System

Abstract

An object of a system according to an embodiment is to provide various and appropriate fortune-telling results to a user.SOLUTION: A system includes a fortune-telling data learning part, a user input analysis part, and a fortune-telling result generation part. A fortune-telling data learning part learns a plurality of fortune-telling patterns. The user input analysis unit analyzes a user's input. A fortune-telling result generation part generates a proper fortune-telling result on the basis of the user's input analyzed by the user input analysis 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 limited fortune-telling patterns, making it difficult to provide users with diverse and appropriate fortune-telling results.

[0005] The system according to the embodiment aims to provide a variety of appropriate fortune-telling results to users. [Means for solving the problem]

[0006] The system according to the embodiment includes a fortune-telling data learning unit, a user input analysis unit, and a fortune-telling result generation unit. The fortune-telling data learning unit learns a plurality of fortune-telling patterns. The user input analysis unit analyzes user input. The fortune-telling result generation unit generates an appropriate fortune-telling result based on the user input analyzed by the user input analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide a variety of appropriate fortune-telling results to 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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 service according to an embodiment of the present invention uses a generation AI to learn a wide variety of fortune-telling patterns and outputs fortune-telling results by combining them individually or in combination. In this fortune-telling service, the generation AI analyzes fortune-telling data and provides appropriate fortune-telling results based on user input. This enables the fortune-telling service to provide users with fortune-telling results based on a variety of fortune-telling patterns.

[0029] A fortune-telling service according to an embodiment includes a fortune-telling data learning unit, a user input analysis unit, and a fortune-telling result generation unit. The fortune-telling data learning unit learns a plurality of fortune-telling patterns. For example, the fortune-telling data learning unit learns fortune-telling patterns such as tarot readings, horoscope readings, palmistry, numerology, and feng shui. The fortune-telling data learning unit can also learn based on past fortune-telling results and the knowledge of fortune tellers. The user input analysis unit analyzes user input. For example, if a user inputs "I want to know my fortune for today," the user input analysis unit analyzes the request. The user input analysis unit can also analyze text input, voice input, and image input depending on the user's input format. The fortune-telling result generation unit generates an appropriate fortune-telling result based on the user input analyzed by the user input analysis unit. For example, the fortune-telling result generation unit analyzes the meaning of a tarot card and provides a fortune-telling result appropriate for the user. The fortune-telling result generation unit can also generate a fortune-telling result by combining the results of horoscope readings, palmistry, and the like. As a result, the fortune-telling service according to the embodiment can learn a wide variety of fortune-telling patterns and provide appropriate fortune-telling results based on the user's input.

[0030] The fortune-telling data learning unit can reflect user feedback data in real time. For example, the fortune-telling data learning unit collects feedback provided by users on fortune-telling results in real time and has the generation AI learn the feedback. For example, it collects feedback on whether the user was satisfied with the fortune-telling results. In this way, by reflecting user feedback data in real time, the accuracy of the fortune-telling results can be improved.

[0031] The fortune-telling data learning unit incorporates fortune-telling data from different cultural spheres or historical backgrounds, making it possible to provide fortune-telling results from a global perspective. For example, the fortune-telling data learning unit collects fortune-telling data from different cultural spheres and has the generation AI learn it. For example, fortune-telling data from various cultures, such as Chinese feng shui and Indian astrology, can be incorporated. By incorporating fortune-telling data from different cultural spheres and historical backgrounds, fortune-telling results can be provided from a global perspective.

[0032] The fortune-telling data learning unit can combine predictive data other than fortune-telling to add a multifaceted perspective to the fortune-telling results. For example, the fortune-telling data learning unit combines weather forecast data with fortune-telling data and has the generation AI learn it. For example, the impact that changes in the weather have on fortunes can be reflected in the fortune-telling results. In this way, by combining predictive data other than fortune-telling, a multifaceted perspective can be added to the fortune-telling results.

[0033] The user input analysis unit can refer to the user's past input history to provide more personalized fortune-telling results. For example, the user input analysis unit collects the user's past input history and has the generation AI learn from it. For example, it incorporates fortune-telling requests and feedback entered by the user in the past as data. In this way, by referring to the user's past input history, it is possible to provide more personalized fortune-telling results.

[0034] The user input analysis unit can incorporate voice input or image input to accommodate a wider variety of input formats. The user input analysis unit, for example, analyzes voice input and trains the generation AI. For example, it converts requests input by voice by the user into text and generates fortune-telling results. This allows for a wider variety of input formats to be accommodated by incorporating voice input or image input.

