System

Abstract

An object of a system according to an embodiment is to analyze daily behavior and conversation data of an individual and suggest an optimal course and directionality to the individual.SOLUTION: A system includes a collection part, an analysis part, an understanding part, a suggestion part, and a protection part. The collection unit collects daily behavior and conversation of an individual. The analysis unit analyzes the data collected by the collection unit. The understanding unit understands personal preferences and thought patterns based on the data analyzed by the analysis unit. The suggestion unit suggests a course or directionality to the individual based on the information obtained by the understanding unit. The protection unit anonymizes or encrypts data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not fully utilizing an individual's daily behavior and conversation data to understand their preferences and thought patterns and suggest appropriate career paths and directions.

[0005] The system according to the embodiment aims to analyze an individual's daily behavior and conversation data and suggest the best course and direction for the individual. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an understanding unit, a suggestion unit, and a protection unit. The collection unit collects an individual's daily behavior and conversations. The analysis unit analyzes the data collected by the collection unit. The understanding unit understands the individual's preferences and thought patterns based on the data analyzed by the analysis unit. The suggestion unit suggests a path or direction to the individual based on the information obtained by the understanding unit. The protection unit anonymizes or encrypts the data. [Effects of the Invention]

[0007] The system according to the embodiment can analyze an individual's daily behavior and conversation data and suggest the best career path and direction for the individual. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A career suggestion system according to an embodiment of the present invention collects an individual's daily behavior, conversations, and other data associated with the individual, analyzes them using a generation AI, understands the individual's preferences and thought patterns, and suggests optimal career paths and directions. The career suggestion system collects an individual's daily behavior, conversations, and other personal data, analyzes them using a generation AI, and understands the individual's preferences and thought patterns. For example, the career suggestion system collects detailed data, such as the actions an individual takes and the conversations they have. Then, the career suggestion system uses a generation AI to analyze the collected data and understand the individual's preferences and thought patterns. For example, the generation AI analyzes the individual's hobbies and ways of thinking. Next, the career suggestion system suggests optimal career paths and directions for the individual based on the understood preferences and thought patterns. For example, the generation AI suggests occupations and lifestyles suitable for the individual. This allows the career suggestion system to increase an individual's success rate and satisfaction. This allows an individual to find the optimal career path and direction for themselves. For example, by finding an occupation that suits them, individuals can increase their job satisfaction and improve their success rate. Additionally, finding a lifestyle that suits you improves your quality of life and increases your satisfaction.

[0029] A career suggestion system according to an embodiment includes a collection unit, an analysis unit, an understanding unit, a suggestion unit, and a protection unit. The collection unit collects an individual's daily activities and conversations. Examples of the individual's daily activities and conversations include, but are not limited to, voice data, text messages, and location information. The collection unit collects the individual's daily activities and conversations using, for example, a smartphone or a wearable device. The collection unit can also collect the individual's behavioral history and social media activity. For example, the collection unit collects the user's stress level and fatigue level using sensor data from the smartphone. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, machine learning or natural language processing. For example, the analysis unit uses deep learning technology to analyze the collected data with high accuracy. The analysis unit can also improve the accuracy of the analysis by taking into account interrelationships between data. For example, the analysis unit analyzes correlations between data to improve the accuracy of the analysis results. The understanding unit understands the individual's preferences and thought patterns based on the data analyzed by the analysis unit. The understanding unit, for example, understands an individual's hobbies and ways of thinking based on the analyzed data. The understanding unit can also improve the accuracy of understanding preferences and thought patterns by referring to past data. For example, the understanding unit understands preferences and thought patterns by referring to the user's past behavioral data. The suggestion unit suggests a career path or direction to the individual based on the information obtained by the understanding unit. For example, the suggestion unit suggests the most suitable occupation or lifestyle for the individual. The suggestion unit can also introduce new techniques to increase the individual's success rate and satisfaction. For example, the suggestion unit suggests the optimal career path based on the user's past success stories. The protection unit anonymizes or encrypts the collected data. The protection unit improves the accuracy of data protection by, for example, introducing the latest security technology. The protection unit can also improve the accuracy of protection by taking into account the interrelationships between data. For example, the protection unit analyzes correlations between data to improve the accuracy of anonymization and encryption. As a result, the career suggestion system according to the embodiment can suggest the optimal career path or direction based on the individual's preferences and thought patterns.

[0030] The collection unit can collect the individual's daily activities and conversations using a smartphone or a wearable device. The collection unit collects the individual's daily activities and conversations using, for example, sensor data from the smartphone. For example, the collection unit analyzes heart rate data from a smartwatch to estimate the user's stress level. The collection unit can also estimate the user's fatigue level from the user's walking pattern using an acceleration sensor in the smartphone. The collection unit can also analyze electrodermal activity data from a wearable device to evaluate the user's stress level. This allows the individual's daily activities and conversations to be efficiently collected using a smartphone or a wearable device. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the smartphone's sensor data into a generation AI and have the generation AI analyze the data.

[0031] The analysis unit can analyze the collected data using machine learning or natural language processing. The analysis unit analyzes the collected data using, for example, a machine learning algorithm. For example, the analysis unit analyzes the collected data with high accuracy using deep learning technology. The analysis unit can also analyze text data using natural language processing technology. For example, the analysis unit analyzes the meaning of the text data using morphological analysis. The analysis unit can also improve the accuracy of the analysis by taking into account interrelationships between data. For example, the analysis unit analyzes correlations between data to improve the accuracy of the analysis results. In this way, the accuracy of data analysis is improved by using machine learning or natural language processing. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI analyze the data.

[0032] The understanding unit can understand an individual's preferences and thought patterns based on the data analyzed by the analysis unit. The understanding unit, for example, understands an individual's hobbies and way of thinking based on the analyzed data. For example, the understanding unit refers to the user's past behavioral data to understand the preferences and thought patterns. The understanding unit can also refer to the user's past conversation data to understand the preferences and thought patterns. The understanding unit can also refer to the user's past feedback to understand the preferences and thought patterns. This allows an accurate understanding of an individual's preferences and thought patterns based on the analyzed data. Some or all of the above-described processing in the understanding unit may be performed using, for example, AI, or may be performed without using AI. For example, the understanding unit can input the analyzed data to a generation AI and cause the generation AI to understand the preferences and thought patterns.

[0033] The suggestion unit can suggest a career path or direction to an individual based on the information obtained by the understanding unit. The suggestion unit, for example, suggests the most suitable occupation or lifestyle for an individual. For example, the suggestion unit suggests the most suitable career path based on the user's past success stories. The suggestion unit can also suggest the most suitable direction based on the user's past satisfaction data. The suggestion unit can also improve the accuracy of the suggestions based on the user's past feedback. This can increase the success rate and satisfaction by suggesting the most suitable career path or direction for an individual. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the information obtained by the understanding unit into a generation AI and have the generation AI execute the suggestions on a career path or direction.

