system

Abstract

The system according to the embodiment aims to provide specific advice based on past information of an individual. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects past information about an individual. The analysis unit analyzes the information collected by the collection unit to identify the individual's interests and goals. The provision unit provides specific advice based on the analysis results obtained by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to make specific plans for the future based on an individual's past information.

[0005] The system according to the embodiment aims to provide specific advice based on past information of an individual. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects past information about an individual. The analysis unit analyzes the information collected by the collection unit to identify the individual's interests and goals. The provision unit provides specific advice based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide specific advice based on past information of an individual. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A future planning support system according to an embodiment of the present invention collects an individual's past information, analyzes it using a generation AI, and provides specific advice. This future planning support system collects an individual's past information, interests, and goals, and then uses the generation AI to analyze them to predict future possibilities and provide specific advice. For example, the generation AI analyzes an individual's past career interests, skills, and future goals. Based on the results, the generation AI proposes specific career paths, study plans, improvements to daily life, and ways to develop hobbies to the individual. This enables the individual to take specific steps toward their future goals. For example, the generation AI collects data from sources such as an individual's social media posts, resume, and past learning history to identify the individual's past career interests and skills. Next, the generation AI analyzes the collected information to identify the individual's interests and goals. For example, it analyzes the individual's future goals and what career path is suitable for them. Finally, the generation AI provides specific advice. Based on the analysis results, the generation AI proposes specific career paths, study plans, improvements to daily life, and ways to develop hobbies to the individual. For example, it can specifically show an individual what skills they should acquire and what kind of study plan they should make to reach their future goals. This allows the individual to take specific steps toward their future goals. This allows the future planning support system to provide specific advice based on the individual's past information.

[0029] A future planning support system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects past information about an individual. Examples of past information about an individual include, but are not limited to, social media posts, resumes, and past learning histories. For example, the collection unit collects social media posts to identify the individual's past career interests. The collection unit can also collect resumes to understand the individual's work history and educational background. The collection unit can also collect past learning histories to identify the individual's skills. For example, the collection unit analyzes social media posts to identify the individual's interests. The collection unit analyzes resumes to understand the individual's work history and educational background. The collection unit analyzes past learning histories to identify the individual's skills. The analysis unit analyzes the information collected by the collection unit to identify the individual's interests and goals. The analysis can be performed using methods such as, but not limited to, data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit uses data mining technology to extract the individual's interests and goals from the collected data. The analysis unit may also use statistical analysis to identify the individual's interests and goals. Furthermore, the analysis unit may also use a machine learning algorithm to identify the individual's interests and goals. For example, the analysis unit may use data mining technology to extract the individual's interests and goals from collected data. The analysis unit may use statistical analysis to identify the individual's interests and goals. The analysis unit may use a machine learning algorithm to identify the individual's interests and goals. The provision unit may provide specific advice based on the analysis results obtained by the analysis unit. Examples of advice include, but are not limited to, career paths, study plans, improvements to daily life, and ways to develop hobbies. For example, the provision unit may suggest a specific career path to the individual based on the analysis results. Furthermore, the provision unit may suggest a specific study plan to the individual based on the analysis results. Furthermore, the provision unit may suggest improvements to daily life to the individual based on the analysis results. For example, the provision unit may suggest a specific career path to the individual based on the analysis results. A specific study plan may be suggested. A specific study plan may be suggested. Improvements to daily life may be suggested.As a result, the future planning support system according to the embodiment can provide specific advice based on the past information of an individual.

[0030] The collection unit can collect data from at least one source of information, such as social media posts, resumes, and past learning histories. For example, the collection unit collects social media posts and identifies what occupations an individual has been interested in in the past. For example, the collection unit analyzes social media posts and identifies the individual's interests and concerns. The collection unit can also collect resumes and identify the individual's work history and educational background. For example, the collection unit analyzes resumes to identify the individual's work history and educational background. The collection unit can also collect past learning histories and identify the individual's skills. For example, the collection unit analyzes past learning histories to identify the individual's skills. This enables more accurate analysis by collecting data from various sources. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input social media posts into a generation AI, which can analyze the content of the posts to identify the individual's interests and concerns.

[0031] The analysis unit can analyze the collected data and identify individual interests and goals. The analysis unit can extract individual interests and goals from the collected data using, for example, data mining technology. For example, the analysis unit can extract individual interests and goals from the collected data using data mining technology. The analysis unit can also identify individual interests and goals using statistical analysis. For example, the analysis unit can identify individual interests and goals using statistical analysis. Furthermore, the analysis unit can also identify individual interests and goals using a machine learning algorithm. For example, the analysis unit can identify individual interests and goals using a machine learning algorithm. This makes it possible to accurately identify individual interests and goals. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the collected data into a generation AI, which can analyze the data to identify individual interests and goals.

[0032] The providing unit can propose a specific career path or study plan to the individual based on the analysis results. The providing unit, for example, proposes a specific career path to the individual based on the analysis results. For example, the providing unit proposes a specific career path to the individual based on the analysis results. The providing unit can also propose a specific study plan to the individual based on the analysis results. For example, the providing unit proposes a specific study plan to the individual based on the analysis results. This makes it possible to propose a specific career path or study plan to the individual. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the analysis results into a generation AI, which can then propose a specific career path or study plan to the individual.

