System

Abstract

An object of a system according to an embodiment is to provide a highly accurate prediction based on the content of a user's question.SOLUTION: A system includes an analysis unit, a collection unit, a prediction unit, and a provision unit. The analysis unit analyzes the question content of the user. The collection unit collects related data based on the question content analyzed by the analysis unit. The prediction unit performs prediction with high accuracy based on the data collected by the collection unit. The providing unit provides the user with the prediction result obtained by the prediction unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have difficulty in providing highly accurate predictions in fortune-telling, and there is room for improvement.

[0005] The system according to the embodiment aims to provide highly accurate predictions based on the content of a user's question. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a collection unit, a prediction unit, and a provision unit. The analysis unit analyzes the content of a user's question. The collection unit collects related data based on the content of the question analyzed by the analysis unit. The prediction unit makes highly accurate predictions based on the data collected by the collection unit. The provision unit provides the user with the prediction results obtained by the prediction unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide highly accurate predictions based on the content of a user's question. [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 high-precision prediction generation AI fortune-telling system according to an embodiment of the present invention analyzes a user's question and collects and analyzes related data to provide highly accurate predictions and specific advice. The high-precision prediction generation AI fortune-telling system analyzes a user's question and collects and analyzes related data to provide highly accurate predictions and specific advice. For example, a user inputs a fortune-telling question or consultation. For example, the high-precision prediction generation AI fortune-telling system accepts specific questions such as, "What should I do about my future career?" or "I want to know my love luck." This input is analyzed by a generation AI. Next, the high-precision prediction generation AI fortune-telling system analyzes the input question using the generation AI and collects related data, such as past fortune-telling results, statistical data, and the user's profile information. This allows the generation AI to understand the user's situation and background and collect basic information to provide appropriate advice. Next, the high-precision prediction generation AI fortune-telling system makes highly accurate predictions based on the data collected using the generation AI. For example, if the question is about a career, it uses past data and statistical models to suggest the user's optimal career path. If the question is about love luck, it takes into account the user's personality and past romantic experiences to predict their future love luck. Next, the high-precision prediction generation AI fortune-telling system uses the generation AI to provide users with predictions. In addition to the predictions, it also provides specific advice and guidelines for action. For example, specific advice such as "For your future career, it would be a good idea to focus on self-development" or "To improve your love luck, it is important to actively communicate" is conceivable. By analyzing the content of the user's questions and collecting and analyzing related data, the high-precision prediction generation AI fortune-telling system can provide highly accurate predictions and specific advice. This allows the high-precision prediction generation AI fortune-telling system to provide users with hints for self-improvement and problem-solving, helping them pursue a more fulfilling life. For example, users can make better decisions by receiving specific advice about their careers and love lives.Furthermore, by providing individual advice according to the user's situation and background, user satisfaction can be increased.

[0029] A high-precision prediction generation AI fortune-telling system according to an embodiment includes an analysis unit, a collection unit, a prediction unit, and a provision unit. The analysis unit analyzes a user's question. The user's question may include, but is not limited to, technical questions, business questions, and questions about daily life. The analysis unit may analyze the question using, for example, natural language processing technology. The analysis unit may also analyze the question using a machine learning algorithm. The analysis unit may also analyze the question using a generation AI. For example, the generation AI may analyze the question using a text generation AI (e.g., LLM). The collection unit collects related data based on the question analyzed by the analysis unit. The related data may include, but is not limited to, information from a database or information from the Internet. The collection unit may collect, for example, past fortune-telling results and statistical data. The collection unit may also collect user profile information. The collection unit may also collect related data using the generation AI. For example, the generation AI may automatically collect information on the Internet and provide the information to the analysis unit as related data. The prediction unit makes highly accurate predictions based on the data collected by the collection unit. The predictions are made using, for example, past data and statistical models, but are not limited to these examples. For example, in response to a question about a career, the prediction unit suggests an optimal career path using past data and statistical models. In addition, in response to a question about love luck, the prediction unit can make predictions taking into account the user's personality and past romantic experiences. The prediction unit can also make highly accurate predictions using a generation AI. For example, the generation AI makes predictions using a text generation AI. The provision unit provides the prediction results obtained by the prediction unit to the user. The provision can be made in, for example, a text format, an audio format, a visual format, or the like, but is not limited to these examples. For example, the provision unit provides the prediction results to the user in text format. In addition, the provision unit can also provide the prediction results to the user in audio format. In addition, the provision unit can also provide the prediction results to the user in visual format. For example, the provision unit generates the prediction results in text format using the generation AI and provides them to the user.As a result, the high-precision prediction generation AI fortune-telling system according to the embodiment can provide highly accurate predictions and specific advice by analyzing the content of the user's question and collecting and analyzing related data. For example, users can make better decisions by receiving specific advice about their careers or love lives. Furthermore, providing individual advice tailored to the user's situation and background can increase user satisfaction.

[0030] When analyzing the content of a question, the analysis unit can improve the accuracy of the analysis by referring to the user's past question history. For example, the analysis unit allows the generation AI to refer to the content of questions the user has previously asked and perform more accurate analysis of similar questions. The analysis unit can also extract specific patterns from the user's past question history and allow the generation AI to perform analysis based on those patterns. The analysis unit can also allow the generation AI to refer to advice the user has received in the past and perform a consistent analysis of the current question. In this way, by referring to the past question history, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past question history data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0031] When analyzing the question content, the analysis unit can select an analysis algorithm based on the user's living situation or areas of interest. For example, the generation AI of the analysis unit selects an appropriate analysis algorithm by taking into account the user's current occupation and living situation. The analysis unit can also select an optimal analysis algorithm by taking into account the user's areas of interest (e.g., career, romance, health, etc.). The analysis unit can also adjust the analysis algorithm by taking into account the user's current living situation (e.g., stress level, health condition, etc.). This enables analysis according to the user's living situation and areas of interest. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input data on the user's living situation and areas of interest into the generation AI and have the generation AI select an analysis algorithm.

