System

Abstract

The system according to the embodiment aims to interpret a card with content suitable for a user.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, a feedback unit, and a learning unit. The receiving unit receives an image of a card. The analysis unit analyzes the image of the card input by the reception unit. The generation unit generates an interpretation based on the content of the card analyzed by the analysis unit. The feedback unit receives a user's feedback on the interpretation generated by the generation unit. The learning unit learns the preference of the user based on the feedback received by the feedback 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 had the problem that it is difficult to interpret cards and to interpret them in a way that is appropriate for the user.

[0005] The system according to the embodiment aims to interpret the card in a way that is appropriate for the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, a feedback unit, and a learning unit. The reception unit inputs an image of a card. The analysis unit analyzes the image of the card input by the reception unit. The generation unit generates an interpretation based on the content of the card analyzed by the analysis unit. The feedback unit receives user feedback on the interpretation generated by the generation unit. The learning unit learns user preferences based on the feedback received by the feedback unit. [Effects of the Invention]

[0007] The system according to the embodiment can interpret the card in a way that is appropriate for the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A card interpretation system according to an embodiment of the present invention utilizes a generation AI to provide a card interpretation tailored to the user. In the card interpretation system, a user inputs an image of a card, and the generation AI analyzes the image and interprets the card's content. The generation AI generates an optimal interpretation based on the user's past reading history and preferences. This allows the user to receive a card interpretation tailored to their needs. For example, in a card interpretation system, a user takes a photo of a card using a device such as a smartphone or tablet and uploads it to the system. The generation AI uses image recognition technology to identify the card's type and design and interprets its content. The generation AI generates an optimal interpretation based on the user's past reading history and preferences. This allows the user to receive a card interpretation tailored to their needs. For example, even a novice user can easily understand the meaning of the card by referring to the interpretation provided by the generation AI. Even experienced users can gain deeper insight by incorporating new perspectives and interpretations provided by the generation AI. Furthermore, the generation AI can improve the accuracy of its interpretations based on user feedback. For example, by users rating the provided interpretations, the generation AI can learn from the ratings and incorporate them into future interpretations. This allows the card interpretation system to provide more appropriate interpretations to users. For example, even if a user finds it difficult to interpret a card, they can refer to the interpretation provided by the generation AI to make it easier to understand the meaning of the card. The generation AI can also learn from user feedback and improve the accuracy of its interpretations. This allows the card interpretation system to continuously evolve and provide users with more appropriate interpretations.

[0029] A card interpretation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, a feedback unit, and a learning unit. The reception unit receives images of cards from users. The users take photos of the cards using a device such as a smartphone or tablet and upload them to the system. The reception unit can receive images of various types of cards, such as tarot cards and oracle cards. The analysis unit uses a generation AI to analyze the card images received by the reception unit. The analysis unit identifies the card type and design using image recognition technology. The generation AI can use image recognition technology such as a convolutional neural network (CNN) or a scale-invariant feature transform (SIFT). The generation unit generates an interpretation based on the card content analyzed by the analysis unit using the generation AI. The generation unit generates the interpretation by taking into account, for example, the user's past reading history and preferences. The generation AI customizes the content of the interpretation by taking into account, for example, the user's preferred card interpretations in the past and themes of interest. The feedback unit receives user feedback on the interpretation generated by the generation unit. The feedback unit, for example, provides an interface through which the user evaluates the interpretation. The interface may be, for example, a graphical user interface (GUI) or a voice interface. The learning unit learns the user's preferences based on the feedback received by the feedback unit. The learning unit may, for example, use a recommendation system to learn the user's preferences. The recommendation system may, for example, use collaborative filtering or content-based filtering. This allows the card interpretation system according to the embodiment to provide card interpretations with content suited to the user.

[0030] The analysis unit can identify the type and design of a card using image recognition technology. Examples of image recognition technology include, but are not limited to, CNN (Convolutional Neural Network) and SIFT (Scale-Invariant Feature Transform). The analysis unit can identify the type and design of a card using, for example, CNN. The analysis unit can also use SIFT to extract feature points of a card and identify the type and design. The analysis unit can also combine image recognition technology to perform more accurate analysis. For example, the analysis unit can combine CNN and SIFT to identify the type and design of a card. This allows for accurate identification of the type and design of a card. 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 image data of a card to a generation AI and cause the generation AI to identify the type and design of the card from the image data.

[0031] The generation unit can generate an interpretation by taking into account the user's past reading history and preferences. Reading history includes, for example, past interpretation results and user reactions, but is not limited to these examples. The generation unit generates an interpretation based on, for example, the user's past interpretation results. The generation unit can also customize the interpretation by taking into account the user's preferences. For example, the generation unit can learn patterns of interpretations that the user has previously preferred and generate an interpretation based on those patterns. The generation unit can also generate an interpretation by taking into account the user's areas of interest. For example, the generation unit can generate an interpretation related to a topic that the user is interested in. This makes it possible to provide the user with an optimal interpretation. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past reading history and preference data into the generation AI and have the generation AI generate an interpretation.

[0032] The feedback unit can provide an interface through which the user evaluates the interpretation. Examples of the interface include, but are not limited to, a graphical user interface (GUI) or an audio interface. For example, the feedback unit can provide an interface through which the user evaluates the interpretation using a GUI. The feedback unit can also provide an interface through which the user evaluates by voice using an audio interface. The feedback unit can also provide an interface through which the user inputs comments on the interpretation. For example, the feedback unit can provide an interface through which the user inputs comments on the interpretation in text. This allows the user to evaluate the interpretation. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input user evaluation data to a generation AI and cause the generation AI to analyze the evaluation data.

[0033] The learning unit can learn user preferences using a recommendation system. Recommendation systems include, but are not limited to, collaborative filtering and content-based filtering. The learning unit can learn user preferences using, for example, collaborative filtering. The learning unit can also learn user preferences using content-based filtering. The learning unit can also combine recommendation systems to perform more accurate learning. For example, the learning unit can combine collaborative filtering and content-based filtering to learn user preferences. This allows the learning of user preferences and improves the accuracy of interpretation. Some or all of the above-described processing in the learning unit can be performed using, for example, AI, or without AI. For example, the learning unit can input user preference data into the generation AI and cause the generation AI to learn the preferences.