[0034] The protection unit can anonymize or encrypt the collected data. The protection unit can, for example, introduce the latest security technology to improve the accuracy of data protection. For example, the protection unit can introduce the latest encryption technology to improve the accuracy of data protection. The protection unit can also introduce the latest anonymization technology to improve the accuracy of data protection. The protection unit can also improve the accuracy of protection by taking into account the interrelationships between data. For example, the protection unit can analyze the correlations between data and improve the accuracy of anonymization or encryption. As a result, the anonymization and encryption of data enhances the protection of personal information. Some or all of the above-mentioned processing in the protection unit can be performed using, for example, AI, or can be performed without using AI. For example, the protection unit can input the collected data to a generation AI and have the generation AI anonymize or encrypt the data.

[0035] The collection unit can collect the user's stress level and fatigue level by utilizing sensor data from a smartphone or wearable device. The collection unit, for example, analyzes heart rate data from a smartwatch to estimate the user's stress level. For example, the collection unit can use an acceleration sensor in a smartphone to estimate the user's fatigue level from their walking pattern. The collection unit can also analyze electrodermal activity data from a wearable device to evaluate the user's stress level. This allows the user's stress level and fatigue level to be accurately collected by utilizing sensor data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input heart rate data from the smartwatch to a generation AI and have the generation AI estimate the stress level.

[0036] The collection unit can analyze the user's past behavioral history and select the optimal data collection method. The collection unit, for example, optimizes the timing of data collection based on the user's frequent behavior in the past. For example, the collection unit analyzes the user's past behavioral patterns and selects the most efficient data collection method. The collection unit can also concentrate data collection during specific time periods based on the user's past behavioral history. This allows the optimal data collection method to be selected by analyzing the past behavioral history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past behavioral history into a generation AI and have the generation AI select the optimal data collection method.

[0037] The collection unit can customize the frequency and timing of data collection based on the user's lifestyle rhythm. For example, if the user has a morning-type lifestyle rhythm, the collection unit concentrates data collection in the morning hours. For example, if the user has a nocturnal lifestyle rhythm, the collection unit concentrates data collection in the evening hours. The collection unit can also adjust the frequency of data collection according to the user's lifestyle rhythm. This allows more appropriate data to be collected by adjusting the frequency and timing of data collection according to the user's lifestyle rhythm. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's lifestyle rhythm data into the generation AI and cause the generation AI to customize the frequency and timing of data collection.

[0038] The collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific location, the collection unit prioritizes collecting data related to that location. For example, when the user is traveling, the collection unit prioritizes collecting data related to the user's destination. Furthermore, when the user is at home, the collection unit can also prioritize collecting data related to the user's home. In this way, highly relevant data can be collected preferentially by taking into account the geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0039] The collection unit can analyze the user's social media activity and collect related data. For example, the collection unit collects data related to places where the user has checked in on social media. For example, the collection unit analyzes the content of the user's social media posts and collects related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. This allows for efficient collection of related data by analyzing social media activity. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to collect related data.

[0040] The collection unit can customize the data collection method by reflecting the user's past feedback. The collection unit, for example, optimizes the data collection method based on feedback provided by the user in the past. For example, the collection unit preferentially adopts a specific data collection method based on the user's past feedback. The collection unit can also analyze the user's past feedback and improve the data collection method. In this way, the data collection method can be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the data collection method.

[0041] The analysis unit can introduce new techniques to improve the accuracy of data analysis using machine learning or natural language processing. The analysis unit, for example, improves the accuracy of data analysis by improving machine learning algorithms. For example, the analysis unit introduces natural language processing technology to improve the accuracy of text data analysis. The analysis unit can also use deep learning technology to improve the accuracy of data analysis. This improves the accuracy of data analysis by introducing new techniques. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input collected data into a generation AI and have the generation AI implement the new technique.

[0042] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between data during analysis. The analysis unit, for example, analyzes the correlations between data and improves the accuracy of the analysis results. For example, the analysis unit adjusts the analysis algorithm by taking into account the interdependence of data. The analysis unit can also visualize the interrelationships between data to make the analysis results easier to understand. This improves the accuracy of the analysis by taking the interrelationships between data into account. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the interrelationships between data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a natural language processing algorithm to text data. For example, the analysis unit applies a statistical analysis algorithm to numerical data. The analysis unit can also apply an image analysis algorithm to image data. This improves the accuracy of analysis by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. The analysis unit, for example, prioritizes analysis of the most recent data. For example, the analysis unit postpones data that was submitted earlier. The analysis unit can also adjust the analysis schedule based on the time of submission. In this way, by determining the priority of analysis based on the time of data submission, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data submission to the generation AI and have the generation AI determine the analysis priority.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit postpones analysis of less relevant data. The analysis unit can also optimize the order of analysis based on the relevance of the data. This allows highly relevant data to be analyzed preferentially by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis results according to the user's level of expertise. For example, the analysis unit explains the analysis results in simple terms to a user with little technical expertise. For example, the analysis unit explains the analysis results using detailed technical terms to a user with extensive technical expertise. The analysis unit can also adjust the display method of the analysis results according to the user's level of expertise. This makes it possible to provide analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis results according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise to the generation AI and have the generation AI use technical terms in the analysis results.

[0047] During understanding, the understanding unit can improve the accuracy of understanding preferences and thought patterns by referring to past data. The understanding unit, for example, refers to the user's past behavioral data to understand preferences and thought patterns. For example, the understanding unit refers to the user's past conversation data to understand preferences and thought patterns. The understanding unit can also refer to the user's past feedback to understand preferences and thought patterns. By referring to past data, the accuracy of understanding preferences and thought patterns is improved. Some or all of the above-mentioned processing in the understanding unit may be performed using, for example, AI, or may be performed without using AI. For example, the understanding unit can input past data into the generation AI and cause the generation AI to improve the accuracy of understanding preferences and thought patterns.

[0048] The understanding unit can improve the accuracy of understanding by taking into account the interrelationships between data during understanding. The understanding unit, for example, analyzes correlations between data to understand preferences and thought patterns. For example, the understanding unit adjusts the understanding algorithm by taking into account the interdependence of data. The understanding unit can also visualize the interrelationships between data to make it easier to understand preferences and thought patterns. This improves the accuracy of understanding by taking into account the interrelationships between data. Some or all of the above-mentioned processing in the understanding unit may be performed using, for example, AI, or may be performed without using AI. For example, the understanding unit can input the interrelationships between data into the generation AI and cause the generation AI to improve the accuracy of understanding.

[0049] During understanding, the understanding unit can apply different understanding algorithms depending on the data category. For example, the understanding unit applies a natural language processing algorithm to text data. For example, the understanding unit applies a statistical analysis algorithm to numerical data. The understanding unit can also apply an image analysis algorithm to image data. This improves the accuracy of understanding by applying an appropriate understanding algorithm depending on the data category. Some or all of the above-mentioned processing in the understanding unit may be performed using AI, for example, or may be performed without using AI. For example, the understanding unit can input the data category to the generation AI and cause the generation AI to apply an appropriate understanding algorithm.