[0032] When analyzing the content of a question, the analysis unit can select an appropriate analysis method depending on the user's input method. For example, when a user inputs a question by voice, the analysis unit has the generation AI perform voice analysis and convert it into text for analysis. Furthermore, when a user inputs a question by text, the analysis unit can have the generation AI perform analysis using natural language processing. Furthermore, when a user inputs a question using an image, the analysis unit can have the generation AI perform image analysis and extract related information for analysis. This enables analysis according to the user's input method. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's voice data into the generation AI and have the generation AI analyze the voice data.

[0033] When analyzing the content of a question, the analysis unit can prioritize analysis of highly relevant data taking into account the user's geographical location information. For example, if the user lives in a specific area, the analysis unit can cause the generation AI to prioritize analysis of data related to that area. Furthermore, if the user is traveling, the analysis unit can also cause the generation AI to prioritize analysis of data related to the user's current location. Furthermore, if the user is interested in a specific city, the analysis unit can also cause the generation AI to prioritize analysis of data related to that city. This enables analysis based on the user's geographical location information. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's geographical location information data to the generation AI and cause the generation AI to prioritize analysis of highly relevant data.

[0034] When analyzing the question content, the analysis unit can analyze the user's social media activity and analyze the related data. For example, the analysis unit has the generation AI analyze the user's social media postings and analyze the related data. The analysis unit can also have the generation AI analyze the activities of the user's friends on social media and analyze the related data. The analysis unit can also have the generation AI analyze the user's social media check-in information and analyze the related data. This enables analysis based on the user's social media activity. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's social media activity data into the generation AI and have the generation AI analyze the related data.

[0035] When analyzing the content of a question, the analysis unit can customize the analysis method by reflecting the user's past feedback. For example, the analysis unit adjusts the analysis method by having the generation AI refer to feedback provided by the user in the past. Furthermore, if the analysis unit determines that a specific analysis method is effective based on the user's past feedback, the analysis unit can preferentially use that method. Furthermore, the analysis unit can cause the generation AI to continuously improve the analysis method based on the user's feedback. This makes it possible to customize the analysis method based on the user's past feedback. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past feedback data into the generation AI and have the generation AI customize the analysis method.

[0036] When collecting data, the collection unit can select an appropriate collection method by referring to the user's past data collection history. For example, the collection unit has the generation AI refer to the data collection method used by the user in the past and select a similar method. The collection unit can also have the generation AI select the most efficient method from the user's past data collection history. The collection unit can also analyze the user's past data collection history and have the generation AI suggest the optimal collection method. This allows the optimal collection method to be selected based on the user's past data collection history. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's past data collection history data into the generation AI and have the generation AI select the optimal collection method.

[0037] The collection unit can perform filtering based on the user's current living situation and areas of interest when collecting data. For example, the collection unit filters relevant data by having the generation AI take into account the user's current occupation and living situation. The collection unit can also have the generation AI filter data based on the user's areas of interest (e.g., career, romance, health, etc.). The collection unit can also have the generation AI take into account the user's current living situation (e.g., stress level, health condition, etc.) and filter data. This enables data collection according to the user's living situation and areas of interest. Some or all of the above-described processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input data on the user's living situation and areas of interest to the generation AI and have the generation AI filter the data.

[0038] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user inputs data by voice, the collection unit causes the generation AI to collect the voice data and use it for analysis. Furthermore, if the user inputs data by text, the collection unit can also cause the generation AI to collect the text data and use it for analysis. Furthermore, if the user inputs data using an image, the collection unit can also cause the generation AI to collect image data and use it for analysis. This makes it possible to collect data depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's voice data to the generation AI and have the generation AI collect the voice data.

[0039] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user lives in a specific area, the collection unit can cause the generation AI to prioritize collecting data related to that area. Furthermore, if the user is traveling, the collection unit can also cause the generation AI to prioritize collecting data related to the user's current location. Furthermore, if the user is interested in a specific city, the collection unit can also cause the generation AI to prioritize collecting data related to that city. This enables data collection based on the user's geographical location information. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location information data into the generation AI and cause the generation AI to prioritize collecting highly relevant data.

[0040] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit has the generation AI analyze the user's social media posts and collect related data. The collection unit can also have the generation AI analyze the activities of the user's friends on social media and collect related data. The collection unit can also have the generation AI analyze the user's social media check-in information and collect related data. This makes it possible to collect data based on the user's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media activity data into the generation AI and have the generation AI collect related data.

[0041] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit adjusts the collection method by having the generation AI refer to feedback provided by the user in the past. Furthermore, if the collection unit determines that a specific collection method is effective based on the user's past feedback, the collection unit can also preferentially use that method. Furthermore, the collection unit can cause the generation AI to continuously improve the collection method based on the user's feedback. This makes it possible to customize the collection method based on the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and have the generation AI customize the collection method.

[0042] The prediction unit can adjust the level of detail of the prediction based on the importance of the collected data during prediction. For example, the prediction unit allows the generation AI to make a detailed prediction based on data with high importance. The prediction unit can also allow the generation AI to make a simplified prediction based on data with low importance. The prediction unit can also allow the generation AI to dynamically adjust the level of detail of the prediction according to the importance of the data. This adjusts the level of detail of the prediction according to the importance of the data. Some or all of the above-mentioned processing in the prediction unit may be performed using AI, for example, or may be performed without using AI. For example, the prediction unit can input the importance of the collected data to the generation AI and cause the generation AI to adjust the level of detail of the prediction.

[0043] The prediction unit can apply different prediction algorithms depending on the data category when making a prediction. For example, the generation AI of the prediction unit can apply a career-specific prediction algorithm to data related to career. The prediction unit can also apply a love-specific prediction algorithm to data related to romance. The prediction unit can also apply a health-specific prediction algorithm to data related to health. This enables optimal predictions according to the data category. Some or all of the above-mentioned processing in the prediction unit can be performed using AI, for example, or without AI. For example, the prediction unit can input the data category into the generation AI and have the generation AI apply a prediction algorithm according to the category.

[0044] The prediction unit can improve the accuracy of predictions by referring to the user's past prediction results. For example, the prediction unit uses the generation AI to refer to the user's past prediction results and reflect them in the current prediction. The prediction unit can also extract specific patterns from the user's past prediction results, and the generation AI can make predictions based on those patterns. The prediction unit can also adjust the prediction algorithm based on the user's past prediction results. By referring to past prediction results, the accuracy of predictions is improved. Some or all of the above-described processing in the prediction unit may be performed using, or without, an AI. For example, the prediction unit can input the user's past prediction result data into the generation AI and cause the generation AI to improve prediction accuracy.

