system

Abstract

The system according to the embodiment aims to suggest appropriate educational destinations and necessary studies based on the user's aptitude and desired career. [Solution] A system according to an embodiment includes a reception unit, a collection unit, an analysis unit, a determination unit, and a suggestion unit. The reception unit inputs the user's interests and desired occupation. The collection unit collects the information input by the reception unit. The analysis unit analyzes the information collected by the collection unit. The determination unit determines the user's aptitude and required skills based on the information analyzed by the analysis unit. The suggestion unit suggests appropriate educational destinations and necessary studies based on the results of 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 technology has had the problem of making it difficult for users to find schools and necessary studies that match their aptitudes and desired careers.

[0005] The system according to the embodiment aims to suggest appropriate educational destinations and necessary studies based on the user's aptitude and desired career. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a collection unit, an analysis unit, a determination unit, and a suggestion unit. The reception unit inputs the user's interests and desired occupation. The collection unit collects the information input by the reception unit. The analysis unit analyzes the information collected by the collection unit. The determination unit determines the user's aptitude and required skills based on the information analyzed by the analysis unit. The suggestion unit suggests appropriate educational destinations and necessary studies based on the results of the determination by the determination unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest appropriate educational destinations and necessary studies based on the user's aptitude and desired career. [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 embodiment of the education and career suggestion system of the present invention is a system that suggests optimal educational destinations and necessary studies based on a child's aptitude and aspirations when selecting an education or future career (elementary, junior high, high school, or university). This system allows users to input their interests and desired careers, and a generation AI analyzes the information to suggest optimal educational destinations and necessary studies based on the user's aptitude and aspirations. For example, if a user inputs information such as "I want to be a doctor" or "I'm interested in engineering," the generation AI analyzes the information and suggests the required academic ability, educational destinations, and study content. These suggestions are made taking into account the user's age, current academic ability, interests, and other factors. This allows users to select the optimal career path for their future. For example, if a junior high school student, the system suggests high school options and necessary study content, and if a high school student, the system suggests university options and necessary study content. The system also provides information on skills and qualifications necessary for future careers based on the user's interests. This system supports users in selecting the optimal career path for their future, reducing confusion and anxiety when selecting an education or career. For example, users can quickly identify the skills and qualifications necessary for their future careers and systematically study for them. This allows the education and career suggestion system to suggest the most suitable education destination and necessary studies based on the user's interests and wishes.

[0029] The educational and career suggestion system according to the embodiment includes a reception unit, a collection unit, an analysis unit, a determination unit, and a suggestion unit. The reception unit inputs a user's interests and desired career. Examples of the user's interests and desired career include, but are not limited to, doctor, engineer, and teacher. For example, if the user inputs "I want to be a doctor," the reception unit transmits the information to the collection unit. The collection unit collects information such as the user's age, current academic ability, and interests. For example, if the user is a junior high school student, the collection unit collects high school options and required study content. The collection unit also collects information on skills and qualifications required for future careers based on the user's interests. The analysis unit analyzes the collected information and determines the user's aptitude and required skills. For example, using a generation AI, the analysis unit determines the aptitude and required skills based on the user's interests and desired career. The generation AI analyzes the user's information using a text generation AI (e.g., LLM) or a multimodal generation AI. The determination unit determines the user's aptitude and required skills based on the analysis results. The determination unit determines the user's aptitude and required skills based on the analysis results, for example, using a generation AI. The suggestion unit suggests the optimal educational destination and required studies based on the determination results. The suggestion unit suggests the optimal educational destination and required studies based on the determination results, for example, using a generation AI. In this way, the educational / career suggestion system according to the embodiment can suggest the optimal educational destination and required studies based on the user's interests and wishes.

[0030] The reception unit can analyze the user's past input history and provide an appropriate suggestion function when inputting information. For example, the reception unit can automatically display as candidates the interests or desired occupations that the user has frequently input in the past. The reception unit can also prioritize suggestions based on the user's past input history, such as voice or text. The reception unit can also predict and suggest the interests or desired occupations that the user will use during a specific time period based on the user's past input history. This can improve the efficiency of input work by providing appropriate suggestions based on the past input history. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past input data into a generation AI and cause the generation AI to provide a suggestion function.

[0031] The reception unit can additionally display related questions in real time according to the user's input. For example, if the user inputs "I want to be a doctor," the reception unit can display "What specialty are you interested in?" as a related question. Furthermore, if the user inputs "I'm interested in engineering," the reception unit can also display "What field of engineering do you aspire to be?" as a related question. Furthermore, if the user inputs "I want to be a teacher," the reception unit can also display "What subject do you want to teach?" as a related question. By displaying questions according to the user's input in real time, more detailed information can be collected. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and cause the generation AI to display related questions.

[0032] The reception unit can prioritize displaying information about occupations and educational destinations specific to a region based on the user's geographical location information. For example, if the user lives in an urban area, the reception unit can prioritize displaying occupations and educational destinations that are popular in the urban area. Furthermore, if the user lives in a rural area, the reception unit can prioritize displaying occupations and educational destinations that are in demand in the rural area. Furthermore, if the user is interested in a specific region, the reception unit can prioritize displaying information about occupations and educational destinations in that region. This makes it possible to provide information suitable for the user by displaying information specific to the region preferentially. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location data to the generation AI and cause the generation AI to display information specific to the region.

