system

Abstract

The system according to this embodiment aims to analyze the aptitudes, interests, and abilities of individuals with developmental disabilities in a way that is easy for them to understand, and to suggest suitable occupations. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, and a proposal unit. The reception unit receives information from the user. The analysis unit analyzes the information received by the reception unit and analyzes the user's aptitude, interests, and abilities. The proposal unit proposes suitable occupations and roles to the user based on the analysis results obtained by the analysis unit.

Description

Technical Field

[0004] ,

[0006] , , , ,

[0005] , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the analysis of the aptitude, interests, and abilities of people with developmental disabilities in a form that is easy to understand, and the proposal of suitable occupations have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the aptitude, interests, and abilities of people with developmental disabilities in a form that is easy to understand and propose suitable occupations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, and a proposal unit. The reception unit receives information from the user. The analysis unit analyzes the information received by the reception unit to analyze the user's aptitude, interests, and abilities. Based on the analysis results obtained by the analysis unit, the proposal unit proposes suitable occupations and roles for the user. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the aptitudes, interests, and abilities of individuals with developmental disabilities in a way that is easy for them to understand, and can suggest suitable occupations. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The dialogue system according to an embodiment of the present invention is a system that utilizes a multimodal LLM (Large-Scale Language Model) to analyze the aptitudes of individuals with developmental disabilities in a way that is easy for them to understand, and to propose suitable occupations. The dialogue system receives information from the user in the form of text, voice, pictograms, pictures, etc. Next, the multimodal LLM analyzes this information to analyze the user's aptitudes, interests, and abilities. Finally, based on the analysis results, it proposes occupations and roles that are suitable for the user. This system allows individuals with developmental disabilities to understand their own aptitudes, interests, and abilities and gain confidence. It can also help avoid mismatches in employment. For example, a user might input "I like to draw pictures" as text, or "I like animals" as voice. This information is input into the multimodal LLM. Next, the multimodal LLM analyzes the input information. The multimodal LLM learns data from various information sources such as text, voice, pictograms, and pictures, and analyzes the user's aptitudes, interests, and abilities based on that. For example, if a user inputs "I like to draw pictures," the multimodal LLM analyzes this information and determines that the user is suitable for creative occupations. Finally, based on the analysis results, the system suggests suitable occupations and roles for the user. For example, if the multimodal LLM analyzes the user's aptitude and determines that the user is suited to creative occupations, it will suggest occupations such as graphic designer or illustrator. It also provides learning materials and training methods to acquire the skills required for those occupations. This system allows individuals with developmental disabilities to understand their own aptitudes, interests, and abilities, and to gain confidence. For example, by understanding their aptitudes and finding a suitable occupation, users can achieve self-actualization and feel a sense of fulfillment and happiness. It can also help avoid mismatches in employment. For example, by finding a suitable occupation, users can reduce workplace stress and dissatisfaction and work long-term. Furthermore, this system can be used not only by individuals with developmental disabilities but also by their supporters and educators. For example, by understanding the aptitudes of individuals with developmental disabilities and providing appropriate support, supporters and educators can enable individuals with developmental disabilities to contribute to society.Furthermore, families of individuals with developmental disabilities can use this system to alleviate some of their anxieties about their child's future. Thus, the dialogue system utilizing multimodal LLM not only enables individuals with developmental disabilities to understand their aptitudes, interests, and abilities and gain confidence, but also helps avoid employment mismatches and promotes diversity and inclusion in society as a whole. In this way, the dialogue system allows individuals with developmental disabilities to understand their aptitudes, interests, and abilities and gain confidence. It also helps avoid employment mismatches.

[0029] The dialogue system according to this embodiment comprises a reception unit, an analysis unit, and a suggestion unit. The reception unit receives information from the user. Information from the user includes, but is not limited to, text data, voice data, and image data. The reception unit can, for example, receive information entered in text format. The reception unit can also receive information entered in voice format. The reception unit can also receive information entered in the form of pictograms or pictures. For example, the reception unit receives information entered by the user in text format, such as "I like to draw pictures." The reception unit can also receive information entered by the user in voice format, such as "I like animals." The reception unit can also receive information entered by the user using pictograms or pictures. The analysis unit analyzes the information received by the reception unit and analyzes the user's aptitude, interests, and abilities. The analysis unit can, for example, analyze text data and analyze the user's aptitude, interests, and abilities. The analysis unit can also analyze voice data and analyze the user's aptitude, interests, and abilities. The analysis unit can also analyze data such as pictograms and pictures to analyze the user's aptitude, interests, and abilities. For example, the analysis unit can analyze information such as "I like to draw" entered by the user and determine that the user is suited to a creative profession. It can also analyze information such as "I like animals" and determine that the user is suited to an animal-related profession. Furthermore, the analysis unit can analyze information entered by the user using pictograms and pictures to analyze the user's aptitude, interests, and abilities. The suggestion unit proposes suitable professions and roles to the user based on the analysis results obtained by the analysis unit. For example, if the analysis unit determines that the user is suited to a creative profession, the suggestion unit can propose professions such as graphic designer or illustrator. Similarly, if the analysis unit determines that the user is suited to an animal-related profession, the suggestion unit can propose professions such as zookeeper or veterinarian. The suggestion unit can also propose suitable roles to the user based on the analysis results obtained by the analysis unit.For example, if the suggestion unit determines that the user is suited to a creative profession, it will suggest professions such as graphic designer or illustrator. Similarly, if the suggestion unit determines that the user is suited to an animal-related profession, it can suggest professions such as zookeeper or veterinarian. In this way, the dialogue system according to this embodiment can analyze the user's aptitude, interests, and abilities, and suggest suitable professions and roles.

[0030] The reception unit receives information from users. This information includes, but is not limited to, text data, audio data, and image data. The reception unit can, for example, receive information entered in text format. Specifically, it can receive text data entered by the user using a keyboard. The reception unit can also receive information entered in voice format. In the case of voice input, the content spoken by the user through a microphone is received as audio data and converted into text data using speech recognition technology. Furthermore, the reception unit can also receive information entered in the form of pictograms and drawings. For example, it can receive drawings and pictograms drawn by the user using the touchscreen of a tablet or smartphone. This allows the reception unit to receive information such as "I like to draw" entered by the user in text. It can also receive information such as "I like animals" spoken by the user. Furthermore, the reception unit can also receive information entered by the user using pictograms and drawings. For example, if a user draws a picture of an animal, the picture is received as image data and sent to the subsequent analysis unit. This allows the reception desk to accept user input in various formats, thereby improving user convenience.

[0031] The analysis unit analyzes information received by the reception unit to analyze the user's aptitude, interests, and abilities. For example, the analysis unit analyzes text data to analyze the user's aptitude, interests, and abilities. Specifically, it uses natural language processing technology to analyze text data and extract the user's interests and concerns. For example, if a user enters "I like to draw," the analysis unit analyzes the text and determines that the user is interested in creative activities. The analysis unit can also analyze voice data to analyze the user's aptitude, interests, and abilities. For voice data analysis, speech recognition technology is used to convert the voice into text, and the user's interests and concerns are analyzed based on that text. Furthermore, the analysis unit can also analyze data such as pictograms and pictures to analyze the user's aptitude, interests, and abilities. Using image recognition technology, it analyzes pictures and pictograms drawn by the user and extracts the user's interests and concerns from their content. For example, if a user draws a picture of an animal, the analysis unit analyzes the picture and determines that the user is interested in animals. This allows the analysis unit to analyze information such as "I like drawing" entered by the user and determine that the user is suited to a creative profession. It can also analyze information such as "I like animals" and determine that the user is suited to an animal-related profession. Furthermore, the analysis unit can analyze information entered by the user using pictograms and drawings to analyze the user's aptitude, interests, and abilities. This enables the analysis unit to analyze diverse data formats and comprehensively analyze the user's aptitude, interests, and abilities.