[0045] The prediction unit can determine the priority of predictions based on the time of data submission when making predictions. For example, the prediction unit allows the generation AI to prioritize predictions based on the latest data. The prediction unit can also allow the generation AI to lower the priority of predictions based on older data. The prediction unit can also allow the generation AI to dynamically adjust the priority of predictions based on the time of data submission. This determines the priority of predictions based on the time of data submission. Some or all of the above-described processing in the prediction unit may be performed using AI, for example, or may be performed without using AI. For example, the prediction unit can input the time of data submission to the generation AI and cause the generation AI to determine the priority of predictions based on the time of submission.

[0046] The prediction unit can adjust the order of predictions based on the relevance of the data during prediction. For example, the prediction unit allows the generation AI to prioritize predictions based on data with high relevance. The prediction unit can also postpone the order of predictions based on data with low relevance. The prediction unit can also allow the generation AI to dynamically adjust the order of predictions based on the relevance of the data. This adjusts the order of predictions based on the relevance of the data. Some or all of the above-described processing in the prediction unit may be performed using AI, for example, or may be performed without using AI. For example, the prediction unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of predictions based on the relevance.

[0047] The prediction unit can adjust the use of technical terminology in the prediction according to the user's level of expertise when making a prediction. For example, if the user has technical expertise, the prediction unit can cause the generation AI to provide a prediction that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the prediction unit can also cause the generation AI to provide a prediction in simpler terms. Furthermore, the prediction unit can dynamically adjust the use of technical terminology in the prediction according to the user's level of expertise. This enables prediction according to the user's level of expertise. Some or all of the above-described processing in the prediction unit can be performed, for example, using AI or without AI. For example, the prediction unit can input the user's level of expertise into the generation AI and cause the generation AI to use technical terminology in the prediction based on the level of expertise.

[0048] The providing unit can adjust the level of detail of the advice based on the importance of the prediction result when providing the advice. For example, the providing unit causes the generation AI to provide detailed advice based on a prediction result with high importance. The providing unit can also cause the generation AI to provide simplified advice based on a prediction result with low importance. The providing unit can also cause the generation AI to dynamically adjust the level of detail of the advice based on the importance of the prediction result. This adjusts the level of detail of the advice based on the importance of the prediction result. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the importance of the prediction result to the generation AI and cause the generation AI to adjust the level of detail of the advice based on the importance.

[0049] The providing unit can improve the accuracy of advice by referring to the user's past advice history when providing advice. For example, the providing unit causes the generation AI to refer to the user's past advice history and reflect it in the current advice. The providing unit can also extract a specific pattern from the user's past advice history, and the generation AI can provide advice based on that pattern. The providing unit can also cause the generation AI to adjust the advice algorithm based on the user's past advice history. In this way, the accuracy of advice is improved by referring to the past advice history. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past advice history data into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0050] The providing unit can customize the content of the advice based on the user's current living situation and areas of interest when providing the advice. For example, the providing unit provides appropriate advice by having the generation AI take into account the user's current occupation and living situation. The providing unit can also customize the advice by having the generation AI take into account the user's areas of interest (e.g., career, romance, health, etc.). The providing unit can also adjust the advice by having the generation AI take into account the user's current living situation (e.g., stress level, health condition, etc.). This makes it possible to provide advice tailored to the user's living situation and areas of interest. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data on the user's living situation and areas of interest into the generation AI and cause the generation AI to customize the advice content.

[0051] When providing advice, the providing unit can prioritize providing highly relevant advice by taking into account the user's geographical location information. For example, if the user lives in a specific area, the providing unit can cause the generation AI to prioritize providing advice related to that area. Furthermore, if the user is traveling, the providing unit can cause the generation AI to prioritize providing advice related to the user's current location. Furthermore, if the user is interested in a specific city, the providing unit can cause the generation AI to prioritize providing advice related to that city. This makes it possible to provide advice based on the user's geographical location information. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input the user's geographical location information data into the generation AI and cause the generation AI to prioritize providing highly relevant advice.

[0052] The providing unit can analyze the user's social media activity and provide relevant advice at the time of providing. For example, the providing unit can have the generation AI analyze the content posted by the user on social media and provide relevant advice. The providing unit can also have the generation AI analyze the activity of the user's friends on social media and provide relevant advice. The providing unit can also have the generation AI analyze the user's check-in information on social media and provide relevant advice. This makes it possible to provide advice based on the user's social media activity. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input the user's social media activity data into the generation AI and cause the generation AI to provide relevant advice.

[0053] The providing unit can customize the content of the advice by reflecting the user's past feedback when providing the advice. For example, the providing unit causes the generation AI to refer to feedback provided by the user in the past and adjust the content of the advice. Furthermore, if specific advice is found to be effective from the user's past feedback, the providing unit can also preferentially use that content. Furthermore, the providing unit can cause the generation AI to continuously improve the content of the advice based on the user's feedback. This makes it possible to customize the advice based on the user's past feedback. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the content of the advice.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] When collecting related data based on the content of a user's question, the collection unit can refer to the user's past search history and prioritize collecting more relevant data. For example, if the user has asked many questions about career in the past, the collection unit can prioritize collecting career-related data. Also, if the user has asked many questions about love in the past, the collection unit can prioritize collecting love-related data. Furthermore, if the user has asked many questions about health in the past, the collection unit can prioritize collecting health-related data. This makes it possible to optimally collect data based on the user's past search history.

[0056] When providing a prediction result to a user, the providing unit can customize the content of the advice to be provided by referring to the user's past feedback. For example, if the user has given positive feedback on advice provided in the past, the providing unit can preferentially provide similar advice. Also, if the user has given negative feedback on advice provided in the past, the providing unit can provide advice with a different approach. Furthermore, the providing unit can continuously improve the content of the advice based on the user's feedback. This makes it possible to provide optimal advice based on the user's past feedback.

[0057] When analyzing the content of a user's question, the analysis unit can analyze the user's social media activity and collect related data. For example, the analysis unit can analyze the content that the user frequently posts on social media and gain a deeper understanding of the question based on that content. The analysis unit can also analyze the activity of the user's friends on social media and collect related data. Furthermore, the analysis unit can analyze the user's check-in information on social media and collect related data. This makes it possible to perform analysis based on the user's social media activity.