[0033] The reception unit can analyze the user's social media activity and automatically suggest related interests and desired occupations. For example, if the user frequently posts about "medicine" or "health" on social media, the reception unit can suggest medical-related occupations. Alternatively, if the user posts about "technology" or "programming," the reception unit can suggest engineer or programmer occupations. Alternatively, if the user posts about "education" or "children," the reception unit can suggest teacher or education-related occupations. This allows for suggestions that match the user's interests by suggesting appropriate occupations based on social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI suggest related occupations.

[0034] The collection unit can analyze the user's past academic ability data and optimize the range of information to be collected. For example, if the user has demonstrated high academic ability in the past, the collection unit can collect more advanced information. Furthermore, if the user has previously struggled with a subject, the collection unit can also focus on collecting information related to that subject. Furthermore, the collection unit can collect information related to the user's optimal educational destination or career based on the user's past academic ability data. By optimizing the range of information collection based on the past academic ability data, efficient information collection becomes possible. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past academic ability data into a generation AI and cause the generation AI to optimize the range of information collection.

[0035] The collection unit can dynamically change the categories of information to be collected based on the user's current living situation. For example, if the user is a student, the collection unit can collect information related to further education and studies. If the user is a working adult, the collection unit can also collect information related to career changes and skill development. If the user has specific hobbies or interests, the collection unit can also collect information on occupations and further education related to those hobbies or interests. This makes it possible to collect information according to the user's current living situation. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's living situation data into a generation AI and cause the generation AI to dynamically change the information categories.

[0036] The collection unit can collect region-specific academic ability data and interests taking into account the user's geographical location information. For example, if the user lives in an urban area, the collection unit collects academic ability data and interests for the urban area. Furthermore, if the user lives in a rural area, the collection unit can collect regional academic ability data and interests. Furthermore, if the user is interested in a specific region, the collection unit can collect academic ability data and interests for that region. By collecting region-specific information, it is possible to provide information suitable for the user. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location data into the generation AI and cause the generation AI to collect region-specific information.

[0037] The collection unit can analyze a user's social media activity and automatically collect related information. For example, if a user frequently posts about "medical care" or "health" on social media, the collection unit can collect medical-related information. Alternatively, if a user posts about "technology" or "programming," the collection unit can collect information related to engineering or programming. Alternatively, if a user posts about "education" or "children," the collection unit can collect education-related information. By collecting appropriate information based on social media activity, it becomes possible to provide information tailored to the user's interests. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related information.

[0038] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between the collected information. The analysis unit, for example, analyzes the interrelationships between the user's academic ability data and interests to suggest the optimal educational destination. The analysis unit can also analyze the interrelationships between the user's age and current academic ability to suggest the necessary study content. The analysis unit can also analyze the interrelationships between the user's interests and desired career to suggest the optimal educational destination or career. By taking into account the interrelationships between the information, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected information into a generation AI and have the generation AI perform an analysis of the interrelationships.

[0039] The analysis unit can optimize the analysis algorithm by referencing the user's past analysis results. The analysis unit, for example, optimizes the current analysis algorithm based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results and reflect them in the current analysis. The analysis unit can also suggest optimal educational destinations or careers by referencing the user's past analysis results. This makes it possible to optimize the analysis algorithm by referencing the past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis data into the generation AI and have the generation AI optimize the analysis algorithm.

[0040] The analysis unit can perform analysis taking into account the geographical distribution of the collected information. For example, if the user lives in an urban area, the analysis unit can prioritize analyzing information about urban areas. Furthermore, if the user lives in a rural area, the analysis unit can prioritize analyzing information about the rural area. Furthermore, if the user is interested in a particular region, the analysis unit can prioritize analyzing information about that region. This enables region-specific analysis by taking geographical distribution into account. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input geographical data of the collected information into a generation AI and cause the generation AI to perform an analysis of the geographical distribution.

[0041] The analysis unit can improve the accuracy of the analysis by referring to literature related to the collected information. For example, the analysis unit can improve the accuracy of the analysis by referring to academic papers related to the collected information. The analysis unit can also improve the accuracy of the analysis by referring to industry reports related to the collected information. The analysis unit can also improve the accuracy of the analysis by referring to books related to the collected information. In this way, the accuracy of the analysis is improved by referring to related literature. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input related literature data into the generation AI and cause the generation AI to refer to literature and improve the accuracy of the analysis.

[0042] The determination unit can improve the accuracy of the determination by taking into account the correlations between the analysis results. The determination unit, for example, analyzes the correlation between the user's academic ability data and interests to determine the optimal educational destination. The determination unit can also analyze the correlation between the user's age and current academic ability to determine the necessary study content. The determination unit can also analyze the correlation between the user's interests and desired career to determine the optimal educational destination or career. By taking into account the correlations between the analysis results, the accuracy of the determination is improved. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the analysis result data to a generation AI and cause the generation AI to analyze the correlations and improve the accuracy of the determination.

[0043] The judgment unit can optimize the judgment algorithm by referring to the user's past judgment results. The judgment unit, for example, optimizes the current judgment algorithm based on the user's past judgment results. The judgment unit can also extract specific patterns from the user's past judgment results and reflect them in the current judgment. The judgment unit can also refer to the user's past judgment results to determine the user's optimal educational destination or occupation. This makes it possible to optimize the judgment algorithm by referring to the past judgment results. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the user's past judgment data into the generation AI and cause the generation AI to optimize the judgment algorithm.