[0032] The suggestion department proposes suitable occupations and roles to the user based on the analysis results obtained by the analysis department. For example, if the analysis department determines that the user is suited to a creative occupation, the suggestion department will propose occupations such as graphic designer or illustrator. Specifically, the suggestion department generates a list of related occupations based on the user's interests and aptitudes and selects the most suitable occupation from among them. In addition, if the analysis department determines that the user is suited to an animal-related occupation, the suggestion department can also propose occupations such as zookeeper or veterinarian. The suggestion department generates a list of related occupations based on the user's interests and aptitudes and selects the most suitable occupation from among them. In addition, the suggestion department can also propose suitable roles to the user based on the analysis results obtained by the analysis department. For example, if the suggestion department determines that the user is suited to a creative occupation, it will propose occupations such as graphic designer or illustrator. In addition, if the suggestion department determines that the user is suited to an animal-related occupation, it can also propose occupations such as zookeeper or veterinarian. Furthermore, the suggestion unit generates a list of relevant occupations based on the user's interests and aptitudes, and selects the most suitable occupation from among them. This allows the suggestion unit to analyze the user's aptitudes, interests, and abilities, and propose suitable occupations and roles. Additionally, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can collect user reactions and opinions on suggested occupations and adjust the suggestion algorithm based on this feedback. This enables the suggestion unit to provide users with more appropriate and satisfying suggestions.

[0033] The suggestion unit includes a provision unit that provides learning materials and training methods to acquire the necessary skills based on the suggested occupation. For example, if the user is suggested the occupation of graphic designer, the suggestion unit provides online courses and materials to acquire graphic design skills. The suggestion unit can also provide training programs on animal care and breeding if the user is suggested the occupation of zookeeper. The suggestion unit can also provide learning materials and training methods to acquire veterinary knowledge if the user is suggested the occupation of veterinarian. For example, the suggestion unit provides online courses to acquire graphic design skills. The suggestion unit can also provide training programs on animal care and breeding. The suggestion unit can also provide learning materials and training methods to acquire veterinary knowledge. In this way, the suggestion unit can provide learning materials and training methods to acquire the skills necessary for the suggested occupation. Some or all of the above processing in the provision unit may be performed using AI, for example, or not using AI. For example, the provision unit can input the user's learning history and progress into the AI ​​and have the AI ​​select the optimal learning materials and training methods.

[0034] The reception desk can accept information in various formats, such as text, voice, pictograms, and pictures. For example, the reception desk can accept information entered by a user in text format, such as "I like drawing pictures." It can also accept information entered by a user in voice format, such as "I like animals." Furthermore, the reception desk can accept information entered by a user using pictograms or pictures. For example, the reception desk can accept information entered by a user in text format. It can also accept information entered by a user in voice format. Furthermore, the reception desk can accept information entered by a user using pictograms or pictures. This allows the reception desk to accept information from users in various formats. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input text data entered by a user into an AI and have the AI ​​perform analysis of the text data.

[0035] The analysis unit can learn data from various sources such as text, audio, pictograms, and pictures, and analyze the user's aptitudes, interests, and abilities based on that data. For example, the analysis unit can analyze text data to analyze the user's aptitudes, interests, and abilities. It can also analyze audio data to analyze the user's aptitudes, interests, and abilities. Furthermore, the analysis unit can analyze data such as pictograms and pictures to analyze the user's aptitudes, interests, and abilities. For example, the analysis unit can analyze information that the user has entered, such as "I like drawing," and determine that the user is suited for a creative profession. It can also analyze information that the user has said, such as "I like animals," and determine that the user is suited for an animal-related profession. Furthermore, the analysis unit can analyze information that the user has entered using pictograms and pictures to analyze the user's aptitudes, interests, and abilities. In this way, the analysis unit can learn data from various sources and analyze the user's aptitudes, interests, and abilities. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input text data entered by the user into the AI ​​and have the AI ​​perform the analysis of that text data.

[0036] The suggestion unit can suggest suitable occupations and roles based on the user's aptitude, interests, and abilities. For example, if the analysis unit analyzes the user's aptitude and determines that the user is suited to a creative occupation, the suggestion unit can suggest occupations such as graphic designer or illustrator. Similarly, if the analysis unit analyzes the user's aptitude and determines that the user is suited to an animal-related occupation, the suggestion unit can suggest occupations such as zookeeper or veterinarian. The suggestion unit can also suggest suitable roles based on the analysis unit's analysis of the user's aptitude. For example, if the suggestion unit determines that the user is suited to a creative occupation, it can suggest occupations such as graphic designer or illustrator. Similarly, if the suggestion unit determines that the user is suited to an animal-related occupation, it can suggest occupations such as zookeeper or veterinarian. Thus, the suggestion unit can suggest suitable occupations and roles based on the user's aptitude, interests, and abilities. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the proposal department can make suggestions using an AI model that proposes suitable occupations and roles based on the user's aptitude, interests, and abilities.

[0037] The service provider can provide learning materials and training methods to help users acquire the necessary skills based on the proposed occupation. For example, if a user is proposed to be a graphic designer, the service provider can provide online courses and materials to help them acquire graphic design skills. If a user is proposed to be a zookeeper, the service provider can also provide training programs related to animal care and breeding. Furthermore, if a user is proposed to be a veterinarian, the service provider can provide learning materials and training methods to help them acquire veterinary knowledge. For example, the service provider can provide online courses to help users acquire graphic design skills. The service provider can also provide training programs related to animal care and breeding. Furthermore, the service provider can provide learning materials and training methods to help users acquire veterinary knowledge. This allows the service provider to provide learning materials and training methods to help users acquire the skills necessary for the proposed occupation. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's learning history and progress into the AI ​​and have the AI ​​select the optimal learning materials and training methods.

[0038] The reception desk can analyze the user's past input history and suggest the optimal input format. For example, the reception desk can prioritize suggesting input formats (text, voice, etc.) that the user has frequently used in the past. The reception desk can also predict and suggest input formats to be used during specific time periods based on the user's past input history. Furthermore, the reception desk can automatically display relevant input formats based on the user's past input. This allows the reception desk to suggest the optimal input format based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history into AI and have the AI ​​select the optimal input format.

[0039] The reception desk can automatically select the input format based on the user's current situation and environment when receiving information. For example, if the user is on the move, the reception desk can prioritize voice input, allowing information to be entered without using hands. Alternatively, if the user is in a quiet environment, the reception desk can prioritize text input, allowing information to be entered without being disturbed by surrounding noise. Furthermore, if the user is in a public place, the reception desk can prioritize input formats using pictograms or pictures, allowing information to be entered visually. This allows the reception desk to automatically select the optimal input format according to the user's current situation and environment. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's current status and environmental data into the AI, allowing the AI ​​to select the optimal input format.