[0033] The providing unit can suggest improvements to daily life or methods for developing hobbies. The providing unit can suggest improvements to daily life to an individual based on, for example, the analysis results. For example, the providing unit can suggest improvements to daily life to an individual based on the analysis results. The providing unit can also suggest methods for developing hobbies to an individual based on the analysis results. For example, the providing unit can suggest methods for developing hobbies to an individual based on the analysis results. This makes it possible to provide specific advice that is useful for developing an individual's daily life or hobbies. Some or all of the above-mentioned processing in the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the analysis results into a generation AI, which can then suggest improvements to daily life to an individual or methods for developing hobbies.

[0034] The collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the collection unit prioritizes selecting a data collection method that the user has frequently used in the past. For example, the collection unit prioritizes selecting a data collection method that the user has frequently used in the past. The collection unit can also identify and propose the most efficient collection method from the user's past data collection history. For example, the collection unit can identify and propose the most efficient collection method from the user's past data collection history. Furthermore, the collection unit can analyze the user's past data collection history and identify and optimize improvements to the collection method. For example, the collection unit analyzes the user's past data collection history and identifies and optimizes improvements to the collection method. This enables efficient data collection by selecting the optimal collection method based on the past data collection history. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's past data collection history into the generation AI, which can select the optimal collection method.

[0035] When collecting data, the collection unit can filter the data based on the user's current living situation and areas of interest. For example, the collection unit prioritizes collecting only data related to areas in which the user is currently interested. For example, the collection unit prioritizes collecting only data related to areas in which the user is currently interested. The collection unit can also filter and collect necessary data according to the user's living situation. For example, the collection unit filters and collects necessary data according to the user's living situation. Furthermore, the collection unit can exclude and collect unnecessary data based on the user's current living situation and areas of interest. For example, the collection unit excludes and collects unnecessary data based on the user's current living situation and areas of interest. This allows more relevant data to be collected by filtering data based on the user's current living situation and areas of interest. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's current living situation and areas of interest into the generation AI, which can then filter the data.

[0036] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting event information related to the user's current location. For example, the collection unit prioritizes collecting event information related to the user's current location. The collection unit can also collect nearby learning opportunities and career information based on the user's geographical location information. For example, the collection unit collects nearby learning opportunities and career information based on the user's geographical location information. Furthermore, the collection unit can also collect data related to hobbies and activities specific to the region by taking into account the user's geographical location information. For example, the collection unit collects data related to hobbies and activities specific to the region by taking into account the user's geographical location information. In this way, by collecting data by taking into account the user's geographical location information, more relevant data can be collected. Some or all of the above-described processing by the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's geographical location information into the generation AI, which can then prioritize collecting highly relevant data.

[0037] During data collection, the collection unit can analyze the user's social media activity and collect related data. For example, the collection unit collects data related to topics frequently mentioned by the user on social media. For example, the collection unit collects data related to topics frequently mentioned by the user on social media. The collection unit can also collect data related to events and activities of interest from the user's social media activity. For example, the collection unit collects data related to events and activities of interest from the user's social media activity. Furthermore, the collection unit can analyze the user's friendships on social media and collect data related to common interests. For example, the collection unit analyzes the user's friendships on social media and collects data related to common interests. This makes it possible to collect more relevant data by analyzing the user's social media activity. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's social media activity into a generation AI, which can collect related data.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on highly important data to provide specific insights. For example, the analysis unit performs a detailed analysis on highly important data to provide specific insights. The analysis unit can also perform a concise analysis on less important data to provide only the main points. For example, the analysis unit can perform a concise analysis on less important data to provide only the main points. Furthermore, the analysis unit can adjust the depth and scope of the analysis according to the importance of the data to perform an efficient analysis. For example, the analysis unit adjusts the depth and scope of the analysis according to the importance of the data to perform an efficient analysis. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the importance of the data into the generation AI, which can adjust the level of detail of the analysis.

[0039] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies a career aptitude analysis algorithm to career-related data. For example, the analysis unit applies a career aptitude analysis algorithm to career-related data. The analysis unit can also apply a learning effect analysis algorithm to learning-related data. For example, the analysis unit applies a learning effect analysis algorithm to learning-related data. The analysis unit can also apply an interest analysis algorithm to hobby- and activity-related data. For example, the analysis unit applies an interest analysis algorithm to hobby- and activity-related data. This enables more accurate analysis by applying an appropriate analysis algorithm depending on the category of data. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the category of data into the generation AI, which then applies an appropriate analysis algorithm.

[0040] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. For example, the analysis unit prioritizes analysis of recently submitted data and provides the latest information. For example, the analysis unit prioritizes analysis of recently submitted data and provides the latest information. The analysis unit can also postpone analysis of data that was submitted earlier. For example, the analysis unit postpones analysis of data that was submitted earlier. Furthermore, the analysis unit can adjust the analysis schedule based on the time of submission to perform efficient analysis. For example, the analysis unit adjusts the analysis schedule based on the time of submission to perform efficient analysis. In this way, by determining the priority of analysis based on the time of data submission, it is possible to prioritize analysis of the latest information. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the time of data submission to the generation AI, and the generation AI can determine the priority of analysis.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data to provide important insights. For example, the analysis unit prioritizes analysis of highly relevant data to provide important insights. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit postpones analysis of less relevant data. Furthermore, the analysis unit can optimize the order of analysis based on the relevance of the data to perform efficient analysis. For example, the analysis unit optimizes the order of analysis based on the relevance of the data to perform efficient analysis. As a result, adjusting the order of analysis based on the relevance of the data enables efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the relevance of the data into the generation AI, which can adjust the order of analysis.