[0058] 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 filter relevant data taking into account the user's current occupation and living situation. The collection unit can also filter data based on the user's areas of interest (e.g., career, love, health, etc.). Furthermore, the collection unit can filter data taking into account the user's current living situation (e.g., stress level, health condition, etc.). This makes it possible to collect data according to the user's living situation and areas of interest.

[0059] The prediction unit can adjust the use of technical terminology in the prediction depending on the user's level of expertise when making a prediction. For example, if the user has technical expertise, the prediction unit can provide a prediction that uses a lot of technical terminology. Alternatively, if the user does not have technical expertise, the prediction unit can provide a prediction in simple language. Furthermore, depending on the user's level of expertise, the prediction unit can dynamically adjust the use of technical terminology in the prediction. This enables predictions that are tailored to the user's level of expertise.

[0060] When analyzing the content of a user's question, the analysis unit can prioritize the analysis of highly relevant data by taking into account the user's geographical location information. For example, if the user lives in a specific area, data related to that area can be prioritized for analysis. Also, if the user is traveling, data related to the user's current location can be prioritized for analysis. Furthermore, if the user is interested in a specific city, data related to that city can be prioritized for analysis. This makes it possible to perform analysis based on the user's geographical location information.

[0061] The providing unit can improve the accuracy of advice by referring to the user's past advice history when providing advice. For example, the providing unit can refer to the user's past advice history and reflect it in the current advice. It can also extract a specific pattern from the user's past advice history and provide advice based on that pattern. It can also adjust the advice algorithm based on the user's past advice history. In this way, the accuracy of advice can be improved by referring to the past advice history.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The analysis unit analyzes the user's question. The user's question may be technical, business-related, or related to daily life. The analysis unit analyzes the question using natural language processing technology, machine learning algorithms, and generative AI (e.g., text generation AI). Step 2: The collection unit collects relevant data based on the question content analyzed by the analysis unit. The relevant data includes information from databases, information from the Internet, past fortune-telling results and statistical data, and user profile information. The collection unit can also use generation AI to automatically collect information on the Internet and provide it to the analysis unit as relevant data. Step 3: The prediction unit makes highly accurate predictions based on the data collected by the collection unit. Predictions are made using past data and statistical models. For example, in response to a question about careers, the unit suggests the optimal career path, and in response to a question about love luck, the unit makes predictions taking into account the user's personality and past romantic experiences. The prediction unit can also make highly accurate predictions using generative AI (e.g., text generation AI). Step 4: The providing unit provides the prediction results obtained by the prediction unit to the user. The results are provided in text format, audio format, visual format, etc. For example, the providing unit generates the prediction results in text format using the generation AI and provides them to the user.

[0064] (Example 2) A high-precision prediction generation AI fortune-telling system according to an embodiment of the present invention analyzes a user's question and collects and analyzes related data to provide highly accurate predictions and specific advice. The high-precision prediction generation AI fortune-telling system analyzes a user's question and collects and analyzes related data to provide highly accurate predictions and specific advice. For example, a user inputs a fortune-telling question or consultation. For example, the high-precision prediction generation AI fortune-telling system accepts specific questions such as, "What should I do about my future career?" or "I want to know my love luck." This input is analyzed by a generation AI. Next, the high-precision prediction generation AI fortune-telling system analyzes the input question using the generation AI and collects related data, such as past fortune-telling results, statistical data, and the user's profile information. This allows the generation AI to understand the user's situation and background and collect basic information to provide appropriate advice. Next, the high-precision prediction generation AI fortune-telling system makes highly accurate predictions based on the data collected using the generation AI. For example, if the question is about a career, it uses past data and statistical models to suggest the user's optimal career path. If the question is about love luck, it takes into account the user's personality and past romantic experiences to predict their future love luck. Next, the high-precision prediction generation AI fortune-telling system uses the generation AI to provide users with predictions. In addition to the predictions, it also provides specific advice and guidelines for action. For example, specific advice such as "For your future career, it would be a good idea to focus on self-development" or "To improve your love luck, it is important to actively communicate" is conceivable. By analyzing the content of the user's questions and collecting and analyzing related data, the high-precision prediction generation AI fortune-telling system can provide highly accurate predictions and specific advice. This allows the high-precision prediction generation AI fortune-telling system to provide users with hints for self-improvement and problem-solving, helping them pursue a more fulfilling life. For example, users can make better decisions by receiving specific advice about their careers and love lives.Furthermore, by providing individual advice according to the user's situation and background, user satisfaction can be increased.

[0065] A high-precision prediction generation AI fortune-telling system according to an embodiment includes an analysis unit, a collection unit, a prediction unit, and a provision unit. The analysis unit analyzes a user's question. The user's question may include, but is not limited to, technical questions, business questions, and questions about daily life. The analysis unit may analyze the question using, for example, natural language processing technology. The analysis unit may also analyze the question using a machine learning algorithm. The analysis unit may also analyze the question using a generation AI. For example, the generation AI may analyze the question using a text generation AI (e.g., LLM). The collection unit collects related data based on the question analyzed by the analysis unit. The related data may include, but is not limited to, information from a database or information from the Internet. The collection unit may collect, for example, past fortune-telling results and statistical data. The collection unit may also collect user profile information. The collection unit may also collect related data using the generation AI. For example, the generation AI may automatically collect information on the Internet and provide the information to the analysis unit as related data. The prediction unit makes highly accurate predictions based on the data collected by the collection unit. The predictions are made using, for example, past data and statistical models, but are not limited to these examples. For example, in response to a question about a career, the prediction unit suggests an optimal career path using past data and statistical models. In addition, in response to a question about love luck, the prediction unit can make predictions taking into account the user's personality and past romantic experiences. The prediction unit can also make highly accurate predictions using a generation AI. For example, the generation AI makes predictions using a text generation AI. The provision unit provides the prediction results obtained by the prediction unit to the user. The provision can be made in, for example, a text format, an audio format, a visual format, or the like, but is not limited to these examples. For example, the provision unit provides the prediction results to the user in text format. In addition, the provision unit can also provide the prediction results to the user in audio format. In addition, the provision unit can also provide the prediction results to the user in visual format. For example, the provision unit generates the prediction results in text format using the generation AI and provides them to the user.As a result, the high-precision prediction generation AI fortune-telling system according to the embodiment can provide highly accurate predictions and specific advice by analyzing the content of the user's question and collecting and analyzing related data. For example, users can make better decisions by receiving specific advice about their careers or love lives. Furthermore, providing individual advice tailored to the user's situation and background can increase user satisfaction.

