system

Abstract

The system according to the embodiment aims to statistically determine suitable occupations based on the characteristics and tendencies of a user. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a determination unit, and a provision unit. The collection unit collects at least one of the following data: personal information of a user, past work experience, skills, and interests. The analysis unit analyzes the data collected by the collection unit to understand the user's characteristics or tendencies. The determination unit determines an occupation using statistical methods based on the data analyzed by the analysis unit. The provision unit provides the user with the occupation determined by the determination unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not adequately statistically determine suitable occupations based on a user's characteristics and tendencies, and there is room for improvement.

[0005] The system according to the embodiment aims to statistically determine suitable occupations based on the characteristics and tendencies of a user. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a determination unit, and a provision unit. The collection unit collects at least one of the following data: personal information of the user, past work experience, skills, and interests. The analysis unit analyzes the data collected by the collection unit to understand the characteristics or tendencies of the user. The determination unit determines an occupation using statistical methods based on the data analyzed by the analysis unit. The provision unit provides the user with the occupation determined by the determination unit. [Effects of the Invention]

[0007] The system according to the embodiment can statistically determine suitable occupations based on the characteristics and tendencies of a user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An occupational aptitude assessment system according to an embodiment of the present invention collects data such as a user's personal information, past work experience, skills, and interests, and analyzes it using AI to statistically determine the occupation most suitable for the user. This occupational aptitude assessment system collects data such as the user's personal information (e.g., age, gender, address), past work experience, skills, interests, personality test results, past job evaluations, educational background, and qualifications. Next, AI analyzes the collected data to identify the user's characteristics and tendencies. A machine learning algorithm is used for the analysis. Based on the analyzed data, a statistical method is used to determine the user's occupation. A clustering algorithm is used to reference the occupation data of other users with similar characteristics. Finally, the system provides the user with the determined occupation, along with the reasons and detailed information. This system allows users to find the occupation that best suits them and helps them make a career choice. Security measures are also implemented to protect the user's personal information. For example, the user's personal information is encrypted, and access control is strictly managed. Furthermore, the system undergoes regular security audits and responds promptly if any vulnerabilities are discovered. This allows the career aptitude assessment system to collect and analyze data such as a user's personal information, past work experience, skills, and interests, and use statistical methods to assess and provide careers.

[0029] The career aptitude assessment system according to the embodiment includes a collection unit, an analysis unit, a determination unit, and a provision unit. The collection unit collects at least one of a user's personal information, past work experience, skills, and interests. The collection unit can collect, for example, the user's personal information (age, gender, address, etc.). The collection unit can also collect past work experience (job type, period, job title, etc.). The collection unit can also collect the user's skills (technical skills, soft skills, language skills, etc.). The collection unit can also collect the user's interests (hobbies, areas of interest, activity history, etc.). The analysis unit analyzes the data collected by the collection unit to understand the user's characteristics or tendencies. The analysis unit analyzes the data using, for example, a machine learning algorithm. Machine learning algorithms include decision trees, random forests, neural networks, etc. The analysis unit can extract the user's characteristics (personality traits, behavioral traits, psychological traits, etc.). The determination unit determines an occupation using a statistical method based on the data analyzed by the analysis unit. The determination unit, for example, uses a clustering algorithm to refer to occupational data of other users whose characteristics are similar to the user's. Clustering algorithms include K-means, hierarchical clustering, DBSCAN, etc. The provision unit provides the user with the occupation determined by the determination unit. The provision unit, for example, suggests suitable occupations to the user and provides reasons and detailed information. The provision unit takes security measures to protect the user's personal information. Security measures include data encryption, access control, audit logs, etc. As a result, the career aptitude determination system according to the embodiment can collect and analyze data such as the user's personal information, past work experience, skills, and interests, and determine and provide an occupation using statistical methods.

[0030] The collection unit can collect data such as the user's personality diagnostic test results, past job evaluations, educational background, and qualifications. The collection unit, for example, collects the user's personality diagnostic test results. Personality diagnostic tests include MBTI, Big Five, Enneagram, etc. The collection unit can also collect past job evaluations. Job evaluations include performance evaluations, feedback, and achievement evaluations. The collection unit can also collect the user's educational background. Educational background includes highest level of education, major, degree obtained, etc. The collection unit can also collect the user's qualifications. Qualifications include technical qualifications, professional qualifications, language qualifications, etc. In this way, by collecting data such as the user's personality diagnostic test results, past job evaluations, educational background, and qualifications, more detailed information can be obtained.