[0044] The determination unit can make a determination taking into account the geographical distribution of the analysis results. For example, if the user lives in an urban area, the determination unit can prioritize information about urban areas. Furthermore, if the user lives in a rural area, the determination unit can prioritize information about rural areas. Furthermore, if the user is interested in a specific region, the determination unit can prioritize information about that region. This allows for region-specific determination by taking geographical distribution into consideration. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input geographical data of the analysis results into the generation AI and cause the generation AI to determine the geographical distribution.

[0045] The determination unit can improve the accuracy of the determination by referring to literature related to the analysis results. The determination unit can improve the accuracy of the determination by referring to, for example, academic papers related to the analysis results. The determination unit can also improve the accuracy of the determination by referring to industry reports related to the analysis results. The determination unit can also improve the accuracy of the determination by referring to books related to the analysis results. In this way, the accuracy of the determination is improved by referring to related literature. Some or all of the above-described processing in the determination unit can be performed using, for example, AI, or can be performed without using AI. For example, the determination unit can input related literature data into the generation AI and cause the generation AI to refer to literature and improve the accuracy of the determination.

[0046] The suggestion unit can adjust the level of detail of the proposal based on the importance of the judgment result. For example, if the judgment result is important, the suggestion unit can provide a proposal including detailed information. Furthermore, if the judgment result is general, the suggestion unit can also provide a proposal including basic information. Furthermore, if the judgment result is not urgent, the suggestion unit can also provide a proposal including concise information. In this way, by providing a proposal according to the importance of the judgment result, it is possible to provide optimal information for the user. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the judgment result data to a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0047] The suggestion unit can apply different suggestion algorithms depending on the category of the assessment result. For example, if the assessment result is related to further education, the suggestion unit can prioritize suggesting information about further education. Furthermore, if the assessment result is related to occupation, the suggestion unit can prioritize suggesting information related to occupation. Furthermore, if the assessment result is related to skill development, the suggestion unit can prioritize suggesting information related to skill development. This enables appropriate suggestions to be made by applying a suggestion algorithm according to the category. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the assessment result data to a generation AI and cause the generation AI to apply a suggestion algorithm.

[0048] The suggestion unit can make suggestions taking into account the geographical distribution of the determination results. For example, if the user lives in an urban area, the suggestion unit can prioritize suggesting information about urban areas. Also, if the user lives in a rural area, the suggestion unit can prioritize suggesting information about rural areas. Also, if the user is interested in a specific region, the suggestion unit can prioritize suggesting information about that region. This makes it possible to make suggestions specific to the region by taking the geographical distribution into consideration. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input geographical data of the determination results into a generation AI and cause the generation AI to make suggestions about the geographical distribution.

[0049] The suggestion unit can improve the accuracy of the suggestion by referring to literature related to the judgment result. The suggestion unit can improve the accuracy of the suggestion by referring to, for example, academic papers related to the judgment result. The suggestion unit can also improve the accuracy of the suggestion by referring to industry reports related to the judgment result. The suggestion unit can also improve the accuracy of the suggestion by referring to books related to the judgment result. In this way, the accuracy of the suggestion is improved by referring to related literature. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input related literature data into the generation AI and cause the generation AI to refer to literature and improve the accuracy of the suggestion.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The reception unit can provide video interviews of related occupations based on the user's input. For example, if the user inputs "I want to be a doctor," a video interview with a practicing doctor can be displayed, providing information about what a doctor's job is actually like and the skills required. If the user inputs "I'm interested in engineering," a video interview with an engineer can be displayed, providing information about the appeal of engineering work and career paths. Furthermore, if the user inputs "I want to be a teacher," a video interview with a teacher can be displayed, providing information about what it's like in the classroom and what it's rewarding to be a teacher. This allows the user to get a concrete image of the occupation and can serve as a reference for choosing a career path.

[0052] The reception unit can analyze the user's past input history and suggest appropriate learning resources when inputting information. For example, if a user previously input "I want to become a doctor," the reception unit can suggest online courses and reference books related to medicine. If a user inputs "I'm interested in engineering," the reception unit can suggest programming tutorials and technical books. Furthermore, if a user inputs "I want to become a teacher," the reception unit can suggest seminars and teaching materials related to education. This allows users to efficiently find learning resources that are geared toward their goals.

[0053] The reception unit can provide information on the future prospects and market trends of related occupations in real time according to the user's input. For example, if a user inputs "I want to be a doctor," the future prospects and demand forecasts for the medical industry can be displayed. If a user inputs "I'm interested in engineering," trends in the technology industry and job openings can be displayed. Furthermore, if a user inputs "I want to be a teacher," changes and demand forecasts for the education industry can be displayed. This allows users to consider future prospects when choosing a career.

[0054] The reception unit can provide information on scholarships and support programs specific to a region based on the user's geographic location information. For example, if the user lives in an urban area, scholarships and support programs available in the urban area can be displayed. Also, if the user lives in a rural area, scholarships and support programs specific to that region can be displayed. Furthermore, if the user is interested in a specific region, information on scholarships and support programs available in that region can be provided. This allows the user to receive support appropriate for their region.

