation unit that generates a speech of praise based on the identified strengths and advantages. For example, a speech such as, "Your leadership is boosting the morale of the entire team," is generated. Finally, a reading unit is included to read the generated speech aloud. The reading unit uses speech synthesis technology to read the speech. This allows users to reaffirm their strengths and positive attributes and increase their motivation for work. The self-evaluation improvement system enables users to improve their self-esteem and work with confidence, even in a remote work environment. For example, when a user reports on the progress of a project, the generation AI analyzes the report and generates and reads a speech praising the user's contribution, allowing the user to feel a sense of accomplishment for their work. In this way, the self-evaluation improvement system can improve users' self-esteem and increase their motivation for work.

[0029] The self-assessment improvement system according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, and a reading unit. The data collection unit records the user's daily actions and statements in meetings. For example, the data collection unit records instances where the user reports on project progress or offers constructive opinions in meetings. The data collection unit records the user's actions and statements and inputs them into the generation AI. The analysis unit analyzes the data collected by the data collection unit and identifies the user's strengths and advantages. For example, if the user makes a statement that boosts team morale, the analysis unit identifies that statement as the user's strength. The generation unit generates a speech of praise based on the identified strengths and advantages. For example, the generation unit generates a speech such as, "Your leadership is boosting the morale of the entire team." The reading unit reads the generated speech aloud using speech synthesis technology. For example, by reading the generated speech aloud using speech synthesis technology, the user can reaffirm their strengths and advantages and increase their motivation for work. As a result, the self-evaluation improvement system according to the embodiment can enhance the user's motivation for work by recording and analyzing the user's daily actions and statements in meetings, generating and reading aloud a speech of praise.

[0030] The data collection unit records users' daily actions and statements made in meetings. Specifically, it records statements made by users when reporting on project progress and when they offer constructive opinions in meetings. The data collection unit uses speech recognition and natural language processing technologies to record users' statements as text data. For example, when a user speaks in a meeting, their voice is collected by a microphone and converted to text in real time. It can also record users' daily actions, such as sending emails and reporting the completion of tasks. This allows the data collection unit to record users' actions and statements in detail and provide them as data for subsequent analysis and generation. Furthermore, the data collection unit implements strict security measures regarding data collection and storage to protect user privacy. For example, the data is encrypted and can only be viewed by those with access rights. It is also possible to set the collected data to be automatically deleted after a certain period of time. This allows the data collection unit to efficiently collect necessary data while protecting user privacy.

[0031] The analysis unit analyzes data collected by the data collection unit to identify the user's strengths and advantages. Specifically, it uses natural language processing techniques and machine learning algorithms to analyze the user's statements and actions. For example, it analyzes what a user says in a meeting and extracts positive elements and constructive opinions. Furthermore, it can analyze the tone and emotion of the user's statements to identify characteristics such as leadership and collaborativeness. Based on this data, the analysis unit lists and evaluates the user's strengths and advantages. For example, if a user makes a statement that boosts team morale, that statement will be identified as the user's strength. The analysis unit can also evaluate the user's growth and progress by comparing it with past data. This allows the analysis unit to accurately identify the user's strengths and advantages and provide them as input data to the generation unit, which is the next step. In addition, the analysis unit can receive user feedback and continuously improve the accuracy of the analysis algorithm. For example, it can make adjustments to improve the accuracy and reliability of the analysis results based on the feedback provided by the user. This allows the analysis unit to more accurately identify the user's strengths and advantages and improve the overall performance of the system.

[0032] The generation unit generates praise speeches based on the strengths and advantages identified by the analysis unit. Specifically, it uses a generation AI to automatically create speeches that praise the user's strengths and advantages. For example, it might generate a speech such as, "Your leadership is boosting the morale of the entire team." The generation AI selects the most appropriate words of praise based on the user's characteristics and past behavioral data, and constructs them into natural-sounding sentences. Furthermore, the generation unit can adjust the content and tone of the speech, taking into account the user's preferences and feedback. For example, if the user is seeking more specific feedback, the generation unit will generate a speech that includes specific examples and anecdotes. The generation unit can also customize the length and format of the speech to suit the user's needs. This allows the generation unit to provide the most effective praise speeches for the user, improving their self-esteem. In addition, the generation unit can save the generated speeches for later reference. This allows the user to look back on past speeches and confirm their growth and progress.

[0033] The text-to-speech unit reads the generated speech using speech synthesis technology. Specifically, the speech created by the generation unit is input into a speech synthesis engine and output as natural-sounding speech. The text-to-speech unit can adjust the tone and speed of the voice according to the user's preferences and situation. For example, if the user wants to listen to the speech in a relaxed environment, they can set it to read slowly in a gentle tone. The text-to-speech unit can also provide multiple voice models for the user to choose from. This allows the user to listen to the speech in the voice that best suits them. Furthermore, the text-to-speech unit can adjust the intonation and pauses of the voice to emphasize the content of the speech. For example, the tone of voice can be raised or pauses added to emphasize important points or words of praise. This allows the text-to-speech unit to effectively convey the speech to the user and support the improvement of self-esteem. In addition, the text-to-speech unit can record the generated speech and play it back later. This allows the user to reaffirm their strengths and advantages at any time and maintain their motivation.

[0034] The data collection unit can record the user's daily actions and statements in meetings. For example, the data collection unit can record instances where the user reports on project progress or offers constructive opinions in meetings. The data collection unit records the user's actions and statements and inputs them into the generating AI. This allows for the collection of data necessary for subsequent analysis by recording the user's daily actions and statements in meetings. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can collect data for analyzing the user's actions and statements by recording them and inputting them into the generating AI.

