System

Abstract

To provide an appropriate fortune-telling result to a user by effectively utilizing fortune-telling data.SOLUTION: A system includes a fortune-telling data learning part, a fortune-telling result generation part, and a fortune-telling result provision part. A fortune-telling data learning part learns various kinds of fortune-telling data. A fortune-telling result generation part generates a fortune-telling result corresponding to the request of the user on the basis of the data learned by the fortune-telling data learning part. A fortune-telling result providing part provides the user with the fortune-telling result generated by the fortune-telling result generating part.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 not been effective in utilizing fortune-telling data to provide users with appropriate fortune-telling results, and there is room for improvement.

[0005] The system according to the embodiment aims to effectively utilize fortune-telling data and provide appropriate fortune-telling results to users. [Means for solving the problem]

[0006] The system according to the embodiment includes a fortune-telling data learning unit, a fortune-telling result generating unit, and a fortune-telling result providing unit. The fortune-telling data learning unit learns various fortune-telling data. The fortune-telling result generating unit generates a fortune-telling result in response to a user request based on the data learned by the fortune-telling data learning unit. The fortune-telling result providing unit provides the user with the fortune-telling result generated by the fortune-telling result generating unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively utilize fortune-telling data and provide appropriate fortune-telling results to users. [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) The fortune-telling system according to the embodiment of the present invention is a system that uses a generation AI to implement a wide variety of fortune-telling functions and provide them as services. As a result, the fortune-telling system can provide a wide variety of fortune-telling functions using the generation AI, making people happy.

[0029] The fortune-telling system according to the embodiment includes a fortune-telling data learning unit, a fortune-telling result generating unit, and a fortune-telling result providing unit. The fortune-telling data learning unit learns various fortune-telling data. For example, the fortune-telling data learning unit learns data such as tarot readings, horoscope readings, palmistry, and numerology. The fortune-telling data learning unit can also learn past fortune-telling results, interpretations by fortune-tellers, and literature related to fortune-telling. For example, the fortune-telling data learning unit learns the meanings of tarot cards and how to interpret them based on their combinations. The fortune-telling result generating unit generates fortune-telling results in response to a user's request based on the data learned by the fortune-telling data learning unit. For example, when a user inputs their date of birth, the fortune-telling result generating unit generates a horoscope reading result based on the information. When a user uploads an image of their palm, the fortune-telling result generating unit can analyze the image and generate a palm-reading result. Furthermore, when a user selects a tarot card, the fortune-telling result generating unit can generate a tarot reading result based on the user's selection. The fortune-telling result providing unit provides the fortune-telling result generated by the fortune-telling result generating unit to the user. For example, the fortune-telling result providing unit provides the generated fortune-telling result to the user in text format. In addition, the fortune-telling result providing unit may provide the generated fortune-telling result in an audio format. Furthermore, the fortune-telling result providing unit may provide the generated fortune-telling result in a graphical format. As a result, the fortune-telling system according to the embodiment can realize a wide variety of fortune-telling functions and provide happiness to the user.

[0030] The fortune-telling data learning unit can learn the user's past behavioral data or social media posts in addition to the fortune-telling data to generate more personalized fortune-telling results. The fortune-telling data learning unit, for example, collects the user's past behavioral data and integrates it with the fortune-telling data to learn. For example, it analyzes what kind of fortune-telling the user has received in the past and what kind of feedback they have left, to generate personalized fortune-telling results. The fortune-telling data learning unit also analyzes social media posts to understand the user's interests and concerns. For example, it reflects the content the user frequently posts and the hashtags they use in the fortune-telling results. The fortune-telling data learning unit also combines the user's behavioral data with social media posts to generate more accurate fortune-telling results. For example, it provides fortune-telling results that take into account the user's life events and emotional changes. This allows for more personalized fortune-telling results to be provided by taking into account the user's past behavioral data and social media posts.

[0031] The fortune-telling data learning unit analyzes the fortune teller's audio or video and includes the fortune teller's facial expressions and tone of voice during the reading in the learning data, thereby providing a more realistic fortune-telling experience. For example, the fortune-telling data learning unit collects the fortune teller's audio data and analyzes their tone of voice and speaking style. For example, it learns how the fortune teller's voice changes when they draw a specific card, and the generation AI provides fortune-telling results in a similar tone. The fortune-telling data learning unit also analyzes the fortune teller's video data and learns their facial expressions and gestures. For example, reproducing the fortune teller's facial expressions and hand movements when they draw a card provides a more realistic fortune-telling experience. The fortune-telling data learning unit also integrates the audio and video data to learn the fortune teller's overall performance. For example, reproducing the fortune teller's speaking style and movements provides the user with a more realistic fortune-telling experience. In this way, by analyzing the fortune teller's audio and video, a more realistic fortune-telling experience can be provided.