[0050] During comprehension, the comprehension unit can determine the priority of comprehension based on the time of data submission. The comprehension unit, for example, prioritizes understanding the most recent data. For example, the comprehension unit postpones data that was submitted earlier. The comprehension unit can also adjust the comprehension schedule based on the time of submission. In this way, by determining the priority of comprehension based on the time of data submission, the most recent data can be prioritized for comprehension. Some or all of the above-described processing in the comprehension unit may be performed using, for example, AI, or may be performed without using AI. For example, the comprehension unit can input the time of data submission to the generation AI and have the generation AI determine the priority of comprehension.

[0051] During comprehension, the comprehension unit can adjust the order of comprehension based on the relevance of the data. For example, the comprehension unit prioritizes understanding of highly relevant data. For example, the comprehension unit postpones understanding of less relevant data. The comprehension unit can also optimize the order of comprehension based on the relevance of the data. This allows highly relevant data to be prioritized for comprehension by adjusting the order of comprehension based on the relevance of the data. Some or all of the above-described processing in the comprehension unit may be performed using, or without, AI, for example. For example, the comprehension unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of comprehension.

[0052] During understanding, the understanding unit can adjust the use of technical terms in the understanding result according to the user's level of expertise. For example, the understanding unit explains the understanding result in simple terms to a user with little technical expertise. For example, the understanding unit explains the understanding result using detailed technical terms to a user with extensive technical expertise. The understanding unit can also adjust the display method of the understanding result according to the user's level of expertise. In this way, by adjusting the use of technical terms in the understanding result according to the user's level of expertise, it is possible to provide an understanding result that is easy for the user to understand. Some or all of the above-mentioned processing in the understanding unit may be performed using AI, for example, or may be performed without using AI. For example, the understanding unit can input the user's level of expertise to the generation AI and cause the generation AI to use technical terms in the understanding result.

[0053] When making a suggestion, the suggestion unit can introduce a new method to increase an individual's success rate or satisfaction. The suggestion unit, for example, suggests the optimal path based on the user's past success stories. For example, the suggestion unit suggests the optimal direction based on the user's past satisfaction data. The suggestion unit can also improve the accuracy of the suggestions based on the user's past feedback. This makes it possible to introduce a new method to increase an individual's success rate or satisfaction. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past data into the generation AI and have the generation AI implement the new method.

[0054] The suggestion unit can improve the accuracy of suggestions by taking into account the interrelationships between data when making suggestions. The suggestion unit, for example, analyzes correlations between data and improves the accuracy of suggestions. For example, the suggestion unit adjusts the suggestion algorithm by taking into account the interdependence of data. The suggestion unit can also visualize the interrelationships between data to make the suggestions easier to understand. In this way, the accuracy of suggestions is improved by taking the interrelationships between data into consideration. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the interrelationships between data into the generation AI and cause the generation AI to improve the accuracy of suggestions.

[0055] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the data category. For example, the suggestion unit applies a natural language processing algorithm to text data. For example, the suggestion unit applies a statistical analysis algorithm to numerical data. The suggestion unit can also apply an image analysis algorithm to image data. This improves the accuracy of suggestions by applying an appropriate suggestion algorithm depending on the data category. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the data category to the generation AI and cause the generation AI to apply an appropriate suggestion algorithm.

[0056] When making a suggestion, the suggestion unit can determine the priority of the suggestion based on the time of data submission. The suggestion unit provides suggestions based on, for example, the latest data. For example, the suggestion unit postpones data that has been submitted earlier. The suggestion unit can also adjust the schedule of suggestions based on the time of submission. In this way, by determining the priority of suggestions based on the time of data submission, suggestions can be provided based on the latest data. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the time of data submission to the generation AI and have the generation AI determine the priority of suggestions.

[0057] The suggestion unit can adjust the order of suggestions based on the relevance of the data when making a suggestion. The suggestion unit, for example, provides suggestions based on highly relevant data. For example, the suggestion unit postpones less relevant data. The suggestion unit can also optimize the order of suggestions based on the relevance of the data. In this way, by adjusting the order of suggestions based on the relevance of the data, suggestions can be provided based on highly relevant data. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of suggestions.

[0058] When making a suggestion, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. For example, the suggestion unit provides suggestions in simple language to a user with little technical expertise. For example, the suggestion unit provides suggestions using detailed technical terminology to a user with extensive technical expertise. The suggestion unit can also adjust the display method of the suggestion according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the suggestion according to the user's level of expertise, suggestions that are easy for the user to understand can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise to the generation AI and cause the generation AI to use technical terminology in the suggestion.

[0059] The protection unit can improve the accuracy of protection by introducing the latest security technology when anonymizing or encrypting data. The protection unit, for example, introduces the latest encryption technology to improve the accuracy of data protection. For example, the protection unit introduces the latest anonymization technology to improve the accuracy of data protection. The protection unit can also introduce the latest security protocol to improve the accuracy of data protection. As a result, the accuracy of data protection is improved by introducing the latest security technology. Some or all of the above-mentioned processing in the protection unit may be performed using AI, for example, or may be performed without using AI. For example, the protection unit can input the latest security technology into the generation AI and cause the generation AI to improve the accuracy of protection.

[0060] The protection unit can improve the accuracy of protection by taking into account the interrelationships between data when anonymizing or encrypting data. The protection unit, for example, analyzes the correlations between data and improves the accuracy of anonymization or encryption. For example, the protection unit adjusts the anonymization or encryption algorithm by taking into account the interdependence of data. The protection unit can also visualize the interrelationships between data to make the anonymization or encryption easier to understand. In this way, the accuracy of data protection is improved by taking into account the interrelationships between data. Some or all of the above-mentioned processing in the protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the protection unit can input the interrelationships between data into the generation AI and cause the generation AI to improve the accuracy of protection.

[0061] When anonymizing or encrypting data, the protection unit can determine the priority of protection based on the time of data submission. For example, the protection unit prioritizes anonymizing or encrypting the most recent data. For example, the protection unit postpones data that was submitted earlier. The protection unit can also adjust the data protection schedule based on the time of submission. In this way, by determining the priority of protection based on the time of data submission, the most recent data can be protected preferentially. Some or all of the above-mentioned processing in the protection unit may be performed using AI, for example, or may be performed without using AI. For example, the protection unit can input the time of data submission to the generation AI and have the generation AI determine the priority of protection.