[0034] The reception unit can analyze the user's past card image reception history and select the optimal reception method. For example, the reception unit prioritizes and suggests reception methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the user's past reception history. The reception unit can also customize the optimal reception method based on the user's past preferred reception methods. This makes it possible to provide the optimal reception method based on the user's past history. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input the user's past reception history data into the generation AI and have the generation AI select the optimal reception method.

[0035] The reception unit can perform filtering based on the user's current field of interest when receiving card images. For example, the reception unit preferentially receives card images related to themes in which the user is currently interested. The reception unit can also filter related card images based on the user's current field of interest. The reception unit can also preferentially receive specific card images based on the user's field of interest. This makes it possible to preferentially receive related card images based on the user's field of interest. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's field of interest data to the generation AI and cause the generation AI to perform filtering based on the field of interest.

[0036] When accepting a card image, the acceptance unit can select the optimal acceptance means depending on the user's input method. For example, if the user uses voice input, the acceptance unit accepts the card image using voice recognition technology. Furthermore, if the user uses text input, the acceptance unit can also accept the card image using text analysis technology. Furthermore, if the user uses image input, the acceptance unit can also accept the card image using image recognition technology. This makes it possible to provide the optimal acceptance means depending on the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using AI, for example, or may be performed without using AI. For example, the acceptance unit can input the user's input method data into the generation AI and cause the generation AI to select the optimal acceptance means.

[0037] When accepting card images, the reception unit can prioritize accepting highly relevant images taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes accepting card images related to that area. The reception unit can also filter highly relevant card images based on the user's current location. The reception unit can also prioritize accepting specific card images based on the user's geographical location information. This makes it possible to prioritize accepting highly relevant card images based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to perform filtering based on the geographical location information.

[0038] When receiving a card image, the reception unit can analyze the user's social media activity and receive related images. For example, the reception unit receives related card images based on images shared by the user on social media. The reception unit can also analyze the user's social media activity and filter related card images. The reception unit can also receive related card images with reference to the activity of the user's friends on social media. This makes it possible to receive related card images based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to perform filtering based on the social media activity.

[0039] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a card image. The reception unit, for example, suggests an optimal reception method based on feedback provided by the user in the past. The reception unit can also customize the reception method by reflecting the user's past feedback. The reception unit can also analyze the user's feedback history and select the optimal reception method. This makes it possible to provide the optimal reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's feedback history data into the generation AI and cause the generation AI to customize the reception method based on the feedback.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the card. For example, the analysis unit performs a detailed analysis for an important card. The analysis unit can also perform a normal analysis for an ordinary card. The analysis unit can also perform a simplified analysis for a card with low importance. This allows the level of detail of the analysis to be adjusted according to the importance of the card. The importance is evaluated based on, for example, the user's evaluation or the type of card. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input card importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis based on the importance.

[0041] The analysis unit can apply different analysis algorithms depending on the category of the card during analysis. For example, in the case of tarot cards, the analysis unit uses an analysis algorithm dedicated to tarot. In addition, in the case of oracle cards, the analysis unit can also use an analysis algorithm dedicated to oracles. In addition, in the case of other cards, the analysis unit can select an appropriate analysis algorithm. This makes it possible to apply the optimal analysis algorithm depending on the category of the card. Category classification is performed, for example, based on the type or purpose of the card. 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 card category data into the generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also analyze the user's past analysis results and select the optimal analysis method. This allows the accuracy of the analysis to be improved based on the user's past analysis results. Past analysis results include, for example, analysis accuracy and user evaluation, but are not limited to these examples. 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 analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time of card submission. For example, the analysis unit prioritizes analysis of recently submitted cards. The analysis unit can also postpone analysis of older submitted cards. The analysis unit can also adjust the priority of analysis based on the time of submission. This makes it possible to determine the priority of analysis based on the time of card submission. The evaluation of the submission time is performed based on, for example, the submission date and time or the frequency of submission. 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 card submission time data into the generation AI and cause the generation AI to determine the priority of analysis based on the time of submission.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the cards. For example, the analysis unit prioritizes analysis of highly relevant cards. The analysis unit can also postpone analysis of less relevant cards. The analysis unit can also adjust the order of analysis based on the relevance of the cards. This makes it possible to adjust the order of analysis based on the relevance of the cards. The relevance is evaluated based on, for example, the content of the cards or the user's areas of interest. 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 card relevance data to the generation AI and cause the generation AI to adjust the order of analysis based on the relevance.

[0045] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit uses simple technical terminology for a novice user. The analysis unit can also use appropriate technical terminology for an intermediate user. The analysis unit can also use detailed technical terminology for an advanced user. This allows appropriate technical terminology to be used according to the user's level of expertise. The expertise level is evaluated based on, for example, the user's occupation or past learning history. 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 expertise level data into the generation AI and cause the generation AI to use technical terminology based on the expertise level.

[0046] When generating an interpretation, the generation unit can adjust the level of detail of the interpretation based on the importance of the card. For example, the generation unit generates a detailed interpretation for an important card. The generation unit can also generate a normal interpretation for an ordinary card. The generation unit can also generate a simplified interpretation for a card with low importance. This makes it possible to adjust the level of detail of the interpretation according to the importance of the card. The importance is evaluated based on, for example, the user's evaluation or the type of card. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input card importance data into the generation AI and cause the generation AI to adjust the level of detail of the interpretation based on the importance.

[0047] When generating an interpretation, the generation unit can apply different interpretation algorithms depending on the card category. For example, in the case of tarot cards, the generation unit uses an interpretation algorithm dedicated to tarot. In addition, in the case of oracle cards, the generation unit can also use an interpretation algorithm dedicated to oracles. In addition, in the case of other cards, the generation unit can select an appropriate interpretation algorithm. This makes it possible to apply the optimal interpretation algorithm depending on the card category. Category classification is performed, for example, based on the type or purpose of the card. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input card category data into the generation AI and cause the generation AI to apply an interpretation algorithm depending on the category.

[0048] When generating an interpretation, the generation unit can improve the accuracy of the interpretation by referring to the user's past interpretation results. For example, the generation unit adjusts the interpretation algorithm based on the user's past interpretation results. The generation unit can also improve the accuracy of the interpretation by referring to the user's past interpretation results. The generation unit can also analyze the user's past interpretation results and select an optimal interpretation method. This makes it possible to improve the accuracy of the interpretation based on the user's past interpretation results. Past interpretation results include, for example, the accuracy of the interpretation and user evaluations, but are not limited to these examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past interpretation result data into the generation AI and cause the generation AI to improve the accuracy of the interpretation.