[0035] The analysis unit can analyze the data collected by the data collection unit to identify the user's strengths and advantages. For example, if a user makes a statement that motivates the team, the analysis unit will identify that statement as the user's strength. The analysis unit analyzes the data collected by the data collection unit to identify the user's strengths and advantages. In this way, the user's strengths and advantages can be identified by analyzing the collected data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can identify the user's strengths by analyzing the data collected by the data collection unit to identify the user's strengths and advantages.

[0036] The generation unit can generate praise speeches based on identified strengths and advantages. For example, the generation unit can generate a speech such as, "Your leadership is boosting the morale of the entire team." The generation unit generates praise speeches based on identified strengths and advantages. This allows the user to improve their self-esteem by generating praise speeches based on identified strengths and advantages. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit uses a generation AI to generate speeches in order to generate praise speeches based on identified strengths and advantages.

[0037] The reading unit can read aloud the generated speech using speech synthesis technology. For example, by reading aloud the generated speech using speech synthesis technology, the reading unit can help users re-evaluate their strengths and abilities, thereby increasing their motivation for work. The reading unit reads aloud the generated speech using speech synthesis technology. This allows for direct feedback to be provided to the user by reading aloud the generated speech using speech synthesis technology. Some or all of the above-described processes in the reading unit may be performed using AI or not. For example, the reading unit can provide direct feedback to the user by reading aloud the generated speech using speech synthesis technology.

[0038] The data collection unit can analyze the user's past behavior and speech history and select the optimal recording method. For example, the data collection unit prioritizes recording actions that the user has frequently performed in the past. The data collection unit analyzes the user's past behavior and speech history and selects the optimal recording method. This allows the optimal recording method to be selected by analyzing the user's past behavior and speech history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can effectively record the user's behavior and speech by analyzing the user's past behavior and speech history and selecting the optimal recording method.

[0039] The data collection unit can filter recordings of actions and statements based on the user's current projects and areas of interest. For example, the data collection unit can prioritize recording statements related to the user's current projects. The data collection unit filters recordings of actions and statements based on the user's current projects and areas of interest. This allows for the recording of highly relevant actions and statements. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can record highly relevant actions and statements by filtering based on the user's current projects and areas of interest.

[0040] The data collection unit can prioritize recording highly relevant actions and statements by considering the user's geographical location when recording actions and statements. For example, if the user is in a specific location, the data collection unit will prioritize recording actions and statements related to that location. The data collection unit prioritizes recording highly relevant actions and statements by considering the user's geographical location when recording actions and statements. This allows for the priority recording of highly relevant actions and statements by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can effectively record user actions and statements by prioritizing the recording of highly relevant actions and statements by considering the user's geographical location.

[0041] The data collection unit can analyze a user's social media activity and record relevant actions and statements when recording actions and statements. For example, the data collection unit can record relevant actions and statements based on content shared by the user on social media. The data collection unit analyzes a user's social media activity and records relevant actions and statements when recording actions and statements. This allows the data collection unit to record relevant actions and statements by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can effectively record a user's actions and statements by analyzing the user's social media activity and recording relevant actions and statements.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of actions and statements during the analysis. For example, the analysis unit performs a detailed analysis on actions and statements of high importance. The analysis unit adjusts the level of detail of the analysis based on the importance of actions and statements during the analysis. This allows for a more detailed analysis of important information. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can effectively analyze user actions and statements by adjusting the level of detail of the analysis based on the importance of actions and statements.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the behavior or statement during analysis. For example, the analysis unit applies a leadership evaluation algorithm to statements related to leadership. The analysis unit applies different analysis algorithms depending on the category of the behavior or statement during analysis. By applying different analysis algorithms depending on the category of the behavior or statement, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can effectively analyze the user's behavior and statements by applying different analysis algorithms depending on the category of the behavior or statement.

[0044] The analysis unit can determine the priority of analysis based on the timing of the submission of actions and statements during analysis. For example, the analysis unit may prioritize the analysis of recent actions and statements. The analysis unit determines the priority of analysis based on the timing of the submission of actions and statements. This allows for the prioritization of analysis of more important information. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can effectively analyze user actions and statements by determining the priority of analysis based on the timing of the submission of actions and statements.

[0045] The analysis unit can adjust the order of analysis based on the relevance of actions and statements during analysis. For example, the analysis unit prioritizes the analysis of highly relevant actions and statements. The analysis unit adjusts the order of analysis based on the relevance of actions and statements during analysis. This allows for the prioritization of analysis of more relevant information by adjusting the order of analysis based on the relevance of actions and statements. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can effectively analyze user actions and statements by adjusting the order of analysis based on the relevance of actions and statements.

[0046] The generation unit can adjust the level of detail in the speech based on the importance of identified strengths and advantages during speech generation. For example, the generation unit generates detailed speech for strengths and advantages of high importance. The generation unit adjusts the level of detail in the speech based on the importance of identified strengths and advantages during speech generation. This allows more important information to be included in the speech in detail. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can effectively include the user's strengths and advantages in the speech by adjusting the level of detail in the speech based on the importance of identified strengths and advantages during speech generation.

[0047] The generation unit can apply different speech generation algorithms depending on the identified categories of strengths and advantages when generating a speech. For example, the generation unit applies a leadership evaluation algorithm to strengths related to leadership. The generation unit applies different speech generation algorithms depending on the identified categories of strengths and advantages when generating a speech. This allows for the generation of more effective speeches by applying the appropriate speech generation algorithm according to the identified categories of strengths and advantages. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can effectively include the user's strengths and advantages in the speech by applying different speech generation algorithms depending on the identified categories of strengths and advantages when generating a speech.

