System

Abstract

To provide a system for immediately generating a fortune-telling result on the basis of a fortune-telling method selected by a user.SOLUTION: A system includes a selection unit, a generation unit, and a display unit. The selection part allows a user to select a fortune-telling method. The generation part generates a fortune-telling result on the basis of the fortune-telling method selected by the selection part. The display part displays the fortune-telling result generated by the generation 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 had the problem that it is difficult to immediately generate fortune-telling results based on the fortune-telling method selected by the user.

[0005] The system according to the embodiment aims to instantly generate a fortune-telling result based on the fortune-telling method selected by the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a selection unit, a generation unit, and a display unit. The selection unit allows a user to select a fortune-telling method. The generation unit generates a fortune-telling result based on the fortune-telling method selected by the selection unit. The display unit displays the fortune-telling result generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can instantly generate a fortune-telling result based on the fortune-telling method selected by the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A fortune-telling system according to an embodiment of the present invention instantly generates and displays fortune-telling results based on a fortune-telling method selected by a user. In the fortune-telling system, a user selects a fortune-telling method, and a generation AI generates fortune-telling results based on the selected fortune-telling method. The fortune-telling results provide a personalized fortune-telling experience, allowing the user to easily explore their own destiny. For example, the fortune-telling system allows a user to select from multiple fortune-telling methods, such as tarot readings, horoscope readings, and palmistry. The generation AI analyzes appropriate data based on the selected fortune-telling method to generate fortune-telling results. For example, in the case of tarot readings, the generation AI analyzes the placement and meaning of the tarot cards to generate fortune-telling results. The generated fortune-telling results are displayed to the user, allowing the user to explore their own destiny through the fortune-telling results generated by the generation AI. This allows the fortune-telling system to easily receive fortune-telling at home or on the go. For example, users can enjoy fortune-telling anytime, anywhere using a smartphone or tablet. Furthermore, because the generation AI instantly generates fortune-telling results, the user can receive fortune-telling without waiting. This improves user convenience and provides a more comfortable fortune-telling experience. This allows the fortune-telling system to instantly generate and display fortune-telling results based on the fortune-telling method selected by the user. For example, even if a user is busy with daily life, the user can receive fortune telling in a short time, so the user can enjoy fortune telling casually.

[0029] A fortune-telling system according to an embodiment includes a selection unit, a generation unit, and a display unit. The selection unit allows a user to select a fortune-telling method. For example, fortune-telling methods selectable by the user include, but are not limited to, tarot card readings, horoscope readings, and palmistry. For example, when a user selects tarot card readings, the selection unit provides the generation unit with data for analyzing the arrangement and meaning of tarot cards. The generation unit uses a generation AI to generate a fortune-telling result based on the fortune-telling method selected by the selection unit. The generation unit, for example, analyzes the arrangement and meaning of tarot cards to generate a fortune-telling result. The generation unit can also analyze the user's date of birth and the position of their zodiac sign to provide fortunes and advice. The generation unit can also use the generation AI to provide specific advice based on the fortune-telling result. The display unit displays the fortune-telling result generated by the generation unit. The display unit, for example, displays the generated fortune-telling result on a smartphone or tablet. The display unit can also store the generated fortune-telling result in a database. This allows the fortune-telling system according to an embodiment to instantly generate and display a fortune-telling result based on the fortune-telling method selected by the user. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit may input data for analyzing the arrangement and meaning of tarot cards into the generation AI and cause the generation AI to generate fortune-telling results. This allows the fortune-telling system to instantly generate and display fortune-telling results based on the fortune-telling method selected by the user.

[0030] The generation unit can analyze the arrangement and meaning of tarot cards and generate a fortune-telling result. The generation unit can, for example, analyze the arrangement method of tarot cards and their meaning. For example, the generation unit can analyze arrangement methods such as a Celtic cross or a three-card spread and generate a fortune-telling result. The generation unit can also analyze the positions and card combinations of tarot cards and generate a fortune-telling result. For example, the generation unit can provide fortunes and advice based on the positions and combinations of cards. This allows the generation unit to generate a fortune-telling result specialized for tarot readings. Some or all of the above-mentioned processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input tarot card arrangement data into the generation AI and have the generation AI generate a fortune-telling result.

[0031] The generation unit can analyze the user's date of birth and the position of the zodiac sign to provide fortunes and advice. The generation unit, for example, analyzes the user's date of birth and the position of the zodiac sign. For example, the generation unit can analyze the position of celestial bodies and a horoscope to provide fortunes and advice. The generation unit can also provide fortunes such as love luck, career luck, and health luck based on the user's date of birth and the position of the zodiac sign. For example, the generation unit can analyze the user's date of birth and the position of the zodiac sign to provide advice regarding love luck. The generation unit can also analyze the user's date of birth and the position of the zodiac sign to provide advice regarding career luck. This allows the generation unit to generate fortune-telling results specialized for zodiac sign fortune-telling. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's date of birth and the position of the zodiac sign data into the generation AI and cause the generation AI to generate fortunes and advice.

[0032] The display unit can display the generated fortune-telling result on a smartphone or tablet. For example, the display unit displays the generated fortune-telling result on a smartphone or tablet. The display unit can support multiple operating systems, such as iOS and Android. For example, the display unit can provide a display method optimized for iOS devices. The display unit can also provide a display method optimized for Android devices. This allows the display unit to display the generated fortune-telling result on a smartphone or tablet. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the generated fortune-telling result into AI and have the AI ​​execute the optimal display method.

[0033] The display unit can save the generated fortune-telling results in a database. The display unit, for example, saves the generated fortune-telling results in a database. Databases include, but are not limited to, SQL databases and NoSQL databases. For example, the display unit saves the fortune-telling results in an SQL database. The display unit can also save the fortune-telling results in a NoSQL database. This allows the display unit to save the generated fortune-telling results in a database. Some or all of the above-mentioned processing in the display unit may be performed, for example, using AI or may be performed without using AI. For example, the display unit can input the generated fortune-telling results into AI and have the AI ​​save the results in the database.