[0049] When generating interpretations, the generation unit can determine the priority of interpretations based on the time of card submission. For example, the generation unit prioritizes interpretation of recently submitted cards. The generation unit can also postpone interpretation of older submitted cards. The generation unit can also adjust the priority of interpretations based on the time of submission. This makes it possible to determine the priority of interpretations based on the time of card submission. The evaluation of the submission time is performed based on, for example, the submission date and time or the submission frequency. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input card submission time data into the generation AI and cause the generation AI to determine the priority of interpretations based on the submission time.

[0050] The generation unit can adjust the order of interpretations based on the relevance of the cards when generating interpretations. For example, the generation unit prioritizes interpretation of highly relevant cards. The generation unit can also postpone interpretation of less relevant cards. The generation unit can also adjust the order of interpretations based on the relevance of the cards. This makes it possible to adjust the order of interpretations based on the relevance of the cards. The relevance is evaluated based on, for example, the content of the cards or the user's areas of interest. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input card relevance data into the generation AI and cause the generation AI to adjust the order of interpretations based on the relevance.

[0051] When generating an interpretation, the generation unit can adjust the use of technical terminology in the interpretation according to the user's level of expertise. For example, the generation unit uses simple technical terminology for a novice user. The generation unit can also use appropriate technical terminology for an intermediate user. The generation unit can also use detailed technical terminology for an advanced user. This allows appropriate technical terminology to be used according to the user's level of expertise. The expertise level is evaluated based on, for example, the user's occupation or past learning history. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's expertise level data into the generation AI and cause the generation AI to use technical terminology based on the expertise level.

[0052] When receiving feedback, the feedback unit can select the optimal reception method by referring to the user's past feedback history. For example, the feedback unit can suggest the optimal reception method based on feedback provided by the user in the past. The feedback unit can also customize the reception method by reflecting the user's past feedback history. The feedback unit can also analyze the user's feedback history and select the optimal reception method. This makes it possible to provide the optimal reception method based on the user's past feedback history. The feedback history includes, for example, past ratings and comments, but is not limited to such examples. Some or all of the above-mentioned processing in the feedback unit may be performed using, or without, AI. For example, the feedback unit can input the user's feedback history data into a generation AI and cause the generation AI to select a reception method based on the feedback history.

[0053] The feedback unit may perform filtering based on the user's current areas of interest when receiving feedback. For example, the feedback unit may preferentially receive feedback related to topics in which the user is currently interested. The feedback unit may also filter related feedback based on the user's current areas of interest. The feedback unit may also preferentially receive specific feedback based on the user's areas of interest. This allows for preferential reception of feedback related to the user's areas of interest. The areas of interest may be identified based on, for example, past search history or survey results. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input the user's area of ​​interest data into a generation AI and cause the generation AI to perform filtering based on the areas of interest.

[0054] When receiving feedback, the feedback unit can prioritize receiving highly relevant feedback taking into account the user's geographical location information. For example, if the user is in a specific area, the feedback unit can prioritize receiving feedback related to that area. The feedback unit can also filter highly relevant feedback based on the user's current location. The feedback unit can also prioritize receiving specific feedback based on the user's geographical location information. This allows highly relevant feedback to be prioritized based on the user's geographical location information. The geographical location information can be acquired based on, for example, GPS data or an IP address. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or without AI. For example, the feedback unit can input the user's geographical location information data to a generation AI and cause the generation AI to determine the priority of feedback based on the geographical location information.

[0055] When receiving feedback, the feedback unit can analyze the user's social media activity and receive relevant feedback. For example, the feedback unit can receive relevant feedback based on content shared by the user on social media. The feedback unit can also analyze the user's social media activity and filter relevant feedback. The feedback unit can also receive relevant feedback based on the activity of the user's friends on social media. This makes it possible to receive relevant feedback based on the user's social media activity. The analysis of social media activity is performed based on, for example, the content of posts, the number of likes, the number of followers, etc. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's social media activity data into a generation AI and cause the generation AI to receive feedback based on the social media activity.

[0056] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit, for example, adjusts the learning algorithm based on past learning data. The learning unit can also improve learning accuracy by referring to past learning data. The learning unit can also analyze past learning data and select an optimal learning method. This makes it possible to optimize the learning algorithm based on past learning data. Past learning data includes, for example, learning accuracy and user evaluations, but is not limited to these examples. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into a generation AI and cause the generation AI to optimize the learning algorithm.

[0057] During learning, the learning unit can update the learning data by reflecting user feedback. The learning unit updates the learning data based on, for example, feedback provided by the user. The learning unit can also adjust the learning algorithm by reflecting user feedback. The learning unit can also analyze the user's feedback history and optimize the learning data. This allows the learning data to be updated based on user feedback. Feedback includes, for example, user ratings, comments, scoring, etc., but is not limited to these examples. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input user feedback data to a generation AI and cause the generation AI to update the learning data based on the feedback.

[0058] During learning, the learning unit can weight the learning data based on the time of card submission. For example, the learning unit may weight data on recently submitted cards during learning. The learning unit may also weight data on older submitted cards during learning. The learning unit may also adjust the weighting of the learning data based on the time of submission. This allows weighting of the learning data based on the time of card submission. The evaluation of the submission time is performed based on, for example, the date and time of submission or the frequency of submission. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit may input data on the time of card submission into the generation AI and cause the generation AI to weight the learning data based on the time of submission.

[0059] During learning, the learning unit can integrate information from different data sources to enrich the training data. For example, the learning unit integrates information from different data sources to enrich the training data. The learning unit can also analyze information from different data sources and reflect the information in the training data. The learning unit can also optimize the learning algorithm based on information from different data sources. This allows the training data to be enriched by integrating information from different data sources. Examples of data sources include, but are not limited to, social media, sensor data, and public databases. Some or all of the above-described processing in the learning unit may be performed using, or without, AI. For example, the learning unit can input information from different data sources into a generation AI and cause the generation AI to integrate the information.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The analysis unit can collect background information about the card and reflect it in the analysis results. For example, it can collect information about the year the card was issued and the issuer, and add the card's historical background to the analysis. It can also collect information about the events and circumstances in which the card was used, and reflect that in the analysis results. It can also collect information about the card's owner, and customize the analysis results based on the owner's background. This allows for highly accurate analysis that takes into account the card's background information.