[0062] When anonymizing or encrypting data, the protection unit can adjust the order of protection based on the relevance of the data. For example, the protection unit prioritizes anonymizing or encrypting highly relevant data. For example, the protection unit postpones data with low relevance. The protection unit can also optimize the order of anonymization or encryption based on the relevance of the data. As a result, highly relevant data can be protected preferentially by adjusting the order of protection based on the relevance of the data. Some or all of the above-mentioned processing in the protection unit may be performed using AI, for example, or may be performed without using AI. For example, the protection unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of protection.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The career suggestion system may further include a feedback collection unit. The feedback collection unit collects feedback provided by the user and transmits it to the analysis unit. For example, the feedback collection unit may collect feedback such as how the user felt about the career suggestion and what improvements could be made. The feedback collection unit may also collect feedback about the results of the user actually attempting the suggested career. This allows the career suggestion system to improve the accuracy of the career suggestions based on the user's feedback.

[0065] The career suggestion system may further include a real-time monitoring unit. The real-time monitoring unit monitors the user's daily actions and conversations in real time and sends the information to the collection unit. For example, if the user starts a new hobby, the information is collected immediately and sent to the analysis unit. The real-time monitoring unit can also detect changes in the user's behavioral patterns and update the content of the career suggestions as appropriate. This allows the career suggestion system to suggest the optimal career path based on the user's latest situation.

[0066] The career suggestion system can also collect health data of the user and send it to the analysis unit. For example, the system can collect the user's sleep patterns and exercise volume and analyze them in the analysis unit. The system can also suggest suitable occupations and lifestyles for the user based on the health data. This allows the career suggestion system to suggest the optimal career path based on the user's health condition.

[0067] The career suggestion system can further collect social network data of the user and send it to the analysis unit. For example, the occupations and lifestyles of the user's friends and family are collected and analyzed by the analysis unit. The system can also suggest occupations and lifestyles suitable for the user based on the social network data. This allows the career suggestion system to suggest the optimal career path based on the user's social network.

[0068] The career suggestion system can further suggest career paths based on the user's hobbies and interests. For example, if the user is interested in music, it can suggest music-related occupations and lifestyles. Also, if the user is interested in sports, it can suggest sports-related career paths. In this way, the career suggestion system can suggest the optimal career path based on the user's hobbies and interests.

[0069] The career suggestion system can also collect the user's geographical location information and send it to the analysis unit. For example, if the user lives in a specific area, it can suggest occupations and lifestyles related to that area. Also, if the user is traveling, it can suggest careers related to the user's destination. This allows the career suggestion system to suggest the optimal career path based on the user's geographical location information.

[0070] The processing flow of the first embodiment will be briefly explained below.

[0071] Step 1: The collection unit collects an individual's daily activities and conversations. These activities and conversations include voice data, text messages, and location information. The collection unit collects this data using smartphones and wearable devices. The collection unit can also collect an individual's behavioral history and social media activity. For example, sensor data from a smartphone can be used to collect the user's stress level and fatigue level. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses machine learning and natural language processing to analyze the data, and deep learning technology to perform high-precision analysis. It also analyzes correlations between data to improve the accuracy of the analysis results. Step 3: The understanding unit understands the individual's preferences and thought patterns based on the data analyzed by the analysis unit. The understanding unit understands the individual's hobbies and ways of thinking based on the analyzed data and improves the accuracy of the understanding by referring to past data. For example, it may understand preferences and thought patterns by referring to the user's past behavioral data. Step 4: The suggestion unit suggests career paths and directions to the individual based on the information obtained by the understanding unit. The suggestion unit suggests the best career and lifestyle for the individual and introduces new methods to increase the individual's success rate and satisfaction. For example, it suggests the best career path based on the user's past success stories. Step 5: The protection unit anonymizes or encrypts the collected data. The protection unit uses the latest security technology to improve the accuracy of data protection and analyzes correlations between data to improve the accuracy of anonymization or encryption.

[0072] (Example 2) A career suggestion system according to an embodiment of the present invention collects an individual's daily behavior, conversations, and other data associated with the individual, analyzes them using a generation AI, understands the individual's preferences and thought patterns, and suggests optimal career paths and directions. The career suggestion system collects an individual's daily behavior, conversations, and other personal data, analyzes them using a generation AI, and understands the individual's preferences and thought patterns. For example, the career suggestion system collects detailed data, such as the actions an individual takes and the conversations they have. Then, the career suggestion system uses a generation AI to analyze the collected data and understand the individual's preferences and thought patterns. For example, the generation AI analyzes the individual's hobbies and ways of thinking. Next, the career suggestion system suggests optimal career paths and directions for the individual based on the understood preferences and thought patterns. For example, the generation AI suggests occupations and lifestyles suitable for the individual. This allows the career suggestion system to increase an individual's success rate and satisfaction. This allows an individual to find the optimal career path and direction for themselves. For example, by finding an occupation that suits them, individuals can increase their job satisfaction and improve their success rate. Additionally, finding a lifestyle that suits you improves your quality of life and increases your satisfaction.

[0073] A career suggestion system according to an embodiment includes a collection unit, an analysis unit, an understanding unit, a suggestion unit, and a protection unit. The collection unit collects an individual's daily activities and conversations. Examples of the individual's daily activities and conversations include, but are not limited to, voice data, text messages, and location information. The collection unit collects the individual's daily activities and conversations using, for example, a smartphone or a wearable device. The collection unit can also collect the individual's behavioral history and social media activity. For example, the collection unit collects the user's stress level and fatigue level using sensor data from the smartphone. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, machine learning or natural language processing. For example, the analysis unit uses deep learning technology to analyze the collected data with high accuracy. The analysis unit can also improve the accuracy of the analysis by taking into account interrelationships between data. For example, the analysis unit analyzes correlations between data to improve the accuracy of the analysis results. The understanding unit understands the individual's preferences and thought patterns based on the data analyzed by the analysis unit. The understanding unit, for example, understands an individual's hobbies and ways of thinking based on the analyzed data. The understanding unit can also improve the accuracy of understanding preferences and thought patterns by referring to past data. For example, the understanding unit understands preferences and thought patterns by referring to the user's past behavioral data. The suggestion unit suggests a career path or direction to the individual based on the information obtained by the understanding unit. For example, the suggestion unit suggests the most suitable occupation or lifestyle for the individual. The suggestion unit can also introduce new techniques to increase the individual's success rate and satisfaction. For example, the suggestion unit suggests the optimal career path based on the user's past success stories. The protection unit anonymizes or encrypts the collected data. The protection unit improves the accuracy of data protection by, for example, introducing the latest security technology. The protection unit can also improve the accuracy of protection by taking into account the interrelationships between data. For example, the protection unit analyzes correlations between data to improve the accuracy of anonymization and encryption. As a result, the career suggestion system according to the embodiment can suggest the optimal career path or direction based on the individual's preferences and thought patterns.

[0074] The collection unit can collect the individual's daily activities and conversations using a smartphone or a wearable device. The collection unit collects the individual's daily activities and conversations using, for example, sensor data from the smartphone. For example, the collection unit analyzes heart rate data from a smartwatch to estimate the user's stress level. The collection unit can also estimate the user's fatigue level from the user's walking pattern using an acceleration sensor in the smartphone. The collection unit can also analyze electrodermal activity data from a wearable device to evaluate the user's stress level. This allows the individual's daily activities and conversations to be efficiently collected using a smartphone or a wearable device. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the smartphone's sensor data into a generation AI and have the generation AI analyze the data.