[0048] The generation unit can prioritize speeches based on the timing of identified strengths and advantages during speech generation. For example, the generation unit prioritizes the inclusion of recently identified strengths and advantages in the speech. The generation unit prioritizes speeches based on the timing of identified strengths and advantages during speech generation. This allows for the generation of more timely speeches by prioritizing speeches based on the timing of identified strengths and advantages. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can effectively include the user's strengths and advantages in the speech by prioritizing speeches based on the timing of identified strengths and advantages during speech generation.

[0049] The generation unit can adjust the order of speech based on the relevance of identified strengths and advantages during speech generation. For example, the generation unit prioritizes the inclusion of highly relevant strengths and advantages in the speech. The generation unit adjusts the order of speech based on the relevance of identified strengths and advantages during speech generation. This allows for the generation of a more effective speech by adjusting the order of speech based on the relevance of identified strengths and advantages. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can effectively include the user's strengths and advantages in the speech by adjusting the order of speech based on the relevance of identified strengths and advantages during speech generation.

[0050] The text-to-speech unit can select the optimal reading method by referring to the user's past response history during reading. For example, the text-to-speech unit may prioritize selecting a reading method that the user has previously preferred. The text-to-speech unit selects the optimal reading method by referring to the user's past response history during reading. This allows for the selection of a more effective reading method by referring to the user's past response history. Some or all of the above processing in the text-to-speech unit may be performed using AI or not. For example, the text-to-speech unit can perform an effective reading for the user by selecting the optimal reading method by referring to the user's past response history.

[0051] The text-to-speech unit can select the optimal reading method while considering the user's device information. For example, if the user is using a smartphone, the text-to-speech unit adjusts the volume of the audio. The text-to-speech unit selects the optimal reading method while considering the user's device information. This allows for the selection of a more appropriate reading method by considering the user's device information. Some or all of the above processing in the text-to-speech unit may be performed using AI or not. For example, by selecting the optimal reading method while considering the user's device information, the text-to-speech unit can provide more effective reading to the user.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The self-assessment improvement system can also include a schedule management unit to manage the user's schedule. This unit integrates with the user's calendar and task management tools, providing feedback to improve self-assessment before and after important meetings and tasks. For example, before an important presentation, it can generate and read aloud a speech reviewing the user's past successes, thereby boosting the user's confidence. Furthermore, the schedule management unit can provide feedback at the optimal time, tailored to the user's busy schedule. This allows for effective self-assessment improvement that aligns with the user's schedule.

[0054] The self-assessment improvement system can also include a learning management unit that manages the user's learning history. The learning management unit records the training and learning content the user has received in the past and provides it to the analysis unit. For example, if a user has received training on a specific skill, the system can prioritize the analysis of actions and statements related to that skill. The learning management unit can also suggest further training and resources for skill improvement based on the user's learning history. This allows for more effective self-assessment improvement by providing feedback that takes the user's learning history into account.

[0055] The self-assessment improvement system can also include a social analysis unit that analyzes the user's social interactions. The social analysis unit analyzes the user's interactions on social media and in the workplace to evaluate the user's communication skills and influence. For example, by identifying posts that received many responses on social media and analyzing their content, the system can identify the user's strengths. Furthermore, the social analysis unit can provide advice for improving communication skills based on the user's social interactions. This allows for more effective self-assessment improvement by providing feedback that takes the user's social interactions into consideration.

[0056] The self-assessment improvement system can further include a goal-setting unit to assist users in setting goals. The goal-setting unit monitors progress based on the goals set by the user and provides feedback according to the degree of achievement. For example, if a user sets a goal for a specific project, the system can analyze its progress and provide praise and advice according to the degree of achievement. The goal-setting unit can also suggest specific steps and resources to help the user achieve their goals. This is expected to improve self-assessment by supporting the user in achieving their goals.

[0057] The self-esteem improvement system can also include a "Hobbies and Interests" section that provides feedback considering the user's hobbies and interests. The Hobbies and Interests section provides relevant feedback based on the user's interests. For example, if the user is interested in music, it can provide success stories and inspiring speeches related to music. Furthermore, the Hobbies and Interests section can also provide advice on relaxation methods and stress relief based on the user's interests. This allows for more effective self-esteem improvement by providing feedback that takes the user's hobbies and interests into account.

[0058] The self-assessment improvement system can also include a career path section that provides feedback while considering the user's career path. The career path section provides relevant feedback based on the user's career goals and past work experience. For example, if a user wants to improve their leadership skills, it can provide success stories and commendation speeches related to leadership. The career path section can also suggest training and resources for further skill development based on the user's career goals. This allows for more effective self-assessment improvement by providing feedback that considers the user's career path.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The data collection unit records the user's daily actions and statements in meetings. For example, it records instances where the user reports on project progress or offers constructive opinions in meetings. The data collection unit records the user's actions and statements and inputs them into the generating AI. Step 2: The analysis unit analyzes the data collected by the collection unit to identify the user's strengths and advantages. For example, if a user makes a statement that motivates the team, that statement will be identified as the user's strength. Step 3: The generation unit generates a praise speech based on the strengths and advantages identified by the analysis unit. For example, it might generate a speech such as, "Your leadership is boosting the morale of the entire team." Step 4: The reading unit reads the generated speech aloud using speech synthesis technology. This allows users to reaffirm their strengths and advantages and boost their motivation for work.