[0042] When providing advice, the providing unit can analyze the user's past behavioral history and select optimal advice. The providing unit can provide optimal advice based on, for example, behavioral patterns that the user has been successful in the past. For example, the providing unit can provide optimal advice based on behavioral patterns that the user has been successful in the past. The providing unit can also analyze the user's past failure experiences and provide advice to avoid making the same mistakes. For example, the providing unit can analyze the user's past failure experiences and provide advice to avoid making the same mistakes. The providing unit can also select and provide the most effective advice from the user's past behavioral history. For example, the providing unit can select and provide the most effective advice from the user's past behavioral history. This makes it possible to provide more effective advice by selecting optimal advice based on the user's past behavioral history. Some or all of the above-described processing in the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the user's past behavioral history into the generation AI, which can select optimal advice.

[0043] When providing advice, the providing unit can customize the means of advice based on the user's current living situation. For example, when the user is busy, the providing unit provides advice that can be implemented in a short time. For example, when the user is busy, the providing unit provides advice that can be implemented in a short time. Furthermore, when the user is relaxed, the providing unit can provide detailed advice and deep insight. For example, when the user is relaxed, the providing unit can provide detailed advice and deep insight. Furthermore, the providing unit can select and provide the optimal means of advice based on the user's current living situation. For example, the providing unit selects and provides the optimal means of advice based on the user's current living situation. In this way, by customizing the means of advice based on the user's current living situation, more appropriate advice can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's current living situation into the generation AI, which can customize the means of advice.

[0044] When providing advice, the providing unit can select optimal advice by taking into account the user's geographical location information. For example, the providing unit prioritizes providing career information related to the user's current location. For example, the providing unit prioritizes providing career information related to the user's current location. The providing unit can also provide information on nearby learning opportunities and events based on the user's geographical location information. For example, the providing unit provides information on nearby learning opportunities and events based on the user's geographical location information. Furthermore, the providing unit can also provide advice related to hobbies and activities specific to the region by taking into account the user's geographical location information. For example, the providing unit provides advice related to hobbies and activities specific to the region by taking into account the user's geographical location information. In this way, by providing advice by taking into account the user's geographical location information, more relevant advice can be provided. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's geographical location information into the generation AI, which can select optimal advice.

[0045] When providing advice, the providing unit can analyze the user's social media activity and suggest a means of providing the advice. The providing unit, for example, provides advice related to topics frequently mentioned by the user on social media. For example, the providing unit provides advice related to topics frequently mentioned by the user on social media. The providing unit can also provide advice related to events or activities of interest from the user's social media activity. For example, the providing unit can provide advice related to events or activities of interest from the user's social media activity. Furthermore, the providing unit can analyze the user's friendships on social media and provide advice related to common interests. For example, the providing unit analyzes the user's friendships on social media and provides advice related to common interests. In this way, more relevant advice can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's social media activity into the generation AI, which can then suggest a means of providing the advice.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the collection unit can prioritize the selection of a data collection method that the user has frequently used in the past. The collection unit can also identify and suggest the most efficient collection method from the user's past data collection history. Furthermore, the collection unit can analyze the user's past data collection history and identify and optimize improvements to the collection method. This enables efficient data collection by selecting the optimal collection method based on the past data collection history. Some or all of the above-described processing in the collection unit can be performed, for example, using or without the generation AI. For example, the collection unit can input the user's past data collection history into the generation AI, which can select the optimal collection method.

[0048] When providing advice, the providing unit can analyze the user's past behavioral history and select optimal advice. For example, the providing unit can provide optimal advice based on the user's past successful behavioral patterns. The providing unit can also analyze the user's past failure experiences and provide advice to avoid making the same mistakes. Furthermore, the providing unit can select and provide the most effective advice from the user's past behavioral history. In this way, by selecting optimal advice based on the user's past behavioral history, more effective advice can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI, for example. For example, the providing unit can input the user's past behavioral history into the generation AI, which can select optimal advice.

[0049] When collecting data, the collection unit can filter the data based on the user's current living situation and areas of interest. For example, the collection unit can prioritize collecting only data related to areas in which the user is currently interested. The collection unit can also filter and collect necessary data according to the user's living situation. Furthermore, the collection unit can also exclude and collect unnecessary data based on the user's current living situation and areas of interest. This allows for more relevant data to be collected by filtering data based on the user's current living situation and areas of interest. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's current living situation and areas of interest into the generation AI, which can then filter the data.

[0050] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, a detailed analysis can be performed on highly important data to provide specific insights. Alternatively, a concise analysis can be performed on less important data to provide only the main points. Furthermore, the analysis unit can adjust the depth and scope of the analysis according to the importance of the data to perform an efficient analysis. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the importance of the data into the generation AI, which can then adjust the level of detail of the analysis.