[0034] The generation unit can provide specific advice based on the fortune-telling result. The generation unit provides specific advice based on, for example, the fortune-telling result. The advice includes, but is not limited to, suggested actions and points of caution. For example, the generation unit suggests actions to the user based on the fortune-telling result. The generation unit can also provide points of caution to the user based on the fortune-telling result. This allows the generation unit to provide advice to the user based on the fortune-telling result. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input fortune-telling result data to the generation AI and cause the generation AI to generate advice.

[0035] The fortune-telling system includes a selection unit that analyzes a user's past fortune-telling history and suggests an optimal fortune-telling method. The selection unit, for example, prioritizes suggesting fortune-telling methods that the user has frequently used in the past. The selection unit can also prioritize suggesting fortune-telling methods that the user has given high ratings to in the past. The selection unit can also suggest fortune-telling methods related to a specific theme from the user's past fortune-telling history. This allows the selection unit to suggest an optimal fortune-telling method based on the user's past fortune-telling history. Some or all of the above-described processing in the selection unit may be performed using, or without, AI. For example, the selection unit may input the user's past fortune-telling history data into a generation AI and cause the generation AI to suggest an optimal fortune-telling method.

[0036] The fortune-telling system includes a selection unit that filters fortune-telling methods based on the user's current living situation and areas of interest when selecting a fortune-telling method. For example, if the user has work-related concerns, the selection unit may preferentially present career-related fortune-telling methods. For example, if the user is interested in love, the selection unit may preferentially present fortune-telling methods related to love luck. For example, if the user is interested in health, the selection unit may preferentially present fortune-telling methods related to health luck. This allows the selection unit to present fortune-telling methods according to the user's living situation and areas of interest. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit may input data on the user's living situation and areas of interest into the generation AI and cause the generation AI to filter for the most appropriate fortune-telling method.

[0037] The fortune-telling system includes a selection unit that provides an optimal selection means according to the user's input method when selecting a fortune-telling method. For example, when the user uses voice input, the selection unit provides an interface that allows the user to select a fortune-telling method by voice. For example, when the user uses text input, the selection unit can also provide an interface that allows the user to select a fortune-telling method by text. For example, when the user uses image input, the selection unit can also analyze the image and suggest a related fortune-telling method. This allows the selection unit to provide an optimal selection means according to the user's input method. Some or all of the above-mentioned processing in the selection unit may be performed using AI, for example, or may be performed without using AI. For example, the selection unit can input the user's input data to a generation AI and have the generation AI provide an optimal selection means.

[0038] The fortune-telling system includes a selection unit that, when selecting a fortune-telling method, prioritizes presenting highly relevant fortune-telling methods in consideration of the user's geographical location information. For example, if the user is in a specific area, the selection unit prioritizes presenting fortune-telling methods related to that area. For example, if the user is traveling, the selection unit can also prioritize presenting fortune-telling methods related to the user's travel destination. For example, if the user is at home, the selection unit can also prioritize presenting fortune-telling methods that can be performed at home. This allows the selection unit to present highly relevant fortune-telling methods based on the user's geographical location information. Some or all of the above-described processing in the selection unit may be performed using AI, for example, or may be performed without using AI. For example, the selection unit can input the user's geographical location information to a generation AI and cause the generation AI to present highly relevant fortune-telling methods.

[0039] The fortune-telling system includes a selection unit that analyzes a user's social media activity and suggests a related fortune-telling method when selecting a fortune-telling method. The selection unit, for example, suggests fortune-telling methods related to a theme in which the user has shown interest on social media. The selection unit can also analyze the content of the user's social media posts and suggest a related fortune-telling method. The selection unit can also suggest a related fortune-telling method, for example, by referring to the activities of the user's friends on social media. This allows the selection unit to suggest a related fortune-telling method based on the user's social media activity. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit may input the user's social media activity data into a generation AI and cause the generation AI to suggest a related fortune-telling method.

[0040] The fortune-telling system includes a selection unit that customizes options by reflecting the user's past feedback when selecting a fortune-telling method. The selection unit, for example, preferentially presents fortune-telling methods that the user has previously rated highly. The selection unit can also exclude fortune-telling methods that the user has previously rated poorly. The selection unit can also suggest an optimal fortune-telling method based on the user's past feedback. This allows the selection unit to customize the options based on the user's past feedback. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's past feedback data into a generation AI and have the generation AI customize the options.

[0041] The fortune-telling system includes a generation unit that adjusts the level of detail of the result based on the importance of the fortune-telling method when generating a fortune-telling result. For example, the generation unit provides a detailed fortune-telling result for a fortune-telling method with a high level of importance. For example, the generation unit can also provide a concise fortune-telling result for a fortune-telling method with a low level of importance. The generation unit can also gradually adjust the level of detail of the result according to the importance of the fortune-telling method. This allows the generation unit to adjust the level of detail of the result according to the importance of the fortune-telling method. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input importance data of the fortune-telling method into the generation AI and have the generation AI adjust the level of detail of the result.