[0062] The feedback unit can analyze user feedback in real time and immediately reflect it in system improvements. For example, if a user gives a low rating to an interpretation, the feedback is immediately analyzed and reflected in the next interpretation. Also, if a user gives a high rating to a particular interpretation, the system can learn the interpretation pattern and use it in future interpretations. Furthermore, it can analyze user feedback and identify areas for improvement across the entire system. This allows user feedback to be quickly reflected and the accuracy of the system to be improved.

[0063] The reception unit can provide different card interpretations for each region, taking into account the user's geographic location information. For example, if the user is in a specific region, it can provide card interpretations based on the culture and traditions of that region. It can also provide region-specific card interpretations preferentially based on the user's current location. Furthermore, it can learn interpretation patterns for each region based on the user's geographic location information and reflect them in future interpretations. This allows it to provide more appropriate card interpretations based on the user's geographic location information.

[0064] The analysis unit can analyze a user's social media activity and reflect it in the analysis results. For example, it can provide relevant card interpretations based on images and posts shared by the user on social media. It can also analyze the user's social media activity and provide analysis results based on areas of interest. It can also provide relevant card interpretations based on the activity of the user's friends on social media. This makes it possible to provide more personalized analysis results based on the user's social media activity.

[0065] The feedback unit can analyze user feedback and identify areas for improvement in the system based on the content of the feedback. For example, if a user gives a low rating to a particular interpretation, the unit can identify problems with that interpretation and improve it. It can also learn patterns of interpretations that users have given high ratings to and use them for future interpretations. Furthermore, it can analyze user feedback and identify areas for improvement in the entire system. This allows the accuracy of the system to be improved based on user feedback.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The user inputs an image of the card into the reception unit. For example, the user takes a photo of the card using a device such as a smartphone or tablet and uploads it to the system. The reception unit can accept images of various types of cards, such as tarot cards and oracle cards. Step 2: The analysis unit uses the generation AI to analyze the image of the card input by the reception unit. The analysis unit identifies the type and design of the card using, for example, image recognition technology. The generation AI can use, for example, CNN (Convolutional Neural Network) or SIFT (Scale-Invariant Feature Transform) as image recognition technology. Step 3: The generator uses the generation AI to generate an interpretation based on the content of the card analyzed by the analysis unit. The generator generates the interpretation, for example, taking into account the user's past reading history and preferences. The generation AI customizes the content of the interpretation, for example, taking into account what card interpretations the user has preferred in the past and what themes the user is interested in. Step 4: The feedback unit receives user feedback on the interpretation generated by the generation unit. The feedback unit provides, for example, an interface through which the user can evaluate the interpretation. The interface can be, for example, a graphical user interface (GUI) or a voice interface. Step 5: The learning unit learns the user's preferences based on the feedback received by the feedback unit. The learning unit may use, for example, a recommendation system to learn the user's preferences. The recommendation system may use, for example, collaborative filtering or content-based filtering.

[0068] (Example 2) A card interpretation system according to an embodiment of the present invention utilizes a generation AI to provide a card interpretation tailored to the user. In the card interpretation system, a user inputs an image of a card, and the generation AI analyzes the image and interprets the card's content. The generation AI generates an optimal interpretation based on the user's past reading history and preferences. This allows the user to receive a card interpretation tailored to their needs. For example, in a card interpretation system, a user takes a photo of a card using a device such as a smartphone or tablet and uploads it to the system. The generation AI uses image recognition technology to identify the card's type and design and interprets its content. The generation AI generates an optimal interpretation based on the user's past reading history and preferences. This allows the user to receive a card interpretation tailored to their needs. For example, even a novice user can easily understand the meaning of the card by referring to the interpretation provided by the generation AI. Even experienced users can gain deeper insight by incorporating new perspectives and interpretations provided by the generation AI. Furthermore, the generation AI can improve the accuracy of its interpretations based on user feedback. For example, by users rating the provided interpretations, the generation AI can learn from the ratings and incorporate them into future interpretations. This allows the card interpretation system to provide more appropriate interpretations to users. For example, even if a user finds it difficult to interpret a card, they can refer to the interpretation provided by the generation AI to make it easier to understand the meaning of the card. The generation AI can also learn from user feedback and improve the accuracy of its interpretations. This allows the card interpretation system to continuously evolve and provide users with more appropriate interpretations.

[0069] A card interpretation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, a feedback unit, and a learning unit. The reception unit receives images of cards from users. The users take photos of the cards using a device such as a smartphone or tablet and upload them to the system. The reception unit can receive images of various types of cards, such as tarot cards and oracle cards. The analysis unit uses a generation AI to analyze the card images received by the reception unit. The analysis unit identifies the card type and design using image recognition technology. The generation AI can use image recognition technology such as a convolutional neural network (CNN) or a scale-invariant feature transform (SIFT). The generation unit generates an interpretation based on the card content analyzed by the analysis unit using the generation AI. The generation unit generates the interpretation by taking into account, for example, the user's past reading history and preferences. The generation AI customizes the content of the interpretation by taking into account, for example, the user's preferred card interpretations in the past and themes of interest. The feedback unit receives user feedback on the interpretation generated by the generation unit. The feedback unit, for example, provides an interface through which the user evaluates the interpretation. The interface may be, for example, a graphical user interface (GUI) or a voice interface. The learning unit learns the user's preferences based on the feedback received by the feedback unit. The learning unit may, for example, use a recommendation system to learn the user's preferences. The recommendation system may, for example, use collaborative filtering or content-based filtering. This allows the card interpretation system according to the embodiment to provide card interpretations with content suited to the user.

[0070] The analysis unit can identify the type and design of a card using image recognition technology. Examples of image recognition technology include, but are not limited to, CNN (Convolutional Neural Network) and SIFT (Scale-Invariant Feature Transform). The analysis unit can identify the type and design of a card using, for example, CNN. The analysis unit can also use SIFT to extract feature points of a card and identify the type and design. The analysis unit can also combine image recognition technology to perform more accurate analysis. For example, the analysis unit can combine CNN and SIFT to identify the type and design of a card. This allows for accurate identification of the type and design of a card. 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 image data of a card to a generation AI and cause the generation AI to identify the type and design of the card from the image data.