[0051] When providing advice, the providing unit can select optimal advice by taking into account the user's geographical location information. For example, the providing unit can prioritize providing career information related to the user's current location. The providing unit can also provide nearby learning opportunities and event information based on the user's geographical location information. Furthermore, the providing unit can provide advice related to hobbies and activities specific to the region by taking into account the user's geographical location information. In this way, by providing advice by taking into account the user's geographical location information, more relevant advice can be provided. Some or all of the above-described processing in the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the user's geographical location information into the generation AI, which can then select optimal advice.

[0052] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, a career aptitude analysis algorithm can be applied to career-related data. A learning effect analysis algorithm can be applied to learning-related data. Furthermore, an interest analysis algorithm can be applied to hobby and activity-related data. This enables more accurate analysis by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the data category into the generation AI, which then applies an appropriate analysis algorithm.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The collection unit collects past information about an individual. Past information about an individual includes, for example, social media posts, resumes, and past learning history. The collection unit collects social media posts to identify what occupations the individual was interested in in the past. It can also collect resumes to understand the individual's work history and educational background. It also collects past learning history to identify what skills the individual has. Step 2: The analysis unit analyzes the information collected by the collection unit and identifies individual interests and goals. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, data mining techniques are used to extract individual interests and goals from the collected data; statistical analysis is used to identify individual interests and goals; and machine learning algorithms are used to identify individual interests and goals. Step 3: The provision unit provides specific advice based on the analysis results obtained by the analysis unit. The advice may include career paths, study plans, improvements to daily life, and methods for developing hobbies. For example, based on the analysis results, the provision unit may suggest a specific career path for the individual, a specific study plan, or improvements to daily life.

[0055] (Example 2) A future planning support system according to an embodiment of the present invention collects an individual's past information, analyzes it using a generation AI, and provides specific advice. This future planning support system collects an individual's past information, interests, and goals, and then uses the generation AI to analyze them to predict future possibilities and provide specific advice. For example, the generation AI analyzes an individual's past career interests, skills, and future goals. Based on the results, the generation AI proposes specific career paths, study plans, improvements to daily life, and ways to develop hobbies to the individual. This enables the individual to take specific steps toward their future goals. For example, the generation AI collects data from sources such as an individual's social media posts, resume, and past learning history to identify the individual's past career interests and skills. Next, the generation AI analyzes the collected information to identify the individual's interests and goals. For example, it analyzes the individual's future goals and what career path is suitable for them. Finally, the generation AI provides specific advice. Based on the analysis results, the generation AI proposes specific career paths, study plans, improvements to daily life, and ways to develop hobbies to the individual. For example, it can specifically show an individual what skills they should acquire and what kind of study plan they should make to reach their future goals. This allows the individual to take specific steps toward their future goals. This allows the future planning support system to provide specific advice based on the individual's past information.

[0056] A future planning support system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects past information about an individual. Examples of past information about an individual include, but are not limited to, social media posts, resumes, and past learning histories. For example, the collection unit collects social media posts to identify the individual's past career interests. The collection unit can also collect resumes to understand the individual's work history and educational background. The collection unit can also collect past learning histories to identify the individual's skills. For example, the collection unit analyzes social media posts to identify the individual's interests. The collection unit analyzes resumes to understand the individual's work history and educational background. The collection unit analyzes past learning histories to identify the individual's skills. The analysis unit analyzes the information collected by the collection unit to identify the individual's interests and goals. The analysis can be performed using methods such as, but not limited to, data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit uses data mining technology to extract the individual's interests and goals from the collected data. The analysis unit may also use statistical analysis to identify the individual's interests and goals. Furthermore, the analysis unit may also use a machine learning algorithm to identify the individual's interests and goals. For example, the analysis unit may use data mining technology to extract the individual's interests and goals from collected data. The analysis unit may use statistical analysis to identify the individual's interests and goals. The analysis unit may use a machine learning algorithm to identify the individual's interests and goals. The provision unit may provide specific advice based on the analysis results obtained by the analysis unit. Examples of advice include, but are not limited to, career paths, study plans, improvements to daily life, and ways to develop hobbies. For example, the provision unit may suggest a specific career path to the individual based on the analysis results. Furthermore, the provision unit may suggest a specific study plan to the individual based on the analysis results. Furthermore, the provision unit may suggest improvements to daily life to the individual based on the analysis results. For example, the provision unit may suggest a specific career path to the individual based on the analysis results. A specific study plan may be suggested. A specific study plan may be suggested. Improvements to daily life may be suggested.As a result, the future planning support system according to the embodiment can provide specific advice based on the past information of an individual.

[0057] The collection unit can collect data from at least one source of information, such as social media posts, resumes, and past learning histories. For example, the collection unit collects social media posts and identifies what occupations an individual has been interested in in the past. For example, the collection unit analyzes social media posts and identifies the individual's interests and concerns. The collection unit can also collect resumes and identify the individual's work history and educational background. For example, the collection unit analyzes resumes to identify the individual's work history and educational background. The collection unit can also collect past learning histories and identify the individual's skills. For example, the collection unit analyzes past learning histories to identify the individual's skills. This enables more accurate analysis by collecting data from various sources. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input social media posts into a generation AI, which can analyze the content of the posts to identify the individual's interests and concerns.