[0042] The fortune-telling system includes a generation unit that applies different generation algorithms depending on the category of the fortune-telling method when generating a fortune-telling result. For example, in the case of tarot card readings, the generation unit applies an algorithm that analyzes the arrangement and meaning of tarot cards. For example, in the case of horoscope readings, the generation unit can also apply an algorithm that analyzes the position of the zodiac signs and the user's date of birth. For example, in the case of palmistry readings, the generation unit can also apply an algorithm that analyzes the characteristics of the palm lines. This allows the generation unit to apply a generation algorithm depending on the category of the fortune-telling method. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input category data of the fortune-telling method into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0043] The fortune-telling system includes a generation unit that, when generating a fortune-telling result, improves the accuracy of generation by referring to the user's past fortune-telling results. The generation unit, for example, adjusts the current fortune-telling result based on the user's past fortune-telling results. The generation unit can also, for example, analyze trends from the user's past fortune-telling results to improve accuracy. The generation unit can also, for example, refer to the user's past fortune-telling results to provide consistent fortune-telling results. This allows the generation unit to improve the accuracy of generation by referring to the user's past fortune-telling results. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past fortune-telling result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0044] The fortune-telling system includes a generation unit that, when generating fortune-telling results, determines the priority of the results based on the time when the fortune-telling method was selected. For example, if a user had their fortune told in the morning, the generation unit may prioritize providing the fortune for that day. For example, if a user had their fortune told on the weekend, the generation unit may also prioritize providing the fortune for the weekend. For example, if a user had their fortune told before a specific event, the generation unit may also prioritize providing the fortune related to the event. This allows the generation unit to determine the priority of the results based on the time when the fortune-telling method was selected. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit may input data on the time when the fortune-telling method was selected into the generation AI and have the generation AI determine the priority of the results.

[0045] The fortune-telling system includes a generation unit that adjusts the order of results based on the relevance of fortune-telling methods when generating fortune-telling results. For example, if a user has their love fortune told, the generation unit may display love-related results first. For example, if a user has their career fortune told, the generation unit may also display work-related results first. For example, if a user has their health fortune told, the generation unit may also display health-related results first. This allows the generation unit to adjust the order of results based on the relevance of fortune-telling methods. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit may input relevance data of fortune-telling methods into the generation AI and cause the generation AI to adjust the order of the results.

[0046] The fortune-telling system includes a generation unit that adjusts the use of technical terms in the results according to the user's level of expertise when generating a fortune-telling result. For example, if the user is knowledgeable about fortune-telling, the generation unit provides a fortune-telling result that uses a lot of technical terms. For example, if the user is not knowledgeable about fortune-telling, the generation unit can also provide a fortune-telling result explained in simple language. For example, the generation unit can gradually adjust the use of technical terms according to the user's level of expertise. This allows the generation unit to adjust the use of technical terms according to the user's level of expertise. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terms.

[0047] The fortune-telling system includes a display unit that, when displaying, selects an optimal display method by referring to the user's past operation history. The display unit, for example, preferentially provides a display method that the user has previously preferred. The display unit can also suggest an optimal display method based on the user's past operation history. The display unit can also customize the display method based on the user's past operation history. This allows the display unit to select an optimal display method based on the user's past operation history. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's past operation history data into a generation AI and have the generation AI select an optimal display method.

[0048] The fortune-telling system includes a display unit that customizes the display content according to the user's current task when displaying the display content. For example, when the user is at work, the display unit prioritizes displaying work-related fortune-telling results. For example, when the user is on vacation, the display unit can also prioritize displaying relaxation-related fortune-telling results. For example, when the user is participating in a specific event, the display unit can also prioritize displaying fortune-telling results related to the event. This allows the display unit to customize the display content according to the user's current task. Some or all of the above-described processing in the display unit may be performed using, or without, AI. For example, the display unit may input the user's current task data into a generation AI and have the generation AI customize the display content.

[0049] The fortune-telling system includes a display unit that selects an optimal display method in consideration of the user's device information when displaying. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. For example, if the user is using a tablet, the display unit can also provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the display unit can also provide a simple, highly visible display method. This allows the display unit to select the optimal display method based on the user's device information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's device information to a generation AI and have the generation AI select the optimal display method.

[0050] The fortune-telling system includes a display unit that, when displayed, makes the display content multilingual according to the user's language setting. The display unit automatically sets the display content based on, for example, the language setting of the user's device. The display unit can also provide a language switching function, for example, when the user uses multiple languages. For example, when the user selects a specific language, the display unit can provide the display content in that language. This allows the display unit to make the display content multilingual based on the user's language setting. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's language setting data into a generation AI and cause the generation AI to set the display content to be multilingual.

[0051] The fortune-telling system includes a display unit that, when displayed, analyzes a user's social media activity and provides related information. The display unit, for example, provides information about places where the user has checked in on social media. The display unit, for example, can analyze the content of the user's social media posts and provide information about related tourist spots and stores. The display unit, for example, can provide information about related places and events by referring to the activity of the user's friends on social media. This allows the display unit to provide related information based on the user's social media activity. Some or all of the above-described processing in the display unit may be performed using, or without, AI. For example, the display unit may input the user's social media activity data into a generation AI and cause the generation AI to provide related information.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The fortune-telling system includes an analysis unit that analyzes the user's past fortune-telling results and identifies the user's tendencies. The analysis unit, for example, analyzes what fortune-telling results the user has received in the past and identifies the user's preferences and interests. The analysis unit can, for example, prioritize displaying fortune-telling results that the user has previously rated highly. The analysis unit can, for example, suggest fortune-telling results related to a specific theme from the user's past fortune-telling results. This allows the analysis unit to provide optimal fortune-telling results based on the user's past fortune-telling results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past fortune-telling result data into a generation AI and have the generation AI understand the tendencies.

[0054] The fortune-telling system includes a generation unit that customizes fortune-telling results by taking into account the user's current living situation. For example, if the user has work-related concerns, the generation unit provides fortune-telling results that include career-related advice. For example, if the user is interested in romance, the generation unit can also provide fortune-telling results that include advice related to love luck. For example, if the user is interested in health, the generation unit can also provide fortune-telling results that include advice related to health luck. This allows the generation unit to provide optimal fortune-telling results according to the user's living situation. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the user's living situation data into the generation AI and cause the generation AI to customize the fortune-telling results.