[0061] (Example of form 2) The self-evaluation improvement system according to an embodiment of the present invention is a system for improving self-evaluation in a remote work environment. The self-evaluation improvement system records the user's daily actions and statements in meetings, and a generating AI analyzes these records to identify the user's strengths and advantages. The generating AI generates a speech of praise based on the identified strengths and advantages and reads it aloud using speech synthesis technology. This allows the user to reaffirm their strengths and advantages and increase their motivation for work. For example, the self-evaluation improvement system includes a collection unit that records the user's daily actions and statements in meetings. The collection unit records the user's actions and statements and inputs them into the generating AI. For example, it records situations where the user reports on the progress of a project or makes constructive opinions in a meeting. Next, it includes an analysis unit that analyzes the collected data. The analysis unit analyzes the data collected by the collection unit and identifies the user's strengths and advantages. For example, if a user makes a statement that boosts the team's motivation, that statement is identified as the user's strength. The generating AI includes a generation unit that generates a speech of praise based on the identified strengths and advantages. For example, a speech such as, "Your leadership is boosting the morale of the entire team," is generated. Finally, a reading unit is included to read the generated speech aloud. The reading unit uses speech synthesis technology to read the speech. This allows users to reaffirm their strengths and positive attributes and increase their motivation for work. The self-evaluation improvement system enables users to improve their self-esteem and work with confidence, even in a remote work environment. For example, when a user reports on the progress of a project, the generation AI analyzes the report and generates and reads a speech praising the user's contribution, allowing the user to feel a sense of accomplishment for their work. In this way, the self-evaluation improvement system can improve users' self-esteem and increase their motivation for work.

[0062] The self-assessment improvement system according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, and a reading unit. The data collection unit records the user's daily actions and statements in meetings. For example, the data collection unit records instances where the user reports on project progress or offers constructive opinions in meetings. The data collection unit records the user's actions and statements and inputs them into the generation AI. The analysis unit analyzes the data collected by the data collection unit and identifies the user's strengths and advantages. For example, if the user makes a statement that boosts team morale, the analysis unit identifies that statement as the user's strength. The generation unit generates a speech of praise based on the identified strengths and advantages. For example, the generation unit generates a speech such as, "Your leadership is boosting the morale of the entire team." The reading unit reads the generated speech aloud using speech synthesis technology. For example, by reading the generated speech aloud using speech synthesis technology, the user can reaffirm their strengths and advantages and increase their motivation for work. As a result, the self-evaluation improvement system according to the embodiment can enhance the user's motivation for work by recording and analyzing the user's daily actions and statements in meetings, generating and reading aloud a speech of praise.

[0063] The data collection unit records users' daily actions and statements made in meetings. Specifically, it records statements made by users when reporting on project progress and when they offer constructive opinions in meetings. The data collection unit uses speech recognition and natural language processing technologies to record users' statements as text data. For example, when a user speaks in a meeting, their voice is collected by a microphone and converted to text in real time. It can also record users' daily actions, such as sending emails and reporting the completion of tasks. This allows the data collection unit to record users' actions and statements in detail and provide them as data for subsequent analysis and generation. Furthermore, the data collection unit implements strict security measures regarding data collection and storage to protect user privacy. For example, the data is encrypted and can only be viewed by those with access rights. It is also possible to set the collected data to be automatically deleted after a certain period of time. This allows the data collection unit to efficiently collect necessary data while protecting user privacy.

[0064] The analysis unit analyzes data collected by the data collection unit to identify the user's strengths and advantages. Specifically, it uses natural language processing techniques and machine learning algorithms to analyze the user's statements and actions. For example, it analyzes what a user says in a meeting and extracts positive elements and constructive opinions. Furthermore, it can analyze the tone and emotion of the user's statements to identify characteristics such as leadership and collaborativeness. Based on this data, the analysis unit lists and evaluates the user's strengths and advantages. For example, if a user makes a statement that boosts team morale, that statement will be identified as the user's strength. The analysis unit can also evaluate the user's growth and progress by comparing it with past data. This allows the analysis unit to accurately identify the user's strengths and advantages and provide them as input data to the generation unit, which is the next step. In addition, the analysis unit can receive user feedback and continuously improve the accuracy of the analysis algorithm. For example, it can make adjustments to improve the accuracy and reliability of the analysis results based on the feedback provided by the user. This allows the analysis unit to more accurately identify the user's strengths and advantages and improve the overall performance of the system.

[0065] The generation unit generates praise speeches based on the strengths and advantages identified by the analysis unit. Specifically, it uses a generation AI to automatically create speeches that praise the user's strengths and advantages. For example, it might generate a speech such as, "Your leadership is boosting the morale of the entire team." The generation AI selects the most appropriate words of praise based on the user's characteristics and past behavioral data, and constructs them into natural-sounding sentences. Furthermore, the generation unit can adjust the content and tone of the speech, taking into account the user's preferences and feedback. For example, if the user is seeking more specific feedback, the generation unit will generate a speech that includes specific examples and anecdotes. The generation unit can also customize the length and format of the speech to suit the user's needs. This allows the generation unit to provide the most effective praise speeches for the user, improving their self-esteem. In addition, the generation unit can save the generated speeches for later reference. This allows the user to look back on past speeches and confirm their growth and progress.