[0058] The analysis unit can analyze the collected data and identify individual interests and goals. The analysis unit can extract individual interests and goals from the collected data using, for example, data mining technology. For example, the analysis unit can extract individual interests and goals from the collected data using data mining technology. The analysis unit can also identify individual interests and goals using statistical analysis. For example, the analysis unit can identify individual interests and goals using statistical analysis. Furthermore, the analysis unit can also identify individual interests and goals using a machine learning algorithm. For example, the analysis unit can identify individual interests and goals using a machine learning algorithm. This makes it possible to accurately identify individual interests and goals. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the collected data into a generation AI, which can analyze the data to identify individual interests and goals.

[0059] The providing unit can propose a specific career path or study plan to the individual based on the analysis results. The providing unit, for example, proposes a specific career path to the individual based on the analysis results. For example, the providing unit proposes a specific career path to the individual based on the analysis results. The providing unit can also propose a specific study plan to the individual based on the analysis results. For example, the providing unit proposes a specific study plan to the individual based on the analysis results. This makes it possible to propose a specific career path or study plan to the individual. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the analysis results into a generation AI, which can then propose a specific career path or study plan to the individual.

[0060] The providing unit can suggest improvements to daily life or methods for developing hobbies. The providing unit can suggest improvements to daily life to an individual based on, for example, the analysis results. For example, the providing unit can suggest improvements to daily life to an individual based on the analysis results. The providing unit can also suggest methods for developing hobbies to an individual based on the analysis results. For example, the providing unit can suggest methods for developing hobbies to an individual based on the analysis results. This makes it possible to provide specific advice that is useful for developing an individual's daily life or hobbies. Some or all of the above-mentioned processing in the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the analysis results into a generation AI, which can then suggest improvements to daily life to an individual or methods for developing hobbies.

[0061] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit temporarily stops data collection and resumes it when the user is relaxed. For example, if the user is feeling stressed, the collection unit temporarily stops data collection and resumes it when the user is relaxed. The collection unit can also actively collect data and collect detailed information when the user is relaxed. For example, if the user is relaxed, the collection unit actively collects data and collects detailed information. Furthermore, if the user is excited, the collection unit can refrain from collecting data until the user's emotions calm down and then collect again later. For example, if the user is excited, the collection unit refrains from collecting data until the user's emotions calm down and then collects again later. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input the user's emotion data into the generation AI, which may infer the emotion and adjust the timing of data collection.

[0062] The collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the collection unit prioritizes selecting a data collection method that the user has frequently used in the past. For example, the collection unit prioritizes selecting a data collection method that the user has frequently used in the past. The collection unit can also identify and propose the most efficient collection method from the user's past data collection history. For example, the collection unit can identify and propose the most efficient collection method from the user's past data collection history. Furthermore, the collection unit can analyze the user's past data collection history and identify and optimize improvements to the collection method. For example, the collection unit analyzes the user's past data collection history and identifies and optimizes improvements to the collection method. This enables efficient data collection by selecting the optimal collection method based on the past data collection history. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's past data collection history into the generation AI, which can select the optimal collection method.

[0063] When collecting data, the collection unit can filter the data based on the user's current living situation and areas of interest. For example, the collection unit prioritizes collecting only data related to areas in which the user is currently interested. For example, the collection unit prioritizes collecting only data related to areas in which the user is currently interested. The collection unit can also filter and collect necessary data according to the user's living situation. For example, the collection unit filters and collects necessary data according to the user's living situation. Furthermore, the collection unit can exclude and collect unnecessary data based on the user's current living situation and areas of interest. For example, the collection unit excludes and collects unnecessary data based on the user's current living situation and areas of interest. This allows more relevant data to be collected by filtering data based on the user's current living situation and areas of interest. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's current living situation and areas of interest into the generation AI, which can then filter the data.

[0064] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting data related to relaxation. For example, when the user is feeling stressed, the collection unit prioritizes collecting data related to relaxation. Furthermore, when the user is relaxed, the collection unit can also prioritize collecting data related to learning or career. For example, when the user is relaxed, the collection unit prioritizes collecting data related to learning or career. Furthermore, when the user is excited, the collection unit can also prioritize collecting data related to entertainment. For example, when the user is excited, the collection unit prioritizes collecting data related to entertainment. This enables more appropriate data collection by determining the priority of data to be collected according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the collection unit can input the user's emotional data into the generation AI, which can then infer the emotions and determine the priority of the data to be collected.

[0065] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting event information related to the user's current location. For example, the collection unit prioritizes collecting event information related to the user's current location. The collection unit can also collect nearby learning opportunities and career information based on the user's geographical location information. For example, the collection unit collects nearby learning opportunities and career information based on the user's geographical location information. Furthermore, the collection unit can also collect data related to hobbies and activities specific to the region by taking into account the user's geographical location information. For example, the collection unit collects data related to hobbies and activities specific to the region by taking into account the user's geographical location information. In this way, by collecting data by taking into account the user's geographical location information, more relevant data can be collected. Some or all of the above-described processing by the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's geographical location information into the generation AI, which can then prioritize collecting highly relevant data.