[0031] The analysis unit can analyze the data using a machine learning algorithm and extract user characteristics. The analysis unit can analyze the data using, for example, a decision tree algorithm. The decision tree algorithm hierarchically divides the data and performs classification and regression. The analysis unit can also analyze the data using a random forest algorithm. The random forest algorithm combines multiple decision trees to improve prediction accuracy. The analysis unit can also analyze the data using a neural network algorithm. The neural network algorithm uses multiple layers of artificial neurons to learn complex patterns. As a result, the use of a machine learning algorithm can more accurately extract user characteristics.

[0032] The determination unit can use a clustering algorithm to refer to the occupation data of other users whose characteristics are similar to the user's, and suggest appropriate occupations. The determination unit clusters the user's characteristics using, for example, a K-means clustering algorithm. The K-means clustering algorithm divides the data into K clusters and calculates the center of each cluster. The determination unit can also cluster the user's characteristics using a hierarchical clustering algorithm. The hierarchical clustering algorithm clusters the data hierarchically and generates a dendrogram. The determination unit can also cluster the user's characteristics using a DBSCAN clustering algorithm. The DBSCAN clustering algorithm clusters the data based on density and removes noise. As a result, the clustering algorithm can suggest the most suitable occupation for the user.

[0033] The providing unit can suggest suitable occupations to the user and provide the reasons and detailed information for the suggestion. The providing unit, for example, suggests suitable occupations to the user. Suitable occupations include occupations selected based on the user's characteristics and tendencies. The providing unit can also provide the user with reasons for suggesting the occupation. The reasons for the suggestion include analysis results based on the user's characteristics and tendencies. The providing unit can also provide the user with detailed information about the occupation. The detailed information includes the job content, salary, career path, etc. of the occupation. In this way, by suggesting suitable occupations to the user and providing the reasons and detailed information, the user can select an occupation with confidence.

[0034] The providing unit can take security measures to protect the user's personal information. The providing unit protects the user's personal information using, for example, data encryption. Data encryption includes encryption algorithms such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman). The providing unit can also protect the user's personal information using access control. Access control includes user authentication, authority management, and access log recording. The providing unit can also protect the user's personal information using an audit log. The audit log records data access history and change history. As a result, by taking security measures to protect the user's personal information, the user's privacy can be protected.

[0035] The collection unit can classify the user's past work experience and collect data based on a specific skill set. For example, the collection unit classifies the user's past work experience by industry. Classification by industry includes the IT industry, manufacturing, service industry, etc. The collection unit can also classify the user's work experience by job title. Classification by job title includes manager, engineer, analyst, etc. The collection unit can also classify the user's work experience by project. Classification by project includes the results and skills of a specific project. In this way, by classifying the user's past work experience in detail, data based on a specific skill set can be collected.

[0036] The collection unit can filter data based on the user's current living situation and interests when collecting data. For example, the collection unit prioritizes collecting relevant data by taking into account the user's current living situation (family composition, living environment, etc.). The collection unit can also collect relevant work experience and skills based on the user's interests and hobbies. The collection unit can also collect data related to the user's future career by taking into account the user's current working situation. In this way, more relevant data can be collected by filtering data based on the user's current living situation and interests.

[0037] When collecting data, the collection unit can prioritize collection of highly relevant data based on the user's geographical location information. For example, the collection unit collects region-specific work experience and skills based on the user's place of residence. The collection unit can also collect data related to commuting time and transportation means based on the user's place of work. The collection unit can also collect data taking into account the local labor market situation based on the user's geographical location information. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information.

[0038] When collecting data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit analyzes the content of the user's social media posts and collects data related to their interests. The collection unit can also analyze the user's social media network and collect data related to their work experience and skills. The collection unit can also analyze the frequency of the user's social media activities and collect data related to their job suitability. In this way, by analyzing the user's social media activities, related data can be collected.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. For data with low importance, the analysis unit performs a brief analysis. The analysis unit can also determine the priority of the analysis according to the importance of the data. This allows for more effective analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a clustering algorithm to work experience data. Clustering algorithms include K-means, hierarchical clustering, DBSCAN, etc. The analysis unit can also apply regression analysis to skill data. Regression analysis includes linear regression, logistic regression, etc. The analysis unit can also apply collaborative filtering to interest data. Collaborative filtering includes user-based collaborative filtering and item-based collaborative filtering, etc. This makes it possible to obtain more accurate analysis results by applying different analysis algorithms depending on the data category.

[0041] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the analysis unit prioritizes analysis of the most recent data. Older data is analyzed as needed. The analysis unit can also adjust the analysis priority based on the time when the data was collected. This allows for more effective analysis by determining the analysis priority based on the time when the data was collected.

[0042] 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, and postpones analysis of less relevant data. The analysis unit can also adjust the order of analysis according to the relevance of the data. This allows for more effective analysis by adjusting the order of analysis based on the relevance of the data.