[0040] The reception unit can prioritize accepting input formats that are highly relevant to the user's geographical location when receiving information. For example, if the user is in a specific region, the reception unit can prioritize accepting information related to that region. Furthermore, if the user is traveling, the reception unit can prioritize accepting information related to their travel destination. Also, if the user is at home, the reception unit can prioritize accepting information related to their home. This allows the reception unit to prioritize accepting input formats that are highly relevant based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location into AI and have AI select the most relevant input formats.

[0041] The reception unit can analyze the user's social media activity and receive relevant information when information is received. For example, the reception unit can suggest relevant input formats based on information shared by the user on social media. The reception unit can also suggest relevant input formats based on information about accounts the user follows on social media. Furthermore, the reception unit can suggest relevant input formats based on information about groups the user participates in on social media. For example, the reception unit can suggest relevant input formats based on information shared by the user on social media. Furthermore, the reception unit can also suggest relevant input formats based on information about accounts the user follows on social media. Furthermore, the reception unit can also suggest relevant input formats based on information about groups the user participates in on social media. This allows the reception unit to receive relevant information based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input the user's social media activity data into AI and have AI select relevant information.

[0042] The analysis unit can optimize the analysis algorithm by referring to the user's past data during analysis. For example, the analysis unit can select the optimal analysis algorithm based on the user's past input data. The analysis unit can also analyze the user's past behavior patterns and adjust the analysis algorithm. Furthermore, the analysis unit can improve the accuracy of the analysis algorithm by referring to the user's past result data. For example, the analysis unit can select the optimal analysis algorithm based on the user's past input data. Furthermore, the analysis unit can analyze the user's past behavior patterns and adjust the analysis algorithm. Furthermore, the analysis unit can improve the accuracy of the analysis algorithm by referring to the user's past result data. In this way, the analysis unit can optimize the analysis algorithm based on the user's past data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's past data into AI and have AI perform the optimization of the analysis algorithm.

[0043] The analysis unit can apply different analysis methods depending on the user's input format during analysis. For example, in the case of text input, the analysis unit applies an analysis method using natural language processing. The analysis unit can also apply an analysis method using speech recognition technology in the case of voice input. Furthermore, the analysis unit can apply an analysis method using image recognition technology in the case of pictograms or pictures. This allows the analysis unit to apply the most suitable analysis method depending on the user's input format. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's input data into AI and have AI select the most suitable analysis method.

[0044] The analysis unit can perform analysis while taking into account the user's geographical location information. For example, if the user is in a specific region, the analysis unit will prioritize analyzing data related to that region. Furthermore, if the user is traveling, the analysis unit can prioritize analyzing data related to the travel destination. Also, if the user is at home, the analysis unit can prioritize analyzing data related to the home. This allows the analysis unit to perform optimal analysis based on the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into AI and have the AI ​​perform the optimal analysis.

[0045] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature during the analysis. For example, the analysis unit can improve the accuracy of its analysis based on literature the user has previously referenced. Furthermore, if the user is interested in a particular field, the analysis unit can perform the analysis by referring to literature in that field. Furthermore, if the user is interested in a particular theme, the analysis unit can perform the analysis by referring to literature related to that theme. In this way, the analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's relevant literature data into AI and have AI perform the analysis accuracy improvement.

[0046] The suggestion unit can make optimal suggestions by referring to the user's past selection history when making suggestions. For example, the suggestion unit can suggest related occupations based on the occupations the user has previously selected. The suggestion unit can also suggest occupations that the user might be interested in based on the user's past selection history. Furthermore, the suggestion unit can analyze the user's past selection history and suggest the most suitable occupation. For example, the suggestion unit can suggest related occupations based on the occupations the user has previously selected. Furthermore, the suggestion unit can suggest occupations that the user might be interested in based on the user's past selection history. Furthermore, the suggestion unit can analyze the user's past selection history and suggest the most suitable occupation. In this way, the suggestion unit can make optimal suggestions based on the user's past selection history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's past selection history data into AI and have the AI ​​execute the optimal suggestions.

[0047] The suggestion unit can customize its suggestions based on the user's current situation. For example, if the user is satisfied with their current job, the suggestion unit can offer suggestions for career advancement. It can also suggest new jobs if the user is considering a career change. Furthermore, if the user is currently learning, the suggestion unit can suggest jobs related to their learning. This allows the suggestion unit to provide optimal suggestions tailored to the user's current situation. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's current situation data into AI and have the AI ​​select the optimal suggestions.

[0048] The suggestion unit can make optimal suggestions by considering the user's geographical location information when making suggestions. For example, if the user is in a specific region, the suggestion unit can suggest occupations related to that region. Also, if the user is traveling, the suggestion unit can suggest occupations related to the travel destination. Also, if the user is at home, the suggestion unit can suggest occupations related to home. In this way, the suggestion unit can make optimal suggestions based on the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's geographical location information into AI and have the AI ​​execute the optimal suggestions.

[0049] The suggestion unit can analyze the user's social media activity and adjust the suggested content when making suggestions. For example, the suggestion unit can suggest relevant occupations based on information the user has shared on social media. It can also suggest relevant occupations based on information about accounts the user follows on social media. Furthermore, it can suggest relevant occupations based on information about groups the user participates in on social media. This allows the suggestion unit to provide optimal suggestions based on the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's social media activity data into AI and have AI select the optimal suggestions.

[0050] The service provider can select the most suitable learning material by referring to the user's past learning history at the time of provision. For example, the service provider can provide relevant learning material based on what the user has learned in the past. The service provider can also suggest what the user should learn next based on their past learning history. Furthermore, the service provider can analyze the user's past learning history and provide the most effective learning material. For example, the service provider can provide relevant learning material based on what the user has learned in the past. The service provider can also suggest what the user should learn next based on their past learning history. Furthermore, the service provider can analyze the user's past learning history and provide the most effective learning material. In this way, the service provider can provide the most suitable learning material based on the user's past learning history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past learning history data into AI and have the AI ​​select the most suitable learning material.

[0051] The service provider can provide optimal learning materials by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider can provide learning materials related to that region. Furthermore, if the user is traveling, the service provider can provide learning materials related to their travel destination. Furthermore, if the user is at home, the service provider can provide learning materials related to their home. This allows the service provider to provide optimal learning materials based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into AI and have AI select the optimal learning materials.

[0052] The service provider can customize learning materials based on the user's current situation and environment at the time of delivery. For example, if the user is at work, the service provider can provide materials that can be learned in a short amount of time. If the user is on vacation, the service provider can also provide detailed materials that can be learned over a longer period of time. Furthermore, if the user is traveling, the service provider can provide learning materials using audio and video. This allows the service provider to provide optimal learning materials according to the user's current situation and environment. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's current situation and environment data into the AI ​​and have the AI ​​select the optimal learning materials.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The dialogue system can provide more accurate career suggestions by referring to the user's past work history. For example, the suggestion unit can suggest relevant occupations based on the occupations the user has previously experienced. The suggestion unit can also suggest occupations that the user might be interested in based on their past work history. Furthermore, the suggestion unit can analyze the user's past work history and suggest the most suitable occupation. In this way, the suggestion unit can provide optimal career suggestions based on the user's past work history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's past work history data into AI and have the AI ​​perform optimal career suggestions.