[0032] The fortune-telling data learning unit can learn historical events or astronomical data in addition to fortune-telling data, thereby adding a new perspective to the fortune-telling results. The fortune-telling data learning unit, for example, includes historical events in the learning data and reflects them in the fortune-telling results. For example, a new perspective is added to the fortune-telling results based on historical events that occurred at a specific time. The fortune-telling data learning unit also learns astronomical data and reflects it in the fortune-telling results. For example, a new perspective is added to the fortune-telling results based on the positions of specific constellations or planets. The fortune-telling data learning unit also integrates historical events and astronomical data to add a new perspective to the fortune-telling results. For example, fortune-telling results are generated based on events that occurred at a specific time and the astronomical conditions at that time. In this way, by learning historical events and astronomical data, a new perspective can be added to the fortune-telling results.

[0033] The fortune-telling data learning unit can compare fortune-telling data with fortune-telling from different cultural spheres and provide fortune-telling results that take cultural background into account. The fortune-telling data learning unit, for example, collects fortune-telling data from different cultural spheres and has the generation AI learn it. For example, it compares Western fortune-telling data with Eastern fortune-telling data and provides fortune-telling results that take cultural background into account. The fortune-telling data learning unit also learns the interpretations of fortune-tellers from different cultural spheres to generate fortune-telling results that take cultural background into account. For example, the same fortune-telling result can be interpreted differently depending on the culture. The fortune-telling data learning unit also integrates fortune-telling data from different cultural spheres and provides fortune-telling results that correspond to the user's cultural background. For example, it customizes the fortune-telling results taking into account the cultural sphere the user belongs to. In this way, by comparing fortune-telling data from different cultural spheres, it is possible to provide fortune-telling results that take cultural background into account.

[0034] The fortune-telling result generation unit can provide more accurate fortune-telling results by taking into account the user's past fortune-telling results or feedback. The fortune-telling result generation unit, for example, stores the user's past fortune-telling results in a database, and the generation AI generates fortune-telling results based on them. For example, the unit compares the past fortune-telling results with the current situation to provide more accurate fortune-telling results. The fortune-telling result generation unit also collects user feedback, and the generation AI uses it to improve the fortune-telling results. For example, the unit analyzes what feedback the user left on past fortune-telling results and reflects this in the next fortune-telling result. The fortune-telling result generation unit also integrates the past fortune-telling results and feedback, and the generation AI generates fortune-telling results based on that. For example, the unit takes into account the user's past actions and feedback to provide more accurate fortune-telling results. In this way, more accurate fortune-telling results can be provided by taking into account the user's past fortune-telling results and feedback.

[0035] The fortune-telling result generation unit can collect user reactions to the fortune-telling results in real time and dynamically revise the fortune-telling results based on those reactions. The fortune-telling result generation unit, for example, collects user reactions to the fortune-telling results in real time, and the generation AI revise the fortune-telling results based on that. For example, if the user has a positive reaction to the fortune-telling results, the fortune-telling results are improved based on that reaction. The fortune-telling result generation unit also analyzes the user's reaction, and the generation AI dynamically revise the fortune-telling results based on that. For example, if the user has a negative reaction to the fortune-telling results, the fortune-telling results are revise based on that reaction. The fortune-telling result generation unit also builds a system in which the generation AI dynamically revises the fortune-telling results based on user reactions collected in real time. For example, the fortune-telling results are instantly revise depending on the user's reaction. In this way, user reactions can be collected in real time and the fortune-telling results can be dynamically revise.

[0036] When generating a fortune-telling result, the fortune-telling result generation unit can take into account the user's health data or fitness data and include health advice. The fortune-telling result generation unit, for example, collects the user's health data, and the generation AI generates a fortune-telling result based on that data. For example, a fortune-telling result including health advice is provided based on the user's heart rate and sleep data. The fortune-telling result generation unit also analyzes fitness data, and the generation AI generates a fortune-telling result based on that data. For example, a fortune-telling result including health advice is provided based on the user's exercise habits and dietary data. The fortune-telling result generation unit also integrates health data and fitness data, and the generation AI generates a fortune-telling result based on that. For example, a fortune-telling result including health advice is provided by taking into account the user's health condition and fitness level. In this way, fortune-telling results including health advice can be provided by taking the user's health data and fitness data into account.

[0037] When generating fortune-telling results, the fortune-telling result generation unit takes into account information about the user's occupation or hobbies, allowing the unit to provide more personalized fortune-telling results. The fortune-telling result generation unit, for example, collects the user's occupation data, and the generation AI generates fortune-telling results based on that data. For example, fortune-telling results that include advice related to the user's occupation are provided. The fortune-telling result generation unit also analyzes information about the user's hobbies, and the generation AI generates fortune-telling results based on that data. For example, fortune-telling results that include advice related to the user's hobbies are provided. The fortune-telling result generation unit also integrates the occupation data and information about hobbies, and the generation AI generates fortune-telling results based on that data. For example, more personalized fortune-telling results are provided by taking into account the user's occupation and hobbies. This allows the unit to provide more personalized fortune-telling results by taking into account information about the user's occupation and hobbies.