[0055] The fortune-telling system includes a generation unit that analyzes a user's past fortune-telling history and customizes fortune-telling results based on the user's preferences. The generation unit, for example, analyzes trends in fortune-telling results that the user has previously preferred and provides fortune-telling results with similar trends. The generation unit can, for example, preferentially display fortune-telling results that the user has previously rated highly. The generation unit can, for example, suggest fortune-telling results related to a specific theme from the user's past fortune-telling history. This allows the generation unit to provide optimal fortune-telling results based on the user's past fortune-telling history. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past fortune-telling history data into the generation AI and have the generation AI customize the fortune-telling results.

[0056] The fortune-telling system includes a generation unit that customizes fortune-telling results by taking into account the user's current geographical location information. For example, if the user is in a specific area, the generation unit provides fortune-telling results related to that area. For example, if the user is traveling, the generation unit can also provide fortune-telling results related to the user's travel destination. For example, if the user is at home, the generation unit can also provide fortune-telling results that can be performed at home. This allows the generation unit to provide optimal fortune-telling results based on the user's geographical location information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the user's geographical location information data into the generation AI and cause the generation AI to customize the fortune-telling results.

[0057] The fortune-telling system includes a generation unit that analyzes a user's social media activity and customizes fortune-telling results based on the user's interests. The generation unit, for example, provides fortune-telling results related to themes in which the user has shown interest on social media. The generation unit can also analyze the content of the user's social media posts and provide related fortune-telling results. The generation unit can also provide related fortune-telling results by referring to the activities of the user's friends on social media, for example. This allows the generation unit to provide optimal fortune-telling results based on the user's social media activity. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's social media activity data into the generation AI and have the generation AI customize the fortune-telling results.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The selection unit allows the user to select a fortune-telling method. For example, fortune-telling methods that the user can select include tarot reading, horoscope reading, palmistry, etc. The selection unit provides the generation unit with necessary data based on the fortune-telling method selected by the user. Step 2: The generation unit generates a fortune-telling result based on the fortune-telling method selected by the selection unit. The generation unit, for example, analyzes the arrangement and meaning of tarot cards to generate a fortune-telling result. The generation unit can also analyze the user's date of birth and the position of their zodiac sign to provide fortunes and advice. The generation unit can also use a generation AI to provide specific advice based on the fortune-telling result. Step 3: The display unit displays the fortune-telling result generated by the generation unit. The display unit displays the generated fortune-telling result on a smartphone or tablet, for example. The display unit can also store the generated fortune-telling result in a database.

[0060] (Example 2) A fortune-telling system according to an embodiment of the present invention instantly generates and displays fortune-telling results based on a fortune-telling method selected by a user. In the fortune-telling system, a user selects a fortune-telling method, and a generation AI generates fortune-telling results based on the selected fortune-telling method. The fortune-telling results provide a personalized fortune-telling experience, allowing the user to easily explore their own destiny. For example, the fortune-telling system allows a user to select from multiple fortune-telling methods, such as tarot readings, horoscope readings, and palmistry. The generation AI analyzes appropriate data based on the selected fortune-telling method to generate fortune-telling results. For example, in the case of tarot readings, the generation AI analyzes the placement and meaning of the tarot cards to generate fortune-telling results. The generated fortune-telling results are displayed to the user, allowing the user to explore their own destiny through the fortune-telling results generated by the generation AI. This allows the fortune-telling system to easily receive fortune-telling at home or on the go. For example, users can enjoy fortune-telling anytime, anywhere using a smartphone or tablet. Furthermore, because the generation AI instantly generates fortune-telling results, the user can receive fortune-telling without waiting. This improves user convenience and provides a more comfortable fortune-telling experience. This allows the fortune-telling system to instantly generate and display fortune-telling results based on the fortune-telling method selected by the user. For example, even if a user is busy with daily life, the user can receive fortune telling in a short time, so the user can enjoy fortune telling casually.

[0061] A fortune-telling system according to an embodiment includes a selection unit, a generation unit, and a display unit. The selection unit allows a user to select a fortune-telling method. For example, fortune-telling methods selectable by the user include, but are not limited to, tarot card readings, horoscope readings, and palmistry. For example, when a user selects tarot card readings, the selection unit provides the generation unit with data for analyzing the arrangement and meaning of tarot cards. The generation unit uses a generation AI to generate a fortune-telling result based on the fortune-telling method selected by the selection unit. The generation unit, for example, analyzes the arrangement and meaning of tarot cards to generate a fortune-telling result. The generation unit can also analyze the user's date of birth and the position of their zodiac sign to provide fortunes and advice. The generation unit can also use the generation AI to provide specific advice based on the fortune-telling result. The display unit displays the fortune-telling result generated by the generation unit. The display unit, for example, displays the generated fortune-telling result on a smartphone or tablet. The display unit can also store the generated fortune-telling result in a database. This allows the fortune-telling system according to an embodiment to instantly generate and display a fortune-telling result based on the fortune-telling method selected by the user. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit may input data for analyzing the arrangement and meaning of tarot cards into the generation AI and cause the generation AI to generate fortune-telling results. This allows the fortune-telling system to instantly generate and display fortune-telling results based on the fortune-telling method selected by the user.

[0062] The generation unit can analyze the arrangement and meaning of tarot cards and generate a fortune-telling result. The generation unit can, for example, analyze the arrangement method of tarot cards and their meaning. For example, the generation unit can analyze arrangement methods such as a Celtic cross or a three-card spread and generate a fortune-telling result. The generation unit can also analyze the positions and card combinations of tarot cards and generate a fortune-telling result. For example, the generation unit can provide fortunes and advice based on the positions and combinations of cards. This allows the generation unit to generate a fortune-telling result specialized for tarot readings. Some or all of the above-mentioned processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input tarot card arrangement data into the generation AI and have the generation AI generate a fortune-telling result.