[0055] The dialogue system can customize career suggestions based on the user's current situation and environment. For example, if the user is satisfied with their current job, the suggestion unit can offer suggestions for career advancement. The suggestion unit can also suggest new jobs if the user is considering a career change. Furthermore, if the user is learning, the suggestion unit can suggest jobs related to their learning. This allows the suggestion unit to provide optimal career suggestions according to the user's current situation. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's current situation data into the AI ​​and have the AI ​​select the most suitable career suggestion.

[0056] The dialogue system can provide optimal learning materials by referring to the user's past learning history. For example, the providing unit can provide relevant learning materials based on what the user has learned in the past. The providing unit can also suggest what the user should learn next based on their past learning history. Furthermore, the providing unit can analyze the user's past learning history and provide the most effective learning materials. In this way, the providing unit can provide optimal learning materials based on the user's past learning history. Some or all of the above processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input the user's past learning history data into AI and have the AI ​​select the optimal learning materials.

[0057] The dialogue system can provide optimal learning materials by taking into account the user's geographical location. For example, if the user is in a specific region, the system can provide learning materials related to that region. Furthermore, if the user is traveling, the system can provide learning materials related to their travel destination. Additionally, if the user is at home, the system can provide learning materials related to their home. This allows the system to provide optimal learning materials based on the user's geographical location. Some or all of the above processing in the system may be performed using AI, for example, or without AI. For instance, the system can input the user's geographical location into the AI ​​and have the AI ​​select the optimal learning materials.

[0058] The dialogue system can analyze a user's social media activity and receive relevant information. For example, the reception unit can suggest relevant input formats based on information the user has shared on social media. It can also suggest relevant input formats based on information about accounts the user follows on social media. Furthermore, it can suggest relevant input formats based on information about groups the user participates in on social media. This allows the reception unit to receive relevant information based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into AI and have AI select relevant information.

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

[0060] Step 1: The reception desk receives information from the user. This information includes text data, audio data, and image data. For example, it accepts information such as the user typing "I like to draw pictures," speaking "I like animals," and inputting information using pictograms or pictures. Step 2: The analysis unit analyzes the information received by the reception unit to determine the user's aptitude, interests, and abilities. For example, it might analyze text data to determine if the user is suited to creative professions, or analyze voice data to determine if the user is suited to animal-related professions. It can also analyze data such as pictograms and pictures to determine the user's aptitude, interests, and abilities. Step 3: The suggestion unit proposes suitable occupations and roles for the user based on the analysis results obtained by the analysis unit. For example, if the analysis unit determines that the user is suited to a creative occupation, it will suggest occupations such as graphic designer or illustrator. If it determines that the user is suited to an animal-related occupation, it can also suggest occupations such as zookeeper or veterinarian.

[0061] (Example of form 2) The dialogue system according to an embodiment of the present invention is a system that utilizes a multimodal LLM (Large-Scale Language Model) to analyze the aptitudes of individuals with developmental disabilities in a way that is easy for them to understand, and to propose suitable occupations. The dialogue system receives information from the user in the form of text, voice, pictograms, pictures, etc. Next, the multimodal LLM analyzes this information to analyze the user's aptitudes, interests, and abilities. Finally, based on the analysis results, it proposes occupations and roles that are suitable for the user. This system allows individuals with developmental disabilities to understand their own aptitudes, interests, and abilities and gain confidence. It can also help avoid mismatches in employment. For example, a user might input "I like to draw pictures" as text, or "I like animals" as voice. This information is input into the multimodal LLM. Next, the multimodal LLM analyzes the input information. The multimodal LLM learns data from various information sources such as text, voice, pictograms, and pictures, and analyzes the user's aptitudes, interests, and abilities based on that. For example, if a user inputs "I like to draw pictures," the multimodal LLM analyzes this information and determines that the user is suitable for creative occupations. Finally, based on the analysis results, the system suggests suitable occupations and roles for the user. For example, if the multimodal LLM analyzes the user's aptitude and determines that the user is suited to creative occupations, it will suggest occupations such as graphic designer or illustrator. It also provides learning materials and training methods to acquire the skills required for those occupations. This system allows individuals with developmental disabilities to understand their own aptitudes, interests, and abilities, and to gain confidence. For example, by understanding their aptitudes and finding a suitable occupation, users can achieve self-actualization and feel a sense of fulfillment and happiness. It can also help avoid mismatches in employment. For example, by finding a suitable occupation, users can reduce workplace stress and dissatisfaction and work long-term. Furthermore, this system can be used not only by individuals with developmental disabilities but also by their supporters and educators. For example, by understanding the aptitudes of individuals with developmental disabilities and providing appropriate support, supporters and educators can enable individuals with developmental disabilities to contribute to society.Furthermore, families of individuals with developmental disabilities can use this system to alleviate some of their anxieties about their child's future. Thus, the dialogue system utilizing multimodal LLM not only enables individuals with developmental disabilities to understand their aptitudes, interests, and abilities and gain confidence, but also helps avoid employment mismatches and promotes diversity and inclusion in society as a whole. In this way, the dialogue system allows individuals with developmental disabilities to understand their aptitudes, interests, and abilities and gain confidence. It also helps avoid employment mismatches.

[0062] The dialogue system according to this embodiment comprises a reception unit, an analysis unit, and a suggestion unit. The reception unit receives information from the user. Information from the user includes, but is not limited to, text data, voice data, and image data. The reception unit can, for example, receive information entered in text format. The reception unit can also receive information entered in voice format. The reception unit can also receive information entered in the form of pictograms or pictures. For example, the reception unit receives information entered by the user in text format, such as "I like to draw pictures." The reception unit can also receive information entered by the user in voice format, such as "I like animals." The reception unit can also receive information entered by the user using pictograms or pictures. The analysis unit analyzes the information received by the reception unit and analyzes the user's aptitude, interests, and abilities. The analysis unit can, for example, analyze text data and analyze the user's aptitude, interests, and abilities. The analysis unit can also analyze voice data and analyze the user's aptitude, interests, and abilities. The analysis unit can also analyze data such as pictograms and pictures to analyze the user's aptitude, interests, and abilities. For example, the analysis unit can analyze information such as "I like to draw" entered by the user and determine that the user is suited to a creative profession. It can also analyze information such as "I like animals" and determine that the user is suited to an animal-related profession. Furthermore, the analysis unit can analyze information entered by the user using pictograms and pictures to analyze the user's aptitude, interests, and abilities. The suggestion unit proposes suitable professions and roles to the user based on the analysis results obtained by the analysis unit. For example, if the analysis unit determines that the user is suited to a creative profession, the suggestion unit can propose professions such as graphic designer or illustrator. Similarly, if the analysis unit determines that the user is suited to an animal-related profession, the suggestion unit can propose professions such as zookeeper or veterinarian. The suggestion unit can also propose suitable roles to the user based on the analysis results obtained by the analysis unit.For example, if the suggestion unit determines that the user is suited to a creative profession, it will suggest professions such as graphic designer or illustrator. Similarly, if the suggestion unit determines that the user is suited to an animal-related profession, it can suggest professions such as zookeeper or veterinarian. In this way, the dialogue system according to this embodiment can analyze the user's aptitude, interests, and abilities, and suggest suitable professions and roles.