[0035] The user input analysis unit can automatically translate different languages ​​and dialects, making it possible to accommodate international users. For example, the user input analysis unit can automatically translate inputs in different languages ​​and train the generation AI. For example, it can translate requests entered by a user in English or French into Japanese and generate fortune-telling results. This allows it to automatically translate different languages ​​and dialects, making it possible to accommodate international users.

[0036] The fortune-telling result generation unit can explain in detail the basis of the fortune-telling result, deepening the user's understanding. For example, the fortune-telling result generation unit is equipped with a function that allows the generation AI to explain in detail the basis of the fortune-telling result. For example, the meaning of the tarot cards or the characteristics of the zodiac signs are specifically explained. This allows the user to deepen their understanding by explaining in detail the basis of the fortune-telling result.

[0037] The fortune-telling result generation unit can reflect user feedback and continuously improve the accuracy of the fortune-telling results. The fortune-telling result generation unit, for example, collects user feedback and makes the generation AI learn from it. For example, it incorporates feedback provided by users on fortune-telling results as data. In this way, by reflecting user feedback, it is possible to continuously improve the accuracy of the fortune-telling results.

[0038] The fortune-telling result generation unit can combine different fortune-telling patterns to provide composite fortune-telling results. For example, the fortune-telling result generation unit combines different fortune-telling patterns and has the generation AI learn them. For example, it can incorporate data that combines tarot readings and horoscope readings. This allows it to provide composite fortune-telling results by combining different fortune-telling patterns.

[0039] The fortune-telling result generation unit can visualize fortune-telling results and provide them to users in a format that is visually easy to understand. For example, the fortune-telling result generation unit is equipped with a function to visualize fortune-telling results and has the generation AI learn this. For example, it displays images of tarot cards or diagrams of constellations. By visualizing fortune-telling results, the unit can provide them to users in a format that is visually easy to understand.

[0040] The fortune-telling result generation unit can analyze the interrelationships between different fortune-telling patterns and provide more consistent fortune-telling results. For example, the fortune-telling result generation unit analyzes the interrelationships between different fortune-telling patterns and has the generation AI learn from them. For example, the interrelationships between tarot readings and horoscope readings can be incorporated as data. This allows for the analysis of the interrelationships between different fortune-telling patterns to provide more consistent fortune-telling results.

[0041] The fortune-telling result generation unit can refer to the user's past fortune-telling results and provide long-term fortune trends. The fortune-telling result generation unit, for example, collects the user's past fortune-telling results and has the generation AI learn them. For example, it incorporates the fortune-telling results that the user has received in the past as data. In this way, it is possible to provide long-term fortune trends by referring to the user's past fortune-telling results.

[0042] The fortune-telling result generation unit can combine different fortune-telling patterns to generate new fortune-telling patterns. For example, the fortune-telling result generation unit combines different fortune-telling patterns and has the generation AI learn them. For example, it can incorporate data that combines tarot readings and horoscope readings. This makes it possible to generate new fortune-telling patterns by combining different fortune-telling patterns.

[0043] The fortune-telling result generation unit can provide fortune-telling results in an interactive format, allowing users to customize their fortune-telling results themselves. The fortune-telling result generation unit, for example, is equipped with a function for providing fortune-telling results in an interactive format and has the generation AI learn this. For example, it provides an interface that allows users to customize their fortune-telling results. By providing fortune-telling results in an interactive format, it allows users to customize their fortune-telling results themselves.

[0044] The fortune-telling result generation unit can provide a customizable output format according to the user's preferences. For example, the fortune-telling result generation unit is equipped with a function that enables customization of the output format of fortune-telling results, and has the generation AI learn this function. For example, it provides an interface that allows the user to select the display format of the fortune-telling results. This improves user satisfaction by providing a customizable output format according to the user's preferences.

[0045] The fortune-telling result generation unit can reflect user feedback and continuously improve the accuracy of the output format. The fortune-telling result generation unit, for example, collects user feedback and makes the generation AI learn from it. For example, the unit incorporates feedback provided by the user regarding the output format of the fortune-telling result as data. In this way, the accuracy of the output format can be continuously improved by reflecting user feedback.