[0075] The analysis unit can analyze the collected data using machine learning or natural language processing. The analysis unit analyzes the collected data using, for example, a machine learning algorithm. For example, the analysis unit analyzes the collected data with high accuracy using deep learning technology. The analysis unit can also analyze text data using natural language processing technology. For example, the analysis unit analyzes the meaning of the text data using morphological analysis. The analysis unit can also improve the accuracy of the analysis by taking into account interrelationships between data. For example, the analysis unit analyzes correlations between data to improve the accuracy of the analysis results. In this way, the accuracy of data analysis is improved by using machine learning or natural language processing. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI analyze the data.

[0076] The understanding unit can understand an individual's preferences and thought patterns based on the data analyzed by the analysis unit. The understanding unit, for example, understands an individual's hobbies and way of thinking based on the analyzed data. For example, the understanding unit refers to the user's past behavioral data to understand the preferences and thought patterns. The understanding unit can also refer to the user's past conversation data to understand the preferences and thought patterns. The understanding unit can also refer to the user's past feedback to understand the preferences and thought patterns. This allows an accurate understanding of an individual's preferences and thought patterns based on the analyzed data. Some or all of the above-described processing in the understanding unit may be performed using, for example, AI, or may be performed without using AI. For example, the understanding unit can input the analyzed data to a generation AI and cause the generation AI to understand the preferences and thought patterns.

[0077] The suggestion unit can suggest a career path or direction to an individual based on the information obtained by the understanding unit. The suggestion unit, for example, suggests the most suitable occupation or lifestyle for an individual. For example, the suggestion unit suggests the most suitable career path based on the user's past success stories. The suggestion unit can also suggest the most suitable direction based on the user's past satisfaction data. The suggestion unit can also improve the accuracy of the suggestions based on the user's past feedback. This can increase the success rate and satisfaction by suggesting the most suitable career path or direction for an individual. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the information obtained by the understanding unit into a generation AI and have the generation AI execute the suggestions on a career path or direction.

[0078] The protection unit can anonymize or encrypt the collected data. The protection unit can, for example, introduce the latest security technology to improve the accuracy of data protection. For example, the protection unit can introduce the latest encryption technology to improve the accuracy of data protection. The protection unit can also introduce the latest anonymization technology to improve the accuracy of data protection. The protection unit can also improve the accuracy of protection by taking into account the interrelationships between data. For example, the protection unit can analyze the correlations between data and improve the accuracy of anonymization or encryption. As a result, the anonymization and encryption of data enhances the protection of personal information. Some or all of the above-mentioned processing in the protection unit can be performed using, for example, AI, or can be performed without using AI. For example, the protection unit can input the collected data to a generation AI and have the generation AI anonymize or encrypt the data.

[0079] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit temporarily stops data collection and resumes it when the user is relaxed. For example, if the user is relaxed, the collection unit actively collects data and collects detailed data. Furthermore, if the user is in a hurry, the collection unit can minimize data collection and collect detailed data later. This allows for more appropriate data to be collected by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of data collection.

[0080] The collection unit can collect the user's stress level and fatigue level by utilizing sensor data from a smartphone or wearable device. The collection unit, for example, analyzes heart rate data from a smartwatch to estimate the user's stress level. For example, the collection unit can use an acceleration sensor in a smartphone to estimate the user's fatigue level from their walking pattern. The collection unit can also analyze electrodermal activity data from a wearable device to evaluate the user's stress level. This allows the user's stress level and fatigue level to be accurately collected by utilizing sensor data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input heart rate data from the smartwatch to a generation AI and have the generation AI estimate the stress level.

[0081] The collection unit can analyze the user's past behavioral history and select the optimal data collection method. The collection unit, for example, optimizes the timing of data collection based on the user's frequent behavior in the past. For example, the collection unit analyzes the user's past behavioral patterns and selects the most efficient data collection method. The collection unit can also concentrate data collection during specific time periods based on the user's past behavioral history. This allows the optimal data collection method to be selected by analyzing the past behavioral history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past behavioral history into a generation AI and have the generation AI select the optimal data collection method.

[0082] The collection unit can customize the frequency and timing of data collection based on the user's lifestyle rhythm. For example, if the user has a morning-type lifestyle rhythm, the collection unit concentrates data collection in the morning hours. For example, if the user has a nocturnal lifestyle rhythm, the collection unit concentrates data collection in the evening hours. The collection unit can also adjust the frequency of data collection according to the user's lifestyle rhythm. This allows more appropriate data to be collected by adjusting the frequency and timing of data collection according to the user's lifestyle rhythm. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's lifestyle rhythm data into the generation AI and cause the generation AI to customize the frequency and timing of data collection.

[0083] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit prioritizes collecting stress-related data. For example, if the user is relaxed, the collection unit prioritizes collecting relaxation-related data. Furthermore, if the user is in a hurry, the collection unit can also prioritize collecting data related to urgent actions. This allows for the priority of data to be collected to be determined according to the user's emotions, thereby enabling more important data to be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data to be collected.

[0084] The collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific location, the collection unit prioritizes collecting data related to that location. For example, when the user is traveling, the collection unit prioritizes collecting data related to the user's destination. Furthermore, when the user is at home, the collection unit can also prioritize collecting data related to the user's home. In this way, highly relevant data can be collected preferentially by taking into account the geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0085] The collection unit can analyze the user's social media activity and collect related data. For example, the collection unit collects data related to places where the user has checked in on social media. For example, the collection unit analyzes the content of the user's social media posts and collects related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. This allows for efficient collection of related data by analyzing social media activity. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to collect related data.

[0086] The collection unit can customize the data collection method by reflecting the user's past feedback. The collection unit, for example, optimizes the data collection method based on feedback provided by the user in the past. For example, the collection unit preferentially adopts a specific data collection method based on the user's past feedback. The collection unit can also analyze the user's past feedback and improve the data collection method. In this way, the data collection method can be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the data collection method.

[0087] The analysis unit can estimate the user's emotions and adjust the data analysis algorithm based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit prioritizes stress-related data in the analysis. For example, if the user is relaxed, the analysis unit prioritizes relaxation-related data in the analysis. Furthermore, if the user is in a hurry, the analysis unit can also prioritize data related to urgent actions in the analysis. This improves analysis accuracy by adjusting the data analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the data analysis algorithm.

[0088] The analysis unit can introduce new techniques to improve the accuracy of data analysis using machine learning or natural language processing. The analysis unit, for example, improves the accuracy of data analysis by improving machine learning algorithms. For example, the analysis unit introduces natural language processing technology to improve the accuracy of text data analysis. The analysis unit can also use deep learning technology to improve the accuracy of data analysis. This improves the accuracy of data analysis by introducing new techniques. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input collected data into a generation AI and have the generation AI implement the new technique.