[0055] The reception unit can analyze the user's social media activity and suggest related communities and networking events. For example, if the user posts on social media about "medicine" or "health," the reception unit can suggest medical-related communities and networking events. If the user posts about "technology" or "programming," the reception unit can suggest technology-related communities and events. Furthermore, if the user posts about "education" or "children," the reception unit can suggest education-related communities and events. This allows the user to participate in communities and events that match their interests.

[0056] The collection unit can analyze the user's past academic ability data and optimize the update frequency of the collected information. For example, if the user has shown high academic ability in the past, the latest information can be collected frequently. Also, if the user has previously struggled with a subject, information related to that subject can be updated regularly. Furthermore, information related to further education and career can be updated regularly based on the user's past academic ability data. In this way, by optimizing the update frequency of information collection based on past academic ability data, it is possible to always provide the latest information.

[0057] The collection unit can evaluate the reliability of the information to be collected based on the user's current living situation. For example, if the user is a student, information from academic sources can be collected with priority. Alternatively, if the user is a working professional, industry reports and expert opinions can be collected with priority. Furthermore, if the user has a specific hobby or interest, information can be collected from experts in that field or from reliable sources. This makes it possible to provide reliable information that is appropriate for the user's current living situation.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The reception unit inputs the user's interests and desired occupation. For example, the user can input "I want to be a doctor." Step 2: The collection unit collects the information input by the reception unit, such as the user's age, current academic ability, and interests. Step 3: The analysis unit analyzes the information collected by the collection unit. For example, using a generation AI, it determines aptitude and necessary skills based on the user's interests and desired occupation. Step 4: The judgment unit judges the user's aptitude and required skills based on the information analyzed by the analysis unit. For example, the judgment unit uses a generation AI to judge the user's aptitude and required skills based on the analysis results. Step 5: The suggestion unit suggests appropriate schools and necessary studies based on the results of the assessment by the assessment unit. For example, using a generative AI, it suggests the optimal schools and necessary studies based on the assessment results.

[0060] (Example 2) An embodiment of the education and career suggestion system of the present invention is a system that suggests optimal educational destinations and necessary studies based on a child's aptitude and aspirations when selecting an education or future career (elementary, junior high, high school, or university). This system allows users to input their interests and desired careers, and a generation AI analyzes the information to suggest optimal educational destinations and necessary studies based on the user's aptitude and aspirations. For example, if a user inputs information such as "I want to be a doctor" or "I'm interested in engineering," the generation AI analyzes the information and suggests the required academic ability, educational destinations, and study content. These suggestions are made taking into account the user's age, current academic ability, interests, and other factors. This allows users to select the optimal career path for their future. For example, if a junior high school student, the system suggests high school options and necessary study content, and if a high school student, the system suggests university options and necessary study content. The system also provides information on skills and qualifications necessary for future careers based on the user's interests. This system supports users in selecting the optimal career path for their future, reducing confusion and anxiety when selecting an education or career. For example, users can quickly identify the skills and qualifications necessary for their future careers and systematically study for them. This allows the education and career suggestion system to suggest the most suitable education destination and necessary studies based on the user's interests and wishes.

[0061] The educational and career suggestion system according to the embodiment includes a reception unit, a collection unit, an analysis unit, a determination unit, and a suggestion unit. The reception unit inputs a user's interests and desired career. Examples of the user's interests and desired career include, but are not limited to, doctor, engineer, and teacher. For example, if the user inputs "I want to be a doctor," the reception unit transmits the information to the collection unit. The collection unit collects information such as the user's age, current academic ability, and interests. For example, if the user is a junior high school student, the collection unit collects high school options and required study content. The collection unit also collects information on skills and qualifications required for future careers based on the user's interests. The analysis unit analyzes the collected information and determines the user's aptitude and required skills. For example, using a generation AI, the analysis unit determines the aptitude and required skills based on the user's interests and desired career. The generation AI analyzes the user's information using a text generation AI (e.g., LLM) or a multimodal generation AI. The determination unit determines the user's aptitude and required skills based on the analysis results. The determination unit determines the user's aptitude and required skills based on the analysis results, for example, using a generation AI. The suggestion unit suggests the optimal educational destination and required studies based on the determination results. The suggestion unit suggests the optimal educational destination and required studies based on the determination results, for example, using a generation AI. In this way, the educational / career suggestion system according to the embodiment can suggest the optimal educational destination and required studies based on the user's interests and wishes.

[0062] The reception unit can estimate the user's emotions and dynamically change the design of the input interface based on the estimated user emotions. For example, if the user is nervous, the reception unit can provide an interface with subdued colors to reduce visual stress. Furthermore, if the user is having fun, the reception unit can provide an interface with bright colors to make input work more enjoyable. Furthermore, if the user is tired, the reception unit can provide a simple, highly visible interface to make input work easier. This improves the comfort of input work by providing an interface that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0063] The reception unit can analyze the user's past input history and provide an appropriate suggestion function when inputting information. For example, the reception unit can automatically display as candidates the interests or desired occupations that the user has frequently input in the past. The reception unit can also prioritize suggestions based on the user's past input history, such as voice or text. The reception unit can also predict and suggest the interests or desired occupations that the user will use during a specific time period based on the user's past input history. This can improve the efficiency of input work by providing appropriate suggestions based on the past input history. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past input data into a generation AI and cause the generation AI to provide a suggestion function.