[0066] The analysis unit can estimate the user's emotions and adjust the analysis method for the question content based on the estimated user emotions. For example, if the user is stressed, the analysis unit can cause the generation AI to select a simple analysis method and avoid complex analysis. Alternatively, if the user is relaxed, the analysis unit can cause the generation AI to select a detailed analysis method and consider more data. Alternatively, if the user is in a hurry, the analysis unit can cause the generation AI to perform analysis quickly and provide results immediately. This enables more appropriate analysis by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the analysis method based on the emotion.

[0067] When analyzing the content of a question, the analysis unit can improve the accuracy of the analysis by referring to the user's past question history. For example, the analysis unit allows the generation AI to refer to the content of questions the user has previously asked and perform more accurate analysis of similar questions. The analysis unit can also extract specific patterns from the user's past question history and allow the generation AI to perform analysis based on those patterns. The analysis unit can also allow the generation AI to refer to advice the user has received in the past and perform a consistent analysis of the current question. In this way, by referring to the past question history, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past question history data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0068] When analyzing the question content, the analysis unit can select an analysis algorithm based on the user's living situation or areas of interest. For example, the generation AI of the analysis unit selects an appropriate analysis algorithm by taking into account the user's current occupation and living situation. The analysis unit can also select an optimal analysis algorithm by taking into account the user's areas of interest (e.g., career, romance, health, etc.). The analysis unit can also adjust the analysis algorithm by taking into account the user's current living situation (e.g., stress level, health condition, etc.). This enables analysis according to the user's living situation and areas of interest. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input data on the user's living situation and areas of interest into the generation AI and have the generation AI select an analysis algorithm.

[0069] When analyzing the content of a question, the analysis unit can select an appropriate analysis method depending on the user's input method. For example, when a user inputs a question by voice, the analysis unit has the generation AI perform voice analysis and convert it into text for analysis. Furthermore, when a user inputs a question by text, the analysis unit can have the generation AI perform analysis using natural language processing. Furthermore, when a user inputs a question using an image, the analysis unit can have the generation AI perform image analysis and extract related information for analysis. This enables analysis according to the user's input method. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's voice data into the generation AI and have the generation AI analyze the voice data.

[0070] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can cause the generation AI to prioritize analysis results that provide a sense of security. Furthermore, if the user is excited, the analysis unit can cause the generation AI to prioritize analysis results that are interesting. Furthermore, if the user is calm, the analysis unit can cause the generation AI to prioritize detailed and comprehensive analysis results. This allows the priority of analysis results to be determined according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and cause the generation AI to prioritize the analysis results based on the emotion.

[0071] When analyzing the content of a question, the analysis unit can prioritize analysis of highly relevant data taking into account the user's geographical location information. For example, if the user lives in a specific area, the analysis unit can cause the generation AI to prioritize analysis of data related to that area. Furthermore, if the user is traveling, the analysis unit can also cause the generation AI to prioritize analysis of data related to the user's current location. Furthermore, if the user is interested in a specific city, the analysis unit can also cause the generation AI to prioritize analysis of data related to that city. This enables analysis based on the user's geographical location information. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's geographical location information data to the generation AI and cause the generation AI to prioritize analysis of highly relevant data.

[0072] When analyzing the question content, the analysis unit can analyze the user's social media activity and analyze the related data. For example, the analysis unit has the generation AI analyze the user's social media postings and analyze the related data. The analysis unit can also have the generation AI analyze the activities of the user's friends on social media and analyze the related data. The analysis unit can also have the generation AI analyze the user's social media check-in information and analyze the related data. This enables analysis based on the user's social media activity. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's social media activity data into the generation AI and have the generation AI analyze the related data.

[0073] When analyzing the content of a question, the analysis unit can customize the analysis method by reflecting the user's past feedback. For example, the analysis unit adjusts the analysis method by having the generation AI refer to feedback provided by the user in the past. Furthermore, if the analysis unit determines that a specific analysis method is effective based on the user's past feedback, the analysis unit can preferentially use that method. Furthermore, the analysis unit can cause the generation AI to continuously improve the analysis method based on the user's feedback. This makes it possible to customize the analysis method based on the user's past feedback. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past feedback data into the generation AI and have the generation AI customize the analysis method.

[0074] 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 relaxed, the collection unit can cause the generation AI to immediately collect data. Furthermore, if the user is stressed, the collection unit can also cause the generation AI to delay data collection. Furthermore, if the user is in a hurry, the collection unit can also cause the generation AI to quickly collect data. This adjusts the timing of data collection according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, with 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 collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of data collection based on the emotion.

[0075] When collecting data, the collection unit can select an appropriate collection method by referring to the user's past data collection history. For example, the collection unit has the generation AI refer to the data collection method used by the user in the past and select a similar method. The collection unit can also have the generation AI select the most efficient method from the user's past data collection history. The collection unit can also analyze the user's past data collection history and have the generation AI suggest the optimal collection method. This allows the optimal collection method to be selected based on the user's past data collection history. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's past data collection history data into the generation AI and have the generation AI select the optimal collection method.

[0076] The collection unit can perform filtering based on the user's current living situation and areas of interest when collecting data. For example, the collection unit filters relevant data by having the generation AI take into account the user's current occupation and living situation. The collection unit can also have the generation AI filter data based on the user's areas of interest (e.g., career, romance, health, etc.). The collection unit can also have the generation AI take into account the user's current living situation (e.g., stress level, health condition, etc.) and filter data. This enables data collection according to the user's living situation and areas of interest. Some or all of the above-described processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input data on the user's living situation and areas of interest to the generation AI and have the generation AI filter the data.