[0063] The generation unit can analyze the user's date of birth and the position of the zodiac sign to provide fortunes and advice. The generation unit, for example, analyzes the user's date of birth and the position of the zodiac sign. For example, the generation unit can analyze the position of celestial bodies and a horoscope to provide fortunes and advice. The generation unit can also provide fortunes such as love luck, career luck, and health luck based on the user's date of birth and the position of the zodiac sign. For example, the generation unit can analyze the user's date of birth and the position of the zodiac sign to provide advice regarding love luck. The generation unit can also analyze the user's date of birth and the position of the zodiac sign to provide advice regarding career luck. This allows the generation unit to generate fortune-telling results specialized for zodiac sign fortune-telling. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's date of birth and the position of the zodiac sign data into the generation AI and cause the generation AI to generate fortunes and advice.

[0064] The display unit can display the generated fortune-telling result on a smartphone or tablet. For example, the display unit displays the generated fortune-telling result on a smartphone or tablet. The display unit can support multiple operating systems, such as iOS and Android. For example, the display unit can provide a display method optimized for iOS devices. The display unit can also provide a display method optimized for Android devices. This allows the display unit to display the generated fortune-telling result on a smartphone or tablet. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the generated fortune-telling result into AI and have the AI ​​execute the optimal display method.

[0065] The display unit can save the generated fortune-telling results in a database. The display unit, for example, saves the generated fortune-telling results in a database. Databases include, but are not limited to, SQL databases and NoSQL databases. For example, the display unit saves the fortune-telling results in an SQL database. The display unit can also save the fortune-telling results in a NoSQL database. This allows the display unit to save the generated fortune-telling results in a database. Some or all of the above-mentioned processing in the display unit may be performed, for example, using AI or may be performed without using AI. For example, the display unit can input the generated fortune-telling results into AI and have the AI ​​save the results in the database.

[0066] The generation unit can provide specific advice based on the fortune-telling result. The generation unit provides specific advice based on, for example, the fortune-telling result. The advice includes, but is not limited to, suggested actions and points of caution. For example, the generation unit suggests actions to the user based on the fortune-telling result. The generation unit can also provide points of caution to the user based on the fortune-telling result. This allows the generation unit to provide advice to the user based on the fortune-telling result. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input fortune-telling result data to the generation AI and cause the generation AI to generate advice.

[0067] The fortune-telling system includes a selection unit that estimates a user's emotions and presents options for fortune-telling methods based on the estimated user emotions. For example, if the user is feeling anxious, the selection unit may preferentially present fortune-telling methods with a relaxing effect. For example, if the user is excited, the selection unit may preferentially present fortune-telling methods with a high level of entertainment. For example, if the user is feeling depressed, the selection unit may preferentially present fortune-telling methods that include encouraging messages. This allows the selection unit to present options for fortune-telling methods according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the selection unit may input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0068] The fortune-telling system includes a selection unit that analyzes a user's past fortune-telling history and suggests an optimal fortune-telling method. The selection unit, for example, prioritizes suggesting fortune-telling methods that the user has frequently used in the past. The selection unit can also prioritize suggesting fortune-telling methods that the user has given high ratings to in the past. The selection unit can also suggest fortune-telling methods related to a specific theme from the user's past fortune-telling history. This allows the selection unit to suggest an optimal fortune-telling method based on the user's past fortune-telling history. Some or all of the above-described processing in the selection unit may be performed using, or without, AI. For example, the selection unit may input the user's past fortune-telling history data into a generation AI and cause the generation AI to suggest an optimal fortune-telling method.

[0069] The fortune-telling system includes a selection unit that filters fortune-telling methods based on the user's current living situation and areas of interest when selecting a fortune-telling method. For example, if the user has work-related concerns, the selection unit may preferentially present career-related fortune-telling methods. For example, if the user is interested in love, the selection unit may preferentially present fortune-telling methods related to love luck. For example, if the user is interested in health, the selection unit may preferentially present fortune-telling methods related to health luck. This allows the selection unit to present fortune-telling methods according to the user's living situation and areas of interest. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit may input data on the user's living situation and areas of interest into the generation AI and cause the generation AI to filter for the most appropriate fortune-telling method.

[0070] The fortune-telling system includes a selection unit that provides an optimal selection means according to the user's input method when selecting a fortune-telling method. For example, when the user uses voice input, the selection unit provides an interface that allows the user to select a fortune-telling method by voice. For example, when the user uses text input, the selection unit can also provide an interface that allows the user to select a fortune-telling method by text. For example, when the user uses image input, the selection unit can also analyze the image and suggest a related fortune-telling method. This allows the selection unit to provide an optimal selection means according to the user's input method. Some or all of the above-mentioned processing in the selection unit may be performed using AI, for example, or may be performed without using AI. For example, the selection unit can input the user's input data to a generation AI and have the generation AI provide an optimal selection means.

[0071] The fortune-telling system includes a selection unit that estimates a user's emotions and adjusts the display order of options based on the estimated user emotions. For example, if the user is relaxed, the selection unit first displays fortune-telling methods that have a relaxing effect. For example, if the user is excited, the selection unit can also first display fortune-telling methods that are highly entertaining. For example, if the user is anxious, the selection unit can also first display fortune-telling methods that provide a sense of security. This allows the selection unit to adjust the display order of options according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the selection unit may be performed using AI, or may be performed without AI. For example, the selection unit may input user emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0072] The fortune-telling system includes a selection unit that, when selecting a fortune-telling method, prioritizes presenting highly relevant fortune-telling methods in consideration of the user's geographical location information. For example, if the user is in a specific area, the selection unit prioritizes presenting fortune-telling methods related to that area. For example, if the user is traveling, the selection unit can also prioritize presenting fortune-telling methods related to the user's travel destination. For example, if the user is at home, the selection unit can also prioritize presenting fortune-telling methods that can be performed at home. This allows the selection unit to present highly relevant fortune-telling methods based on the user's geographical location information. Some or all of the above-described processing in the selection unit may be performed using AI, for example, or may be performed without using AI. For example, the selection unit can input the user's geographical location information to a generation AI and cause the generation AI to present highly relevant fortune-telling methods.