[0064] The reception unit can additionally display related questions in real time according to the user's input. For example, if the user inputs "I want to be a doctor," the reception unit can display "What specialty are you interested in?" as a related question. Furthermore, if the user inputs "I'm interested in engineering," the reception unit can also display "What field of engineering do you aspire to be?" as a related question. Furthermore, if the user inputs "I want to be a teacher," the reception unit can also display "What subject do you want to teach?" as a related question. By displaying questions according to the user's input in real time, more detailed information can be collected. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and cause the generation AI to display related questions.

[0065] The reception unit can estimate the user's emotions and dynamically change the priority of inputs based on the estimated user emotions. For example, if the user is nervous, the reception unit can display the simplest questions first and gradually move on to more detailed questions. Alternatively, if the user is relaxed, the reception unit can display detailed questions first to pique the user's interest. Alternatively, if the user is in a hurry, the reception unit can prioritize the most important questions to allow the user to complete input quickly. This improves the efficiency of input work by changing the priority of inputs according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0066] The reception unit can prioritize displaying information about occupations and educational destinations specific to a region based on the user's geographical location information. For example, if the user lives in an urban area, the reception unit can prioritize displaying occupations and educational destinations that are popular in the urban area. Furthermore, if the user lives in a rural area, the reception unit can prioritize displaying occupations and educational destinations that are in demand in the rural area. Furthermore, if the user is interested in a specific region, the reception unit can prioritize displaying information about occupations and educational destinations in that region. This makes it possible to provide information suitable for the user by displaying information specific to the region preferentially. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location data to the generation AI and cause the generation AI to display information specific to the region.

[0067] The reception unit can analyze the user's social media activity and automatically suggest related interests and desired occupations. For example, if the user frequently posts about "medicine" or "health" on social media, the reception unit can suggest medical-related occupations. Alternatively, if the user posts about "technology" or "programming," the reception unit can suggest engineer or programmer occupations. Alternatively, if the user posts about "education" or "children," the reception unit can suggest teacher or education-related occupations. This allows for suggestions that match the user's interests by suggesting appropriate occupations based on social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI suggest related occupations.

[0068] The collection unit can estimate the user's emotions and adjust the level of detail of the information to be collected based on the estimated user's emotions. For example, if the user is nervous, the collection unit can collect only basic information and collect detailed information later. Alternatively, if the user is relaxed, the collection unit can collect detailed information to dig deeper into the user's interests. Alternatively, if the user is in a hurry, the collection unit can collect only the most important information and quickly proceed to the next step. This enables appropriate information collection by adjusting the level of detail of the information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0069] The collection unit can analyze the user's past academic ability data and optimize the range of information to be collected. For example, if the user has demonstrated high academic ability in the past, the collection unit can collect more advanced information. Furthermore, if the user has previously struggled with a subject, the collection unit can also focus on collecting information related to that subject. Furthermore, the collection unit can collect information related to the user's optimal educational destination or career based on the user's past academic ability data. By optimizing the range of information collection based on the past academic ability data, efficient information collection becomes possible. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past academic ability data into a generation AI and cause the generation AI to optimize the range of information collection.

[0070] The collection unit can dynamically change the categories of information to be collected based on the user's current living situation. For example, if the user is a student, the collection unit can collect information related to further education and studies. If the user is a working adult, the collection unit can also collect information related to career changes and skill development. If the user has specific hobbies or interests, the collection unit can also collect information on occupations and further education related to those hobbies or interests. This makes it possible to collect information according to the user's current living situation. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's living situation data into a generation AI and cause the generation AI to dynamically change the information categories.

[0071] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, if the user is nervous, the collection unit can prioritize collecting the most important information and collect detailed information later. Also, if the user is relaxed, the collection unit can prioritize collecting detailed information to dig deeper into the user's interests. Also, if the user is in a hurry, the collection unit can quickly collect the most important information and proceed to the next step. This enables efficient information collection by determining the priority of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0072] The collection unit can collect region-specific academic ability data and interests taking into account the user's geographical location information. For example, if the user lives in an urban area, the collection unit collects academic ability data and interests for the urban area. Furthermore, if the user lives in a rural area, the collection unit can collect regional academic ability data and interests. Furthermore, if the user is interested in a specific region, the collection unit can collect academic ability data and interests for that region. By collecting region-specific information, it is possible to provide information suitable for the user. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location data into the generation AI and cause the generation AI to collect region-specific information.

[0073] The collection unit can analyze a user's social media activity and automatically collect related information. For example, if a user frequently posts about "medical care" or "health" on social media, the collection unit can collect medical-related information. Alternatively, if a user posts about "technology" or "programming," the collection unit can collect information related to engineering or programming. Alternatively, if a user posts about "education" or "children," the collection unit can collect education-related information. By collecting appropriate information based on social media activity, it becomes possible to provide information tailored to the user's interests. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related information.