[0038] When providing a fortune-telling result, the fortune-telling result providing unit can provide a more personalized fortune-telling result by taking into account the user's past fortune-telling results or feedback. The fortune-telling result providing unit, for example, stores the user's past fortune-telling results in a database, and the generation AI generates a fortune-telling result based on them. For example, the fortune-telling result providing unit compares the past fortune-telling results with the current situation to provide a more personalized fortune-telling result. The fortune-telling result providing unit also collects user feedback, and the generation AI improves the fortune-telling result based on that. For example, it analyzes what kind of feedback the user left on the past fortune-telling result and reflects it in the next fortune-telling result. The fortune-telling result providing unit also integrates the past fortune-telling results and feedback, and the generation AI generates a fortune-telling result based on that. For example, it takes into account the user's past actions and feedback to provide a more personalized fortune-telling result. In this way, by taking into account the user's past fortune-telling results and feedback, a more personalized fortune-telling result can be provided.

[0039] When providing a fortune-telling result, the fortune-telling result providing unit can take into account the user's health data or fitness data and include health advice. The fortune-telling result providing unit, for example, collects the user's health data, and the generation AI generates a fortune-telling result based on that data. For example, a fortune-telling result including health advice is provided based on the user's heart rate and sleep data. The fortune-telling result providing unit also analyzes fitness data, and the generation AI generates a fortune-telling result based on that data. For example, a fortune-telling result including health advice is provided based on the user's exercise habits and dietary data. The fortune-telling result providing unit also integrates health data and fitness data, and the generation AI generates a fortune-telling result based on that. For example, a fortune-telling result including health advice is provided by taking into account the user's health condition and fitness level. In this way, fortune-telling results including health advice can be provided by taking the user's health data and fitness data into account.

[0040] When providing fortune-telling results, the fortune-telling result providing unit can provide more personalized fortune-telling results by taking into account information about the user's occupation or hobbies. The fortune-telling result providing unit, for example, collects the user's occupation data, and the generation AI generates fortune-telling results based on that data. For example, fortune-telling results including advice related to the user's occupation are provided. The fortune-telling result providing unit also analyzes information about the user's hobbies, and the generation AI generates fortune-telling results based on that data. For example, fortune-telling results including advice related to the user's hobbies are provided. The fortune-telling result providing unit also integrates the occupation data and information about hobbies, and the generation AI generates fortune-telling results based on that data. For example, more personalized fortune-telling results are provided by taking into account the user's occupation and hobbies. In this way, more personalized fortune-telling results can be provided by taking into account information about the user's occupation and hobbies.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The fortune-telling system can also collect the user's health data and include health advice in the fortune-telling results. For example, it can evaluate the user's health condition based on their heart rate and sleep data and provide appropriate advice. It can also analyze fitness data and incorporate advice on exercise habits and diet into the fortune-telling results. This allows the fortune-telling system to provide fortune-telling results that take the user's health into consideration, enabling more personalized service.

[0043] The fortune-telling system can further collect the user's occupational data and include career-related advice in the fortune-telling results. For example, it can provide advice on stress factors and career paths related to the user's occupation. It can also analyze information about the user's hobbies and reflect advice related to the hobbies in the fortune-telling results. This allows the fortune-telling system to provide fortune-telling results that take the user's occupation and hobbies into consideration, thereby realizing a more personalized service.

[0044] The fortune-telling system can further generate fortune-telling results by taking into account the user's past fortune-telling results and feedback. For example, it can compare past fortune-telling results with the current situation to provide more accurate fortune-telling results. It can also collect user feedback and improve the fortune-telling results based on that. This allows the fortune-telling system to utilize the user's past data to provide more personalized fortune-telling results.

[0045] The fortune-telling system can also analyze the user's social media posts and reflect them in the fortune-telling results. For example, it can add new perspectives to the fortune-telling results based on the content the user frequently posts and the hashtags they use. It can also provide fortune-telling results that take into account the user's life events and emotional changes. This allows the fortune-telling system to utilize the user's social media data to provide more personalized fortune-telling results.