[0089] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between data during analysis. The analysis unit, for example, analyzes the correlations between data and improves the accuracy of the analysis results. For example, the analysis unit adjusts the analysis algorithm by taking into account the interdependence of data. The analysis unit can also visualize the interrelationships between data to make the analysis results easier to understand. This improves the accuracy of the analysis by taking the interrelationships between data into account. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the interrelationships between data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0090] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a natural language processing algorithm to text data. For example, the analysis unit applies a statistical analysis algorithm to numerical data. The analysis unit can also apply an image analysis algorithm to image data. This improves the accuracy of analysis by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0091] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method including detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This allows the display method of the analysis results to be adjusted according to the user's emotions, making it easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0092] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. The analysis unit, for example, prioritizes analysis of the most recent data. For example, the analysis unit postpones data that was submitted earlier. The analysis unit can also adjust the analysis schedule based on the time of submission. In this way, by determining the priority of analysis based on the time of data submission, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data submission to the generation AI and have the generation AI determine the analysis priority.

[0093] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit postpones analysis of less relevant data. The analysis unit can also optimize the order of analysis based on the relevance of the data. This allows highly relevant data to be analyzed preferentially by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0094] During analysis, the analysis unit can adjust the use of technical terms in the analysis results according to the user's level of expertise. For example, the analysis unit explains the analysis results in simple terms to a user with little technical expertise. For example, the analysis unit explains the analysis results using detailed technical terms to a user with extensive technical expertise. The analysis unit can also adjust the display method of the analysis results according to the user's level of expertise. This makes it possible to provide analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis results according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise to the generation AI and have the generation AI use technical terms in the analysis results.

[0095] The understanding unit can estimate the user's emotions and adjust the method of understanding the preferences and thought patterns based on the estimated user emotions. For example, if the user is feeling stressed, the understanding unit emphasizes stress-related data for understanding. For example, if the user is relaxed, the understanding unit emphasizes relaxation-related data for understanding. Furthermore, if the user is in a hurry, the understanding unit can also emphasize data related to urgent actions for understanding. This allows for more accurate understanding by adjusting the method of understanding the preferences and thought patterns according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the understanding unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the understanding unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the method of understanding the preferences and thought patterns.

[0096] During understanding, the understanding unit can improve the accuracy of understanding preferences and thought patterns by referring to past data. The understanding unit, for example, refers to the user's past behavioral data to understand preferences and thought patterns. For example, the understanding unit refers to the user's past conversation data to understand preferences and thought patterns. The understanding unit can also refer to the user's past feedback to understand preferences and thought patterns. By referring to past data, the accuracy of understanding preferences and thought patterns is improved. Some or all of the above-mentioned processing in the understanding unit may be performed using, for example, AI, or may be performed without using AI. For example, the understanding unit can input past data into the generation AI and cause the generation AI to improve the accuracy of understanding preferences and thought patterns.

[0097] The understanding unit can improve the accuracy of understanding by taking into account the interrelationships between data during understanding. The understanding unit, for example, analyzes correlations between data to understand preferences and thought patterns. For example, the understanding unit adjusts the understanding algorithm by taking into account the interdependence of data. The understanding unit can also visualize the interrelationships between data to make it easier to understand preferences and thought patterns. This improves the accuracy of understanding by taking into account the interrelationships between data. Some or all of the above-mentioned processing in the understanding unit may be performed using, for example, AI, or may be performed without using AI. For example, the understanding unit can input the interrelationships between data into the generation AI and cause the generation AI to improve the accuracy of understanding.

[0098] During understanding, the understanding unit can apply different understanding algorithms depending on the data category. For example, the understanding unit applies a natural language processing algorithm to text data. For example, the understanding unit applies a statistical analysis algorithm to numerical data. The understanding unit can also apply an image analysis algorithm to image data. This improves the accuracy of understanding by applying an appropriate understanding algorithm depending on the data category. Some or all of the above-mentioned processing in the understanding unit may be performed using AI, for example, or may be performed without using AI. For example, the understanding unit can input the data category to the generation AI and cause the generation AI to apply an appropriate understanding algorithm.

[0099] The understanding unit can estimate the user's emotions and adjust the display method of the understanding results based on the estimated user emotions. For example, if the user is nervous, the understanding unit provides a simple, highly visible display method. For example, if the user is relaxed, the understanding unit provides a display method including detailed information. Furthermore, if the user is in a hurry, the understanding unit can also provide a display method that focuses on the main points. This allows the display method of the understanding results to be adjusted according to the user's emotions, making it easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the understanding unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the understanding unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the understanding results.

[0100] During comprehension, the comprehension unit can determine the priority of comprehension based on the time of data submission. The comprehension unit, for example, prioritizes understanding the most recent data. For example, the comprehension unit postpones data that was submitted earlier. The comprehension unit can also adjust the comprehension schedule based on the time of submission. In this way, by determining the priority of comprehension based on the time of data submission, the most recent data can be prioritized for comprehension. Some or all of the above-described processing in the comprehension unit may be performed using, for example, AI, or may be performed without using AI. For example, the comprehension unit can input the time of data submission to the generation AI and have the generation AI determine the priority of comprehension.

[0101] During comprehension, the comprehension unit can adjust the order of comprehension based on the relevance of the data. For example, the comprehension unit prioritizes understanding of highly relevant data. For example, the comprehension unit postpones understanding of less relevant data. The comprehension unit can also optimize the order of comprehension based on the relevance of the data. This allows highly relevant data to be prioritized for comprehension by adjusting the order of comprehension based on the relevance of the data. Some or all of the above-described processing in the comprehension unit may be performed using, or without, AI, for example. For example, the comprehension unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of comprehension.

[0102] During understanding, the understanding unit can adjust the use of technical terms in the understanding result according to the user's level of expertise. For example, the understanding unit explains the understanding result in simple terms to a user with little technical expertise. For example, the understanding unit explains the understanding result using detailed technical terms to a user with extensive technical expertise. The understanding unit can also adjust the display method of the understanding result according to the user's level of expertise. In this way, by adjusting the use of technical terms in the understanding result according to the user's level of expertise, it is possible to provide an understanding result that is easy for the user to understand. Some or all of the above-mentioned processing in the understanding unit may be performed using AI, for example, or may be performed without using AI. For example, the understanding unit can input the user's level of expertise to the generation AI and cause the generation AI to use technical terms in the understanding result.

[0103] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit provides simple, highly visible suggestions. For example, if the user is relaxed, the suggestion unit can provide suggestions that include detailed information. Furthermore, if the user is in a hurry, the suggestion unit can also provide suggestions that are more concise. This allows the suggestion unit to adjust the way the suggestions are expressed based on the user's emotions, making the suggestions easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the suggestions are expressed.