[0071] The generation unit can generate an interpretation by taking into account the user's past reading history and preferences. Reading history includes, for example, past interpretation results and user reactions, but is not limited to these examples. The generation unit generates an interpretation based on, for example, the user's past interpretation results. The generation unit can also customize the interpretation by taking into account the user's preferences. For example, the generation unit can learn patterns of interpretations that the user has previously preferred and generate an interpretation based on those patterns. The generation unit can also generate an interpretation by taking into account the user's areas of interest. For example, the generation unit can generate an interpretation related to a topic that the user is interested in. This makes it possible to provide the user with an optimal interpretation. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past reading history and preference data into the generation AI and have the generation AI generate an interpretation.

[0072] The feedback unit can provide an interface through which the user evaluates the interpretation. Examples of the interface include, but are not limited to, a graphical user interface (GUI) or an audio interface. For example, the feedback unit can provide an interface through which the user evaluates the interpretation using a GUI. The feedback unit can also provide an interface through which the user evaluates by voice using an audio interface. The feedback unit can also provide an interface through which the user inputs comments on the interpretation. For example, the feedback unit can provide an interface through which the user inputs comments on the interpretation in text. This allows the user to evaluate the interpretation. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input user evaluation data to a generation AI and cause the generation AI to analyze the evaluation data.

[0073] The learning unit can learn user preferences using a recommendation system. Recommendation systems include, but are not limited to, collaborative filtering and content-based filtering. The learning unit can learn user preferences using, for example, collaborative filtering. The learning unit can also learn user preferences using content-based filtering. The learning unit can also combine recommendation systems to perform more accurate learning. For example, the learning unit can combine collaborative filtering and content-based filtering to learn user preferences. This allows the learning of user preferences and improves the accuracy of interpretation. Some or all of the above-described processing in the learning unit can be performed using, for example, AI, or without AI. For example, the learning unit can input user preference data into the generation AI and cause the generation AI to learn the preferences.

[0074] The reception unit can estimate the user's emotions and adjust the timing of receiving card images based on the estimated user emotions. For example, if the user is relaxed, the reception unit can immediately receive card images. Furthermore, if the user is feeling stressed, the reception unit can slightly delay the reception of card images to give the user time to calm down. Furthermore, if the user is excited, the reception unit can quickly receive card images to maintain the user's excitement. This allows the timing of receiving card images to be adjusted 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 reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0075] The reception unit can analyze the user's past card image reception history and select the optimal reception method. For example, the reception unit prioritizes and suggests reception methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the user's past reception history. The reception unit can also customize the optimal reception method based on the user's past preferred reception methods. This makes it possible to provide the optimal reception method based on the user's past history. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input the user's past reception history data into the generation AI and have the generation AI select the optimal reception method.

[0076] The reception unit can perform filtering based on the user's current field of interest when receiving card images. For example, the reception unit preferentially receives card images related to themes in which the user is currently interested. The reception unit can also filter related card images based on the user's current field of interest. The reception unit can also preferentially receive specific card images based on the user's field of interest. This makes it possible to preferentially receive related card images based on the user's field of interest. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's field of interest data to the generation AI and cause the generation AI to perform filtering based on the field of interest.

[0077] When accepting a card image, the acceptance unit can select the optimal acceptance means depending on the user's input method. For example, if the user uses voice input, the acceptance unit accepts the card image using voice recognition technology. Furthermore, if the user uses text input, the acceptance unit can also accept the card image using text analysis technology. Furthermore, if the user uses image input, the acceptance unit can also accept the card image using image recognition technology. This makes it possible to provide the optimal acceptance means depending on the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using AI, for example, or may be performed without using AI. For example, the acceptance unit can input the user's input method data into the generation AI and cause the generation AI to select the optimal acceptance means.

[0078] The reception unit can estimate the user's emotions and determine the priority of the card images to be received based on the estimated user's emotions. For example, when the user is relaxed, the reception unit receives card images in normal priority order. Furthermore, when the user is stressed, the reception unit can also prioritize important card images. Furthermore, when the user is excited, the reception unit can also prioritize relevant card images to maintain the user's excitement. This allows the priority of card images to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority based on the emotion.

[0079] When accepting card images, the reception unit can prioritize accepting highly relevant images taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes accepting card images related to that area. The reception unit can also filter highly relevant card images based on the user's current location. The reception unit can also prioritize accepting specific card images based on the user's geographical location information. This makes it possible to prioritize accepting highly relevant card images based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to perform filtering based on the geographical location information.

[0080] When receiving a card image, the reception unit can analyze the user's social media activity and receive related images. For example, the reception unit receives related card images based on images shared by the user on social media. The reception unit can also analyze the user's social media activity and filter related card images. The reception unit can also receive related card images with reference to the activity of the user's friends on social media. This makes it possible to receive related card images based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to perform filtering based on the social media activity.

[0081] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a card image. The reception unit, for example, suggests an optimal reception method based on feedback provided by the user in the past. The reception unit can also customize the reception method by reflecting the user's past feedback. The reception unit can also analyze the user's feedback history and select the optimal reception method. This makes it possible to provide the optimal reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's feedback history data into the generation AI and cause the generation AI to customize the reception method based on the feedback.

[0082] The analysis unit can estimate the user's emotions and adjust the analysis method of the card images based on the estimated user emotions. For example, the analysis unit can use a normal analysis method when the user is relaxed. The analysis unit can also use a simplified analysis method when the user is stressed. The analysis unit can also use a detailed analysis method when the user is excited. This allows the analysis method of the card images to be adjusted 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 analysis unit can be performed using AI, for example, or without 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.

[0083] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the card. For example, the analysis unit performs a detailed analysis for an important card. The analysis unit can also perform a normal analysis for an ordinary card. The analysis unit can also perform a simplified analysis for a card with low importance. This allows the level of detail of the analysis to be adjusted according to the importance of the card. The importance is evaluated based on, for example, the user's evaluation or the type of card. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input card importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis based on the importance.

[0084] The analysis unit can apply different analysis algorithms depending on the category of the card during analysis. For example, in the case of tarot cards, the analysis unit uses an analysis algorithm dedicated to tarot. In addition, in the case of oracle cards, the analysis unit can also use an analysis algorithm dedicated to oracles. In addition, in the case of other cards, the analysis unit can select an appropriate analysis algorithm. This makes it possible to apply the optimal analysis algorithm depending on the category of the card. Category classification is performed, for example, based on the type or purpose of the card. 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 card category data into the generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0085] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also analyze the user's past analysis results and select the optimal analysis method. This allows the accuracy of the analysis to be improved based on the user's past analysis results. Past analysis results include, for example, analysis accuracy and user evaluation, but are not limited to these examples. 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 analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0086] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is relaxed, the analysis unit uses a normal analysis time. The analysis unit can also shorten the analysis time if the user is stressed. The analysis unit can also perform a detailed analysis and extend the analysis time if the user is excited. This allows the length of the analysis to be adjusted 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 length of the analysis based on the emotion.