[0046] The fortune-telling system can further collect user behavioral data and reflect it in the fortune-telling results. For example, it can analyze what kind of fortune-telling the user has received in the past and what kind of feedback the user has left, and generate personalized fortune-telling results. It can also provide fortune-telling results that take into account the user's life events and emotional changes. In this way, the fortune-telling system can utilize the user's behavioral data to provide more personalized fortune-telling results.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The fortune-telling data learning unit learns various fortune-telling data. For example, it learns data on tarot cards, horoscopes, palmistry, numerology, etc. It also learns past fortune-telling results, fortune-tellers' interpretations, and literature on fortune-telling. Specifically, it learns the meanings of tarot cards and how to interpret them based on their combinations. Step 2: The fortune-telling result generation unit generates fortune-telling results according to the user's request based on the data learned by the fortune-telling data learning unit. For example, if the user inputs their date of birth, the horoscope results are generated based on that information. Also, if the user uploads an image of their palm, the image can be analyzed to generate palm reading results. Furthermore, if the user selects a tarot card, the tarot reading results can be generated based on that selection. Step 3: The fortune-telling result providing unit provides the fortune-telling result generated by the fortune-telling result generating unit to the user. For example, the generated fortune-telling result is provided to the user in text format. The generated fortune-telling result can also be provided in audio format. Furthermore, the generated fortune-telling result can also be provided in graphical format.

[0049] (Example 2) The fortune-telling system according to the embodiment of the present invention is a system that uses a generation AI to implement a wide variety of fortune-telling functions and provide them as services. As a result, the fortune-telling system can provide a wide variety of fortune-telling functions using the generation AI, making people happy.

[0050] The fortune-telling system according to the embodiment includes a fortune-telling data learning unit, a fortune-telling result generating unit, and a fortune-telling result providing unit. The fortune-telling data learning unit learns various fortune-telling data. For example, the fortune-telling data learning unit learns data such as tarot readings, horoscope readings, palmistry, and numerology. The fortune-telling data learning unit can also learn past fortune-telling results, interpretations by fortune-tellers, and literature related to fortune-telling. For example, the fortune-telling data learning unit learns the meanings of tarot cards and how to interpret them based on their combinations. The fortune-telling result generating unit generates fortune-telling results in response to a user's request based on the data learned by the fortune-telling data learning unit. For example, when a user inputs their date of birth, the fortune-telling result generating unit generates a horoscope reading result based on the information. When a user uploads an image of their palm, the fortune-telling result generating unit can analyze the image and generate a palm-reading result. Furthermore, when a user selects a tarot card, the fortune-telling result generating unit can generate a tarot reading result based on the user's selection. The fortune-telling result providing unit provides the fortune-telling result generated by the fortune-telling result generating unit to the user. For example, the fortune-telling result providing unit provides the generated fortune-telling result to the user in text format. In addition, the fortune-telling result providing unit may provide the generated fortune-telling result in an audio format. Furthermore, the fortune-telling result providing unit may provide the generated fortune-telling result in a graphical format. As a result, the fortune-telling system according to the embodiment can realize a wide variety of fortune-telling functions and provide happiness to the user.

[0051] The fortune-telling data learning unit can learn the user's past behavioral data or social media posts in addition to the fortune-telling data to generate more personalized fortune-telling results. The fortune-telling data learning unit, for example, collects the user's past behavioral data and integrates it with the fortune-telling data to learn. For example, it analyzes what kind of fortune-telling the user has received in the past and what kind of feedback they have left, to generate personalized fortune-telling results. The fortune-telling data learning unit also analyzes social media posts to understand the user's interests and concerns. For example, it reflects the content the user frequently posts and the hashtags they use in the fortune-telling results. The fortune-telling data learning unit also combines the user's behavioral data with social media posts to generate more accurate fortune-telling results. For example, it provides fortune-telling results that take into account the user's life events and emotional changes. This allows for more personalized fortune-telling results to be provided by taking into account the user's past behavioral data and social media posts.

[0052] The fortune-telling data learning unit analyzes the fortune teller's audio or video and includes the fortune teller's facial expressions and tone of voice during the reading in the learning data, thereby providing a more realistic fortune-telling experience. For example, the fortune-telling data learning unit collects the fortune teller's audio data and analyzes their tone of voice and speaking style. For example, it learns how the fortune teller's voice changes when they draw a specific card, and the generation AI provides fortune-telling results in a similar tone. The fortune-telling data learning unit also analyzes the fortune teller's video data and learns their facial expressions and gestures. For example, reproducing the fortune teller's facial expressions and hand movements when they draw a card provides a more realistic fortune-telling experience. The fortune-telling data learning unit also integrates the audio and video data to learn the fortune teller's overall performance. For example, reproducing the fortune teller's speaking style and movements provides the user with a more realistic fortune-telling experience. In this way, by analyzing the fortune teller's audio and video, a more realistic fortune-telling experience can be provided.