[0066] The text-to-speech unit reads the generated speech using speech synthesis technology. Specifically, the speech created by the generation unit is input into a speech synthesis engine and output as natural-sounding speech. The text-to-speech unit can adjust the tone and speed of the voice according to the user's preferences and situation. For example, if the user wants to listen to the speech in a relaxed environment, they can set it to read slowly in a gentle tone. The text-to-speech unit can also provide multiple voice models for the user to choose from. This allows the user to listen to the speech in the voice that best suits them. Furthermore, the text-to-speech unit can adjust the intonation and pauses of the voice to emphasize the content of the speech. For example, the tone of voice can be raised or pauses added to emphasize important points or words of praise. This allows the text-to-speech unit to effectively convey the speech to the user and support the improvement of self-esteem. In addition, the text-to-speech unit can record the generated speech and play it back later. This allows the user to reaffirm their strengths and advantages at any time and maintain their motivation.

[0067] The data collection unit can record the user's daily actions and statements in meetings. For example, the data collection unit can record instances where the user reports on project progress or offers constructive opinions in meetings. The data collection unit records the user's actions and statements and inputs them into the generating AI. This allows for the collection of data necessary for subsequent analysis by recording the user's daily actions and statements in meetings. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can collect data for analyzing the user's actions and statements by recording them and inputting them into the generating AI.

[0068] The analysis unit can analyze the data collected by the data collection unit to identify the user's strengths and advantages. For example, if a user makes a statement that motivates the team, the analysis unit will identify that statement as the user's strength. The analysis unit analyzes the data collected by the data collection unit to identify the user's strengths and advantages. In this way, the user's strengths and advantages can be identified by analyzing the collected data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can identify the user's strengths by analyzing the data collected by the data collection unit to identify the user's strengths and advantages.

[0069] The generation unit can generate praise speeches based on identified strengths and advantages. For example, the generation unit can generate a speech such as, "Your leadership is boosting the morale of the entire team." The generation unit generates praise speeches based on identified strengths and advantages. This allows the user to improve their self-esteem by generating praise speeches based on identified strengths and advantages. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit uses a generation AI to generate speeches in order to generate praise speeches based on identified strengths and advantages.

[0070] The reading unit can read aloud the generated speech using speech synthesis technology. For example, by reading aloud the generated speech using speech synthesis technology, the reading unit can help users re-evaluate their strengths and abilities, thereby increasing their motivation for work. The reading unit reads aloud the generated speech using speech synthesis technology. This allows for direct feedback to be provided to the user by reading aloud the generated speech using speech synthesis technology. Some or all of the above-described processes in the reading unit may be performed using AI or not. For example, the reading unit can provide direct feedback to the user by reading aloud the generated speech using speech synthesis technology.

[0071] The data collection unit can estimate the user's emotions and adjust the timing of recording actions and statements based on the estimated emotions. For example, if the user is stressed, the data collection unit will record actions and statements when the user is relaxed. The data collection unit estimates the user's emotions and adjusts the timing of recording actions and statements based on the estimated emotions. By adjusting the recording timing based on the user's emotions, actions and statements can be recorded at more appropriate times. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can record actions and statements at more appropriate times by estimating the user's emotions and adjusting the timing of recording actions and statements based on the estimated emotions.

[0072] The data collection unit can analyze the user's past behavior and speech history and select the optimal recording method. For example, the data collection unit prioritizes recording actions that the user has frequently performed in the past. The data collection unit analyzes the user's past behavior and speech history and selects the optimal recording method. This allows the optimal recording method to be selected by analyzing the user's past behavior and speech history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can effectively record the user's behavior and speech by analyzing the user's past behavior and speech history and selecting the optimal recording method.

[0073] The data collection unit can filter recordings of actions and statements based on the user's current projects and areas of interest. For example, the data collection unit can prioritize recording statements related to the user's current projects. The data collection unit filters recordings of actions and statements based on the user's current projects and areas of interest. This allows for the recording of highly relevant actions and statements. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can record highly relevant actions and statements by filtering based on the user's current projects and areas of interest.

[0074] The data collection unit can estimate the user's emotions and determine the priority of actions and statements to record based on the estimated emotions. For example, if the user has positive emotions, the data collection unit will prioritize recording actions and statements related to those emotions. The data collection unit estimates the user's emotions and determines the priority of actions and statements to record based on the estimated emotions. This allows for the recording of more important actions and statements based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can prioritize recording more important actions and statements by estimating the user's emotions and determining the priority of actions and statements to record based on the estimated emotions.

[0075] The data collection unit can prioritize recording highly relevant actions and statements by considering the user's geographical location when recording actions and statements. For example, if the user is in a specific location, the data collection unit will prioritize recording actions and statements related to that location. The data collection unit prioritizes recording highly relevant actions and statements by considering the user's geographical location when recording actions and statements. This allows for the priority recording of highly relevant actions and statements by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can effectively record user actions and statements by prioritizing the recording of highly relevant actions and statements by considering the user's geographical location.

[0076] The data collection unit can analyze a user's social media activity and record relevant actions and statements when recording actions and statements. For example, the data collection unit can record relevant actions and statements based on content shared by the user on social media. The data collection unit analyzes a user's social media activity and records relevant actions and statements when recording actions and statements. This allows the data collection unit to record relevant actions and statements by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can effectively record a user's actions and statements by analyzing the user's social media activity and recording relevant actions and statements.

[0077] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user has positive emotions, the analysis unit will present the analysis results in a bright tone. The analysis unit estimates the user's emotions and adjusts the presentation of the analysis based on the estimated emotions. This allows for the provision of more appropriate analysis results by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can provide more appropriate analysis results by estimating the user's emotions and adjusting the presentation of the analysis based on the estimated emotions.