[0043] The determination unit can improve the accuracy of the determination by taking into account the interrelationships of data when making the determination. The determination unit makes the determination by taking into account, for example, the interrelationships between work experience and skills. The determination unit can also make the determination by taking into account the interrelationships between interests and personality diagnosis results. The determination unit can also make the determination by taking into account the interrelationships between educational background and qualifications. In this way, the accuracy of the determination can be improved by taking into account the interrelationships of data.

[0044] The determination unit can make a determination taking into account the user's attribute information. For example, the determination unit makes a determination taking into account the user's age. The determination unit can also make a determination taking into account the user's gender. The determination unit can also make a determination taking into account the user's address. In this way, by taking into account the user's attribute information, a more appropriate determination can be made.

[0045] The determination unit can make a determination taking into account the geographical distribution of the data. For example, the determination unit determines a region-specific occupation based on the user's place of residence. The determination unit can also make a determination taking into account commuting time and transportation means based on the user's place of work. The determination unit can also make a determination taking into account the state of the local labor market based on the user's geographical distribution. In this way, by taking into account the geographical distribution of the data, more appropriate determinations can be made.

[0046] The determination unit can improve the accuracy of the determination by referring to related literature when making the determination. The determination unit makes the determination by referring to, for example, related academic papers. The determination unit can also make the determination by referring to industry reports. The determination unit can also make the determination by referring to the opinions of experts. In this way, by referring to related literature, the accuracy of the determination can be improved.

[0047] The providing unit can provide optimal information by referring to the user's past job evaluations when providing the information. The providing unit provides related occupational information based on the user's past job evaluations, for example. The providing unit can also provide information for skill improvement based on the user's past job evaluations. The providing unit can also provide career path information based on the user's past job evaluations. In this way, more appropriate information can be provided by referring to the user's past job evaluations.

[0048] The providing unit can customize the information based on the user's current living situation when providing the information. For example, the providing unit provides relevant occupational information taking into consideration the user's current living situation (family composition, living environment, etc.). The providing unit can also provide information related to the user's future career taking into consideration the user's current work situation. The providing unit can also provide relevant occupational information based on the user's current interests and hobbies. In this way, by customizing the information based on the user's current living situation, more appropriate information can be provided.

[0049] The providing unit can provide optimal information by taking into account the user's geographical location information when providing the information. For example, the providing unit provides area-specific occupational information based on the user's place of residence. The providing unit can also provide information related to commuting time and transportation means based on the user's place of work. The providing unit can also provide information by taking into account the local labor market situation based on the user's geographical location information. In this way, more appropriate information can be provided by taking into account the user's geographical location information.

[0050] At the time of providing the information, the providing unit can analyze the user's social media activity and provide the information. For example, the providing unit can analyze the content of the user's social media posts and provide occupational information related to the user's interests. The providing unit can also analyze the user's social media network and provide information related to work experience and skills. The providing unit can also analyze the frequency of the user's social media activity and provide information related to job suitability. In this way, by analyzing the user's social media activity, more appropriate information can be provided.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The analysis unit can predict a user's future career path based on their past work experience and skills. For example, if the user has worked as an engineer in the past, the analysis unit can predict occupations such as project manager or technical leader as future career paths based on that experience. Also, if the user has a specific skill set, the analysis unit can suggest related occupations and career paths based on that skill set. Furthermore, the analysis unit can predict a user's future career path based on their interests and concerns. This allows the user to more clearly define their career direction.

[0053] The determination unit can take the user's health condition into consideration when determining the user's occupational aptitude. For example, if the user has had health problems in the past, the determination unit can suggest occupations that take health into consideration based on that information. Also, if the user has a specific health condition, the determination unit can suggest occupations that are suitable for that condition. Furthermore, the determination unit can filter occupation options based on the user's health condition. This allows the user to find an occupation that is suitable for their health condition.

[0054] The providing unit can take the user's lifestyle into consideration when suggesting occupations to the user. For example, if the user desires remote work, the providing unit can suggest occupations that match that desire. Also, if the user desires flexible working hours, the providing unit can suggest occupations that match that desire. Furthermore, if the user desires to work in a specific area, the providing unit can suggest occupations related to that area. This allows the user to find an occupation that matches their lifestyle.

[0055] The collection unit can collect data on the user's hobbies and leisure activities and determine occupational aptitude based on that data. For example, if the user's hobby is sports, occupations related to that hobby can be suggested. Also, if the user is interested in art or music, occupations related to that interest can be suggested. Furthermore, if the user is involved in volunteer activities, occupations related to those activities can be suggested. This allows the user to find occupations related to their hobbies and leisure activities.