[0063] The reception unit receives information from users. This information includes, but is not limited to, text data, audio data, and image data. The reception unit can, for example, receive information entered in text format. Specifically, it can receive text data entered by the user using a keyboard. The reception unit can also receive information entered in voice format. In the case of voice input, the content spoken by the user through a microphone is received as audio data and converted into text data using speech recognition technology. Furthermore, the reception unit can also receive information entered in the form of pictograms and drawings. For example, it can receive drawings and pictograms drawn by the user using the touchscreen of a tablet or smartphone. This allows the reception unit to receive information such as "I like to draw" entered by the user in text. It can also receive information such as "I like animals" spoken by the user. Furthermore, the reception unit can also receive information entered by the user using pictograms and drawings. For example, if a user draws a picture of an animal, the picture is received as image data and sent to the subsequent analysis unit. This allows the reception desk to accept user input in various formats, thereby improving user convenience.

[0064] The analysis unit analyzes information received by the reception unit to analyze the user's aptitude, interests, and abilities. For example, the analysis unit analyzes text data to analyze the user's aptitude, interests, and abilities. Specifically, it uses natural language processing technology to analyze text data and extract the user's interests and concerns. For example, if a user enters "I like to draw," the analysis unit analyzes the text and determines that the user is interested in creative activities. The analysis unit can also analyze voice data to analyze the user's aptitude, interests, and abilities. For voice data analysis, speech recognition technology is used to convert the voice into text, and the user's interests and concerns are analyzed based on that text. Furthermore, the analysis unit can also analyze data such as pictograms and pictures to analyze the user's aptitude, interests, and abilities. Using image recognition technology, it analyzes pictures and pictograms drawn by the user and extracts the user's interests and concerns from their content. For example, if a user draws a picture of an animal, the analysis unit analyzes the picture and determines that the user is interested in animals. This allows the analysis unit to analyze information such as "I like drawing" entered by the user and determine that the user is suited to a creative profession. It can also analyze information such as "I like animals" and determine that the user is suited to an animal-related profession. Furthermore, the analysis unit can analyze information entered by the user using pictograms and drawings to analyze the user's aptitude, interests, and abilities. This enables the analysis unit to analyze diverse data formats and comprehensively analyze the user's aptitude, interests, and abilities.

[0065] The suggestion department proposes suitable occupations and roles to the user based on the analysis results obtained by the analysis department. For example, if the analysis department determines that the user is suited to a creative occupation, the suggestion department will propose occupations such as graphic designer or illustrator. Specifically, the suggestion department generates a list of related occupations based on the user's interests and aptitudes and selects the most suitable occupation from among them. In addition, if the analysis department determines that the user is suited to an animal-related occupation, the suggestion department can also propose occupations such as zookeeper or veterinarian. The suggestion department generates a list of related occupations based on the user's interests and aptitudes and selects the most suitable occupation from among them. In addition, the suggestion department can also propose suitable roles to the user based on the analysis results obtained by the analysis department. For example, if the suggestion department determines that the user is suited to a creative occupation, it will propose occupations such as graphic designer or illustrator. In addition, if the suggestion department determines that the user is suited to an animal-related occupation, it can also propose occupations such as zookeeper or veterinarian. Furthermore, the suggestion unit generates a list of relevant occupations based on the user's interests and aptitudes, and selects the most suitable occupation from among them. This allows the suggestion unit to analyze the user's aptitudes, interests, and abilities, and propose suitable occupations and roles. Additionally, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can collect user reactions and opinions on suggested occupations and adjust the suggestion algorithm based on this feedback. This enables the suggestion unit to provide users with more appropriate and satisfying suggestions.

[0066] The suggestion unit includes a provision unit that provides learning materials and training methods to acquire the necessary skills based on the suggested occupation. For example, if the user is suggested the occupation of graphic designer, the suggestion unit provides online courses and materials to acquire graphic design skills. The suggestion unit can also provide training programs on animal care and breeding if the user is suggested the occupation of zookeeper. The suggestion unit can also provide learning materials and training methods to acquire veterinary knowledge if the user is suggested the occupation of veterinarian. For example, the suggestion unit provides online courses to acquire graphic design skills. The suggestion unit can also provide training programs on animal care and breeding. The suggestion unit can also provide learning materials and training methods to acquire veterinary knowledge. In this way, the suggestion unit can provide learning materials and training methods to acquire the skills necessary for the suggested occupation. Some or all of the above processing in the provision unit may be performed using AI, for example, or not using AI. For example, the provision unit can input the user's learning history and progress into the AI ​​and have the AI ​​select the optimal learning materials and training methods.

[0067] The reception desk can accept information in various formats, such as text, voice, pictograms, and pictures. For example, the reception desk can accept information entered by a user in text format, such as "I like drawing pictures." It can also accept information entered by a user in voice format, such as "I like animals." Furthermore, the reception desk can accept information entered by a user using pictograms or pictures. For example, the reception desk can accept information entered by a user in text format. It can also accept information entered by a user in voice format. Furthermore, the reception desk can accept information entered by a user using pictograms or pictures. This allows the reception desk to accept information from users in various formats. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input text data entered by a user into an AI and have the AI ​​perform analysis of the text data.

[0068] The analysis unit can learn data from various sources such as text, audio, pictograms, and pictures, and analyze the user's aptitudes, interests, and abilities based on that data. For example, the analysis unit can analyze text data to analyze the user's aptitudes, interests, and abilities. It can also analyze audio data to analyze the user's aptitudes, interests, and abilities. Furthermore, the analysis unit can analyze data such as pictograms and pictures to analyze the user's aptitudes, interests, and abilities. For example, the analysis unit can analyze information that the user has entered, such as "I like drawing," and determine that the user is suited for a creative profession. It can also analyze information that the user has said, such as "I like animals," and determine that the user is suited for an animal-related profession. Furthermore, the analysis unit can analyze information that the user has entered using pictograms and pictures to analyze the user's aptitudes, interests, and abilities. In this way, the analysis unit can learn data from various sources and analyze the user's aptitudes, interests, and abilities. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input text data entered by the user into the AI ​​and have the AI ​​perform the analysis of that text data.

[0069] The suggestion unit can suggest suitable occupations and roles based on the user's aptitude, interests, and abilities. For example, if the analysis unit analyzes the user's aptitude and determines that the user is suited to a creative occupation, the suggestion unit can suggest occupations such as graphic designer or illustrator. Similarly, if the analysis unit analyzes the user's aptitude and determines that the user is suited to an animal-related occupation, the suggestion unit can suggest occupations such as zookeeper or veterinarian. The suggestion unit can also suggest suitable roles based on the analysis unit's analysis of the user's aptitude. For example, if the suggestion unit determines that the user is suited to a creative occupation, it can suggest occupations such as graphic designer or illustrator. Similarly, if the suggestion unit determines that the user is suited to an animal-related occupation, it can suggest occupations such as zookeeper or veterinarian. Thus, the suggestion unit can suggest suitable occupations and roles based on the user's aptitude, interests, and abilities. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the proposal department can make suggestions using an AI model that proposes suitable occupations and roles based on the user's aptitude, interests, and abilities.