[0074] The analysis unit can estimate the user's emotions and dynamically adjust the analysis algorithm based on the estimated user emotions. For example, if the user is nervous, the analysis unit can use a simple analysis algorithm to quickly provide results. Alternatively, if the user is relaxed, the analysis unit can use a detailed analysis algorithm to provide deeper insights. Alternatively, if the user is in a hurry, the analysis unit can prioritize analyzing the most important information and quickly provide results. This allows appropriate analysis results to be provided by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0075] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between the collected information. The analysis unit, for example, analyzes the interrelationships between the user's academic ability data and interests to suggest the optimal educational destination. The analysis unit can also analyze the interrelationships between the user's age and current academic ability to suggest the necessary study content. The analysis unit can also analyze the interrelationships between the user's interests and desired career to suggest the optimal educational destination or career. By taking into account the interrelationships between the information, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected information into a generation AI and have the generation AI perform an analysis of the interrelationships.

[0076] The analysis unit can optimize the analysis algorithm by referencing the user's past analysis results. The analysis unit, for example, optimizes the current analysis algorithm based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results and reflect them in the current analysis. The analysis unit can also suggest optimal educational destinations or careers by referencing the user's past analysis results. This makes it possible to optimize the analysis algorithm by referencing the past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis data into the generation AI and have the generation AI optimize the analysis algorithm.

[0077] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows for a deeper understanding of the analysis results by providing a display method that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0078] The analysis unit can perform analysis taking into account the geographical distribution of the collected information. For example, if the user lives in an urban area, the analysis unit can prioritize analyzing information about urban areas. Furthermore, if the user lives in a rural area, the analysis unit can prioritize analyzing information about the rural area. Furthermore, if the user is interested in a particular region, the analysis unit can prioritize analyzing information about that region. This enables region-specific analysis by taking geographical distribution into account. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input geographical data of the collected information into a generation AI and cause the generation AI to perform an analysis of the geographical distribution.

[0079] The analysis unit can improve the accuracy of the analysis by referring to literature related to the collected information. For example, the analysis unit can improve the accuracy of the analysis by referring to academic papers related to the collected information. The analysis unit can also improve the accuracy of the analysis by referring to industry reports related to the collected information. The analysis unit can also improve the accuracy of the analysis by referring to books related to the collected information. In this way, the accuracy of the analysis is improved by referring to related literature. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input related literature data into the generation AI and cause the generation AI to refer to literature and improve the accuracy of the analysis.

[0080] The determination unit can estimate the user's emotions and dynamically adjust the determination criteria based on the estimated user emotions. For example, if the user is nervous, the determination unit can use simple determination criteria to quickly provide results. Alternatively, if the user is relaxed, the determination unit can use detailed determination criteria to provide deeper insights. Alternatively, if the user is in a hurry, the determination unit can prioritize the most important information and quickly provide results. This allows the determination criteria to be adjusted according to the user's emotions, providing an appropriate determination result. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the determination unit can be performed using, for example, an AI, or without an AI. For example, the determination unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0081] The determination unit can improve the accuracy of the determination by taking into account the correlations between the analysis results. The determination unit, for example, analyzes the correlation between the user's academic ability data and interests to determine the optimal educational destination. The determination unit can also analyze the correlation between the user's age and current academic ability to determine the necessary study content. The determination unit can also analyze the correlation between the user's interests and desired career to determine the optimal educational destination or career. By taking into account the correlations between the analysis results, the accuracy of the determination is improved. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the analysis result data to a generation AI and cause the generation AI to analyze the correlations and improve the accuracy of the determination.

[0082] The judgment unit can optimize the judgment algorithm by referring to the user's past judgment results. The judgment unit, for example, optimizes the current judgment algorithm based on the user's past judgment results. The judgment unit can also extract specific patterns from the user's past judgment results and reflect them in the current judgment. The judgment unit can also refer to the user's past judgment results to determine the user's optimal educational destination or occupation. This makes it possible to optimize the judgment algorithm by referring to the past judgment results. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the user's past judgment data into the generation AI and cause the generation AI to optimize the judgment algorithm.

[0083] The determination unit can estimate the user's emotion and adjust the display method of the determination result based on the estimated user emotion. For example, if the user is nervous, the determination unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the determination unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the determination unit can provide a display method that focuses on the main points. This provides a display method that corresponds to the user's emotion, thereby deepening understanding of the determination result. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0084] The determination unit can make a determination taking into account the geographical distribution of the analysis results. For example, if the user lives in an urban area, the determination unit can prioritize information about urban areas. Furthermore, if the user lives in a rural area, the determination unit can prioritize information about rural areas. Furthermore, if the user is interested in a specific region, the determination unit can prioritize information about that region. This allows for region-specific determination by taking geographical distribution into consideration. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input geographical data of the analysis results into the generation AI and cause the generation AI to determine the geographical distribution.

[0085] The determination unit can improve the accuracy of the determination by referring to literature related to the analysis results. The determination unit can improve the accuracy of the determination by referring to, for example, academic papers related to the analysis results. The determination unit can also improve the accuracy of the determination by referring to industry reports related to the analysis results. The determination unit can also improve the accuracy of the determination by referring to books related to the analysis results. In this way, the accuracy of the determination is improved by referring to related literature. Some or all of the above-described processing in the determination unit can be performed using, for example, AI, or can be performed without using AI. For example, the determination unit can input related literature data into the generation AI and cause the generation AI to refer to literature and improve the accuracy of the determination.