[0056] The provision unit can provide information about future career paths based on the user's past work experience and skills. For example, if the user has worked as an engineer in the past, information about occupations such as project manager or technical leader as future career paths can be provided based on that experience. Also, if the user has a specific skill set, information about related occupations and career paths can be provided based on that skill set. Furthermore, information about future career paths can be provided based on the user's interests. This allows the user to more clearly define the direction of their career.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The collection unit collects at least one of the user's personal information, past work experience, skills, and interests. For example, the collection unit may collect the user's personal information (age, gender, address, etc.), past work experience (type of work, duration, job title, etc.), skills (technical skills, soft skills, language skills, etc.), and interests (hobbies, areas of interest, activity history, etc.). Step 2: The analysis unit analyzes the data collected by the collection unit to understand the characteristics or tendencies of the user. The analysis unit analyzes the data using, for example, a machine learning algorithm (decision tree, random forest, neural network, etc.) and extracts the user's characteristics (personality traits, behavioral traits, psychological traits, etc.). Step 3: The determination unit determines the occupation based on the data analyzed by the analysis unit using a statistical method. For example, the determination unit uses a clustering algorithm (e.g., K-means, hierarchical clustering, DBSCAN, etc.) to refer to the occupation data of other users whose characteristics are similar to the user's. Step 4: The providing unit provides the user with the occupation determined by the determining unit. For example, the providing unit suggests suitable occupations to the user and provides the reasons and detailed information. The providing unit also takes security measures (data encryption, access control, audit logs, etc.) to protect the user's personal information.

[0059] (Example 2) An occupational aptitude assessment system according to an embodiment of the present invention collects data such as a user's personal information, past work experience, skills, and interests, and analyzes it using AI to statistically determine the occupation most suitable for the user. This occupational aptitude assessment system collects data such as the user's personal information (e.g., age, gender, address), past work experience, skills, interests, personality test results, past job evaluations, educational background, and qualifications. Next, AI analyzes the collected data to identify the user's characteristics and tendencies. A machine learning algorithm is used for the analysis. Based on the analyzed data, a statistical method is used to determine the user's occupation. A clustering algorithm is used to reference the occupation data of other users with similar characteristics. Finally, the system provides the user with the determined occupation, along with the reasons and detailed information. This system allows users to find the occupation that best suits them and helps them make a career choice. Security measures are also implemented to protect the user's personal information. For example, the user's personal information is encrypted, and access control is strictly managed. Furthermore, the system undergoes regular security audits and responds promptly if any vulnerabilities are discovered. This allows the career aptitude assessment system to collect and analyze data such as a user's personal information, past work experience, skills, and interests, and use statistical methods to assess and provide careers.

[0060] The career aptitude assessment system according to the embodiment includes a collection unit, an analysis unit, a determination unit, and a provision unit. The collection unit collects at least one of a user's personal information, past work experience, skills, and interests. The collection unit can collect, for example, the user's personal information (age, gender, address, etc.). The collection unit can also collect past work experience (job type, period, job title, etc.). The collection unit can also collect the user's skills (technical skills, soft skills, language skills, etc.). The collection unit can also collect the user's interests (hobbies, areas of interest, activity history, etc.). The analysis unit analyzes the data collected by the collection unit to understand the user's characteristics or tendencies. The analysis unit analyzes the data using, for example, a machine learning algorithm. Machine learning algorithms include decision trees, random forests, neural networks, etc. The analysis unit can extract the user's characteristics (personality traits, behavioral traits, psychological traits, etc.). The determination unit determines an occupation using a statistical method based on the data analyzed by the analysis unit. The determination unit, for example, uses a clustering algorithm to refer to occupational data of other users whose characteristics are similar to the user's. Clustering algorithms include K-means, hierarchical clustering, DBSCAN, etc. The provision unit provides the user with the occupation determined by the determination unit. The provision unit, for example, suggests suitable occupations to the user and provides reasons and detailed information. The provision unit takes security measures to protect the user's personal information. Security measures include data encryption, access control, audit logs, etc. As a result, the career aptitude determination system according to the embodiment can collect and analyze data such as the user's personal information, past work experience, skills, and interests, and determine and provide an occupation using statistical methods.

[0061] The collection unit can collect data such as the user's personality diagnostic test results, past job evaluations, educational background, and qualifications. The collection unit, for example, collects the user's personality diagnostic test results. Personality diagnostic tests include MBTI, Big Five, Enneagram, etc. The collection unit can also collect past job evaluations. Job evaluations include performance evaluations, feedback, and achievement evaluations. The collection unit can also collect the user's educational background. Educational background includes highest level of education, major, degree obtained, etc. The collection unit can also collect the user's qualifications. Qualifications include technical qualifications, professional qualifications, language qualifications, etc. In this way, by collecting data such as the user's personality diagnostic test results, past job evaluations, educational background, and qualifications, more detailed information can be obtained.