[0066] During data collection, the collection unit can analyze the user's social media activity and collect related data. For example, the collection unit collects data related to topics frequently mentioned by the user on social media. For example, the collection unit collects data related to topics frequently mentioned by the user on social media. The collection unit can also collect data related to events and activities of interest from the user's social media activity. For example, the collection unit collects data related to events and activities of interest from the user's social media activity. Furthermore, the collection unit can analyze the user's friendships on social media and collect data related to common interests. For example, the collection unit analyzes the user's friendships on social media and collects data related to common interests. This makes it possible to collect more relevant data by analyzing the user's social media activity. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's social media activity into a generation AI, which can collect related data.

[0067] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit provides a simple and visually easy-to-understand analysis result. For example, if the user is feeling stressed, the analysis unit provides a simple and visually easy-to-understand analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result and provide deep insight. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result and provide deep insight. Furthermore, if the user is excited, the analysis unit can provide the analysis result using visually stimulating graphics. For example, if the user is excited, the analysis unit provides the analysis result using visually stimulating graphics. This allows for adjusting the presentation method of the analysis according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the user's emotional data into the generation AI, which can then infer the emotion and adjust the way the analysis is presented.

[0068] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on highly important data to provide specific insights. For example, the analysis unit performs a detailed analysis on highly important data to provide specific insights. The analysis unit can also perform a concise analysis on less important data to provide only the main points. For example, the analysis unit can perform a concise analysis on less important data to provide only the main points. Furthermore, the analysis unit can adjust the depth and scope of the analysis according to the importance of the data to perform an efficient analysis. For example, the analysis unit adjusts the depth and scope of the analysis according to the importance of the data to perform an efficient analysis. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the importance of the data into the generation AI, which can adjust the level of detail of the analysis.

[0069] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies a career aptitude analysis algorithm to career-related data. For example, the analysis unit applies a career aptitude analysis algorithm to career-related data. The analysis unit can also apply a learning effect analysis algorithm to learning-related data. For example, the analysis unit applies a learning effect analysis algorithm to learning-related data. The analysis unit can also apply an interest analysis algorithm to hobby- and activity-related data. For example, the analysis unit applies an interest analysis algorithm to hobby- and activity-related data. This enables more accurate analysis by applying an appropriate analysis algorithm depending on the category of data. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the category of data into the generation AI, which then applies an appropriate analysis algorithm.

[0070] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and to-the-point analysis result. For example, if the user is in a hurry, the analysis unit provides a short and to-the-point analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a longer analysis result with detailed explanations. For example, if the user is relaxed, the analysis unit can provide a longer analysis result with detailed explanations. Furthermore, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. For example, if the user is excited, the analysis unit provides an analysis result with visually stimulating effects. This allows for adjusting the length of the analysis according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input the user's emotional data into the generation AI, which can then infer the emotion and adjust the length of the analysis.

[0071] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. For example, the analysis unit prioritizes analysis of recently submitted data and provides the latest information. For example, the analysis unit prioritizes analysis of recently submitted data and provides the latest information. The analysis unit can also postpone analysis of data that was submitted earlier. For example, the analysis unit postpones analysis of data that was submitted earlier. Furthermore, the analysis unit can adjust the analysis schedule based on the time of submission to perform efficient analysis. For example, the analysis unit adjusts the analysis schedule based on the time of submission to perform efficient analysis. In this way, by determining the priority of analysis based on the time of data submission, it is possible to prioritize analysis of the latest information. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the time of data submission to the generation AI, and the generation AI can determine the priority of analysis.

[0072] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data to provide important insights. For example, the analysis unit prioritizes analysis of highly relevant data to provide important insights. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit postpones analysis of less relevant data. Furthermore, the analysis unit can optimize the order of analysis based on the relevance of the data to perform efficient analysis. For example, the analysis unit optimizes the order of analysis based on the relevance of the data to perform efficient analysis. As a result, adjusting the order of analysis based on the relevance of the data enables efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the relevance of the data into the generation AI, which can adjust the order of analysis.

[0073] The providing unit can estimate the user's emotions and adjust the way in which advice is presented based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit provides simple and easy-to-understand advice. For example, if the user is feeling stressed, the providing unit provides simple and easy-to-understand advice. Furthermore, if the user is relaxed, the providing unit can provide detailed advice and deep insight. For example, if the user is relaxed, the providing unit can provide detailed advice and deep insight. Furthermore, if the user is excited, the providing unit can provide advice using visually stimulating graphics. For example, if the user is excited, the providing unit provides advice using visually stimulating graphics. This allows for adjusting the way in which advice is presented according to the user's emotions, thereby providing more appropriate advice. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's emotional data into the generating AI, which can then estimate the emotion and adjust the way the advice is expressed.