[0046] The fortune-telling result generation unit can incorporate audio or video to provide the fortune-telling results in a richer media format. For example, the fortune-telling result generation unit is equipped with a function to provide fortune-telling results in audio format and has the generation AI learn this. For example, it provides a function to read out the fortune-telling results aloud. This allows fortune-telling results to be provided in a richer media format by incorporating audio or video.

[0047] The fortune-telling result generation unit can provide a multi-platform format that is compatible with different devices. For example, the fortune-telling result generation unit is equipped with a function that provides a multi-platform format that allows fortune-telling results to be displayed on different devices, and has the generation AI learn this. For example, fortune-telling results can be displayed on smartphones, tablets, and PCs. This allows compatibility with different devices, allowing users to use fortune-telling results on any device.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] Fortune-telling services can also incorporate a user's health data and provide fortune-telling results based on their health status. For example, they can analyze data obtained from a user's fitness tracker or smartwatch and include advice based on their health status in their fortune-telling results. They can also incorporate a user's sleep data and provide fortune-telling results based on their sleep quality. They can also analyze a user's dietary data and provide fortune-telling results based on their nutritional balance.

[0050] Fortune-telling services can also provide fortune-telling results based on a user's hobbies and interests. For example, if a user likes music, a fortune-telling service can provide a music-related fortune. If a user likes sports, a fortune-telling service can provide a sports-related fortune. Furthermore, if a user likes traveling, the fortune-telling service can include travel destinations and travel advice in the fortune-telling results.

[0051] Fortune-telling services can also provide fortune-telling results that take into account a user's social connections. For example, they can analyze a user's social media data and predict their future based on their friendships and family relationships. They can also provide fortune-telling and advice for the workplace by taking into account the user's relationships at work. They can also predict their future based on the user's community activities.

[0052] The fortune-telling service can also provide fortune-telling results based on the user's life events. For example, if the user is about to get married, fortunes and advice regarding the marriage can be provided. If the user is considering changing jobs, fortunes and advice regarding the job change can also be provided. Furthermore, if the user is planning to move, fortunes and advice regarding the new residence can also be included in the fortune-telling results.

[0053] The fortune-telling service can also incorporate a user's learning data and provide fortune-telling results related to academic achievement and skill improvement. For example, if the user is a student, the service can provide fortune-telling results and study advice based on their academic performance. If the user wants to learn a new skill, the service can provide fortune-telling results and study advice related to that skill. Furthermore, if the user is planning to take a qualification exam, the fortune-telling results can include fortune-telling results and advice on how to prepare for the exam.

[0054] Fortune-telling services can also incorporate a user's travel data and provide fortune-telling results related to travel destinations. For example, when a user is choosing a travel destination, the service can provide fortune-telling results and advice about that location. Fortune-telling services can also provide fortune-telling results for the places and activities the user will visit during their trip. Furthermore, when the user reflects on their experience after their trip, the fortune-telling results can include advice about the outcome of the trip and future fortunes.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The fortune-telling data learning unit learns multiple fortune-telling patterns. For example, it learns fortune-telling patterns such as tarot cards, horoscopes, palmistry, numerology, and feng shui. It can also learn based on past fortune-telling results and the knowledge of fortune-tellers. Step 2: The user input analysis unit analyzes the user's input. For example, if the user inputs "I want to know today's fortune," the unit analyzes that request. It can also analyze text input, voice input, and image input depending on the user's input format. Step 3: The fortune-telling result generator generates an appropriate fortune-telling result based on the user's input analyzed by the user input analyzer. For example, it analyzes the meaning of tarot cards and provides a fortune-telling result appropriate for the user. It can also generate a fortune-telling result by combining the results of horoscopes, palmistry, etc.

[0057] (Example 2) A fortune-telling service according to an embodiment of the present invention uses a generation AI to learn a wide variety of fortune-telling patterns and outputs fortune-telling results by combining them individually or in combination. In this fortune-telling service, the generation AI analyzes fortune-telling data and provides appropriate fortune-telling results based on user input. This enables the fortune-telling service to provide users with fortune-telling results based on a variety of fortune-telling patterns.