[0087] During analysis, the analysis unit can determine the priority of analysis based on the time of card submission. For example, the analysis unit prioritizes analysis of recently submitted cards. The analysis unit can also postpone analysis of older submitted cards. The analysis unit can also adjust the priority of analysis based on the time of submission. This makes it possible to determine the priority of analysis based on the time of card submission. The evaluation of the submission time is performed based on, for example, the submission date and time or the frequency of submission. 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 card submission time data into the generation AI and cause the generation AI to determine the priority of analysis based on the time of submission.

[0088] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the cards. For example, the analysis unit prioritizes analysis of highly relevant cards. The analysis unit can also postpone analysis of less relevant cards. The analysis unit can also adjust the order of analysis based on the relevance of the cards. This makes it possible to adjust the order of analysis based on the relevance of the cards. The relevance is evaluated based on, for example, the content of the cards or the user's areas of interest. 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 card relevance data to the generation AI and cause the generation AI to adjust the order of analysis based on the relevance.

[0089] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit uses simple technical terminology for a novice user. The analysis unit can also use appropriate technical terminology for an intermediate user. The analysis unit can also use detailed technical terminology for an advanced user. This allows appropriate technical terminology to be used according to the user's level of expertise. The expertise level is evaluated based on, for example, the user's occupation or past learning history. 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 expertise level data into the generation AI and cause the generation AI to use technical terminology based on the expertise level.

[0090] The generation unit can estimate the user's emotions and adjust the way the interpretation is expressed based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can use a calm expression. If the user is stressed, the generation unit can use a concise and clear expression. If the user is excited, the generation unit can use a visually stimulating expression. This allows the way the interpretation is expressed to be adjusted 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 generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the interpretation is expressed based on the emotion.

[0091] When generating an interpretation, the generation unit can adjust the level of detail of the interpretation based on the importance of the card. For example, the generation unit generates a detailed interpretation for an important card. The generation unit can also generate a normal interpretation for an ordinary card. The generation unit can also generate a simplified interpretation for a card with low importance. This makes it possible to adjust the level of detail of the interpretation according to the importance of the card. The importance is evaluated based on, for example, the user's evaluation or the type of card. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input card importance data into the generation AI and cause the generation AI to adjust the level of detail of the interpretation based on the importance.

[0092] When generating an interpretation, the generation unit can apply different interpretation algorithms depending on the card category. For example, in the case of tarot cards, the generation unit uses an interpretation algorithm dedicated to tarot. In addition, in the case of oracle cards, the generation unit can also use an interpretation algorithm dedicated to oracles. In addition, in the case of other cards, the generation unit can select an appropriate interpretation algorithm. This makes it possible to apply the optimal interpretation algorithm depending on the card category. Category classification is performed, for example, based on the type or purpose of the card. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input card category data into the generation AI and cause the generation AI to apply an interpretation algorithm depending on the category.

[0093] When generating an interpretation, the generation unit can improve the accuracy of the interpretation by referring to the user's past interpretation results. For example, the generation unit adjusts the interpretation algorithm based on the user's past interpretation results. The generation unit can also improve the accuracy of the interpretation by referring to the user's past interpretation results. The generation unit can also analyze the user's past interpretation results and select an optimal interpretation method. This makes it possible to improve the accuracy of the interpretation based on the user's past interpretation results. Past interpretation results include, for example, the accuracy of the interpretation and user evaluations, but are not limited to these examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past interpretation result data into the generation AI and cause the generation AI to improve the accuracy of the interpretation.

[0094] The generation unit can estimate the user's emotion and adjust the length of the interpretation based on the estimated user emotion. For example, if the user is relaxed, the generation unit uses a normal interpretation length. Furthermore, if the user is stressed, the generation unit can shorten the interpretation length. Furthermore, if the user is excited, the generation unit can provide a detailed interpretation and extend the interpretation length. This allows the length of the interpretation to be adjusted according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the interpretation based on the emotion.

[0095] When generating interpretations, the generation unit can determine the priority of interpretations based on the time of card submission. For example, the generation unit prioritizes interpretation of recently submitted cards. The generation unit can also postpone interpretation of older submitted cards. The generation unit can also adjust the priority of interpretations based on the time of submission. This makes it possible to determine the priority of interpretations based on the time of card submission. The evaluation of the submission time is performed based on, for example, the submission date and time or the submission frequency. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input card submission time data into the generation AI and cause the generation AI to determine the priority of interpretations based on the submission time.

[0096] The generation unit can adjust the order of interpretations based on the relevance of the cards when generating interpretations. For example, the generation unit prioritizes interpretation of highly relevant cards. The generation unit can also postpone interpretation of less relevant cards. The generation unit can also adjust the order of interpretations based on the relevance of the cards. This makes it possible to adjust the order of interpretations based on the relevance of the cards. The relevance is evaluated based on, for example, the content of the cards or the user's areas of interest. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input card relevance data into the generation AI and cause the generation AI to adjust the order of interpretations based on the relevance.

[0097] When generating an interpretation, the generation unit can adjust the use of technical terminology in the interpretation according to the user's level of expertise. For example, the generation unit uses simple technical terminology for a novice user. The generation unit can also use appropriate technical terminology for an intermediate user. The generation unit can also use detailed technical terminology for an advanced user. This allows appropriate technical terminology to be used according to the user's level of expertise. The expertise level is evaluated based on, for example, the user's occupation or past learning history. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's expertise level data into the generation AI and cause the generation AI to use technical terminology based on the expertise level.

[0098] The feedback unit can estimate the user's emotion and adjust the feedback acceptance method based on the estimated user's emotion. For example, the feedback unit can use a normal feedback acceptance method when the user is relaxed. The feedback unit can also use a simplified feedback acceptance method when the user is stressed. The feedback unit can also use a detailed feedback acceptance method when the user is excited. This allows the feedback acceptance method to be adjusted according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the feedback unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the feedback unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the feedback acceptance method based on the emotion.