[0053] The fortune-telling data learning unit can use the emotion estimation function to analyze the user's emotional state in real time and generate fortune-telling results according to that emotion. The fortune-telling data learning unit, for example, analyzes the user's facial expression and estimates the emotional state in real time. For example, it uses a camera to read the user's facial expression and calculates an emotion score. The fortune-telling data learning unit also analyzes the user's voice and estimates the emotional state from the tone of voice and speaking style. For example, it uses a microphone to collect the user's voice and calculates an emotion score. The fortune-telling data learning unit also generates fortune-telling results according to the user's emotional state based on the emotion estimation data. For example, if the user is feeling stressed, it provides fortune-telling results that help the user to relax. This makes it possible to analyze the user's emotional state in real time and provide fortune-telling results according to that emotion.

[0054] The fortune-telling data learning unit can learn historical events or astronomical data in addition to fortune-telling data, thereby adding a new perspective to the fortune-telling results. The fortune-telling data learning unit, for example, includes historical events in the learning data and reflects them in the fortune-telling results. For example, a new perspective is added to the fortune-telling results based on historical events that occurred at a specific time. The fortune-telling data learning unit also learns astronomical data and reflects it in the fortune-telling results. For example, a new perspective is added to the fortune-telling results based on the positions of specific constellations or planets. The fortune-telling data learning unit also integrates historical events and astronomical data to add a new perspective to the fortune-telling results. For example, fortune-telling results are generated based on events that occurred at a specific time and the astronomical conditions at that time. In this way, by learning historical events and astronomical data, a new perspective can be added to the fortune-telling results.

[0055] The fortune-telling data learning unit can compare fortune-telling data with fortune-telling from different cultural spheres and provide fortune-telling results that take cultural background into account. The fortune-telling data learning unit, for example, collects fortune-telling data from different cultural spheres and has the generation AI learn it. For example, it compares Western fortune-telling data with Eastern fortune-telling data and provides fortune-telling results that take cultural background into account. The fortune-telling data learning unit also learns the interpretations of fortune-tellers from different cultural spheres to generate fortune-telling results that take cultural background into account. For example, the same fortune-telling result can be interpreted differently depending on the culture. The fortune-telling data learning unit also integrates fortune-telling data from different cultural spheres and provides fortune-telling results that correspond to the user's cultural background. For example, it customizes the fortune-telling results taking into account the cultural sphere the user belongs to. In this way, by comparing fortune-telling data from different cultural spheres, it is possible to provide fortune-telling results that take cultural background into account.

[0056] The fortune-telling data learning unit can use the emotion estimation function to analyze the emotions of the user when receiving fortune-telling and suggest a type and method of fortune-telling based on the emotions. The fortune-telling data learning unit, for example, analyzes the user's emotional state in real time and suggests a type of fortune-telling based on the emotions. For example, if the user is relaxed, it suggests a fortune-telling that will help them relax. The fortune-telling data learning unit also suggests a fortune-telling method that suits the user's emotional state based on the emotion estimation data. For example, if the user is feeling stressed, it suggests a fortune-telling method that will help them relax. The fortune-telling data learning unit also analyzes the user's emotional state and customizes the type and method of fortune-telling based on the emotions. For example, if the user is relaxed, it provides a fortune-telling result that will help them relax. This makes it possible to suggest the optimal type and method of fortune-telling based on the user's emotions.

[0057] The fortune-telling result generation unit can provide more accurate fortune-telling results by taking into account the user's past fortune-telling results or feedback. The fortune-telling result generation unit, for example, stores the user's past fortune-telling results in a database, and the generation AI generates fortune-telling results based on them. For example, the unit compares the past fortune-telling results with the current situation to provide more accurate fortune-telling results. The fortune-telling result generation unit also collects user feedback, and the generation AI uses it to improve the fortune-telling results. For example, the unit analyzes what feedback the user left on past fortune-telling results and reflects this in the next fortune-telling result. The fortune-telling result generation unit also integrates the past fortune-telling results and feedback, and the generation AI generates fortune-telling results based on that. For example, the unit takes into account the user's past actions and feedback to provide more accurate fortune-telling results. In this way, more accurate fortune-telling results can be provided by taking into account the user's past fortune-telling results and feedback.

[0058] The fortune-telling result generation unit can collect user reactions to the fortune-telling results in real time and dynamically revise the fortune-telling results based on those reactions. The fortune-telling result generation unit, for example, collects user reactions to the fortune-telling results in real time, and the generation AI revise the fortune-telling results based on that. For example, if the user has a positive reaction to the fortune-telling results, the fortune-telling results are improved based on that reaction. The fortune-telling result generation unit also analyzes the user's reaction, and the generation AI dynamically revise the fortune-telling results based on that. For example, if the user has a negative reaction to the fortune-telling results, the fortune-telling results are revise based on that reaction. The fortune-telling result generation unit also builds a system in which the generation AI dynamically revises the fortune-telling results based on user reactions collected in real time. For example, the fortune-telling results are instantly revise depending on the user's reaction. In this way, user reactions can be collected in real time and the fortune-telling results can be dynamically revise.