[0104] When making a suggestion, the suggestion unit can introduce a new method to increase an individual's success rate or satisfaction. The suggestion unit, for example, suggests the optimal path based on the user's past success stories. For example, the suggestion unit suggests the optimal direction based on the user's past satisfaction data. The suggestion unit can also improve the accuracy of the suggestions based on the user's past feedback. This makes it possible to introduce a new method to increase an individual's success rate or satisfaction. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past data into the generation AI and have the generation AI implement the new method.

[0105] The suggestion unit can improve the accuracy of suggestions by taking into account the interrelationships between data when making suggestions. The suggestion unit, for example, analyzes correlations between data and improves the accuracy of suggestions. For example, the suggestion unit adjusts the suggestion algorithm by taking into account the interdependence of data. The suggestion unit can also visualize the interrelationships between data to make the suggestions easier to understand. In this way, the accuracy of suggestions is improved by taking the interrelationships between data into consideration. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the interrelationships between data into the generation AI and cause the generation AI to improve the accuracy of suggestions.

[0106] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the data category. For example, the suggestion unit applies a natural language processing algorithm to text data. For example, the suggestion unit applies a statistical analysis algorithm to numerical data. The suggestion unit can also apply an image analysis algorithm to image data. This improves the accuracy of suggestions by applying an appropriate suggestion algorithm depending on the data category. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the data category to the generation AI and cause the generation AI to apply an appropriate suggestion algorithm.

[0107] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short, to-the-point suggestions. For example, if the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. Furthermore, if the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. This allows the length of the suggestions to be adjusted according to the user's emotions, thereby providing suggestions of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestions.

[0108] When making a suggestion, the suggestion unit can determine the priority of the suggestion based on the time of data submission. The suggestion unit provides suggestions based on, for example, the latest data. For example, the suggestion unit postpones data that has been submitted earlier. The suggestion unit can also adjust the schedule of suggestions based on the time of submission. In this way, by determining the priority of suggestions based on the time of data submission, suggestions can be provided based on the latest data. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the time of data submission to the generation AI and have the generation AI determine the priority of suggestions.

[0109] The suggestion unit can adjust the order of suggestions based on the relevance of the data when making a suggestion. The suggestion unit, for example, provides suggestions based on highly relevant data. For example, the suggestion unit postpones less relevant data. The suggestion unit can also optimize the order of suggestions based on the relevance of the data. In this way, by adjusting the order of suggestions based on the relevance of the data, suggestions can be provided based on highly relevant data. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of suggestions.

[0110] When making a suggestion, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. For example, the suggestion unit provides suggestions in simple language to a user with little technical expertise. For example, the suggestion unit provides suggestions using detailed technical terminology to a user with extensive technical expertise. The suggestion unit can also adjust the display method of the suggestion according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the suggestion according to the user's level of expertise, suggestions that are easy for the user to understand can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise to the generation AI and cause the generation AI to use technical terminology in the suggestion.

[0111] The protection unit can estimate the user's emotions and adjust the data anonymization and encryption methods based on the estimated user emotions. For example, the protection unit strengthens data anonymization when the user is stressed. For example, the protection unit strengthens data encryption when the user is relaxed. The protection unit can also quickly anonymize and encrypt data when the user is in a hurry. This improves the accuracy of data protection by adjusting the data anonymization and encryption methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the protection unit may be performed using AI, for example, or without AI. For example, the protection unit can input the user's emotion data into the generation AI and have the generation AI adjust the data anonymization and encryption methods.

[0112] The protection unit can improve the accuracy of protection by introducing the latest security technology when anonymizing or encrypting data. The protection unit, for example, introduces the latest encryption technology to improve the accuracy of data protection. For example, the protection unit introduces the latest anonymization technology to improve the accuracy of data protection. The protection unit can also introduce the latest security protocol to improve the accuracy of data protection. As a result, the accuracy of data protection is improved by introducing the latest security technology. Some or all of the above-mentioned processing in the protection unit may be performed using AI, for example, or may be performed without using AI. For example, the protection unit can input the latest security technology into the generation AI and cause the generation AI to improve the accuracy of protection.

[0113] The protection unit can improve the accuracy of protection by taking into account the interrelationships between data when anonymizing or encrypting data. The protection unit, for example, analyzes the correlations between data and improves the accuracy of anonymization or encryption. For example, the protection unit adjusts the anonymization or encryption algorithm by taking into account the interdependence of data. The protection unit can also visualize the interrelationships between data to make the anonymization or encryption easier to understand. In this way, the accuracy of data protection is improved by taking into account the interrelationships between data. Some or all of the above-mentioned processing in the protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the protection unit can input the interrelationships between data into the generation AI and cause the generation AI to improve the accuracy of protection.

[0114] The protection unit can estimate the user's emotions and determine the priority of data protection based on the estimated user emotions. For example, when the user is feeling stressed, the protection unit prioritizes data protection. For example, when the user is relaxed, the protection unit adjusts the priority of data protection. The protection unit can also quickly protect data when the user is in a hurry. This allows important data to be protected preferentially by determining the priority of data protection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the protection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the protection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of data protection.

[0115] When anonymizing or encrypting data, the protection unit can determine the priority of protection based on the time of data submission. For example, the protection unit prioritizes anonymizing or encrypting the most recent data. For example, the protection unit postpones data that was submitted earlier. The protection unit can also adjust the data protection schedule based on the time of submission. In this way, by determining the priority of protection based on the time of data submission, the most recent data can be protected preferentially. Some or all of the above-mentioned processing in the protection unit may be performed using AI, for example, or may be performed without using AI. For example, the protection unit can input the time of data submission to the generation AI and have the generation AI determine the priority of protection.

[0116] When anonymizing or encrypting data, the protection unit can adjust the order of protection based on the relevance of the data. For example, the protection unit prioritizes anonymizing or encrypting highly relevant data. For example, the protection unit postpones data with low relevance. The protection unit can also optimize the order of anonymization or encryption based on the relevance of the data. As a result, highly relevant data can be protected preferentially by adjusting the order of protection based on the relevance of the data. Some or all of the above-mentioned processing in the protection unit may be performed using AI, for example, or may be performed without using AI. For example, the protection unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of protection. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, understanding unit, suggestion unit, and protection unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects an individual's daily behavior and conversations using the camera 42 and microphone 38B of the smart device 14. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using machine learning or natural language processing. The understanding unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and understands the individual's preferences and thought patterns based on the analyzed data. The suggestion unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and suggests the individual's optimal path or direction based on the understood information. The protection unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and anonymizes or encrypts the collected data. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, understanding unit, suggestion unit, and protection unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects an individual's daily behavior and conversations using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using machine learning or natural language processing. The understanding unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and understands an individual's preferences and thought patterns based on the analyzed data. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal path or direction for the individual based on the understood information. The protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and anonymizes or encrypts the collected data. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, understanding unit, suggestion unit, and protection unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects an individual's daily behavior and conversations using the camera 42 and microphone 238 of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using machine learning or natural language processing. The understanding unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and understands an individual's preferences and thought patterns based on the analyzed data. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests the individual's optimal path or direction based on the understood information. The protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and anonymizes or encrypts the collected data. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, understanding unit, suggestion unit, and protection unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects an individual's daily behavior and conversations using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using machine learning or natural language processing. The understanding unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and understands the individual's preferences and thought patterns based on the analyzed data. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests the individual's optimal path or direction based on the understood information. The protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and anonymizes or encrypts the collected data.