[0099] When receiving feedback, the feedback unit can select the optimal reception method by referring to the user's past feedback history. For example, the feedback unit can suggest the optimal reception method based on feedback provided by the user in the past. The feedback unit can also customize the reception method by reflecting the user's past feedback history. The feedback unit can also analyze the user's feedback history and select the optimal reception method. This makes it possible to provide the optimal reception method based on the user's past feedback history. The feedback history includes, for example, past ratings and comments, but is not limited to such examples. Some or all of the above-mentioned processing in the feedback unit may be performed using, or without, AI. For example, the feedback unit can input the user's feedback history data into a generation AI and cause the generation AI to select a reception method based on the feedback history.

[0100] The feedback unit may perform filtering based on the user's current areas of interest when receiving feedback. For example, the feedback unit may preferentially receive feedback related to topics in which the user is currently interested. The feedback unit may also filter related feedback based on the user's current areas of interest. The feedback unit may also preferentially receive specific feedback based on the user's areas of interest. This allows for preferential reception of feedback related to the user's areas of interest. The areas of interest may be identified based on, for example, past search history or survey results. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input the user's area of ​​interest data into a generation AI and cause the generation AI to perform filtering based on the areas of interest.

[0101] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. For example, when the user is relaxed, the feedback unit receives feedback with normal priority. Furthermore, when the user is stressed, the feedback unit can also prioritize important feedback. Furthermore, when the user is excited, the feedback unit can also prioritize feedback related to maintaining excitement. This allows the priority of feedback to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of feedback based on emotions.

[0102] When receiving feedback, the feedback unit can prioritize receiving highly relevant feedback taking into account the user's geographical location information. For example, if the user is in a specific area, the feedback unit can prioritize receiving feedback related to that area. The feedback unit can also filter highly relevant feedback based on the user's current location. The feedback unit can also prioritize receiving specific feedback based on the user's geographical location information. This allows highly relevant feedback to be prioritized based on the user's geographical location information. The geographical location information can be acquired based on, for example, GPS data or an IP address. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or without AI. For example, the feedback unit can input the user's geographical location information data to a generation AI and cause the generation AI to determine the priority of feedback based on the geographical location information.

[0103] When receiving feedback, the feedback unit can analyze the user's social media activity and receive relevant feedback. For example, the feedback unit can receive relevant feedback based on content shared by the user on social media. The feedback unit can also analyze the user's social media activity and filter relevant feedback. The feedback unit can also receive relevant feedback based on the activity of the user's friends on social media. This makes it possible to receive relevant feedback based on the user's social media activity. The analysis of social media activity is performed based on, for example, the content of posts, the number of likes, the number of followers, etc. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's social media activity data into a generation AI and cause the generation AI to receive feedback based on the social media activity.

[0104] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, when the user is relaxed, the learning unit uses normal training data. Furthermore, when the user is stressed, the learning unit can also use simplified training data. Furthermore, when the user is excited, the learning unit can also use detailed training data. This allows the selection of training data 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, 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 learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can input the user's emotion data into the generation AI and cause the generation AI to select training data based on the emotion.

[0105] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit, for example, adjusts the learning algorithm based on past learning data. The learning unit can also improve learning accuracy by referring to past learning data. The learning unit can also analyze past learning data and select an optimal learning method. This makes it possible to optimize the learning algorithm based on past learning data. Past learning data includes, for example, learning accuracy and user evaluations, but is not limited to these examples. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into a generation AI and cause the generation AI to optimize the learning algorithm.

[0106] During learning, the learning unit can update the learning data by reflecting user feedback. The learning unit updates the learning data based on, for example, feedback provided by the user. The learning unit can also adjust the learning algorithm by reflecting user feedback. The learning unit can also analyze the user's feedback history and optimize the learning data. This allows the learning data to be updated based on user feedback. Feedback includes, for example, user ratings, comments, scoring, etc., but is not limited to these examples. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input user feedback data to a generation AI and cause the generation AI to update the learning data based on the feedback.

[0107] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated user emotions. For example, when the user is relaxed, the learning unit uses a normal learning frequency. The learning unit can also decrease the learning frequency when the user is stressed. The learning unit can also increase the learning frequency when the user is excited. This allows the learning frequency to be adjusted 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, 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 learning unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the learning unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the learning frequency based on the emotion.

[0108] During learning, the learning unit can weight the learning data based on the time of card submission. For example, the learning unit may weight data on recently submitted cards during learning. The learning unit may also weight data on older submitted cards during learning. The learning unit may also adjust the weighting of the learning data based on the time of submission. This allows weighting of the learning data based on the time of card submission. The evaluation of the submission time is performed based on, for example, the date and time of submission or the frequency of submission. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit may input data on the time of card submission into the generation AI and cause the generation AI to weight the learning data based on the time of submission.

[0109] During learning, the learning unit can integrate information from different data sources to enrich the training data. For example, the learning unit integrates information from different data sources to enrich the training data. The learning unit can also analyze information from different data sources and reflect the information in the training data. The learning unit can also optimize the learning algorithm based on information from different data sources. This allows the training data to be enriched by integrating information from different data sources. Examples of data sources include, but are not limited to, social media, sensor data, and public databases. Some or all of the above-described processing in the learning unit may be performed using, or without, AI. For example, the learning unit can input information from different data sources into a generation AI and cause the generation AI to integrate the information. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, analysis unit, generation unit, feedback unit, and learning unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, allowing a user to take an image of a card using a smartphone or tablet and upload it to the system. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the image of the card using a generation AI. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and generates an interpretation taking into account the user's past reading history and preferences. The feedback unit is implemented, for example, by the control unit 46A of the smart device 14, and provides an interface through which the user can evaluate the interpretation. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and learns the user's preferences based on the feedback. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, feedback unit, and learning unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and allows a user to take an image of a card using the smart glasses and upload it to the system. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the image of the card using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an interpretation taking into account the user's past reading history and preferences. The feedback unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides an interface through which the user can evaluate the interpretation. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and learns the user's preferences based on the feedback. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, feedback unit, and learning unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314, and allows a user to take an image of a card using the headset-type terminal and upload it to the system. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the image of the card using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an interpretation taking into account the user's past reading history and preferences. The feedback unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and provides an interface through which the user can evaluate the interpretation. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and learns the user's preferences based on the feedback. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, generation unit, feedback unit, and learning unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and a user can use the robot to take an image of a card and upload it to the system. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the image of the card using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an interpretation taking into account the user's past reading history and preferences. The feedback unit is realized, for example, by the control unit 46A of the robot 414, and provides an interface through which the user can evaluate the interpretation. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and learns the user's preferences based on the feedback.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The reception unit can analyze the user's voice tone and estimate the user's emotional state. For example, if the user speaks in a calm tone, it can be estimated that the user is relaxed and the card image can be received immediately. If the user speaks in an excited tone, it can be estimated that the user is excited and the card image can be received quickly. Furthermore, if the user speaks in a low tone, it can be estimated that the user is stressed and the card image can be received a little later. This makes it possible to adjust the timing of receiving the card image based on the user's voice tone.