[0058] A fortune-telling service according to an embodiment includes a fortune-telling data learning unit, a user input analysis unit, and a fortune-telling result generation unit. The fortune-telling data learning unit learns a plurality of fortune-telling patterns. For example, the fortune-telling data learning unit learns fortune-telling patterns such as tarot readings, horoscope readings, palmistry, numerology, and feng shui. The fortune-telling data learning unit can also learn based on past fortune-telling results and the knowledge of fortune tellers. The user input analysis unit analyzes user input. For example, if a user inputs "I want to know my fortune for today," the user input analysis unit analyzes the request. The user input analysis unit can also analyze text input, voice input, and image input depending on the user's input format. The fortune-telling result generation unit generates an appropriate fortune-telling result based on the user input analyzed by the user input analysis unit. For example, the fortune-telling result generation unit analyzes the meaning of a tarot card and provides a fortune-telling result appropriate for the user. The fortune-telling result generation unit can also generate a fortune-telling result by combining the results of horoscope readings, palmistry, and the like. As a result, the fortune-telling service according to the embodiment can learn a wide variety of fortune-telling patterns and provide appropriate fortune-telling results based on the user's input.

[0059] The fortune-telling data learning unit can incorporate emotional data of fortune tellers into its learning. For example, the fortune-telling data learning unit collects emotional data when fortune tellers give fortunes and has the generation AI learn it. For example, it can incorporate data on emotional changes and intuitive judgments made by fortune tellers when drawing tarot cards. By incorporating the emotional data of fortune tellers into its learning, it is possible to provide fortune-telling results that reflect more emotional elements.

[0060] The fortune-telling data learning unit can reflect user feedback data in real time. For example, the fortune-telling data learning unit collects feedback provided by users on fortune-telling results in real time and has the generation AI learn the feedback. For example, it collects feedback on whether the user was satisfied with the fortune-telling results. In this way, by reflecting user feedback data in real time, the accuracy of the fortune-telling results can be improved.

[0061] The fortune-telling data learning unit uses the emotion estimation function to learn the user's emotional reactions to fortune-telling results, and can generate fortune-telling results that are easy to empathize with emotionally. The fortune-telling data learning unit, for example, collects the emotional reactions of users when they receive fortune-telling results and has the generation AI learn them. For example, it incorporates data such as changes in facial expressions and voice when the user sees the fortune-telling results. This allows the emotion estimation function to learn the user's emotional reactions and generate fortune-telling results that are easy to empathize with emotionally.

[0062] The fortune-telling data learning unit incorporates fortune-telling data from different cultural spheres or historical backgrounds, making it possible to provide fortune-telling results from a global perspective. For example, the fortune-telling data learning unit collects fortune-telling data from different cultural spheres and has the generation AI learn it. For example, fortune-telling data from various cultures, such as Chinese feng shui and Indian astrology, can be incorporated. By incorporating fortune-telling data from different cultural spheres and historical backgrounds, fortune-telling results can be provided from a global perspective.

[0063] The fortune-telling data learning unit can combine predictive data other than fortune-telling to add a multifaceted perspective to the fortune-telling results. For example, the fortune-telling data learning unit combines weather forecast data with fortune-telling data and has the generation AI learn it. For example, the impact that changes in the weather have on fortunes can be reflected in the fortune-telling results. In this way, by combining predictive data other than fortune-telling, a multifaceted perspective can be added to the fortune-telling results.

[0064] The fortune-telling data learning unit uses the emotion estimation function to mimic the fortune teller's emotions and intuition, making it possible to generate more human-like fortune-telling results. For example, the fortune-telling data learning unit collects emotional data from fortune tellers and has the generation AI learn it. For example, it incorporates data on the fortune teller's emotional changes and intuitive judgments when telling fortunes. This makes it possible to generate more human-like fortune-telling results by using the emotion estimation function to mimic the fortune teller's emotions and intuition.

[0065] The user input analysis unit can refer to the user's past input history to provide more personalized fortune-telling results. For example, the user input analysis unit collects the user's past input history and has the generation AI learn from it. For example, it incorporates fortune-telling requests and feedback entered by the user in the past as data. In this way, by referring to the user's past input history, it is possible to provide more personalized fortune-telling results.

[0066] The user input analysis unit can use natural language processing technology to more accurately understand the user's intentions and emotions. The user input analysis unit, for example, uses natural language processing technology to analyze the user's input and have the generation AI learn from it. For example, it analyzes the intentions and emotions of the text entered by the user. In this way, the use of natural language processing technology can more accurately understand the user's intentions and emotions.

[0067] The user input analysis unit can use the emotion estimation function to generate fortune-telling results that take into account the user's emotional state. The user input analysis unit, for example, uses the emotion estimation function to analyze the user's input and have the generation AI learn from it. For example, it analyzes the emotion of the text entered by the user. In this way, by using the emotion estimation function, fortune-telling results that take into account the user's emotional state can be generated.