[0059] The fortune-telling result generation unit uses the emotion estimation function to generate fortune-telling results according to the user's emotional state, and can provide fortune-telling that will make the user feel positive. The fortune-telling result generation unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and generate fortune-telling results according to that emotion. For example, if the user is feeling stressed, it provides a fortune-telling result that will help the user relax. The fortune-telling result generation unit also generates fortune-telling results that elicit positive emotions using the generation AI based on the user's emotional state. For example, if the user is relaxed, it provides a fortune-telling result that will help the user relax. The fortune-telling result generation unit also customizes fortune-telling results according to the user's emotional state based on the emotion estimation data. For example, if the user is feeling stressed, it provides a fortune-telling result that will help the user relax. This allows emotional elements to be reflected in the evaluation by generating a summary that captures emotional nuances.

[0060] When generating a fortune-telling result, the fortune-telling result generation unit can take into account the user's health data or fitness data and include health advice. The fortune-telling result generation unit, for example, collects the user's health data, and the generation AI generates a fortune-telling result based on that data. For example, a fortune-telling result including health advice is provided based on the user's heart rate and sleep data. The fortune-telling result generation unit also analyzes fitness data, and the generation AI generates a fortune-telling result based on that data. For example, a fortune-telling result including health advice is provided based on the user's exercise habits and dietary data. The fortune-telling result generation unit also integrates health data and fitness data, and the generation AI generates a fortune-telling result based on that. For example, a fortune-telling result including health advice is provided by taking into account the user's health condition and fitness level. In this way, fortune-telling results including health advice can be provided by taking the user's health data and fitness data into account.

[0061] When generating fortune-telling results, the fortune-telling result generation unit takes into account information about the user's occupation or hobbies, allowing the unit to provide more personalized fortune-telling results. The fortune-telling result generation unit, for example, collects the user's occupation data, and the generation AI generates fortune-telling results based on that data. For example, fortune-telling results that include advice related to the user's occupation are provided. The fortune-telling result generation unit also analyzes information about the user's hobbies, and the generation AI generates fortune-telling results based on that data. For example, fortune-telling results that include advice related to the user's hobbies are provided. The fortune-telling result generation unit also integrates the occupation data and information about hobbies, and the generation AI generates fortune-telling results based on that data. For example, more personalized fortune-telling results are provided by taking into account the user's occupation and hobbies. This allows the unit to provide more personalized fortune-telling results by taking into account information about the user's occupation and hobbies.

[0062] The fortune-telling result generation unit uses the emotion estimation function to suggest a type of fortune-telling based on the user's emotional state, and can provide fortune-telling that is most interesting to the user. The fortune-telling result generation unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and suggest a type of fortune-telling based on the emotion. For example, if the user is relaxed, it suggests a fortune-telling that will help them relax. The fortune-telling result generation unit also suggests fortune-telling that the generation AI will be most interested in based on the user's emotional state. For example, if the user is feeling stressed, it suggests a fortune-telling that will help them relax. The fortune-telling result generation unit also customizes the type of fortune-telling according to the user's emotional state based on the emotion estimation data. For example, if the user is relaxed, it suggests a fortune-telling that will help them relax. This makes it possible to suggest the optimal type of fortune-telling based on the user's emotional state and arouse their interest.

[0063] When providing a fortune-telling result, the fortune-telling result providing unit can provide a more personalized fortune-telling result by taking into account the user's past fortune-telling results or feedback. The fortune-telling result providing unit, for example, stores the user's past fortune-telling results in a database, and the generation AI generates a fortune-telling result based on them. For example, the fortune-telling result providing unit compares the past fortune-telling results with the current situation to provide a more personalized fortune-telling result. The fortune-telling result providing unit also collects user feedback, and the generation AI improves the fortune-telling result based on that. For example, it analyzes what kind of feedback the user left on the past fortune-telling result and reflects it in the next fortune-telling result. The fortune-telling result providing unit also integrates the past fortune-telling results and feedback, and the generation AI generates a fortune-telling result based on that. For example, it takes into account the user's past actions and feedback to provide a more personalized fortune-telling result. In this way, by taking into account the user's past fortune-telling results and feedback, a more personalized fortune-telling result can be provided.

[0064] When providing the fortune-telling result, the fortune-telling result providing unit can analyze the emotional state of the user in real time and provide feedback according to that emotion. The fortune-telling result providing unit, for example, analyzes the emotional state of the user in real time and provides feedback according to that emotion. For example, if the user is feeling stressed, it provides feedback that will help them relax. The fortune-telling result providing unit also provides feedback according to the emotional state of the user based on the emotion estimation data. For example, if the user is relaxed, it provides feedback that will help them relax. The fortune-telling result providing unit also analyzes the emotional state of the user and customizes feedback according to that emotion. For example, if the user is feeling stressed, it provides feedback that will help them relax. In this way, the emotional state of the user can be analyzed in real time and feedback according to that emotion can be provided.