[0070] The service provider can provide learning materials and training methods to help users acquire the necessary skills based on the proposed occupation. For example, if a user is proposed to be a graphic designer, the service provider can provide online courses and materials to help them acquire graphic design skills. If a user is proposed to be a zookeeper, the service provider can also provide training programs related to animal care and breeding. Furthermore, if a user is proposed to be a veterinarian, the service provider can provide learning materials and training methods to help them acquire veterinary knowledge. For example, the service provider can provide online courses to help users acquire graphic design skills. The service provider can also provide training programs related to animal care and breeding. Furthermore, the service provider can provide learning materials and training methods to help users acquire veterinary knowledge. This allows the service provider to provide learning materials and training methods to help users acquire the skills necessary for the proposed occupation. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's learning history and progress into the AI ​​and have the AI ​​select the optimal learning materials and training methods.

[0071] The reception desk can estimate the user's emotions and adjust how information is received based on those emotions. For example, if the user is nervous, the reception desk can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception desk can prioritize voice input to allow for quick information entry. This allows the reception desk to adjust how information is received according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user emotion data into the AI ​​and have the AI ​​perform emotion estimation.

[0072] The reception desk can analyze the user's past input history and suggest the optimal input format. For example, the reception desk can prioritize suggesting input formats (text, voice, etc.) that the user has frequently used in the past. The reception desk can also predict and suggest input formats to be used during specific time periods based on the user's past input history. Furthermore, the reception desk can automatically display relevant input formats based on the user's past input. This allows the reception desk to suggest the optimal input format based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history into AI and have the AI ​​select the optimal input format.

[0073] The reception desk can automatically select the input format based on the user's current situation and environment when receiving information. For example, if the user is on the move, the reception desk can prioritize voice input, allowing information to be entered without using hands. Alternatively, if the user is in a quiet environment, the reception desk can prioritize text input, allowing information to be entered without being disturbed by surrounding noise. Furthermore, if the user is in a public place, the reception desk can prioritize input formats using pictograms or pictures, allowing information to be entered visually. This allows the reception desk to automatically select the optimal input format according to the user's current situation and environment. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's current status and environmental data into the AI, allowing the AI ​​to select the optimal input format.

[0074] The reception desk can estimate the user's emotions and prioritize input based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize inputting important information and postpone other inputs. Conversely, if the user is relaxed, the reception desk can prioritize inputting detailed information and complete the overall input. Furthermore, if the user is in a hurry, the reception desk can prioritize inputting the most important information and process it quickly. For example, if the user is stressed, the reception desk will prioritize inputting important information and postpone other inputs. Conversely, if the user is relaxed, the reception desk can prioritize inputting detailed information and complete the overall input. Furthermore, if the user is in a hurry, the reception desk can prioritize inputting the most important information and process it quickly. This allows the reception desk to prioritize input according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user emotion data into the AI ​​and have the AI ​​perform emotion estimation.

[0075] The reception unit can prioritize accepting input formats that are highly relevant to the user's geographical location when receiving information. For example, if the user is in a specific region, the reception unit can prioritize accepting information related to that region. Furthermore, if the user is traveling, the reception unit can prioritize accepting information related to their travel destination. Also, if the user is at home, the reception unit can prioritize accepting information related to their home. This allows the reception unit to prioritize accepting input formats that are highly relevant based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location into AI and have AI select the most relevant input formats.

[0076] The reception unit can analyze the user's social media activity and receive relevant information when information is received. For example, the reception unit can suggest relevant input formats based on information shared by the user on social media. The reception unit can also suggest relevant input formats based on information about accounts the user follows on social media. Furthermore, the reception unit can suggest relevant input formats based on information about groups the user participates in on social media. For example, the reception unit can suggest relevant input formats based on information shared by the user on social media. Furthermore, the reception unit can also suggest relevant input formats based on information about accounts the user follows on social media. Furthermore, the reception unit can also suggest relevant input formats based on information about groups the user participates in on social media. This allows the reception unit to receive relevant information based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input the user's social media activity data into AI and have AI select relevant information.

[0077] The analysis unit can estimate the user's emotions and adjust its analysis method based on the estimated emotions. For example, if the user is nervous, the analysis unit uses a simple and intuitive analysis method. If the user is relaxed, the analysis unit can also use a detailed analysis method to perform a deeper analysis. Furthermore, if the user is in a hurry, the analysis unit can perform a rapid analysis and provide results. For example, if the user is nervous, the analysis unit uses a simple and intuitive analysis method. If the user is relaxed, the analysis unit can also use a detailed analysis method to perform a deeper analysis. Furthermore, if the user is in a hurry, the analysis unit can also perform a rapid analysis and provide results. This allows the analysis unit to adjust its analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the AI ​​and have the AI ​​perform emotion estimation.

[0078] The analysis unit can optimize the analysis algorithm by referring to the user's past data during analysis. For example, the analysis unit can select the optimal analysis algorithm based on the user's past input data. The analysis unit can also analyze the user's past behavior patterns and adjust the analysis algorithm. Furthermore, the analysis unit can improve the accuracy of the analysis algorithm by referring to the user's past result data. For example, the analysis unit can select the optimal analysis algorithm based on the user's past input data. Furthermore, the analysis unit can analyze the user's past behavior patterns and adjust the analysis algorithm. Furthermore, the analysis unit can improve the accuracy of the analysis algorithm by referring to the user's past result data. In this way, the analysis unit can optimize the analysis algorithm based on the user's past data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's past data into AI and have AI perform the optimization of the analysis algorithm.

[0079] The analysis unit can apply different analysis methods depending on the user's input format during analysis. For example, in the case of text input, the analysis unit applies an analysis method using natural language processing. The analysis unit can also apply an analysis method using speech recognition technology in the case of voice input. Furthermore, the analysis unit can apply an analysis method using image recognition technology in the case of pictograms or pictures. This allows the analysis unit to apply the most suitable analysis method depending on the user's input format. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's input data into AI and have AI select the most suitable analysis method.

[0080] 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 and highly visible display method. It can also provide a display method that includes detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. This allows the analysis unit to adjust the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the AI ​​and have the AI ​​perform emotion estimation.

[0081] The analysis unit can perform analysis while taking into account the user's geographical location information. For example, if the user is in a specific region, the analysis unit will prioritize analyzing data related to that region. Furthermore, if the user is traveling, the analysis unit can prioritize analyzing data related to the travel destination. Also, if the user is at home, the analysis unit can prioritize analyzing data related to the home. This allows the analysis unit to perform optimal analysis based on the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into AI and have the AI ​​perform the optimal analysis.

[0082] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature during the analysis. For example, the analysis unit can improve the accuracy of its analysis based on literature the user has previously referenced. Furthermore, if the user is interested in a particular field, the analysis unit can perform the analysis by referring to literature in that field. Furthermore, if the user is interested in a particular theme, the analysis unit can perform the analysis by referring to literature related to that theme. In this way, the analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's relevant literature data into AI and have AI perform the analysis accuracy improvement.

[0083] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is nervous, the suggestion unit can provide a simple and easily understandable suggestion. If the user is relaxed, the suggestion unit can also provide a suggestion that includes detailed information. If the user is in a hurry, the suggestion unit can also provide a concise suggestion. For example, if the user is nervous, the suggestion unit can provide a simple and easily understandable suggestion. If the user is relaxed, the suggestion unit can also provide a suggestion that includes detailed information. If the user is in a hurry, the suggestion unit can also provide a concise suggestion. This allows the suggestion unit to adjust the way it presents suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the proposal department can input user emotion data into the AI ​​and have the AI ​​perform emotion estimation.