[0073] The fortune-telling system includes a selection unit that analyzes a user's social media activity and suggests a related fortune-telling method when selecting a fortune-telling method. The selection unit, for example, suggests fortune-telling methods related to a theme in which the user has shown interest on social media. The selection unit can also analyze the content of the user's social media posts and suggest a related fortune-telling method. The selection unit can also suggest a related fortune-telling method, for example, by referring to the activities of the user's friends on social media. This allows the selection unit to suggest a related fortune-telling method based on the user's social media activity. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit may input the user's social media activity data into a generation AI and cause the generation AI to suggest a related fortune-telling method.

[0074] The fortune-telling system includes a selection unit that customizes options by reflecting the user's past feedback when selecting a fortune-telling method. The selection unit, for example, preferentially presents fortune-telling methods that the user has previously rated highly. The selection unit can also exclude fortune-telling methods that the user has previously rated poorly. The selection unit can also suggest an optimal fortune-telling method based on the user's past feedback. This allows the selection unit to customize the options based on the user's past feedback. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's past feedback data into a generation AI and have the generation AI customize the options.

[0075] The fortune-telling system includes a generation unit that estimates a user's emotions and adjusts the way the fortune-telling result is expressed based on the estimated user's emotions. For example, if the user is relaxed, the generation unit provides the fortune-telling result in a calm expression. For example, if the user is excited, the generation unit can provide the fortune-telling result in an energetic expression. For example, if the user is anxious, the generation unit can provide the fortune-telling result in an expression that gives a sense of security. This allows the generation unit to adjust the way the fortune-telling result is expressed based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit may input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0076] The fortune-telling system includes a generation unit that adjusts the level of detail of the result based on the importance of the fortune-telling method when generating a fortune-telling result. For example, the generation unit provides a detailed fortune-telling result for a fortune-telling method with a high level of importance. For example, the generation unit can also provide a concise fortune-telling result for a fortune-telling method with a low level of importance. The generation unit can also gradually adjust the level of detail of the result according to the importance of the fortune-telling method. This allows the generation unit to adjust the level of detail of the result according to the importance of the fortune-telling method. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input importance data of the fortune-telling method into the generation AI and have the generation AI adjust the level of detail of the result.

[0077] The fortune-telling system includes a generation unit that applies different generation algorithms depending on the category of the fortune-telling method when generating a fortune-telling result. For example, in the case of tarot card readings, the generation unit applies an algorithm that analyzes the arrangement and meaning of tarot cards. For example, in the case of horoscope readings, the generation unit can also apply an algorithm that analyzes the position of the zodiac signs and the user's date of birth. For example, in the case of palmistry readings, the generation unit can also apply an algorithm that analyzes the characteristics of the palm lines. This allows the generation unit to apply a generation algorithm depending on the category of the fortune-telling method. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input category data of the fortune-telling method into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0078] The fortune-telling system includes a generation unit that, when generating a fortune-telling result, improves the accuracy of generation by referring to the user's past fortune-telling results. The generation unit, for example, adjusts the current fortune-telling result based on the user's past fortune-telling results. The generation unit can also, for example, analyze trends from the user's past fortune-telling results to improve accuracy. The generation unit can also, for example, refer to the user's past fortune-telling results to provide consistent fortune-telling results. This allows the generation unit to improve the accuracy of generation by referring to the user's past fortune-telling results. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past fortune-telling result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0079] The fortune-telling system includes a generation unit that estimates a user's emotions and adjusts the length of the fortune-telling result based on the estimated user emotions. For example, if the user is in a hurry, the generation unit provides a short, concise fortune-telling result. For example, if the user is relaxed, the generation unit can provide a longer fortune-telling result with detailed explanations. For example, if the user is excited, the generation unit can provide a fortune-telling result with visually stimulating effects. This allows the generation unit to adjust the length of the fortune-telling result according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI. For example, the generation unit may input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0080] The fortune-telling system includes a generation unit that, when generating fortune-telling results, determines the priority of the results based on the time when the fortune-telling method was selected. For example, if a user had their fortune told in the morning, the generation unit may prioritize providing the fortune for that day. For example, if a user had their fortune told on the weekend, the generation unit may also prioritize providing the fortune for the weekend. For example, if a user had their fortune told before a specific event, the generation unit may also prioritize providing the fortune related to the event. This allows the generation unit to determine the priority of the results based on the time when the fortune-telling method was selected. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit may input data on the time when the fortune-telling method was selected into the generation AI and have the generation AI determine the priority of the results.

[0081] The fortune-telling system includes a generation unit that adjusts the order of results based on the relevance of fortune-telling methods when generating fortune-telling results. For example, if a user has their love fortune told, the generation unit may display love-related results first. For example, if a user has their career fortune told, the generation unit may also display work-related results first. For example, if a user has their health fortune told, the generation unit may also display health-related results first. This allows the generation unit to adjust the order of results based on the relevance of fortune-telling methods. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit may input relevance data of fortune-telling methods into the generation AI and cause the generation AI to adjust the order of the results.

[0082] The fortune-telling system includes a generation unit that adjusts the use of technical terms in the results according to the user's level of expertise when generating a fortune-telling result. For example, if the user is knowledgeable about fortune-telling, the generation unit provides a fortune-telling result that uses a lot of technical terms. For example, if the user is not knowledgeable about fortune-telling, the generation unit can also provide a fortune-telling result explained in simple language. For example, the generation unit can gradually adjust the use of technical terms according to the user's level of expertise. This allows the generation unit to adjust the use of technical terms according to the user's level of expertise. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terms.