[0062] The analysis unit can analyze the data using a machine learning algorithm and extract user characteristics. The analysis unit can analyze the data using, for example, a decision tree algorithm. The decision tree algorithm hierarchically divides the data and performs classification and regression. The analysis unit can also analyze the data using a random forest algorithm. The random forest algorithm combines multiple decision trees to improve prediction accuracy. The analysis unit can also analyze the data using a neural network algorithm. The neural network algorithm uses multiple layers of artificial neurons to learn complex patterns. As a result, the use of a machine learning algorithm can more accurately extract user characteristics.

[0063] The determination unit can use a clustering algorithm to refer to the occupation data of other users whose characteristics are similar to the user's, and suggest appropriate occupations. The determination unit clusters the user's characteristics using, for example, a K-means clustering algorithm. The K-means clustering algorithm divides the data into K clusters and calculates the center of each cluster. The determination unit can also cluster the user's characteristics using a hierarchical clustering algorithm. The hierarchical clustering algorithm clusters the data hierarchically and generates a dendrogram. The determination unit can also cluster the user's characteristics using a DBSCAN clustering algorithm. The DBSCAN clustering algorithm clusters the data based on density and removes noise. As a result, the clustering algorithm can suggest the most suitable occupation for the user.

[0064] The providing unit can suggest suitable occupations to the user and provide the reasons and detailed information for the suggestion. The providing unit, for example, suggests suitable occupations to the user. Suitable occupations include occupations selected based on the user's characteristics and tendencies. The providing unit can also provide the user with reasons for suggesting the occupation. The reasons for the suggestion include analysis results based on the user's characteristics and tendencies. The providing unit can also provide the user with detailed information about the occupation. The detailed information includes the job content, salary, career path, etc. of the occupation. In this way, by suggesting suitable occupations to the user and providing the reasons and detailed information, the user can select an occupation with confidence.

[0065] The providing unit can take security measures to protect the user's personal information. The providing unit protects the user's personal information using, for example, data encryption. Data encryption includes encryption algorithms such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman). The providing unit can also protect the user's personal information using access control. Access control includes user authentication, authority management, and access log recording. The providing unit can also protect the user's personal information using an audit log. The audit log records data access history and change history. As a result, by taking security measures to protect the user's personal information, the user's privacy can be protected.

[0066] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, the collection unit captures the user's facial expressions with a camera and estimates the emotions using an emotion recognition algorithm. The emotion recognition algorithm includes facial expression recognition, voice analysis, text analysis, etc. The collection unit can adjust the timing of data collection based on the user's emotions. For example, if the user is relaxed, detailed questions are asked to collect more data. If the user is stressed, simple questions are asked to complete data collection in a short time. If the user is excited, questions are asked in an interactive format to make data collection a fun experience. This allows data to be collected more effectively by adjusting the timing of data collection based on the user's emotions.

[0067] The collection unit can classify the user's past work experience and collect data based on a specific skill set. For example, the collection unit classifies the user's past work experience by industry. Classification by industry includes the IT industry, manufacturing, service industry, etc. The collection unit can also classify the user's work experience by job title. Classification by job title includes manager, engineer, analyst, etc. The collection unit can also classify the user's work experience by project. Classification by project includes the results and skills of a specific project. In this way, by classifying the user's past work experience in detail, data based on a specific skill set can be collected.

[0068] The collection unit can filter data based on the user's current living situation and interests when collecting data. For example, the collection unit prioritizes collecting relevant data by taking into account the user's current living situation (family composition, living environment, etc.). The collection unit can also collect relevant work experience and skills based on the user's interests and hobbies. The collection unit can also collect data related to the user's future career by taking into account the user's current working situation. In this way, more relevant data can be collected by filtering data based on the user's current living situation and interests.

[0069] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. The collection unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion recognition algorithm. The emotion recognition algorithm includes facial expression recognition, voice analysis, text analysis, etc. The collection unit can determine the priority of data to be collected based on the user's emotions. For example, if the user is relaxed, detailed data is collected with priority. If the user is stressed, basic data is collected with priority. If the user is excited, interactive data collection is prioritized. Thus, by determining the priority of data based on the user's emotions, data can be collected more effectively.

[0070] When collecting data, the collection unit can prioritize collection of highly relevant data based on the user's geographical location information. For example, the collection unit collects region-specific work experience and skills based on the user's place of residence. The collection unit can also collect data related to commuting time and transportation means based on the user's place of work. The collection unit can also collect data taking into account the local labor market situation based on the user's geographical location information. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information.

[0071] When collecting data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit analyzes the content of the user's social media posts and collects data related to their interests. The collection unit can also analyze the user's social media network and collect data related to their work experience and skills. The collection unit can also analyze the frequency of the user's social media activities and collect data related to their job suitability. In this way, by analyzing the user's social media activities, related data can be collected.