[0077] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user inputs data by voice, the collection unit causes the generation AI to collect the voice data and use it for analysis. Furthermore, if the user inputs data by text, the collection unit can also cause the generation AI to collect the text data and use it for analysis. Furthermore, if the user inputs data using an image, the collection unit can also cause the generation AI to collect image data and use it for analysis. This makes it possible to collect data depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's voice data to the generation AI and have the generation AI collect the voice data.

[0078] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling anxious, the collection unit can cause the generation AI to prioritize collecting data that gives a sense of security. Furthermore, if the user is excited, the collection unit can also cause the generation AI to prioritize collecting data that is interesting. Furthermore, if the user is calm, the collection unit can also cause the generation AI to prioritize collecting detailed and comprehensive data. This determines the priority of data collection 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 can 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 collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of data based on emotions.

[0079] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user lives in a specific area, the collection unit can cause the generation AI to prioritize collecting data related to that area. Furthermore, if the user is traveling, the collection unit can also cause the generation AI to prioritize collecting data related to the user's current location. Furthermore, if the user is interested in a specific city, the collection unit can also cause the generation AI to prioritize collecting data related to that city. This enables data collection based on the user's geographical location information. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location information data into the generation AI and cause the generation AI to prioritize collecting highly relevant data.

[0080] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit has the generation AI analyze the user's social media posts and collect related data. The collection unit can also have the generation AI analyze the activities of the user's friends on social media and collect related data. The collection unit can also have the generation AI analyze the user's social media check-in information and collect related data. This makes it possible to collect data based on the user's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media activity data into the generation AI and have the generation AI collect related data.

[0081] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit adjusts the collection method by having the generation AI refer to feedback provided by the user in the past. Furthermore, if the collection unit determines that a specific collection method is effective based on the user's past feedback, the collection unit can also preferentially use that method. Furthermore, the collection unit can cause the generation AI to continuously improve the collection method based on the user's feedback. This makes it possible to customize the collection method based on the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and have the generation AI customize the collection method.

[0082] The prediction unit can estimate the user's emotions and adjust the way the prediction is expressed based on the estimated user emotions. For example, if the user is feeling anxious, the prediction unit causes the generation AI to select a predictive expression that gives a sense of security. Furthermore, if the user is excited, the prediction unit can also cause the generation AI to select an interesting predictive expression. Furthermore, if the user is calm, the prediction unit can also cause the generation AI to select a detailed and comprehensive predictive expression. This enables predictive expressions that correspond 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 may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the predictive expression based on the emotion.

[0083] The prediction unit can adjust the level of detail of the prediction based on the importance of the collected data during prediction. For example, the prediction unit allows the generation AI to make a detailed prediction based on data with high importance. The prediction unit can also allow the generation AI to make a simplified prediction based on data with low importance. The prediction unit can also allow the generation AI to dynamically adjust the level of detail of the prediction according to the importance of the data. This adjusts the level of detail of the prediction according to the importance of the data. Some or all of the above-mentioned processing in the prediction unit may be performed using AI, for example, or may be performed without using AI. For example, the prediction unit can input the importance of the collected data to the generation AI and cause the generation AI to adjust the level of detail of the prediction.

[0084] The prediction unit can apply different prediction algorithms depending on the data category when making a prediction. For example, the generation AI of the prediction unit can apply a career-specific prediction algorithm to data related to career. The prediction unit can also apply a love-specific prediction algorithm to data related to romance. The prediction unit can also apply a health-specific prediction algorithm to data related to health. This enables optimal predictions according to the data category. Some or all of the above-mentioned processing in the prediction unit can be performed using AI, for example, or without AI. For example, the prediction unit can input the data category into the generation AI and have the generation AI apply a prediction algorithm according to the category.

[0085] The prediction unit can improve the accuracy of predictions by referring to the user's past prediction results. For example, the prediction unit uses the generation AI to refer to the user's past prediction results and reflect them in the current prediction. The prediction unit can also extract specific patterns from the user's past prediction results, and the generation AI can make predictions based on those patterns. The prediction unit can also adjust the prediction algorithm based on the user's past prediction results. By referring to past prediction results, the accuracy of predictions is improved. Some or all of the above-described processing in the prediction unit may be performed using, or without, an AI. For example, the prediction unit can input the user's past prediction result data into the generation AI and cause the generation AI to improve prediction accuracy.

[0086] The prediction unit can estimate the user's emotions and adjust the length of the prediction based on the estimated user emotions. For example, if the user is feeling anxious, the prediction unit can cause the generation AI to provide a short, concise prediction. Alternatively, if the user is relaxed, the prediction unit can cause the generation AI to provide a detailed prediction. Alternatively, if the user is in a hurry, the prediction unit can cause the generation AI to make a quick prediction and provide an immediate result. This adjusts the length of the prediction according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the prediction unit can be performed using, for example, an AI, or without an AI. For example, the prediction unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the prediction based on the emotion.

[0087] The prediction unit can determine the priority of predictions based on the time of data submission when making predictions. For example, the prediction unit allows the generation AI to prioritize predictions based on the latest data. The prediction unit can also allow the generation AI to lower the priority of predictions based on older data. The prediction unit can also allow the generation AI to dynamically adjust the priority of predictions based on the time of data submission. This determines the priority of predictions based on the time of data submission. Some or all of the above-described processing in the prediction unit may be performed using AI, for example, or may be performed without using AI. For example, the prediction unit can input the time of data submission to the generation AI and cause the generation AI to determine the priority of predictions based on the time of submission.

[0088] The prediction unit can adjust the order of predictions based on the relevance of the data during prediction. For example, the prediction unit allows the generation AI to prioritize predictions based on data with high relevance. The prediction unit can also postpone the order of predictions based on data with low relevance. The prediction unit can also allow the generation AI to dynamically adjust the order of predictions based on the relevance of the data. This adjusts the order of predictions based on the relevance of the data. Some or all of the above-described processing in the prediction unit may be performed using AI, for example, or may be performed without using AI. For example, the prediction unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of predictions based on the relevance.