[0078] The analysis unit can adjust the level of detail of the analysis based on the importance of actions and statements during the analysis. For example, the analysis unit performs a detailed analysis on actions and statements of high importance. The analysis unit adjusts the level of detail of the analysis based on the importance of actions and statements during the analysis. This allows for a more detailed analysis of important information. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can effectively analyze user actions and statements by adjusting the level of detail of the analysis based on the importance of actions and statements.

[0079] The analysis unit can apply different analysis algorithms depending on the category of the behavior or statement during analysis. For example, the analysis unit applies a leadership evaluation algorithm to statements related to leadership. The analysis unit applies different analysis algorithms depending on the category of the behavior or statement during analysis. By applying different analysis algorithms depending on the category of the behavior or statement, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can effectively analyze the user's behavior and statements by applying different analysis algorithms depending on the category of the behavior or statement.

[0080] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will perform a short, concise analysis. The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated emotions. By adjusting the length of the analysis based on the user's emotions, it is possible to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can provide more appropriate analysis results by estimating the user's emotions and adjusting the length of the analysis based on the estimated emotions.

[0081] The analysis unit can determine the priority of analysis based on the timing of the submission of actions and statements during analysis. For example, the analysis unit may prioritize the analysis of recent actions and statements. The analysis unit determines the priority of analysis based on the timing of the submission of actions and statements. This allows for the prioritization of analysis of more important information. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can effectively analyze user actions and statements by determining the priority of analysis based on the timing of the submission of actions and statements.

[0082] The analysis unit can adjust the order of analysis based on the relevance of actions and statements during analysis. For example, the analysis unit prioritizes the analysis of highly relevant actions and statements. The analysis unit adjusts the order of analysis based on the relevance of actions and statements during analysis. This allows for the prioritization of analysis of more relevant information by adjusting the order of analysis based on the relevance of actions and statements. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can effectively analyze user actions and statements by adjusting the order of analysis based on the relevance of actions and statements.

[0083] The generation unit can estimate the user's emotions and adjust the speech's presentation based on those emotions. For example, if the user is relaxed, the generation unit will generate a speech that proceeds at a relaxed pace. The generation unit estimates the user's emotions and adjusts the speech's presentation based on those emotions. This allows for the generation of a more appropriate speech by adjusting the speech's presentation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI or not. For example, the generation unit can generate a more appropriate speech by estimating the user's emotions and adjusting the speech's presentation based on those emotions.

[0084] The generation unit can adjust the level of detail in the speech based on the importance of identified strengths and advantages during speech generation. For example, the generation unit generates detailed speech for strengths and advantages of high importance. The generation unit adjusts the level of detail in the speech based on the importance of identified strengths and advantages during speech generation. This allows more important information to be included in the speech in detail. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can effectively include the user's strengths and advantages in the speech by adjusting the level of detail in the speech based on the importance of identified strengths and advantages during speech generation.

[0085] The generation unit can apply different speech generation algorithms depending on the identified categories of strengths and advantages when generating a speech. For example, the generation unit applies a leadership evaluation algorithm to strengths related to leadership. The generation unit applies different speech generation algorithms depending on the identified categories of strengths and advantages when generating a speech. This allows for the generation of more effective speeches by applying the appropriate speech generation algorithm according to the identified categories of strengths and advantages. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can effectively include the user's strengths and advantages in the speech by applying different speech generation algorithms depending on the identified categories of strengths and advantages when generating a speech.

[0086] The generation unit can estimate the user's emotions and adjust the length of the speech based on the estimated emotions. For example, if the user is in a hurry, the generation unit will generate a short, concise speech. The generation unit estimates the user's emotions and adjusts the length of the speech based on the estimated emotions. This allows for the generation of more appropriate speeches by adjusting the length of the speech based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI or not. For example, the generation unit can generate more appropriate speeches by estimating the user's emotions and adjusting the length of the speech based on the estimated emotions.

[0087] The generation unit can prioritize speeches based on the timing of identified strengths and advantages during speech generation. For example, the generation unit prioritizes the inclusion of recently identified strengths and advantages in the speech. The generation unit prioritizes speeches based on the timing of identified strengths and advantages during speech generation. This allows for the generation of more timely speeches by prioritizing speeches based on the timing of identified strengths and advantages. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can effectively include the user's strengths and advantages in the speech by prioritizing speeches based on the timing of identified strengths and advantages during speech generation.

[0088] The generation unit can adjust the order of speech based on the relevance of identified strengths and advantages during speech generation. For example, the generation unit prioritizes the inclusion of highly relevant strengths and advantages in the speech. The generation unit adjusts the order of speech based on the relevance of identified strengths and advantages during speech generation. This allows for the generation of a more effective speech by adjusting the order of speech based on the relevance of identified strengths and advantages. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can effectively include the user's strengths and advantages in the speech by adjusting the order of speech based on the relevance of identified strengths and advantages during speech generation.

[0089] The text-to-speech unit can estimate the user's emotions and adjust the speech reading method based on the estimated emotions. For example, if the user is relaxed, the text-to-speech unit will read the speech at a relaxed pace. The text-to-speech unit estimates the user's emotions and adjusts the speech reading method based on the estimated emotions. This allows for more appropriate reading by adjusting the speech reading method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the text-to-speech unit may be performed using AI or not. For example, the text-to-speech unit can perform more appropriate reading by estimating the user's emotions and adjusting the speech reading method based on the estimated emotions.

[0090] The text-to-speech unit can select the optimal reading method by referring to the user's past response history during reading. For example, the text-to-speech unit may prioritize selecting a reading method that the user has previously preferred. The text-to-speech unit selects the optimal reading method by referring to the user's past response history during reading. This allows for the selection of a more effective reading method by referring to the user's past response history. Some or all of the above processing in the text-to-speech unit may be performed using AI or not. For example, the text-to-speech unit can perform an effective reading for the user by selecting the optimal reading method by referring to the user's past response history.