[0072] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion recognition algorithm. Emotion recognition algorithms include facial expression recognition, voice analysis, and text analysis. The analysis unit can adjust the way the analysis is presented based on the user's emotions. For example, if the user is relaxed, detailed analysis results are provided. If the user is stressed, concise and to the point analysis results are provided. If the user is excited, visually appealing analysis results are provided. In this way, by adjusting the way the analysis is presented based on the user's emotions, the analysis results can be provided more effectively.

[0073] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. For data with low importance, the analysis unit performs a brief analysis. The analysis unit can also determine the priority of the analysis according to the importance of the data. This allows for more effective analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0074] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a clustering algorithm to work experience data. Clustering algorithms include K-means, hierarchical clustering, DBSCAN, etc. The analysis unit can also apply regression analysis to skill data. Regression analysis includes linear regression, logistic regression, etc. The analysis unit can also apply collaborative filtering to interest data. Collaborative filtering includes user-based collaborative filtering and item-based collaborative filtering, etc. This makes it possible to obtain more accurate analysis results by applying different analysis algorithms depending on the data category.

[0075] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion recognition algorithm. Emotion recognition algorithms include facial expression recognition, voice analysis, text analysis, and the like. The analysis unit can adjust the length of the analysis based on the user's emotions. For example, if the user is relaxed, a detailed analysis is performed. If the user is stressed, a concise analysis is performed. If the user is excited, a visually appealing analysis is performed. In this way, by adjusting the length of the analysis based on the user's emotions, it is possible to provide analysis results more effectively.

[0076] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the analysis unit prioritizes analysis of the most recent data. Older data is analyzed as needed. The analysis unit can also adjust the analysis priority based on the time when the data was collected. This allows for more effective analysis by determining the analysis priority based on the time when the data was collected.

[0077] 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, and postpones analysis of less relevant data. The analysis unit can also adjust the order of analysis according to the relevance of the data. This allows for more effective analysis by adjusting the order of analysis based on the relevance of the data.

[0078] The determination unit can estimate the user's emotions and adjust the determination criteria based on the estimated user emotions. The determination unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion recognition algorithm. The emotion recognition algorithm includes facial expression recognition, voice analysis, text analysis, etc. The determination unit can adjust the determination criteria based on the user's emotions. For example, if the user is relaxed, detailed criteria are used to make a determination. If the user is stressed, simple criteria are used to make a determination. If the user is excited, visually appealing criteria are used to make a determination. In this way, by adjusting the determination criteria based on the user's emotions, more appropriate determination can be made.

[0079] The determination unit can improve the accuracy of the determination by taking into account the interrelationships of data when making the determination. The determination unit makes the determination by taking into account, for example, the interrelationships between work experience and skills. The determination unit can also make the determination by taking into account the interrelationships between interests and personality diagnosis results. The determination unit can also make the determination by taking into account the interrelationships between educational background and qualifications. In this way, the accuracy of the determination can be improved by taking into account the interrelationships of data.

[0080] The determination unit can make a determination taking into account the user's attribute information. For example, the determination unit makes a determination taking into account the user's age. The determination unit can also make a determination taking into account the user's gender. The determination unit can also make a determination taking into account the user's address. In this way, by taking into account the user's attribute information, a more appropriate determination can be made.

[0081] The determination unit can estimate the user's emotion and adjust the order in which the determination results are displayed based on the estimated user's emotion. The determination unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion recognition algorithm. The emotion recognition algorithm includes facial expression recognition, voice analysis, text analysis, etc. The determination unit can adjust the order in which the determination results are displayed based on the user's emotion. For example, if the user is relaxed, detailed results are preferentially displayed. If the user is stressed, concise results are preferentially displayed. If the user is excited, visually appealing results are preferentially displayed. In this way, by adjusting the order in which the determination results are displayed based on the user's emotion, it is possible to provide results more effectively.

[0082] The determination unit can make a determination taking into account the geographical distribution of the data. For example, the determination unit determines a region-specific occupation based on the user's place of residence. The determination unit can also make a determination taking into account commuting time and transportation means based on the user's place of work. The determination unit can also make a determination taking into account the state of the local labor market based on the user's geographical distribution. In this way, by taking into account the geographical distribution of the data, more appropriate determinations can be made.

[0083] The determination unit can improve the accuracy of the determination by referring to related literature when making the determination. The determination unit makes the determination by referring to, for example, related academic papers. The determination unit can also make the determination by referring to industry reports. The determination unit can also make the determination by referring to the opinions of experts. In this way, by referring to related literature, the accuracy of the determination can be improved.