[0086] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can provide simple, highly visible suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide suggestions that include detailed information. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions that focus on the main points. By providing suggestions that match the user's emotions, the likelihood of the suggestions being accepted increases. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0087] The suggestion unit can adjust the level of detail of the proposal based on the importance of the judgment result. For example, if the judgment result is important, the suggestion unit can provide a proposal including detailed information. Furthermore, if the judgment result is general, the suggestion unit can also provide a proposal including basic information. Furthermore, if the judgment result is not urgent, the suggestion unit can also provide a proposal including concise information. In this way, by providing a proposal according to the importance of the judgment result, it is possible to provide optimal information for the user. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the judgment result data to a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0088] The suggestion unit can apply different suggestion algorithms depending on the category of the assessment result. For example, if the assessment result is related to further education, the suggestion unit can prioritize suggesting information about further education. Furthermore, if the assessment result is related to occupation, the suggestion unit can prioritize suggesting information related to occupation. Furthermore, if the assessment result is related to skill development, the suggestion unit can prioritize suggesting information related to skill development. This enables appropriate suggestions to be made by applying a suggestion algorithm according to the category. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the assessment result data to a generation AI and cause the generation AI to apply a suggestion algorithm.

[0089] The suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can prioritize providing the most important suggestions. Furthermore, if the user is relaxed, the suggestion unit can prioritize providing detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can prioritize providing suggestions that focus on the main points. This enables efficient suggestions by prioritizing suggestions according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0090] The suggestion unit can make suggestions taking into account the geographical distribution of the determination results. For example, if the user lives in an urban area, the suggestion unit can prioritize suggesting information about urban areas. Also, if the user lives in a rural area, the suggestion unit can prioritize suggesting information about rural areas. Also, if the user is interested in a specific region, the suggestion unit can prioritize suggesting information about that region. This makes it possible to make suggestions specific to the region by taking the geographical distribution into consideration. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input geographical data of the determination results into a generation AI and cause the generation AI to make suggestions about the geographical distribution.

[0091] The suggestion unit can improve the accuracy of the suggestion by referring to literature related to the judgment result. The suggestion unit can improve the accuracy of the suggestion by referring to, for example, academic papers related to the judgment result. The suggestion unit can also improve the accuracy of the suggestion by referring to industry reports related to the judgment result. The suggestion unit can also improve the accuracy of the suggestion by referring to books related to the judgment result. In this way, the accuracy of the suggestion is improved by referring to related literature. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input related literature data into the generation AI and cause the generation AI to refer to literature and improve the accuracy of the suggestion. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, collection unit, analysis unit, determination unit, and suggestion unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and inputs the user's interests and desired occupation. The collection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and collects information such as the user's age, current academic ability, and interests. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The determination unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and determines the user's aptitude and necessary skills based on the analysis results. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and suggests the user's optimal educational destination and necessary study based on the determination results. The reception unit is implemented, for example, by the control unit 46A of the smart device 14 and estimates the user's emotions and dynamically changes the design of the input interface based on the estimated user emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, collection unit, analysis unit, determination unit, and suggestion unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and inputs the user's interests and desired occupation. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects information such as the user's age, current academic ability, and interests. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the user's aptitude and necessary skills based on the analysis results. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests the optimal educational destination and necessary study based on the determination results. The reception unit is realized, for example, by the control unit 46A of the smart glasses 214 and estimates the user's emotions and dynamically changes the design of the input interface based on the estimated user emotions. === Hard Collateral 1-3 === Each of the above-described elements, including the reception unit, collection unit, analysis unit, determination unit, and suggestion unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and inputs the user's interests and desired occupation. The collection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and collects information such as the user's age, current academic ability, and interests. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The determination unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and determines the user's aptitude and necessary skills based on the analysis results. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and suggests the user's optimal educational destination and necessary study based on the determination results. The reception unit is implemented, for example, by the control unit 46A of the headset terminal 314 and estimates the user's emotions and dynamically changes the design of the input interface based on the estimated user emotions. === Hard Collateral 1-4 === Each of the above-described elements, including the reception unit, collection unit, analysis unit, determination unit, and suggestion unit, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and inputs the user's interests and desired occupation. The collection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and collects information such as the user's age, current academic ability, and interests. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The determination unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and determines the user's aptitude and necessary skills based on the analysis results. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and suggests the user's optimal educational destination and necessary study based on the determination results. The reception unit is implemented, for example, by the control unit 46A of the robot 414 and estimates the user's emotions and dynamically changes the design of the input interface based on the estimated user emotions.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The reception unit can provide video interviews of related occupations based on the user's input. For example, if the user inputs "I want to be a doctor," a video interview with a practicing doctor can be displayed, providing information about what a doctor's job is actually like and the skills required. If the user inputs "I'm interested in engineering," a video interview with an engineer can be displayed, providing information about the appeal of engineering work and career paths. Furthermore, if the user inputs "I want to be a teacher," a video interview with a teacher can be displayed, providing information about what it's like in the classroom and what it's rewarding to be a teacher. This allows the user to get a concrete image of the occupation and can serve as a reference for choosing a career path.

[0094] The reception unit can estimate the user's emotions and provide feedback on the input content based on the estimated user emotions. For example, if the user is feeling anxious, the reception unit can display an encouraging message to alleviate the user's anxiety. If the user is excited, the reception unit can display a message sharing the user's excitement to motivate the user. Furthermore, if the user is depressed, the reception unit can display a comforting message to soothe the user's feelings. In this way, the user's input experience can be improved by providing feedback according to the user's emotions.