[0089] The prediction unit can adjust the use of technical terminology in the prediction according to the user's level of expertise when making a prediction. For example, if the user has technical expertise, the prediction unit can cause the generation AI to provide a prediction that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the prediction unit can also cause the generation AI to provide a prediction in simpler terms. Furthermore, the prediction unit can dynamically adjust the use of technical terminology in the prediction according to the user's level of expertise. This enables prediction according to the user's level of expertise. Some or all of the above-described processing in the prediction unit can be performed, for example, using AI or without AI. For example, the prediction unit can input the user's level of expertise into the generation AI and cause the generation AI to use technical terminology in the prediction based on the level of expertise.

[0090] 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 anxious, the providing unit can have the generation AI provide reassuring advice. Furthermore, if the user is excited, the providing unit can have the generation AI provide interesting advice. Furthermore, if the user is calm, the providing unit can have the generation AI provide detailed and comprehensive advice. This enables the presentation of advice according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can 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 can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the advice is presented based on the emotion.

[0091] The providing unit can adjust the level of detail of the advice based on the importance of the prediction result when providing the advice. For example, the providing unit causes the generation AI to provide detailed advice based on a prediction result with high importance. The providing unit can also cause the generation AI to provide simplified advice based on a prediction result with low importance. The providing unit can also cause the generation AI to dynamically adjust the level of detail of the advice based on the importance of the prediction result. This adjusts the level of detail of the advice based on the importance of the prediction result. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the importance of the prediction result to the generation AI and cause the generation AI to adjust the level of detail of the advice based on the importance.

[0092] The providing unit can improve the accuracy of advice by referring to the user's past advice history when providing advice. For example, the providing unit causes the generation AI to refer to the user's past advice history and reflect it in the current advice. The providing unit can also extract a specific pattern from the user's past advice history, and the generation AI can provide advice based on that pattern. The providing unit can also cause the generation AI to adjust the advice algorithm based on the user's past advice history. In this way, the accuracy of advice is improved by referring to the past advice history. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past advice history data into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0093] The providing unit can customize the content of the advice based on the user's current living situation and areas of interest when providing the advice. For example, the providing unit provides appropriate advice by having the generation AI take into account the user's current occupation and living situation. The providing unit can also customize the advice by having the generation AI take into account the user's areas of interest (e.g., career, romance, health, etc.). The providing unit can also adjust the advice by having the generation AI take into account the user's current living situation (e.g., stress level, health condition, etc.). This makes it possible to provide advice tailored to the user's living situation and areas of interest. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data on the user's living situation and areas of interest into the generation AI and cause the generation AI to customize the advice content.

[0094] The providing unit can estimate the user's emotions and determine the priority of advice to be provided based on the estimated user emotions. For example, if the user is feeling anxious, the providing unit can cause the generation AI to prioritize advice that provides a sense of security. Furthermore, if the user is excited, the providing unit can cause the generation AI to prioritize advice that is interesting. Furthermore, if the user is calm, the providing unit can cause the generation AI to prioritize detailed and comprehensive advice. This determines the priority of advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to determine the priority of advice based on emotions.

[0095] When providing advice, the providing unit can prioritize providing highly relevant advice by taking into account the user's geographical location information. For example, if the user lives in a specific area, the providing unit can cause the generation AI to prioritize providing advice related to that area. Furthermore, if the user is traveling, the providing unit can cause the generation AI to prioritize providing advice related to the user's current location. Furthermore, if the user is interested in a specific city, the providing unit can cause the generation AI to prioritize providing advice related to that city. This makes it possible to provide advice based on the user's geographical location information. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input the user's geographical location information data into the generation AI and cause the generation AI to prioritize providing highly relevant advice.

[0096] The providing unit can analyze the user's social media activity and provide relevant advice at the time of providing. For example, the providing unit can have the generation AI analyze the content posted by the user on social media and provide relevant advice. The providing unit can also have the generation AI analyze the activity of the user's friends on social media and provide relevant advice. The providing unit can also have the generation AI analyze the user's check-in information on social media and provide relevant advice. This makes it possible to provide advice based on the user's social media activity. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input the user's social media activity data into the generation AI and cause the generation AI to provide relevant advice.

[0097] The providing unit can customize the content of the advice by reflecting the user's past feedback when providing the advice. For example, the providing unit causes the generation AI to refer to feedback provided by the user in the past and adjust the content of the advice. Furthermore, if specific advice is found to be effective from the user's past feedback, the providing unit can also preferentially use that content. Furthermore, the providing unit can cause the generation AI to continuously improve the content of the advice based on the user's feedback. This makes it possible to customize the advice based on the user's past feedback. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the content of the advice. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, collection unit, prediction unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the collection unit collects related data using the camera 42 or the communication I / F 44 of the smart device 14, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. For example, the prediction unit is realized by the specific processing unit 290 of the data processing device 12 and performs highly accurate predictions based on the collected data. For example, the provision unit is realized by the output device 40 of the smart device 14 or the specific processing unit 290 of the data processing device 12 and provides the prediction results to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, collection unit, prediction unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the collection unit collects relevant data using the camera 42 or communication I / F 44 of the smart glasses 214, and the data is analyzed by the specific processing unit 290 of the data processing device 12. For example, the prediction unit is realized by the specific processing unit 290 of the data processing device 12 and performs highly accurate predictions based on the collected data. For example, the provision unit is realized by the speaker 240 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and provides the prediction results to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, collection unit, prediction unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the collection unit collects related data using the camera 42 or communication I / F 44 of the headset type terminal 314, and the data is analyzed by the specific processing unit 290 of the data processing device 12. For example, the prediction unit is realized by the specific processing unit 290 of the data processing device 12, and makes highly accurate predictions based on the collected data. For example, the provision unit is realized by the display 343 of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and provides the prediction results to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, collection unit, prediction unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the collection unit collects related data using the camera 42 and communication I / F 44 of the robot 414, and the data is analyzed by the specific processing unit 290 of the data processing device 12. For example, the prediction unit is realized by the specific processing unit 290 of the data processing device 12 and performs highly accurate predictions based on the collected data. For example, the provision unit is realized by the speaker 240 of the robot 414 or the specific processing unit 290 of the data processing device 12 and provides the prediction results to the user.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] When analyzing the content of a user's question, the analysis unit can analyze the user's voice tone and speaking patterns to infer emotions. For example, if the user is nervous when asking a question, the analysis unit can generate a response in a gentle tone to ease the user's tension. If the user is excited, the analysis unit can generate a response in an energetic tone to maintain the user's excitement. Furthermore, if the user is calm, the analysis unit can perform a detailed and comprehensive analysis and generate a response in a calm tone. This enables appropriate analysis and responses according to the user's emotions.