[0112] The analysis unit can collect background information about the card and reflect it in the analysis results. For example, it can collect information about the year the card was issued and the issuer, and add the card's historical background to the analysis. It can also collect information about the events and circumstances in which the card was used, and reflect that in the analysis results. It can also collect information about the card's owner, and customize the analysis results based on the owner's background. This allows for highly accurate analysis that takes into account the card's background information.

[0113] The generation unit can estimate the user's emotion and adjust the tone of the interpretation based on the estimated user's emotion. For example, if the user is relaxed, the generation unit can provide the interpretation in a calm tone. If the user is stressed, the generation unit can provide the interpretation in a concise and clear tone. Furthermore, if the user is excited, the generation unit can provide the interpretation in a visually stimulating tone. In this way, the generation unit can adjust the tone of the interpretation according to the user's emotion.

[0114] The feedback unit can analyze user feedback in real time and immediately reflect it in system improvements. For example, if a user gives a low rating to an interpretation, the feedback is immediately analyzed and reflected in the next interpretation. Also, if a user gives a high rating to a particular interpretation, the system can learn the interpretation pattern and use it in future interpretations. Furthermore, it can analyze user feedback and identify areas for improvement across the entire system. This allows user feedback to be quickly reflected and the accuracy of the system to be improved.

[0115] The learning unit can estimate the user's emotions and select learning data based on the estimated user's emotions. For example, if the user is relaxed, normal learning data can be used. If the user is stressed, simplified learning data can be used. Furthermore, if the user is excited, detailed learning data can be used. This makes it possible to select learning data according to the user's emotions.

[0116] The reception unit can provide different card interpretations for each region, taking into account the user's geographic location information. For example, if the user is in a specific region, it can provide card interpretations based on the culture and traditions of that region. It can also provide region-specific card interpretations preferentially based on the user's current location. Furthermore, it can learn interpretation patterns for each region based on the user's geographic location information and reflect them in future interpretations. This allows it to provide more appropriate card interpretations based on the user's geographic location information.

[0117] The analysis unit can analyze a user's social media activity and reflect it in the analysis results. For example, it can provide relevant card interpretations based on images and posts shared by the user on social media. It can also analyze the user's social media activity and provide analysis results based on areas of interest. It can also provide relevant card interpretations based on the activity of the user's friends on social media. This makes it possible to provide more personalized analysis results based on the user's social media activity.

[0118] The generation unit can estimate the user's emotion and adjust the length of the interpretation based on the estimated user's emotion. For example, if the user is relaxed, a normal interpretation length can be used. If the user is stressed, the length of the interpretation can be shortened. Furthermore, if the user is excited, a detailed interpretation can be provided and the length of the interpretation can be extended. In this way, the length of the interpretation can be adjusted according to the user's emotion.

[0119] The feedback unit can analyze user feedback and identify areas for improvement in the system based on the content of the feedback. For example, if a user gives a low rating to a particular interpretation, the unit can identify problems with that interpretation and improve it. It can also learn patterns of interpretations that users have given high ratings to and use them for future interpretations. Furthermore, it can analyze user feedback and identify areas for improvement in the entire system. This allows the accuracy of the system to be improved based on user feedback.

[0120] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated user's emotions. For example, if the user is relaxed, a normal learning frequency can be used. If the user is stressed, the learning frequency can be decreased. Furthermore, if the user is excited, the learning frequency can be increased. In this way, the learning frequency can be adjusted according to the user's emotions.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The user inputs an image of the card into the reception unit. For example, the user takes a photo of the card using a device such as a smartphone or tablet and uploads it to the system. The reception unit can accept images of various types of cards, such as tarot cards and oracle cards. Step 2: The analysis unit uses the generation AI to analyze the image of the card input by the reception unit. The analysis unit identifies the type and design of the card using, for example, image recognition technology. The generation AI can use, for example, CNN (Convolutional Neural Network) or SIFT (Scale-Invariant Feature Transform) as image recognition technology. Step 3: The generator uses the generation AI to generate an interpretation based on the content of the card analyzed by the analysis unit. The generator generates the interpretation, for example, taking into account the user's past reading history and preferences. The generation AI customizes the content of the interpretation, for example, taking into account what card interpretations the user has preferred in the past and what themes the user is interested in. Step 4: The feedback unit receives user feedback on the interpretation generated by the generation unit. The feedback unit provides, for example, an interface through which the user can evaluate the interpretation. The interface can be, for example, a graphical user interface (GUI) or a voice interface. Step 5: The learning unit learns the user's preferences based on the feedback received by the feedback unit. The learning unit may use, for example, a recommendation system to learn the user's preferences. The recommendation system may use, for example, collaborative filtering or content-based filtering.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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 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.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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 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.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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 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.

[0174] 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.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting an image of a card; an analysis unit that analyzes the image of the card input by the reception unit; a generation unit that generates an interpretation based on the content of the card analyzed by the analysis unit; a feedback unit that receives user feedback on the interpretation generated by the generation unit; a learning unit that learns user preferences based on the feedback received by the feedback unit. A system characterized by:

2. The analysis unit Identifying card types and designs using image recognition technology 2. The system of claim 1.

3. The generation unit Generates interpretations that take into account the user's past reading history and preferences 2. The system of claim 1.

4. The feedback unit Provide an interface for users to rate interpretations 2. The system of claim 1.

5. The learning unit Learn user preferences with a recommendation system 2. The system of claim 1.

6. The reception unit The user's emotions are estimated, and the timing of accepting the card image is adjusted based on the estimated user's emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past card image reception history and select the optimal reception method 2. The system of claim 1.

8. The reception unit When accepting card images, filter them based on the user's current interests 2. The system of claim 1.

Images