[0065] The fortune-telling result providing unit uses the emotion estimation function to provide fortune-telling results according to the user's emotional state and can provide feedback that will make the user's emotions more positive. The fortune-telling result providing unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and provide fortune-telling results according to the emotions. For example, if the user is feeling stressed, it provides a fortune-telling result that will help the user relax. The fortune-telling result providing unit also provides fortune-telling results in which the generation AI elicits positive emotions based on the user's emotional state. For example, if the user is relaxed, it provides a fortune-telling result that will help the user relax. The fortune-telling result providing unit also customizes the fortune-telling results according to the user's emotional state based on the emotion estimation data. For example, if the user is feeling stressed, it provides a fortune-telling result that will help the user relax. In this way, it is possible to provide fortune-telling results according to the user's emotional state and make the user's emotions more positive.

[0066] When providing a fortune-telling result, the fortune-telling result providing unit can take into account the user's health data or fitness data and include health advice. The fortune-telling result providing unit, for example, collects the user's health data, and the generation AI generates a fortune-telling result based on that data. For example, a fortune-telling result including health advice is provided based on the user's heart rate and sleep data. The fortune-telling result providing unit also analyzes fitness data, and the generation AI generates a fortune-telling result based on that data. For example, a fortune-telling result including health advice is provided based on the user's exercise habits and dietary data. The fortune-telling result providing unit also integrates health data and fitness data, and the generation AI generates a fortune-telling result based on that. For example, a fortune-telling result including health advice is provided by taking into account the user's health condition and fitness level. In this way, fortune-telling results including health advice can be provided by taking the user's health data and fitness data into account.

[0067] When providing fortune-telling results, the fortune-telling result providing unit can provide more personalized fortune-telling results by taking into account information about the user's occupation or hobbies. The fortune-telling result providing unit, for example, collects the user's occupation data, and the generation AI generates fortune-telling results based on that data. For example, fortune-telling results including advice related to the user's occupation are provided. The fortune-telling result providing unit also analyzes information about the user's hobbies, and the generation AI generates fortune-telling results based on that data. For example, fortune-telling results including advice related to the user's hobbies are provided. The fortune-telling result providing unit also integrates the occupation data and information about hobbies, and the generation AI generates fortune-telling results based on that data. For example, more personalized fortune-telling results are provided by taking into account the user's occupation and hobbies. In this way, more personalized fortune-telling results can be provided by taking into account information about the user's occupation and hobbies.

[0068] The fortune-telling result providing unit uses the emotion estimation function to suggest a type of fortune-telling based on the user's emotional state, and can provide fortune-telling that is most interesting to the user. The fortune-telling result providing unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and suggest a type of fortune-telling based on the emotion. For example, if the user is relaxed, it suggests a fortune-telling that will help them relax. The fortune-telling result providing unit also suggests fortune-telling that the generation AI will be most interested in, based on the user's emotional state. For example, if the user is feeling stressed, it suggests a fortune-telling that will help them relax. The fortune-telling result providing unit also customizes the type of fortune-telling according to the user's emotional state, based on the emotion estimation data. For example, if the user is relaxed, it suggests a fortune-telling that will help them relax. This makes it possible to suggest the optimal type of fortune-telling based on the user's emotional state and draw their interest.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The fortune-telling system can also collect the user's health data and include health advice in the fortune-telling results. For example, it can evaluate the user's health condition based on their heart rate and sleep data and provide appropriate advice. It can also analyze fitness data and incorporate advice on exercise habits and diet into the fortune-telling results. This allows the fortune-telling system to provide fortune-telling results that take the user's health into consideration, enabling more personalized service.

[0071] The fortune-telling system can further collect the user's occupational data and include career-related advice in the fortune-telling results. For example, it can provide advice on stress factors and career paths related to the user's occupation. It can also analyze information about the user's hobbies and reflect advice related to the hobbies in the fortune-telling results. This allows the fortune-telling system to provide fortune-telling results that take the user's occupation and hobbies into consideration, thereby realizing a more personalized service.

[0072] The fortune-telling system can further generate fortune-telling results by taking into account the user's past fortune-telling results and feedback. For example, it can compare past fortune-telling results with the current situation to provide more accurate fortune-telling results. It can also collect user feedback and improve the fortune-telling results based on that. This allows the fortune-telling system to utilize the user's past data to provide more personalized fortune-telling results.

[0073] The fortune-telling system can further analyze the user's emotional state in real time and provide fortune-telling results according to those emotions. For example, if the user is feeling stressed, it can provide a fortune-telling result that will help them relax. Also, if the user is relaxed, it can provide a fortune-telling result that will help them relax. In this way, the fortune-telling system can provide fortune-telling results that are according to the user's emotional state and make the user feel positive.