[0083] The fortune-telling system includes a display unit that estimates a user's emotions and adjusts the display method of the fortune-telling results based on the estimated user emotions. For example, when the user is nervous, the display unit provides a simple, highly visible display method. For example, when the user is relaxed, the display unit can also provide a display method that includes detailed information. For example, when the user is in a hurry, the display unit can also provide a display method that focuses on the main points. This allows the display unit to adjust the display method of the fortune-telling results according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit may input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0084] The fortune-telling system includes a display unit that, when displaying, selects an optimal display method by referring to the user's past operation history. The display unit, for example, preferentially provides a display method that the user has previously preferred. The display unit can also suggest an optimal display method based on the user's past operation history. The display unit can also customize the display method based on the user's past operation history. This allows the display unit to select an optimal display method based on the user's past operation history. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's past operation history data into a generation AI and have the generation AI select an optimal display method.

[0085] The fortune-telling system includes a display unit that customizes the display content according to the user's current task when displaying the display content. For example, when the user is at work, the display unit prioritizes displaying work-related fortune-telling results. For example, when the user is on vacation, the display unit can also prioritize displaying relaxation-related fortune-telling results. For example, when the user is participating in a specific event, the display unit can also prioritize displaying fortune-telling results related to the event. This allows the display unit to customize the display content according to the user's current task. Some or all of the above-described processing in the display unit may be performed using, or without, AI. For example, the display unit may input the user's current task data into a generation AI and have the generation AI customize the display content.

[0086] The fortune-telling system includes a display unit that estimates a user's emotions and prioritizes the fortune-telling results to display based on the estimated user emotions. For example, if the user is feeling anxious, the display unit first displays fortune-telling results that provide a sense of security. For example, if the user is excited, the display unit can also first display fortune-telling results that are entertaining. For example, if the user is relaxed, the display unit can also first display fortune-telling results that have a relaxing effect. This allows the display unit to prioritize fortune-telling results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit may input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0087] The fortune-telling system includes a display unit that selects an optimal display method in consideration of the user's device information when displaying. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. For example, if the user is using a tablet, the display unit can also provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the display unit can also provide a simple, highly visible display method. This allows the display unit to select the optimal display method based on the user's device information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's device information to a generation AI and have the generation AI select the optimal display method.

[0088] The fortune-telling system includes a display unit that, when displayed, makes the display content multilingual according to the user's language setting. The display unit automatically sets the display content based on, for example, the language setting of the user's device. The display unit can also provide a language switching function, for example, when the user uses multiple languages. For example, when the user selects a specific language, the display unit can provide the display content in that language. This allows the display unit to make the display content multilingual based on the user's language setting. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's language setting data into a generation AI and cause the generation AI to set the display content to be multilingual.

[0089] The fortune-telling system includes a display unit that, when displayed, analyzes a user's social media activity and provides related information. The display unit, for example, provides information about places where the user has checked in on social media. The display unit, for example, can analyze the content of the user's social media posts and provide information about related tourist spots and stores. The display unit, for example, can provide information about related places and events by referring to the activity of the user's friends on social media. This allows the display unit to provide related information based on the user's social media activity. Some or all of the above-described processing in the display unit may be performed using, or without, AI. For example, the display unit may input the user's social media activity data into a generation AI and cause the generation AI to provide related information. === Hard Collateral 1-1 === For example, each of the multiple elements including the selection unit, generation unit, and display unit is realized by at least one of the smart device 14 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the smart device 14 and provides an interface for the user to select a fortune-telling method. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a fortune-telling result based on the selected fortune-telling method. The display unit is realized by the display 40A of the smart device 14 and displays the generated fortune-telling result to the user. === Hard Collateral 1-2 === For example, each of the multiple elements including the selection unit, the generation unit, and the display unit is realized by at least one of the smart glasses 214 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the smart glasses 214 and provides an interface for the user to select a fortune-telling method. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a fortune-telling result based on the selected fortune-telling method. The display unit is realized by the display of the smart glasses 214 and displays the generated fortune-telling result to the user. === Hard Collateral 1-3 === For example, each of the multiple elements including the selection unit, generation unit, and display unit is realized by at least one of the headset type terminal 314 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the headset type terminal 314, and provides an interface for the user to select a fortune-telling method. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates a fortune-telling result based on the selected fortune-telling method. The display unit is realized by the display 343 of the headset type terminal 314, and displays the generated fortune-telling result to the user. === Hard Collateral 1-4 === For example, each of the multiple elements including the selection unit, generation unit, and display unit is realized by at least one of the robot 414 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the robot 414 and provides an interface for the user to select a fortune-telling method. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a fortune-telling result based on the selected fortune-telling method. The display unit is realized by the display of the robot 414 and displays the generated fortune-telling result to the user.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The fortune-telling system includes a display unit that estimates a user's emotions and adjusts the display format of the fortune-telling results based on the estimated user emotions. For example, if the user is relaxed, the display unit displays the fortune-telling results using calm colors and fonts. For example, if the user is excited, the display unit can display the fortune-telling results using vibrant colors and dynamic animations. For example, if the user is anxious, the display unit can display the fortune-telling results using subdued colors and a simple layout. This allows the display unit to provide an optimal display format according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit may input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0092] The fortune-telling system includes an analysis unit that analyzes the user's past fortune-telling results and identifies the user's tendencies. The analysis unit, for example, analyzes what fortune-telling results the user has received in the past and identifies the user's preferences and interests. The analysis unit can, for example, prioritize displaying fortune-telling results that the user has previously rated highly. The analysis unit can, for example, suggest fortune-telling results related to a specific theme from the user's past fortune-telling results. This allows the analysis unit to provide optimal fortune-telling results based on the user's past fortune-telling results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past fortune-telling result data into a generation AI and have the generation AI understand the tendencies.