[0091] The reading unit can estimate the user's emotions and adjust the reading order of the speech based on the estimated emotions. For example, if the user has positive emotions, the reading unit will read positive content first. The reading unit estimates the user's emotions and adjusts the reading order of the speech based on the estimated emotions. This allows for more effective reading by adjusting the reading order of the speech based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reading unit may be performed using AI or not. For example, the reading unit can perform more effective reading by estimating the user's emotions and adjusting the reading order of the speech based on the estimated emotions.

[0092] The text-to-speech unit can select the optimal reading method while considering the user's device information. For example, if the user is using a smartphone, the text-to-speech unit adjusts the volume of the audio. The text-to-speech unit selects the optimal reading method while considering the user's device information. This allows for the selection of a more appropriate reading method by considering the user's device information. Some or all of the above processing in the text-to-speech unit may be performed using AI or not. For example, by selecting the optimal reading method while considering the user's device information, the text-to-speech unit can provide more effective reading to the user.

[0093] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0094] The self-assessment improvement system can also include a health management unit that monitors the user's health status. The health management unit measures the user's heart rate and stress level and provides this data to the analysis unit. For example, if the user shows a high stress level, the analysis unit can adjust the analysis results based on that data. The health management unit can also suggest appropriate rest and relaxation methods based on the user's health status. This allows for more effective self-assessment improvement by providing feedback that takes the user's health status into account.

[0095] The self-assessment improvement system can also include a schedule management unit to manage the user's schedule. This unit integrates with the user's calendar and task management tools, providing feedback to improve self-assessment before and after important meetings and tasks. For example, before an important presentation, it can generate and read aloud a speech reviewing the user's past successes, thereby boosting the user's confidence. Furthermore, the schedule management unit can provide feedback at the optimal time, tailored to the user's busy schedule. This allows for effective self-assessment improvement that aligns with the user's schedule.

[0096] The self-assessment improvement system can also include a learning management unit that manages the user's learning history. The learning management unit records the training and learning content the user has received in the past and provides it to the analysis unit. For example, if a user has received training on a specific skill, the system can prioritize the analysis of actions and statements related to that skill. The learning management unit can also suggest further training and resources for skill improvement based on the user's learning history. This allows for more effective self-assessment improvement by providing feedback that takes the user's learning history into account.

[0097] The self-assessment improvement system can also include a social analysis unit that analyzes the user's social interactions. The social analysis unit analyzes the user's interactions on social media and in the workplace to evaluate the user's communication skills and influence. For example, by identifying posts that received many responses on social media and analyzing their content, the system can identify the user's strengths. Furthermore, the social analysis unit can provide advice for improving communication skills based on the user's social interactions. This allows for more effective self-assessment improvement by providing feedback that takes the user's social interactions into consideration.

[0098] The self-evaluation improvement system may also include an emotional feedback unit that estimates the user's emotions and adjusts the content of the feedback based on those emotions. The emotional feedback unit monitors the user's emotions in real time, emphasizing praise when the user has positive emotions and providing encouragement when the user has negative emotions. For example, if the user is stressed, the emotional feedback unit can provide advice and words of encouragement to help them relax. Furthermore, the emotional feedback unit can adjust the timing and content of the feedback based on the user's emotions. This allows for more effective self-evaluation improvement by providing appropriate feedback tailored to the user's emotions.

[0099] The self-assessment improvement system can further include a goal-setting unit to assist users in setting goals. The goal-setting unit monitors progress based on the goals set by the user and provides feedback according to the degree of achievement. For example, if a user sets a goal for a specific project, the system can analyze its progress and provide praise and advice according to the degree of achievement. The goal-setting unit can also suggest specific steps and resources to help the user achieve their goals. This is expected to improve self-assessment by supporting the user in achieving their goals.

[0100] The self-evaluation improvement system may further include an emotion format adjustment unit that estimates the user's emotions and adjusts the format of the feedback based on the estimated emotions. The emotion format adjustment unit monitors the user's emotions in real time and provides feedback in the form of a video message when the user has positive emotions, and feedback in the form of a text message when the user has negative emotions. For example, if the user is relaxed, the emotion format adjustment unit can provide a congratulatory speech in the form of a video message. Furthermore, by adjusting the format of the feedback based on the user's emotions, the emotion format adjustment unit can provide more effective feedback. This is expected to improve self-evaluation by providing an appropriate feedback format according to the user's emotions.

[0101] The self-esteem improvement system can also include a "Hobbies and Interests" section that provides feedback considering the user's hobbies and interests. The Hobbies and Interests section provides relevant feedback based on the user's interests. For example, if the user is interested in music, it can provide success stories and inspiring speeches related to music. Furthermore, the Hobbies and Interests section can also provide advice on relaxation methods and stress relief based on the user's interests. This allows for more effective self-esteem improvement by providing feedback that takes the user's hobbies and interests into account.

[0102] The self-evaluation improvement system may further include an emotion frequency adjustment unit that estimates the user's emotions and adjusts the frequency of feedback based on the estimated emotions. The emotion frequency adjustment unit monitors the user's emotions in real time, increasing the frequency of feedback when the user has positive emotions and decreasing it when the user has negative emotions. For example, if the user is highly motivated, the emotion frequency adjustment unit can provide frequent praise. Furthermore, by adjusting the frequency of feedback based on the user's emotions, the emotion frequency adjustment unit can provide more effective feedback. This is expected to improve self-evaluation by providing an appropriate feedback frequency according to the user's emotions.