[0074] The fortune-telling system can further suggest a type of fortune-telling based on the emotional state of the user. For example, if the user is relaxed, a fortune-telling that will help them relax can be suggested. Also, if the user is feeling stressed, a fortune-telling that will help them relax can be suggested. In this way, the fortune-telling system can suggest the optimal type of fortune-telling based on the emotional state of the user and provide the fortune-telling that most interests the user.

[0075] The fortune-telling system can also analyze the user's social media posts and reflect them in the fortune-telling results. For example, it can add new perspectives to the fortune-telling results based on the content the user frequently posts and the hashtags they use. It can also provide fortune-telling results that take into account the user's life events and emotional changes. This allows the fortune-telling system to utilize the user's social media data to provide more personalized fortune-telling results.

[0076] The fortune-telling system can further collect user behavioral data and reflect it in the fortune-telling results. For example, it can analyze what kind of fortune-telling the user has received in the past and what kind of feedback the user has left, and generate personalized fortune-telling results. It can also provide fortune-telling results that take into account the user's life events and emotional changes. In this way, the fortune-telling system can utilize the user's behavioral data to provide more personalized fortune-telling results.

[0077] The fortune-telling system can further analyze the user's emotional state in real time and suggest a type and method of fortune-telling based on that emotion. For example, if the user is relaxed, it can suggest a fortune-telling method that will help them relax. Also, if the user is feeling stressed, it can suggest a fortune-telling method that will help them relax. In this way, the fortune-telling system can suggest the optimal type and method of fortune-telling based on the user's emotional state and provide the fortune-telling that most interests the user.

[0078] The fortune-telling system can further analyze the user's emotional state in real time and provide feedback according to that emotion. For example, if the user is feeling stressed, it can provide feedback to help them relax. Also, if the user is relaxed, it can provide feedback to help them relax. In this way, the fortune-telling system can provide feedback according to the user's emotional state and make the user feel positive.

[0079] The fortune-telling system can further analyze the user's emotional state in real time and provide fortune-telling results according to those emotions. For example, if the user is feeling stressed, it can provide a fortune-telling result that will help them relax. Also, if the user is relaxed, it can provide a fortune-telling result that will help them relax. In this way, the fortune-telling system can provide fortune-telling results that are according to the user's emotional state and make the user feel positive.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The fortune-telling data learning unit learns various fortune-telling data. For example, it learns data on tarot cards, horoscopes, palmistry, numerology, etc. It also learns past fortune-telling results, fortune-tellers' interpretations, and literature on fortune-telling. Specifically, it learns the meanings of tarot cards and how to interpret them based on their combinations. Step 2: The fortune-telling result generation unit generates fortune-telling results according to the user's request based on the data learned by the fortune-telling data learning unit. For example, if the user inputs their date of birth, the horoscope results are generated based on that information. Also, if the user uploads an image of their palm, the image can be analyzed to generate palm reading results. Furthermore, if the user selects a tarot card, the tarot reading results can be generated based on that selection. Step 3: The fortune-telling result providing unit provides the fortune-telling result generated by the fortune-telling result generating unit to the user. For example, the generated fortune-telling result is provided to the user in text format. The generated fortune-telling result can also be provided in audio format. Furthermore, the generated fortune-telling result can also be provided in graphical format.

[0082] 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The 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.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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. [Explanation of symbols]

[0149] 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 fortune-telling data learning unit that learns various fortune-telling data; a fortune-telling result generating unit that generates a fortune-telling result according to a user's request based on the data learned by the fortune-telling data learning unit; a fortune-telling result providing unit that provides the fortune-telling result generated by the fortune-telling result generating unit to a user; A system characterized by:

2. The fortune-telling data learning unit In addition to fortune-telling data, it learns from users' past behavioral data or social media posts to generate more personalized fortune-telling results.

2. The system of claim 1.

3. The fortune-telling data learning unit By analyzing the fortune teller's audio or video and incorporating facial expressions and tone of voice during the reading into the learning data, a more realistic fortune telling experience is provided.

2. The system of claim 1.

4. The fortune-telling data learning unit Analyzes the user's emotional state in real time and generates fortune-telling results based on that emotion 2. The system of claim 1.

5. The fortune-telling data learning unit In addition to fortune-telling data, it also learns historical events or astronomical data to add new perspectives to fortune-telling results.

2. The system of claim 1.

6. The fortune-telling data learning unit Compare fortune-telling data with fortune-telling data from different cultural spheres to provide fortune-telling results that take cultural background into account 2. The system of claim 1.

7. The fortune-telling data learning unit Analyzes the user's emotions when receiving a fortune-telling session and suggests types and methods of fortune-telling based on those emotions 2. The system of claim 1.

8. The fortune-telling result generating unit Provides more accurate fortune-telling results by taking into account the user's past fortune-telling results or feedback 2. The system of claim 1.

Images