[0093] The fortune-telling system includes a generation unit that estimates a user's emotions and adjusts the content of the fortune-telling result based on the estimated user's emotions. For example, if the user is relaxed, the generation unit provides a fortune-telling result that emphasizes positive content. For example, if the user is excited, the generation unit can provide a fortune-telling result that includes energetic content. For example, if the user is feeling anxious, the generation unit can provide a fortune-telling result that includes content that gives a sense of security. This allows the generation unit to provide an optimal fortune-telling result according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0094] The fortune-telling system includes a generation unit that customizes fortune-telling results by taking into account the user's current living situation. For example, if the user has work-related concerns, the generation unit provides fortune-telling results that include career-related advice. For example, if the user is interested in romance, the generation unit can also provide fortune-telling results that include advice related to love luck. For example, if the user is interested in health, the generation unit can also provide fortune-telling results that include advice related to health luck. This allows the generation unit to provide optimal fortune-telling results according to the user's living situation. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the user's living situation data into the generation AI and cause the generation AI to customize the fortune-telling results.

[0095] The fortune-telling system includes a distribution unit that estimates a user's emotions and adjusts the timing of distribution of the fortune-telling results based on the estimated user emotions. For example, if the user is relaxed, the distribution unit immediately distributes the fortune-telling results. For example, if the user is busy, the distribution unit can distribute the fortune-telling results at an appropriate timing. For example, if the user is feeling anxious, the distribution unit can distribute the fortune-telling results at a timing that provides a sense of security. This allows the distribution unit to provide optimal distribution timing according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the distribution unit may be performed using an AI, for example, or without an AI. For example, the distribution unit may input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0096] The fortune-telling system includes a generation unit that analyzes a user's past fortune-telling history and customizes fortune-telling results based on the user's preferences. The generation unit, for example, analyzes trends in fortune-telling results that the user has previously preferred and provides fortune-telling results with similar trends. The generation unit can, for example, preferentially display fortune-telling results that the user has previously rated highly. The generation unit can, for example, suggest fortune-telling results related to a specific theme from the user's past fortune-telling history. This allows the generation unit to provide optimal fortune-telling results based on the user's past fortune-telling history. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past fortune-telling history data into the generation AI and have the generation AI customize the fortune-telling results.

[0097] The fortune-telling system includes a notification unit that estimates a user's emotions and adjusts a notification method for the fortune-telling result based on the estimated user emotions. For example, if the user is relaxed, the notification unit notifies the user with a gentle sound or vibration. For example, if the user is excited, the notification unit can notify the user with an energetic sound or animation. For example, if the user is anxious, the notification unit can notify the user with a sound or message that provides a sense of security. This allows the notification unit to provide an optimal notification method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit may input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0098] The fortune-telling system includes a generation unit that customizes fortune-telling results by taking into account the user's current geographical location information. For example, if the user is in a specific area, the generation unit provides fortune-telling results related to that area. For example, if the user is traveling, the generation unit can also provide fortune-telling results related to the user's travel destination. For example, if the user is at home, the generation unit can also provide fortune-telling results that can be performed at home. This allows the generation unit to provide optimal fortune-telling results based on the user's geographical location information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the user's geographical location information data into the generation AI and cause the generation AI to customize the fortune-telling results.

[0099] The fortune-telling system includes a generation unit that estimates a user's emotions and adjusts the format of the fortune-telling result based on the estimated user emotions. For example, if the user is relaxed, the generation unit provides a detailed fortune-telling result in a long sentence. For example, if the user is excited, the generation unit can provide a fortune-telling result in a short sentence that focuses on the main points. For example, if the user is anxious, the generation unit can provide the fortune-telling result in a format that gives a sense of security. This allows the generation unit to provide an optimal fortune-telling result format according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI. For example, the generation unit may input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0100] The fortune-telling system includes a generation unit that analyzes a user's social media activity and customizes fortune-telling results based on the user's interests. The generation unit, for example, provides fortune-telling results related to themes in which the user has shown interest on social media. The generation unit can also analyze the content of the user's social media posts and provide related fortune-telling results. The generation unit can also provide related fortune-telling results by referring to the activities of the user's friends on social media, for example. This allows the generation unit to provide optimal fortune-telling results based on the user's social media activity. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's social media activity data into the generation AI and have the generation AI customize the fortune-telling results.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The selection unit allows the user to select a fortune-telling method. For example, fortune-telling methods that the user can select include tarot reading, horoscope reading, palmistry, etc. The selection unit provides the generation unit with necessary data based on the fortune-telling method selected by the user. Step 2: The generation unit generates a fortune-telling result based on the fortune-telling method selected by the selection unit. The generation unit, for example, analyzes the arrangement and meaning of tarot cards to generate a fortune-telling result. The generation unit can also analyze the user's date of birth and the position of their zodiac sign to provide fortunes and advice. The generation unit can also use a generation AI to provide specific advice based on the fortune-telling result. Step 3: The display unit displays the fortune-telling result generated by the generation unit. The display unit displays the generated fortune-telling result on a smartphone or tablet, for example. The display unit can also store the generated fortune-telling result in a database.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 selection unit for allowing a user to select a fortune-telling method; a generating unit that generates a fortune-telling result based on the fortune-telling method selected by the selecting unit; a display unit that displays the fortune-telling result generated by the generation unit; Equipped with A system characterized by:

2. The generation unit Analyze the placement and meaning of tarot cards to generate fortune-telling results 2. The system of claim 1.

3. The generation unit Analyzes the user's date of birth and astrological position to provide fortunes and advice 2. The system of claim 1.

4. The display unit Display the generated fortune-telling results on your smartphone or tablet 2. The system of claim 1.

5. The display unit Save the generated fortune-telling results in the database 2. The system of claim 1.

6. The generation unit Providing specific advice based on fortune-telling results 2. The system of claim 1.

7. The selection unit Estimate the user's emotions and present options for fortune-telling methods based on the estimated user emotions.

2. The system of claim 1.

8. The selection unit Analyzes the user's past fortune-telling history and suggests the most suitable fortune-telling method 2. The system of claim 1.

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