[0103] The self-assessment improvement system can also include a career path section that provides feedback while considering the user's career path. The career path section provides relevant feedback based on the user's career goals and past work experience. For example, if a user wants to improve their leadership skills, it can provide success stories and commendation speeches related to leadership. The career path section can also suggest training and resources for further skill development based on the user's career goals. This allows for more effective self-assessment improvement by providing feedback that considers the user's career path.

[0104] The following briefly describes the processing flow for example form 2.

[0105] Step 1: The data collection unit records the user's daily actions and statements in meetings. For example, it records instances where the user reports on project progress or offers constructive opinions in meetings. The data collection unit records the user's actions and statements and inputs them into the generating AI. Step 2: The analysis unit analyzes the data collected by the collection unit to identify the user's strengths and advantages. For example, if a user makes a statement that motivates the team, that statement will be identified as the user's strength. Step 3: The generation unit generates a praise speech based on the strengths and advantages identified by the analysis unit. For example, it might generate a speech such as, "Your leadership is boosting the morale of the entire team." Step 4: The reading unit reads the generated speech aloud using speech synthesis technology. This allows users to reaffirm their strengths and advantages and boost their motivation for work.

[0106] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0107] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0108] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0109] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, and reading unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 38B of the smart device 14 to record the user's daily actions and statements in meetings, and inputs this data into the generation AI via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data to identify the user's strengths and advantages. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which generates a speech of praise based on the identified strengths and advantages. The reading unit is implemented, for example, by the control unit 46A of the smart device 14, which reads the generated speech aloud using speech synthesis technology. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0110] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0111] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0116] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0117] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0118] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0119] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0120] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0122] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0124] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, and reading unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to record the user's daily actions and statements in meetings, and inputs this data into the generation AI via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data to identify the user's strengths and advantages. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which generates a speech of praise based on the identified strengths and advantages. The reading unit is implemented, for example, by the control unit 46A of the smart glasses 214, which reads the generated speech aloud using speech synthesis technology. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0126] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0127] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0129] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0133] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, and reading unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to record the user's daily actions and statements in meetings, and inputs this data into the generation AI via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data to identify the user's strengths and advantages. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which generates a speech of praise based on the identified strengths and advantages. The reading unit is implemented, for example, by the control unit 46A of the headset terminal 314, which reads the generated speech aloud using speech synthesis technology. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0142] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0143] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0150] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, and reading unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the robot 414 to record the user's daily actions and statements in meetings, and inputs this data into the generation AI via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data to identify the user's strengths and advantages. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which generates a speech of praise based on the identified strengths and advantages. The reading unit is implemented, for example, by the control unit 46A of the robot 414, which reads the generated speech aloud using speech synthesis technology. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0159] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0161] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0162] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0163] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0167] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0168] 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.

[0169] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0170] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0171] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0172] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0174] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0175] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0176] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0177] (Note 1) A data collection unit that records users' daily actions and comments made in meetings, An analysis unit analyzes the data collected by the aforementioned collection unit to identify the user's strengths and advantages, A generation unit that generates a speech of praise based on the advantages and strengths identified by the analysis unit, The system includes a reading unit that reads out the speech generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Records users' daily actions and comments in meetings. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The data collected by the data collection unit is analyzed to identify the user's strengths and advantages. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate a praise speech based on identified strengths and advantages. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reading unit, The generated speech is read aloud using speech synthesis technology. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of recording actions and statements based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past behavior and statement history to select the optimal recording method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When recording actions and statements, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and determines the priority of actions and statements to record based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When recording actions and statements, the system prioritizes recording highly relevant actions and statements by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When recording actions and statements, the system analyzes the user's social media activity and records relevant actions and statements. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of actions and statements. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the behavior or statement. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of analysis is determined based on the timing of the submission of actions and statements. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of actions and statements. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It estimates the user's emotions and adjusts the speech delivery based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is During speech generation, the level of detail in the speech is adjusted based on the importance of identified strengths and advantages. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating speech, different speech generation algorithms are applied depending on the identified categories of strengths and advantages. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and adjusts the length of the speech based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating a speech, prioritize the speech based on when the identified strengths and advantages were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During speech generation, the order of speeches is adjusted based on the relevance of identified strengths and advantages. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reading unit, It estimates the user's emotions and adjusts the speech reading method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reading unit, When reading aloud, the system selects the optimal reading method by referring to the user's past response history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reading unit, It estimates the user's emotions and adjusts the order in which the speech is read based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reading unit, When reading aloud, the system selects the optimal reading method by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that records users' daily actions and comments made in meetings, An analysis unit analyzes the data collected by the aforementioned collection unit to identify the user's strengths and advantages, A generation unit that generates a speech of praise based on the advantages and strengths identified by the analysis unit, The system includes a reading unit that reads out the speech generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Records users' daily actions and comments in meetings. The system according to feature 1.

3. The aforementioned analysis unit, The data collected by the aforementioned collection unit is analyzed to identify the user's strengths and advantages. The system according to feature 1.

4. The generating unit is Generate a praise speech based on identified strengths and advantages. The system according to feature 1.

5. The aforementioned reading unit, The generated speech is read aloud using speech synthesis technology. The system according to feature 1.

6. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of recording actions and statements based on the estimated user emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the user's past behavior and statement history to select the optimal recording method. The system according to feature 1.

8. The aforementioned collection unit is When recording actions and statements, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

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