[0068] The user input analysis unit can incorporate voice input or image input to accommodate a wider variety of input formats. The user input analysis unit, for example, analyzes voice input and trains the generation AI. For example, it converts requests input by voice by the user into text and generates fortune-telling results. This allows for a wider variety of input formats to be accommodated by incorporating voice input or image input.

[0069] The user input analysis unit can automatically translate different languages ​​and dialects, making it possible to accommodate international users. For example, the user input analysis unit can automatically translate inputs in different languages ​​and train the generation AI. For example, it can translate requests entered by a user in English or French into Japanese and generate fortune-telling results. This allows it to automatically translate different languages ​​and dialects, making it possible to accommodate international users.

[0070] The user input analysis unit can use the emotion estimation function to monitor the user's emotional state in real time and provide fortune-telling results according to the emotion. The user input analysis unit, for example, uses the emotion estimation function to analyze the user's input in real time and have the generation AI learn from it. For example, it analyzes the emotion of the text entered by the user in real time. This allows the emotion estimation function to monitor the user's emotional state in real time and provide fortune-telling results according to the emotion.

[0071] The fortune-telling result generation unit can explain in detail the basis of the fortune-telling result, deepening the user's understanding. For example, the fortune-telling result generation unit is equipped with a function that allows the generation AI to explain in detail the basis of the fortune-telling result. For example, the meaning of the tarot cards or the characteristics of the zodiac signs are specifically explained. This allows the user to deepen their understanding by explaining in detail the basis of the fortune-telling result.

[0072] The fortune-telling result generation unit can reflect user feedback and continuously improve the accuracy of the fortune-telling results. The fortune-telling result generation unit, for example, collects user feedback and makes the generation AI learn from it. For example, it incorporates feedback provided by users on fortune-telling results as data. In this way, by reflecting user feedback, it is possible to continuously improve the accuracy of the fortune-telling results.

[0073] The fortune-telling result generation unit can use the emotion estimation function to provide advice according to the user's emotions. For example, the fortune-telling result generation unit uses the emotion estimation function to analyze the user's emotions and have the generation AI learn from them. For example, the emotions the user felt when receiving the fortune-telling result can be incorporated as data. In this way, by using the emotion estimation function, advice according to the user's emotions can be provided.

[0074] The fortune-telling result generation unit can combine different fortune-telling patterns to provide composite fortune-telling results. For example, the fortune-telling result generation unit combines different fortune-telling patterns and has the generation AI learn them. For example, it can incorporate data that combines tarot readings and horoscope readings. This allows it to provide composite fortune-telling results by combining different fortune-telling patterns.

[0075] The fortune-telling result generation unit can visualize fortune-telling results and provide them to users in a format that is visually easy to understand. For example, the fortune-telling result generation unit is equipped with a function to visualize fortune-telling results and has the generation AI learn this. For example, it displays images of tarot cards or diagrams of constellations. By visualizing fortune-telling results, the unit can provide them to users in a format that is visually easy to understand.

[0076] The fortune-telling result generation unit can use the emotion estimation function to adjust the fortune-telling result in real time according to the user's emotion. The fortune-telling result generation unit, for example, uses the emotion estimation function to analyze the user's emotion in real time and have the generation AI learn from it. For example, the emotion the user felt when receiving the fortune-telling result is analyzed in real time. In this way, by using the emotion estimation function, the fortune-telling result can be adjusted in real time according to the user's emotion.

[0077] The fortune-telling result generation unit can analyze the interrelationships between different fortune-telling patterns and provide more consistent fortune-telling results. For example, the fortune-telling result generation unit analyzes the interrelationships between different fortune-telling patterns and has the generation AI learn from them. For example, the interrelationships between tarot readings and horoscope readings can be incorporated as data. This allows for the analysis of the interrelationships between different fortune-telling patterns to provide more consistent fortune-telling results.

[0078] The fortune-telling result generation unit can refer to the user's past fortune-telling results and provide long-term fortune trends. The fortune-telling result generation unit, for example, collects the user's past fortune-telling results and has the generation AI learn them. For example, it incorporates the fortune-telling results that the user has received in the past as data. In this way, it is possible to provide long-term fortune trends by referring to the user's past fortune-telling results.