[0074] When providing advice, the providing unit can analyze the user's past behavioral history and select optimal advice. The providing unit can provide optimal advice based on, for example, behavioral patterns that the user has been successful in the past. For example, the providing unit can provide optimal advice based on behavioral patterns that the user has been successful in the past. The providing unit can also analyze the user's past failure experiences and provide advice to avoid making the same mistakes. For example, the providing unit can analyze the user's past failure experiences and provide advice to avoid making the same mistakes. The providing unit can also select and provide the most effective advice from the user's past behavioral history. For example, the providing unit can select and provide the most effective advice from the user's past behavioral history. This makes it possible to provide more effective advice by selecting optimal advice based on the user's past behavioral history. Some or all of the above-described processing in the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the user's past behavioral history into the generation AI, which can select optimal advice.

[0075] When providing advice, the providing unit can customize the means of advice based on the user's current living situation. For example, when the user is busy, the providing unit provides advice that can be implemented in a short time. For example, when the user is busy, the providing unit provides advice that can be implemented in a short time. Furthermore, when the user is relaxed, the providing unit can provide detailed advice and deep insight. For example, when the user is relaxed, the providing unit can provide detailed advice and deep insight. Furthermore, the providing unit can select and provide the optimal means of advice based on the user's current living situation. For example, the providing unit selects and provides the optimal means of advice based on the user's current living situation. In this way, by customizing the means of advice based on the user's current living situation, more appropriate advice can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's current living situation into the generation AI, which can customize the means of advice.

[0076] The providing unit can estimate the user's emotions and determine the priority of advice based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing advice related to relaxation. For example, if the user is feeling stressed, the providing unit can prioritize providing advice related to relaxation. Furthermore, if the user is relaxed, the providing unit can prioritize providing advice related to learning or career. For example, if the user is relaxed, the providing unit can prioritize providing advice related to learning or career. Furthermore, if the user is excited, the providing unit can prioritize providing advice related to entertainment. For example, if the user is excited, the providing unit can prioritize providing advice related to entertainment. In this way, by determining the priority of advice according to the user's emotions, more appropriate advice can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generating AI, which can then estimate the emotion and determine the priority of the advice.

[0077] When providing advice, the providing unit can select optimal advice by taking into account the user's geographical location information. For example, the providing unit prioritizes providing career information related to the user's current location. For example, the providing unit prioritizes providing career information related to the user's current location. The providing unit can also provide information on nearby learning opportunities and events based on the user's geographical location information. For example, the providing unit provides information on nearby learning opportunities and events based on the user's geographical location information. Furthermore, the providing unit can also provide advice related to hobbies and activities specific to the region by taking into account the user's geographical location information. For example, the providing unit provides advice related to hobbies and activities specific to the region by taking into account the user's geographical location information. In this way, by providing advice by taking into account the user's geographical location information, more relevant advice can be provided. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's geographical location information into the generation AI, which can select optimal advice.

[0078] When providing advice, the providing unit can analyze the user's social media activity and suggest a means of providing the advice. The providing unit, for example, provides advice related to topics frequently mentioned by the user on social media. For example, the providing unit provides advice related to topics frequently mentioned by the user on social media. The providing unit can also provide advice related to events or activities of interest from the user's social media activity. For example, the providing unit can provide advice related to events or activities of interest from the user's social media activity. Furthermore, the providing unit can analyze the user's friendships on social media and provide advice related to common interests. For example, the providing unit analyzes the user's friendships on social media and provides advice related to common interests. In this way, more relevant advice can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's social media activity into the generation AI, which can then suggest a means of providing the advice. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects past information of an individual using the camera 42 and microphone 38B of the smart device 14 and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and identifies the individual's interests and goals based on the collected information. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates specific advice based on the analysis results and provides the advice to the individual via the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and provision unit, described above, is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects past information of an individual using the camera 42 and microphone 238 of the smart glasses 214 and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and identifies the individual's interests and goals based on the collected information. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates specific advice based on the analysis results and provides it to the individual through the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, in at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects past information about an individual using the camera 42 and microphone 238 of the headset terminal 314, and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and identifies the individual's interests and goals based on the collected information. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates specific advice based on the analysis results and provides it to the individual via the display 343 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects past information about an individual using the camera 42 and microphone 238 of the robot 414, and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and identifies the individual's interests and goals based on the collected information. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates specific advice based on the analysis results and provides it to the individual via the speaker 240 of the robot 414.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the collection unit can prioritize the selection of a data collection method that the user has frequently used in the past. The collection unit can also identify and suggest the most efficient collection method from the user's past data collection history. Furthermore, the collection unit can analyze the user's past data collection history and identify and optimize improvements to the collection method. This enables efficient data collection by selecting the optimal collection method based on the past data collection history. Some or all of the above-described processing in the collection unit can be performed, for example, using or without the generation AI. For example, the collection unit can input the user's past data collection history into the generation AI, which can select the optimal collection method.

[0081] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide simple, visually easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results and provide deeper insights. If the user is excited, the analysis unit can also provide analysis results using visually stimulating graphics. This allows for more appropriate analysis results to be provided by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generative AI, or without the generative AI. For example, the analysis unit can input the user's emotion data into the generative AI, which can then estimate the emotion and adjust the presentation of the analysis.

[0082] When providing advice, the providing unit can analyze the user's past behavioral history and select optimal advice. For example, the providing unit can provide optimal advice based on the user's past successful behavioral patterns. The providing unit can also analyze the user's past failure experiences and provide advice to avoid making the same mistakes. Furthermore, the providing unit can select and provide the most effective advice from the user's past behavioral history. In this way, by selecting optimal advice based on the user's past behavioral history, more effective advice can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI, for example. For example, the providing unit can input the user's past behavioral history into the generation AI, which can select optimal advice.