[0117] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0118] The career suggestion system may further include a feedback collection unit. The feedback collection unit collects feedback provided by the user and transmits it to the analysis unit. For example, the feedback collection unit may collect feedback such as how the user felt about the career suggestion and what improvements could be made. The feedback collection unit may also collect feedback about the results of the user actually attempting the suggested career. This allows the career suggestion system to improve the accuracy of the career suggestions based on the user's feedback.

[0119] The career suggestion system may further include a real-time monitoring unit. The real-time monitoring unit monitors the user's daily actions and conversations in real time and sends the information to the collection unit. For example, if the user starts a new hobby, the information is collected immediately and sent to the analysis unit. The real-time monitoring unit can also detect changes in the user's behavioral patterns and update the content of the career suggestions as appropriate. This allows the career suggestion system to suggest the optimal career path based on the user's latest situation.

[0120] The career suggestion system can further estimate the user's emotions and adjust the content of the career suggestions based on the estimated emotions. For example, if the user is feeling stressed, it can suggest a relaxing occupation or lifestyle. Also, if the user is excited, it can suggest a challenging career. In this way, the career suggestion system can suggest the optimal career path according to the user's emotions.

[0121] The career suggestion system can also collect health data of the user and send it to the analysis unit. For example, the system can collect the user's sleep patterns and exercise volume and analyze them in the analysis unit. The system can also suggest suitable occupations and lifestyles for the user based on the health data. This allows the career suggestion system to suggest the optimal career path based on the user's health condition.

[0122] The path suggestion system can further estimate the user's emotions and adjust the timing of path suggestions based on the estimated emotions. For example, if the user is relaxed, the system can proactively provide path suggestions. Also, if the user is feeling stressed, the system can temporarily refrain from providing path suggestions. This allows the system to suggest a path at the optimal timing according to the user's emotions.

[0123] The career suggestion system can further collect social network data of the user and send it to the analysis unit. For example, the occupations and lifestyles of the user's friends and family are collected and analyzed by the analysis unit. The system can also suggest occupations and lifestyles suitable for the user based on the social network data. This allows the career suggestion system to suggest the optimal career path based on the user's social network.

[0124] The career suggestion system can further estimate the user's emotions and customize the content of the career suggestions based on the estimated emotions. For example, if the user feels anxious, it can suggest a stable job and lifestyle. Alternatively, if the user feels confident, it can suggest a challenging career path. This allows the career suggestion system to suggest the optimal career path according to the user's emotions.

[0125] The career suggestion system can further suggest career paths based on the user's hobbies and interests. For example, if the user is interested in music, it can suggest music-related occupations and lifestyles. Also, if the user is interested in sports, it can suggest sports-related career paths. In this way, the career suggestion system can suggest the optimal career path based on the user's hobbies and interests.

[0126] The path suggestion system can further estimate the user's emotions and adjust the path suggestion method based on the estimated emotions. For example, if the user is nervous, it can provide simple, highly visible path suggestions. On the other hand, if the user is relaxed, it can provide path suggestions that include detailed information. This allows the path suggestion system to suggest a path in the most optimal way according to the user's emotions.

[0127] The career suggestion system can also collect the user's geographical location information and send it to the analysis unit. For example, if the user lives in a specific area, it can suggest occupations and lifestyles related to that area. Also, if the user is traveling, it can suggest careers related to the user's destination. This allows the career suggestion system to suggest the optimal career path based on the user's geographical location information.

[0128] The processing flow of the second embodiment will be briefly explained below.

[0129] Step 1: The collection unit collects an individual's daily activities and conversations. These activities and conversations include voice data, text messages, and location information. The collection unit collects this data using smartphones and wearable devices. The collection unit can also collect an individual's behavioral history and social media activity. For example, sensor data from a smartphone can be used to collect the user's stress level and fatigue level. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses machine learning and natural language processing to analyze the data, and deep learning technology to perform high-precision analysis. It also analyzes correlations between data to improve the accuracy of the analysis results. Step 3: The understanding unit understands the individual's preferences and thought patterns based on the data analyzed by the analysis unit. The understanding unit understands the individual's hobbies and ways of thinking based on the analyzed data and improves the accuracy of the understanding by referring to past data. For example, it may understand preferences and thought patterns by referring to the user's past behavioral data. Step 4: The suggestion unit suggests career paths and directions to the individual based on the information obtained by the understanding unit. The suggestion unit suggests the best career and lifestyle for the individual and introduces new methods to increase the individual's success rate and satisfaction. For example, it suggests the best career path based on the user's past success stories. Step 5: The protection unit anonymizes or encrypts the collected data. The protection unit uses the latest security technology to improve the accuracy of data protection and analyzes correlations between data to improve the accuracy of anonymization or encryption.

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0134] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0135] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0150] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0151] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0152] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0153] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0154] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0156] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0157] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0162] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0164] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0166] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0167] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0168] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0169] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0170] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0171] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0172] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0173] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0174] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0175] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0176] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0177] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0178] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0179] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0180] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0181] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0182] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0183] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0184] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0185] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0186] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0187] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0188] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0189] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0190] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0191] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0192] 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.

[0193] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0194] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0195] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0196] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0197] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0198] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0199] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0200] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0201] [Explanation of symbols]

[0202] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects information on individuals' daily activities and conversations; an analysis unit that analyzes the data collected by the collection unit; an understanding unit that understands personal preferences and thought patterns based on the data analyzed by the analysis unit; a suggestion unit that suggests a career path or direction to an individual based on the information obtained by the understanding unit; a protection unit that anonymizes or encrypts the data. A system characterized by:

2. The collecting unit Collecting an individual's daily activities and conversations using a smartphone or wearable device 2. The system of claim 1.

3. The analysis unit Analyze the collected data using machine learning or natural language processing 2. The system of claim 1.

4. The understanding unit Understanding personal preferences and thought patterns based on the data analyzed by the analysis unit 2. The system of claim 1.

5. The suggestion section Suggesting career paths and directions to individuals based on the information obtained by the understanding unit 2. The system of claim 1.

6. The protective part is Anonymize or encrypt the collected data 2. The system of claim 1.

7. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Utilizing sensor data from smartphones and wearable devices to collect user stress levels and fatigue levels 2. The system of claim 1.

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