[0084] The suggestion unit can make optimal suggestions by referring to the user's past selection history when making suggestions. For example, the suggestion unit can suggest related occupations based on the occupations the user has previously selected. The suggestion unit can also suggest occupations that the user might be interested in based on the user's past selection history. Furthermore, the suggestion unit can analyze the user's past selection history and suggest the most suitable occupation. For example, the suggestion unit can suggest related occupations based on the occupations the user has previously selected. Furthermore, the suggestion unit can suggest occupations that the user might be interested in based on the user's past selection history. Furthermore, the suggestion unit can analyze the user's past selection history and suggest the most suitable occupation. In this way, the suggestion unit can make optimal suggestions based on the user's past selection history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's past selection history data into AI and have the AI ​​execute the optimal suggestions.

[0085] The suggestion unit can customize its suggestions based on the user's current situation. For example, if the user is satisfied with their current job, the suggestion unit can offer suggestions for career advancement. It can also suggest new jobs if the user is considering a career change. Furthermore, if the user is currently learning, the suggestion unit can suggest jobs related to their learning. This allows the suggestion unit to provide optimal suggestions tailored to the user's current situation. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's current situation data into AI and have the AI ​​select the optimal suggestions.

[0086] The suggestion function can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion function will prioritize important suggestions and postpone others. If the user is relaxed, the suggestion function may prioritize detailed suggestions and complete the overall suggestions. If the user is in a hurry, the suggestion function may prioritize the most important suggestions and process them quickly. For example, if the user is stressed, the suggestion function will prioritize important suggestions and postpone others. If the user is relaxed, the suggestion function may prioritize detailed suggestions and complete the overall suggestions. If the user is in a hurry, the suggestion function may prioritize the most important suggestions and process them quickly. This allows the suggestion function to prioritize suggestions according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input user emotion data into AI and have AI perform emotion estimation.

[0087] The suggestion unit can make optimal suggestions by considering the user's geographical location information when making suggestions. For example, if the user is in a specific region, the suggestion unit can suggest occupations related to that region. Also, if the user is traveling, the suggestion unit can suggest occupations related to the travel destination. Also, if the user is at home, the suggestion unit can suggest occupations related to home. In this way, the suggestion unit can make optimal suggestions based on the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's geographical location information into AI and have the AI ​​execute the optimal suggestions.

[0088] The suggestion unit can analyze the user's social media activity and adjust the suggested content when making suggestions. For example, the suggestion unit can suggest relevant occupations based on information the user has shared on social media. It can also suggest relevant occupations based on information about accounts the user follows on social media. Furthermore, it can suggest relevant occupations based on information about groups the user participates in on social media. This allows the suggestion unit to provide optimal suggestions based on the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's social media activity data into AI and have AI select the optimal suggestions.

[0089] The provider can estimate the user's emotions and adjust the way learning materials and training methods are provided based on the estimated user emotions. For example, if the user is nervous, the provider can provide simple and intuitive learning materials. If the user is relaxed, the provider can also provide detailed learning materials. If the user is in a hurry, the provider can also provide materials that allow for rapid learning. For example, if the user is nervous, the provider can provide simple and intuitive learning materials. If the user is relaxed, the provider can also provide detailed learning materials. If the user is in a hurry, the provider can also provide materials that allow for rapid learning. This allows the provider to adjust the way learning materials and training methods are provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into the AI ​​and have the AI ​​perform emotion estimation.

[0090] The service provider can select the most suitable learning material by referring to the user's past learning history at the time of provision. For example, the service provider can provide relevant learning material based on what the user has learned in the past. The service provider can also suggest what the user should learn next based on their past learning history. Furthermore, the service provider can analyze the user's past learning history and provide the most effective learning material. For example, the service provider can provide relevant learning material based on what the user has learned in the past. The service provider can also suggest what the user should learn next based on their past learning history. Furthermore, the service provider can analyze the user's past learning history and provide the most effective learning material. In this way, the service provider can provide the most suitable learning material based on the user's past learning history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past learning history data into AI and have the AI ​​select the most suitable learning material.

[0091] The system can estimate the user's emotions and prioritize learning materials based on those emotions. For example, if the user is stressed, the system will prioritize important learning materials and postpone others. If the user is relaxed, the system can prioritize detailed learning materials and complete the overall learning. If the user is in a hurry, the system can prioritize the most important learning materials and proceed quickly. For example, if the user is stressed, the system will prioritize important learning materials and postpone others. If the user is relaxed, the system can prioritize detailed learning materials and complete the overall learning. If the user is in a hurry, the system can prioritize the most important learning materials and proceed quickly. This allows the system to prioritize learning materials according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provision unit may be performed using AI, for example, or without using AI. For example, the service provision unit can input user emotion data into AI and have AI perform emotion estimation.

[0092] The service provider can provide optimal learning materials by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider can provide learning materials related to that region. Furthermore, if the user is traveling, the service provider can provide learning materials related to their travel destination. Furthermore, if the user is at home, the service provider can provide learning materials related to their home. This allows the service provider to provide optimal learning materials based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into AI and have AI select the optimal learning materials.

[0093] The service provider can customize learning materials based on the user's current situation and environment at the time of delivery. For example, if the user is at work, the service provider can provide materials that can be learned in a short amount of time. If the user is on vacation, the service provider can also provide detailed materials that can be learned over a longer period of time. Furthermore, if the user is traveling, the service provider can provide learning materials using audio and video. This allows the service provider to provide optimal learning materials according to the user's current situation and environment. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's current situation and environment data into the AI ​​and have the AI ​​select the optimal learning materials.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The dialogue system can estimate the user's emotions and improve the accuracy of the aptitude analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can provide analysis results that make the user feel more secure. The analysis unit can also provide analysis results that calm the user's excitement if the user is excited. Furthermore, if the user is calm, the analysis unit can provide detailed analysis results to allow the user to understand more deeply. This allows the analysis unit to improve the accuracy of the aptitude analysis according to the user's emotions. Emotion estimation is achieved, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the user's emotion data into an AI and have the AI ​​perform emotion estimation.

[0096] The dialogue system can provide more accurate career suggestions by referring to the user's past work history. For example, the suggestion unit can suggest relevant occupations based on the occupations the user has previously experienced. The suggestion unit can also suggest occupations that the user might be interested in based on their past work history. Furthermore, the suggestion unit can analyze the user's past work history and suggest the most suitable occupation. In this way, the suggestion unit can provide optimal career suggestions based on the user's past work history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's past work history data into AI and have the AI ​​perform optimal career suggestions.

[0097] The dialogue system can estimate the user's emotions and adjust the way career suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can provide a simple and easily understandable suggestion. If the user is relaxed, the suggestion unit can also provide a suggestion that includes more detailed information. Furthermore, if the user is in a hurry, the suggestion unit can provide a concise suggestion. In this way, the suggestion unit can adjust the way career suggestions are presented according to the user's emotions. Emotion estimation is achieved, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0098] The dialogue system can customize career suggestions based on the user's current situation and environment. For example, if the user is satisfied with their current job, the suggestion unit can offer suggestions for career advancement. The suggestion unit can also suggest new jobs if the user is considering a career change. Furthermore, if the user is learning, the suggestion unit can suggest jobs related to their learning. This allows the suggestion unit to provide optimal career suggestions according to the user's current situation. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's current situation data into the AI ​​and have the AI ​​select the most suitable career suggestion.