[0095] The reception unit can analyze the user's past input history and suggest appropriate learning resources when inputting information. For example, if a user previously input "I want to become a doctor," the reception unit can suggest online courses and reference books related to medicine. If a user inputs "I'm interested in engineering," the reception unit can suggest programming tutorials and technical books. Furthermore, if a user inputs "I want to become a teacher," the reception unit can suggest seminars and teaching materials related to education. This allows users to efficiently find learning resources that are geared toward their goals.

[0096] The reception unit can provide information on the future prospects and market trends of related occupations in real time according to the user's input. For example, if a user inputs "I want to be a doctor," the future prospects and demand forecasts for the medical industry can be displayed. If a user inputs "I'm interested in engineering," trends in the technology industry and job openings can be displayed. Furthermore, if a user inputs "I want to be a teacher," changes and demand forecasts for the education industry can be displayed. This allows users to consider future prospects when choosing a career.

[0097] The reception unit can estimate the user's emotions and provide advice on the input content based on the estimated user emotions. For example, if the user is nervous, the reception unit can display advice to relax, thereby easing the user's tension. If the user is excited, the reception unit can also display advice to stay calm, thereby supporting the user's decision-making. Furthermore, if the user is depressed, the reception unit can display advice to stay positive, thereby helping the user to regain their spirits. In this way, the user's input experience can be improved by providing advice according to the user's emotions.

[0098] The reception unit can provide information on scholarships and support programs specific to a region based on the user's geographic location information. For example, if the user lives in an urban area, scholarships and support programs available in the urban area can be displayed. Also, if the user lives in a rural area, scholarships and support programs specific to that region can be displayed. Furthermore, if the user is interested in a specific region, information on scholarships and support programs available in that region can be provided. This allows the user to receive support appropriate for their region.

[0099] The reception unit can analyze the user's social media activity and suggest related communities and networking events. For example, if the user posts on social media about "medicine" or "health," the reception unit can suggest medical-related communities and networking events. If the user posts about "technology" or "programming," the reception unit can suggest technology-related communities and events. Furthermore, if the user posts about "education" or "children," the reception unit can suggest education-related communities and events. This allows the user to participate in communities and events that match their interests.

[0100] The collection unit can estimate the user's emotions and evaluate the reliability of the information to be collected based on the estimated user emotions. For example, if the user feels anxious, the collection unit will prioritize collecting information from highly reliable information sources. Also, if the user feels relaxed, the collection unit can collect information from a wide range of information sources to broaden the user's interests. Furthermore, if the user is in a hurry, the collection unit can quickly collect the most reliable information. This makes it possible to collect information according to the user's emotions and provide highly reliable information.

[0101] The collection unit can analyze the user's past academic ability data and optimize the update frequency of the collected information. For example, if the user has shown high academic ability in the past, the latest information can be collected frequently. Also, if the user has previously struggled with a subject, information related to that subject can be updated regularly. Furthermore, information related to further education and career can be updated regularly based on the user's past academic ability data. In this way, by optimizing the update frequency of information collection based on past academic ability data, it is possible to always provide the latest information.

[0102] The collection unit can evaluate the reliability of the information to be collected based on the user's current living situation. For example, if the user is a student, information from academic sources can be collected with priority. Alternatively, if the user is a working professional, industry reports and expert opinions can be collected with priority. Furthermore, if the user has a specific hobby or interest, information can be collected from experts in that field or from reliable sources. This makes it possible to provide reliable information that is appropriate for the user's current living situation.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The reception unit inputs the user's interests and desired occupation. For example, the user can input "I want to be a doctor." Step 2: The collection unit collects the information input by the reception unit, such as the user's age, current academic ability, and interests. Step 3: The analysis unit analyzes the information collected by the collection unit. For example, using a generation AI, it determines aptitude and necessary skills based on the user's interests and desired occupation. Step 4: The judgment unit judges the user's aptitude and required skills based on the information analyzed by the analysis unit. For example, the judgment unit uses a generation AI to judge the user's aptitude and required skills based on the analysis results. Step 5: The suggestion unit suggests appropriate schools and necessary studies based on the results of the assessment by the assessment unit. For example, using a generative AI, it suggests the optimal schools and necessary studies based on the assessment results.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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 reception section for inputting the user's interests and desired occupation; a collection unit that collects the information input by the reception unit; an analysis unit that analyzes the information collected by the collection unit; a determination unit that determines the aptitude and necessary skills of the user based on the information analyzed by the analysis unit; a suggestion unit that suggests appropriate educational destinations and necessary studies based on the results of the judgment by the judgment unit; Equipped with A system characterized by:

2. The reception unit Estimate user emotions and dynamically change the design of the input interface based on the estimated user emotions.

2. The system of claim 1.

3. The reception unit Analyzes the user's input history and provides appropriate suggestions when typing.

2. The system of claim 1.

4. The reception unit Show additional relevant questions in real time as the user types 2. The system of claim 1.

5. The reception unit Estimate user emotions and dynamically change input priorities based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit Prioritize local job and educational information based on the user's geographic location.

2. The system of claim 1.

7. The reception unit Analyzing users' social media activity to automatically suggest related interests and career aspirations 2. The system of claim 1.

8. The collecting unit Estimate the user's emotions and adjust the level of detail of information to be collected based on the estimated user emotions.

2. The system of claim 1.

Images