[0083] When collecting data, the collection unit can filter the data based on the user's current living situation and areas of interest. For example, the collection unit can prioritize collecting only data related to areas in which the user is currently interested. The collection unit can also filter and collect necessary data according to the user's living situation. Furthermore, the collection unit can also exclude and collect unnecessary data based on the user's current living situation and areas of interest. This allows for more relevant data to be collected by filtering data based on the user's current living situation and areas of interest. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's current living situation and areas of interest into the generation AI, which can then filter the data.

[0084] The providing unit can estimate the user's emotions and adjust the way the advice is presented based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide simple and easy-to-understand advice. Furthermore, if the user is relaxed, the providing unit can provide detailed advice and deep insight. Furthermore, if the user is excited, the providing unit can provide advice using visually stimulating graphics. This allows for more appropriate advice to be provided by adjusting the way the advice is presented according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, 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-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI, which can then estimate the emotion and adjust the way the advice is presented.

[0085] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, a detailed analysis can be performed on highly important data to provide specific insights. Alternatively, a concise analysis can be performed on less important data to provide only the main points. Furthermore, the analysis unit can adjust the depth and scope of the analysis according to the importance of the data to perform an efficient analysis. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the importance of the data into the generation AI, which can then adjust the level of detail of the analysis.

[0086] When providing advice, the providing unit can select optimal advice by taking into account the user's geographical location information. For example, the providing unit can prioritize providing career information related to the user's current location. The providing unit can also provide nearby learning opportunities and event information based on the user's geographical location information. Furthermore, the providing unit can provide advice related to hobbies and activities specific to the region by taking into account the user's geographical location information. In this way, by providing advice by taking into account the user's geographical location information, more relevant advice can be provided. Some or all of the above-described processing in the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the user's geographical location information into the generation AI, which can then select optimal advice.

[0087] The collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, data related to relaxation can be prioritized. Furthermore, if the user is relaxed, the collection unit can prioritize collecting data related to learning or career. Furthermore, if the user is excited, the collection unit can prioritize collecting data related to entertainment. This enables more appropriate data collection by prioritizing the data to be collected according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, the generation AI, or without the generation AI. For example, the collection unit can input the user's emotion data into the generation AI, which can then estimate the emotion and prioritize the data to be collected.

[0088] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, a career aptitude analysis algorithm can be applied to career-related data. A learning effect analysis algorithm can be applied to learning-related data. Furthermore, an interest analysis algorithm can be applied to hobby and activity-related data. This enables more accurate analysis by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the data category into the generation AI, which then applies an appropriate analysis algorithm.

[0089] The providing unit can estimate the user's emotions and determine the priority of advice based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can prioritize advice related to relaxation. Furthermore, if the user is relaxed, the providing unit can prioritize advice related to learning or career. Furthermore, if the user is excited, the providing unit can prioritize advice related to entertainment. By determining the priority of advice according to the user's emotions, more appropriate advice can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI, which can estimate the emotion and determine the priority of advice.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The collection unit collects past information about an individual. Past information about an individual includes, for example, social media posts, resumes, and past learning history. The collection unit collects social media posts to identify what occupations the individual was interested in in the past. It can also collect resumes to understand the individual's work history and educational background. It also collects past learning history to identify what skills the individual has. Step 2: The analysis unit analyzes the information collected by the collection unit and identifies individual interests and goals. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, data mining techniques are used to extract individual interests and goals from the collected data; statistical analysis is used to identify individual interests and goals; and machine learning algorithms are used to identify individual interests and goals. Step 3: The provision unit provides specific advice based on the analysis results obtained by the analysis unit. The advice may include career paths, study plans, improvements to daily life, and methods for developing hobbies. For example, based on the analysis results, the provision unit may suggest a specific career path for the individual, a specific study plan, or improvements to daily life.

[0092] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0094] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0095] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0099] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0100] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0102] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0103] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0104] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0107] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0110] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0111] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0122] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0126] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0135] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0136] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0143] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0146] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0147] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0148] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0149] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0150] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0151] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0152] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0153] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0154] 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.

[0155] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0156] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0157] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0158] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0159] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0160] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0161] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0162] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0163] [Explanation of symbols]

[0164] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department that collects past information about individuals; an analysis unit that analyzes the information collected by the collection unit to identify individual interests and goals; a providing unit that provides specific advice based on the analysis results obtained by the analysis unit. A system characterized by:

2. The collecting unit Collect data from at least one of the following sources: social media posts, resumes, and past learning history. The system of claim 1 .

3. The analysis unit Analyze collected data to identify individual interests and goals The system of claim 1 .

4. The providing unit Propose specific career paths or learning plans to individuals based on the analysis results The system of claim 1 .

5. The providing unit Suggest ways to improve your daily life or develop your hobbies The system of claim 1 .

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

7. The collecting unit Analyze the user's past data collection history and select the optimal collection method The system of claim 1 .

8. The collecting unit Filtering data collection based on the user's current life situation and interests The system of claim 1 .

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