[0099] The dialogue system can estimate the user's emotions and adjust the way it provides learning materials and training methods based on the estimated emotions. For example, if the user is nervous, the system can provide simple and intuitive learning materials. If the user is relaxed, it can also provide detailed learning materials. Furthermore, if the user is in a hurry, it can provide materials that allow for rapid learning. In this way, the system can adjust the way it provides learning materials and training methods according to the user's emotions. Emotion estimation is achieved, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the system may be performed using AI or not using AI. For example, the system can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0100] The dialogue system can provide optimal learning materials by referring to the user's past learning history. For example, the providing unit can provide relevant learning materials based on what the user has learned in the past. The providing unit can also suggest what the user should learn next based on their past learning history. Furthermore, the providing unit can analyze the user's past learning history and provide the most effective learning materials. In this way, the providing unit can provide optimal learning materials based on the user's past learning history. Some or all of the above processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input the user's past learning history data into AI and have the AI ​​select the optimal learning materials.

[0101] The dialogue system can estimate the user's emotions and prioritize learning materials based on those emotions. For example, if the user is stressed, the system can prioritize important learning materials and postpone others. If the user is relaxed, the system can prioritize detailed learning materials to complete the overall learning. Furthermore, if the user is in a hurry, the system can prioritize the most important learning materials to expedite learning. In this way, the system can prioritize learning materials according to the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the system may be performed using AI or not. For example, the system can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0102] The dialogue system can provide optimal learning materials by taking into account the user's geographical location. For example, if the user is in a specific region, the system can provide learning materials related to that region. Furthermore, if the user is traveling, the system can provide learning materials related to their travel destination. Additionally, if the user is at home, the system can provide learning materials related to their home. This allows the system to provide optimal learning materials based on the user's geographical location. Some or all of the above processing in the system may be performed using AI, for example, or without AI. For instance, the system can input the user's geographical location into the AI ​​and have the AI ​​select the optimal learning materials.

[0103] The dialogue system can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and easy-to-read display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. In this way, the analysis unit can adjust the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's emotion data into an AI and have the AI ​​perform emotion estimation.

[0104] The dialogue system can analyze a user's social media activity and receive relevant information. For example, the reception unit can suggest relevant input formats based on information the user has shared on social media. It can also suggest relevant input formats based on information about accounts the user follows on social media. Furthermore, it can suggest relevant input formats based on information about groups the user participates in on social media. This allows the reception unit to receive relevant information based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into AI and have AI select relevant information.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The reception desk receives information from the user. This information includes text data, audio data, and image data. For example, it accepts information such as the user typing "I like to draw pictures," speaking "I like animals," and inputting information using pictograms or pictures. Step 2: The analysis unit analyzes the information received by the reception unit to determine the user's aptitude, interests, and abilities. For example, it might analyze text data to determine if the user is suited to creative professions, or analyze voice data to determine if the user is suited to animal-related professions. It can also analyze data such as pictograms and pictures to determine the user's aptitude, interests, and abilities. Step 3: The suggestion unit proposes suitable occupations and roles for the user based on the analysis results obtained by the analysis unit. For example, if the analysis unit determines that the user is suited to a creative occupation, it will suggest occupations such as graphic designer or illustrator. If it determines that the user is suited to an animal-related occupation, it can also suggest occupations such as zookeeper or veterinarian.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0109] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0110] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives information such as text, voice, pictograms, and pictures from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's aptitude, interests, and abilities using a multimodal LLM. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes suitable occupations based on the analysis results. The provision unit is implemented by the control unit 46A of the smart device 14 and provides learning materials and training methods to acquire the skills necessary for the proposed occupation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0114] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0115] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0117] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0118] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0119] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0120] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0121] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0125] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives information such as text, voice, pictograms, and pictures from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's aptitude, interests, and abilities using a multimodal LLM. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes suitable occupations based on the analysis results. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides learning materials and training methods to acquire the skills necessary for the proposed occupation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives information such as text, voice, pictograms, and pictures from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's aptitude, interests, and abilities using multimodal LLM. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes suitable occupations based on the analysis results. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides learning materials and training methods to acquire the skills necessary for the proposed occupation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0151] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives information such as text, voice, pictograms, and pictures from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's aptitude, interests, and abilities using a multimodal LLM. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a suitable occupation based on the analysis results. The provision unit is implemented by the control unit 46A of the robot 414 and provides learning materials and training methods to acquire the skills necessary for the proposed occupation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0160] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0162] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0163] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0164] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0165] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0168] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0169] 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.

[0170] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0171] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0172] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0173] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0175] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0176] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0177] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0178] (Note 1) A reception desk that receives information from users, An analysis unit analyzes the information received by the reception unit and analyzes the user's aptitude, interests, and abilities. The system includes a suggestion unit that proposes suitable occupations and roles for the user based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features. (Note 2) Based on the proposed occupation, it includes a provision department that provides learning materials and training methods to help users acquire the necessary skills. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is We accept information in various formats, including text, audio, pictograms, and pictures. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, It learns data from various sources such as text, audio, pictograms, and pictures, and analyzes the user's aptitude, interests, and abilities based on that data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Based on the user's aptitude, interests, and abilities, we suggest suitable occupations and roles. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Based on the proposed occupation, we provide learning materials and training methods to help you acquire the necessary skills. The system described in Appendix 2, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts how information is received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input format. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving information, the system automatically selects the input format based on the user's current situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of inputs based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving information, the system prioritizes accepting input formats that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving information, the system analyzes the user's social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to the user's past data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis methods are applied depending on the user's input format. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the user's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the system references relevant literature from the user to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making suggestions, the system refers to the user's past selection history to provide the most suitable recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, customize the proposal based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, we take the user's geographical location into consideration to provide the most suitable suggestion. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and adjust the proposal accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts the delivery of learning materials and training methods based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the materials, the system selects the most suitable learning materials by referring to the user's past learning history. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the training material based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the materials, we will consider the user's geographical location to provide the most suitable learning materials. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the materials, the learning materials are customized based on the user's current situation and environment. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives information from users, An analysis unit analyzes the information received by the reception unit and analyzes the user's aptitude, interests, and abilities. The system includes a suggestion unit that proposes suitable occupations and roles for the user based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features.

2. Based on the proposed occupation, it includes a provision department that provides learning materials and training methods to help users acquire the necessary skills. The system according to feature 1.

3. The aforementioned reception unit is We accept information in various formats, including text, audio, pictograms, and pictures. The system according to feature 1.

4. The aforementioned analysis unit, It learns data from various sources such as text, audio, pictograms, and pictures, and analyzes the user's aptitude, interests, and abilities based on that data. The system according to feature 1.

5. The aforementioned proposal section is, Based on the user's aptitude, interests, and abilities, we suggest suitable occupations and roles. The system according to feature 1.

6. The aforementioned supply unit is, Based on the proposed occupation, we provide learning materials and training methods to help you acquire the necessary skills. The system according to feature 2.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts how information is received based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input format. The system according to feature 1.

9. The aforementioned reception unit is When receiving information, the system automatically selects the input format based on the user's current situation and environment. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and determines the priority of inputs based on the estimated user emotions. The system according to feature 1.

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