[0084] The providing unit can estimate the user's emotion and adjust the presentation method of the information to be provided based on the estimated user's emotion. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion recognition algorithm. The emotion recognition algorithm includes facial expression recognition, voice analysis, text analysis, etc. The providing unit can adjust the presentation method of the information to be provided based on the user's emotion. For example, if the user is relaxed, detailed information is provided. If the user is stressed, concise information is provided. If the user is excited, visually appealing information is provided. In this way, by adjusting the presentation method of information based on the user's emotion, it is possible to provide information more effectively.

[0085] The providing unit can provide optimal information by referring to the user's past job evaluations when providing the information. The providing unit provides related occupational information based on the user's past job evaluations, for example. The providing unit can also provide information for skill improvement based on the user's past job evaluations. The providing unit can also provide career path information based on the user's past job evaluations. In this way, more appropriate information can be provided by referring to the user's past job evaluations.

[0086] The providing unit can customize the information based on the user's current living situation when providing the information. For example, the providing unit provides relevant occupational information taking into consideration the user's current living situation (family composition, living environment, etc.). The providing unit can also provide information related to the user's future career taking into consideration the user's current work situation. The providing unit can also provide relevant occupational information based on the user's current interests and hobbies. In this way, by customizing the information based on the user's current living situation, more appropriate information can be provided.

[0087] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user emotions. The providing unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion recognition algorithm. The emotion recognition algorithm includes facial expression recognition, voice analysis, text analysis, etc. The providing unit can determine the priority of information to be provided based on the user's emotions. For example, if the user is relaxed, detailed information is provided preferentially. If the user is stressed, concise information is provided preferentially. If the user is excited, visually appealing information is provided preferentially. In this way, by determining the priority of information based on the user's emotions, information can be provided more effectively.

[0088] The providing unit can provide optimal information by taking into account the user's geographical location information when providing the information. For example, the providing unit provides area-specific occupational information based on the user's place of residence. The providing unit can also provide information related to commuting time and transportation means based on the user's place of work. The providing unit can also provide information by taking into account the local labor market situation based on the user's geographical location information. In this way, more appropriate information can be provided by taking into account the user's geographical location information.

[0089] At the time of providing the information, the providing unit can analyze the user's social media activity and provide the information. For example, the providing unit can analyze the content of the user's social media posts and provide occupational information related to the user's interests. The providing unit can also analyze the user's social media network and provide information related to work experience and skills. The providing unit can also analyze the frequency of the user's social media activity and provide information related to job suitability. In this way, by analyzing the user's social media activity, more appropriate information can be provided. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, determination unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data such as the user's personal information, past work experience, skills, and interests using the camera 42 and microphone 38B of the smart device 14. 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 a machine learning algorithm. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the user's occupation using a clustering algorithm. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the determined occupation to the user, along with the reason and detailed information. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, determination unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data such as the user's personal information, past work experience, skills, and interests 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 a machine learning algorithm. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the user's occupation using a clustering algorithm. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the determined occupation to the user, along with the reason and detailed information. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, determination unit, and provision unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects data such as the user's personal information, past work experience, skills, and interests 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 a machine learning algorithm. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the occupation using a clustering algorithm. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides the determined occupation to the user, along with the reason and detailed information. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, determination unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data such as the user's personal information, past work experience, skills, and interests 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 a machine learning algorithm. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the user's occupation using a clustering algorithm. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the determined occupation to the user, along with the reason and detailed information.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The analysis unit can predict a user's future career path based on their past work experience and skills. For example, if the user has worked as an engineer in the past, the analysis unit can predict occupations such as project manager or technical leader as future career paths based on that experience. Also, if the user has a specific skill set, the analysis unit can suggest related occupations and career paths based on that skill set. Furthermore, the analysis unit can predict a user's future career path based on their interests and concerns. This allows the user to more clearly define their career direction.

[0092] The determination unit can take the user's health condition into consideration when determining the user's occupational aptitude. For example, if the user has had health problems in the past, the determination unit can suggest occupations that take health into consideration based on that information. Also, if the user has a specific health condition, the determination unit can suggest occupations that are suitable for that condition. Furthermore, the determination unit can filter occupation options based on the user's health condition. This allows the user to find an occupation that is suitable for their health condition.

[0093] The providing unit can take the user's lifestyle into consideration when suggesting occupations to the user. For example, if the user desires remote work, the providing unit can suggest occupations that match that desire. Also, if the user desires flexible working hours, the providing unit can suggest occupations that match that desire. Furthermore, if the user desires to work in a specific area, the providing unit can suggest occupations related to that area. This allows the user to find an occupation that matches their lifestyle.