[0100] When collecting related data based on the content of a user's question, the collection unit can refer to the user's past search history and prioritize collecting more relevant data. For example, if the user has asked many questions about career in the past, the collection unit can prioritize collecting career-related data. Also, if the user has asked many questions about love in the past, the collection unit can prioritize collecting love-related data. Furthermore, if the user has asked many questions about health in the past, the collection unit can prioritize collecting health-related data. This makes it possible to optimally collect data based on the user's past search history.

[0101] When making highly accurate predictions based on collected data, the prediction unit can adjust the content of the prediction by taking into account the user's current mood and emotions. For example, if the user is feeling anxious, the prediction unit can provide a prediction that gives a sense of security. Also, if the user is excited, the prediction unit can provide a prediction that maintains that excitement. Furthermore, if the user is calm, the prediction unit can provide a detailed and comprehensive prediction. This makes it possible to make appropriate predictions according to the user's emotions.

[0102] When providing a prediction result to a user, the providing unit can customize the content of the advice to be provided by referring to the user's past feedback. For example, if the user has given positive feedback on advice provided in the past, the providing unit can preferentially provide similar advice. Also, if the user has given negative feedback on advice provided in the past, the providing unit can provide advice with a different approach. Furthermore, the providing unit can continuously improve the content of the advice based on the user's feedback. This makes it possible to provide optimal advice based on the user's past feedback.

[0103] When analyzing the content of a user's question, the analysis unit can analyze the user's social media activity and collect related data. For example, the analysis unit can analyze the content that the user frequently posts on social media and gain a deeper understanding of the question based on that content. The analysis unit can also analyze the activity of the user's friends on social media and collect related data. Furthermore, the analysis unit can analyze the user's check-in information on social media and collect related data. This makes it possible to perform analysis based on the user's social media activity.

[0104] 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 filter relevant data taking into account the user's current occupation and living situation. The collection unit can also filter data based on the user's areas of interest (e.g., career, love, health, etc.). Furthermore, the collection unit can filter data taking into account the user's current living situation (e.g., stress level, health condition, etc.). This makes it possible to collect data according to the user's living situation and areas of interest.

[0105] The prediction unit can adjust the use of technical terminology in the prediction depending on the user's level of expertise when making a prediction. For example, if the user has technical expertise, the prediction unit can provide a prediction that uses a lot of technical terminology. Alternatively, if the user does not have technical expertise, the prediction unit can provide a prediction in simple language. Furthermore, depending on the user's level of expertise, the prediction unit can dynamically adjust the use of technical terminology in the prediction. This enables predictions that are tailored to the user's level of expertise.

[0106] 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 anxious, the providing unit can provide reassuring advice. If the user is excited, the providing unit can also provide interesting advice. Furthermore, if the user is calm, the providing unit can provide detailed and comprehensive advice. This makes it possible to present advice according to the user's emotions.

[0107] When analyzing the content of a user's question, the analysis unit can prioritize the analysis of highly relevant data by taking into account the user's geographical location information. For example, if the user lives in a specific area, data related to that area can be prioritized for analysis. Also, if the user is traveling, data related to the user's current location can be prioritized for analysis. Furthermore, if the user is interested in a specific city, data related to that city can be prioritized for analysis. This makes it possible to perform analysis based on the user's geographical location information.

[0108] The providing unit can improve the accuracy of advice by referring to the user's past advice history when providing advice. For example, the providing unit can refer to the user's past advice history and reflect it in the current advice. It can also extract a specific pattern from the user's past advice history and provide advice based on that pattern. It can also adjust the advice algorithm based on the user's past advice history. In this way, the accuracy of advice can be improved by referring to the past advice history.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The analysis unit analyzes the user's question. The user's question may be technical, business-related, or related to daily life. The analysis unit analyzes the question using natural language processing technology, machine learning algorithms, and generative AI (e.g., text generation AI). Step 2: The collection unit collects relevant data based on the question content analyzed by the analysis unit. The relevant data includes information from databases, information from the Internet, past fortune-telling results and statistical data, and user profile information. The collection unit can also use generation AI to automatically collect information on the Internet and provide it to the analysis unit as relevant data. Step 3: The prediction unit makes highly accurate predictions based on the data collected by the collection unit. Predictions are made using past data and statistical models. For example, in response to a question about careers, the unit suggests the optimal career path, and in response to a question about love luck, the unit makes predictions taking into account the user's personality and past romantic experiences. The prediction unit can also make highly accurate predictions using generative AI (e.g., text generation AI). Step 4: The providing unit provides the prediction results obtained by the prediction unit to the user. The results are provided in text format, audio format, visual format, etc. For example, the providing unit generates the prediction results in text format using the generation AI and provides them to the user.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] 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.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] 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.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0162] 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.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] [Explanation of symbols]

[0183] 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. an analysis unit that analyzes the content of a user's question; a collection unit that collects related data based on the question content analyzed by the analysis unit; a prediction unit that performs highly accurate predictions based on the data collected by the collection unit; a providing unit that provides a user with the prediction result obtained by the prediction unit. A system characterized by:

2. The analysis unit Estimate the user's emotions and adjust the question analysis method based on the estimated user emotions.

2. The system of claim 1.

3. The analysis unit When analyzing questions, the accuracy of the analysis is improved by referring to the user's past question history.

2. The system of claim 1.

4. The analysis unit When analyzing the content of a question, an analysis algorithm is selected based on the user's life situation or area of ​​interest.

2. The system of claim 1.

5. The analysis unit When analyzing the content of a question, select the appropriate analysis method depending on the user's input method.

2. The system of claim 1.

6. The analysis unit Estimate the user's emotions and prioritize the analysis results based on the estimated user emotions.

2. The system of claim 1.

7. The analysis unit When analyzing questions, the system takes into account the user's geographic location information to prioritize the analysis of data that is most relevant.

2. The system of claim 1.

8. The analysis unit When analyzing the content of the question, analyze the user's social media activity and analyze related data.

2. The system of claim 1.

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