[0094] The collection unit can collect data on the user's hobbies and leisure activities and determine occupational aptitude based on that data. For example, if the user's hobby is sports, occupations related to that hobby can be suggested. Also, if the user is interested in art or music, occupations related to that interest can be suggested. Furthermore, if the user is involved in volunteer activities, occupations related to those activities can be suggested. This allows the user to find occupations related to their hobbies and leisure activities.

[0095] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. For example, if the user is relaxed, a detailed analysis can be performed to provide more accurate results. If the user is stressed, a concise analysis can be performed to reduce the user's burden. If the user is excited, a visually appealing analysis result can be provided to attract the user's interest. Thus, by adjusting the accuracy of the analysis based on the user's emotions, the analysis results can be provided more effectively.

[0096] The determination unit can estimate the user's emotions and adjust the timing of the determination based on the estimated user emotions. For example, if the user is relaxed, a detailed determination can be made to provide a more accurate result. If the user is stressed, a concise determination can be made to reduce the burden on the user. If the user is excited, a visually appealing determination result can be provided to attract the user's interest. Thus, by adjusting the timing of the determination based on the user's emotions, it is possible to provide a more effective determination result.

[0097] The providing unit can estimate the user's emotions and adjust the amount of information to be provided based on the estimated user's emotions. For example, when the user is relaxed, detailed information can be provided to allow the user to fully understand. When the user is stressed, concise information can be provided to reduce the burden on the user. When the user is excited, visually appealing information can be provided to attract the user's interest. Thus, by adjusting the amount of information to be provided based on the user's emotions, information can be provided more effectively.

[0098] The collection unit can estimate the user's emotions and adjust the data collection method based on the estimated user emotions. For example, if the user is relaxed, detailed questions can be asked to collect more data. If the user is stressed, simple questions can be asked to complete data collection in a short time. If the user is excited, questions can be asked in an interactive format to make data collection a fun experience. This allows data to be collected more effectively by adjusting the data collection method based on the user's emotions.

[0099] The analysis unit can estimate the user's emotions and adjust the order of analysis based on the estimated user emotions. For example, if the user is relaxed, detailed analysis can be prioritized to provide more accurate results. If the user is stressed, brief analysis can be prioritized to reduce the user's burden. If the user is excited, visually appealing analysis results can be prioritized to attract the user's attention. Thus, by adjusting the order of analysis based on the user's emotions, analysis results can be provided more effectively.

[0100] The provision unit can provide information about future career paths based on the user's past work experience and skills. For example, if the user has worked as an engineer in the past, information about occupations such as project manager or technical leader as future career paths can be provided based on that experience. Also, if the user has a specific skill set, information about related occupations and career paths can be provided based on that skill set. Furthermore, information about future career paths can be provided based on the user's interests. This allows the user to more clearly define the direction of their career.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The collection unit collects at least one of the user's personal information, past work experience, skills, and interests. For example, the collection unit may collect the user's personal information (age, gender, address, etc.), past work experience (type of work, duration, job title, etc.), skills (technical skills, soft skills, language skills, etc.), and interests (hobbies, areas of interest, activity history, etc.). Step 2: The analysis unit analyzes the data collected by the collection unit to understand the characteristics or tendencies of the user. The analysis unit analyzes the data using, for example, a machine learning algorithm (decision tree, random forest, neural network, etc.) and extracts the user's characteristics (personality traits, behavioral traits, psychological traits, etc.). Step 3: The determination unit determines the occupation based on the data analyzed by the analysis unit using a statistical method. For example, the determination unit uses a clustering algorithm (e.g., K-means, hierarchical clustering, DBSCAN, etc.) to refer to the occupation data of other users whose characteristics are similar to the user's. Step 4: The providing unit provides the user with the occupation determined by the determining unit. For example, the providing unit suggests suitable occupations to the user and provides the reasons and detailed information. The providing unit also takes security measures (data encryption, access control, audit logs, etc.) to protect the user's personal information.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects at least one of personal information, past work experience, skills, and interests of the user; an analysis unit that analyzes the data collected by the collection unit and grasps the characteristics or tendencies of users; a determination unit that determines an occupation using a statistical method based on the data analyzed by the analysis unit; a providing unit that provides the user with the occupation determined by the determining unit; Equipped with A system characterized by:

2. The collecting unit Collecting user personality test results, past job evaluations, education, and qualification data 2. The system of claim 1.

3. The analysis unit Analyze data using machine learning algorithms to extract user characteristics 2. The system of claim 1.

4. The determination unit Using a clustering algorithm, the system refers to the occupational data of other users with similar characteristics and suggests suitable occupations.

2. The system of claim 1.

5. The providing unit Suggest suitable occupations to users and provide reasons and detailed information 2. The system of claim 1.

6. The providing unit Implement security measures to protect your personal information 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 Categorize users' past work experience and collect data based on specific skill sets 2. The system of claim 1.

Images