[0079] The fortune-telling result generation unit can use the emotion estimation function to provide comprehensive advice according to the user's emotions. The fortune-telling result generation unit, for example, uses the emotion estimation function to analyze the user's emotions and have the generation AI learn from them. For example, the emotions the user felt when receiving the fortune-telling result can be incorporated as data. In this way, by using the emotion estimation function, comprehensive advice according to the user's emotions can be provided.

[0080] The fortune-telling result generation unit can combine different fortune-telling patterns to generate new fortune-telling patterns. For example, the fortune-telling result generation unit combines different fortune-telling patterns and has the generation AI learn them. For example, it can incorporate data that combines tarot readings and horoscope readings. This makes it possible to generate new fortune-telling patterns by combining different fortune-telling patterns.

[0081] The fortune-telling result generation unit can provide fortune-telling results in an interactive format, allowing users to customize their fortune-telling results themselves. The fortune-telling result generation unit, for example, is equipped with a function for providing fortune-telling results in an interactive format and has the generation AI learn this. For example, it provides an interface that allows users to customize their fortune-telling results. By providing fortune-telling results in an interactive format, it allows users to customize their fortune-telling results themselves.

[0082] The fortune-telling result generation unit can use the emotion estimation function to adjust the overall fortune-telling result in real time according to the user's emotions. The fortune-telling result generation unit, for example, uses the emotion estimation function to analyze the user's emotions in real time and have the generation AI learn from them. For example, the emotion the user felt when receiving the fortune-telling result is analyzed in real time. In this way, by using the emotion estimation function, the overall fortune-telling result can be adjusted in real time according to the user's emotions.

[0083] The fortune-telling result generation unit can provide a customizable output format according to the user's preferences. For example, the fortune-telling result generation unit is equipped with a function that enables customization of the output format of fortune-telling results, and has the generation AI learn this function. For example, it provides an interface that allows the user to select the display format of the fortune-telling results. This improves user satisfaction by providing a customizable output format according to the user's preferences.

[0084] The fortune-telling result generation unit can reflect user feedback and continuously improve the accuracy of the output format. The fortune-telling result generation unit, for example, collects user feedback and makes the generation AI learn from it. For example, the unit incorporates feedback provided by the user regarding the output format of the fortune-telling result as data. In this way, the accuracy of the output format can be continuously improved by reflecting user feedback.

[0085] The fortune-telling result generation unit can use the emotion estimation function to provide an output format that corresponds to the user's emotion. For example, the fortune-telling result generation unit uses the emotion estimation function to analyze the user's emotion and have the generation AI learn it. For example, the emotion the user felt when receiving the fortune-telling result is taken in as data. In this way, by using the emotion estimation function, an output format that corresponds to the user's emotion can be provided.

[0086] The fortune-telling result generation unit can incorporate audio or video to provide the fortune-telling results in a richer media format. For example, the fortune-telling result generation unit is equipped with a function to provide fortune-telling results in audio format and has the generation AI learn this. For example, it provides a function to read out the fortune-telling results aloud. This allows fortune-telling results to be provided in a richer media format by incorporating audio or video.

[0087] The fortune-telling result generation unit can provide a multi-platform format that is compatible with different devices. For example, the fortune-telling result generation unit is equipped with a function that provides a multi-platform format that allows fortune-telling results to be displayed on different devices, and has the generation AI learn this. For example, fortune-telling results can be displayed on smartphones, tablets, and PCs. This allows compatibility with different devices, allowing users to use fortune-telling results on any device.

[0088] The fortune-telling result generation unit can use the emotion estimation function to adjust the output format in real time according to the user's emotion. The fortune-telling result generation unit, for example, uses the emotion estimation function to analyze the user's emotion in real time and have the generation AI learn from it. For example, the emotion the user felt when receiving the fortune-telling result is analyzed in real time. In this way, by using the emotion estimation function, the output format can be adjusted in real time according to the user's emotion.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] Fortune-telling services can also incorporate a user's health data and provide fortune-telling results based on their health status. For example, they can analyze data obtained from a user's fitness tracker or smartwatch and include advice based on their health status in their fortune-telling results. They can also incorporate a user's sleep data and provide fortune-telling results based on their sleep quality. They can also analyze a user's dietary data and provide fortune-telling results based on their nutritional balance.

[0091] Fortune-telling services can also provide fortune-telling results based on a user's hobbies and interests. For example, if a user likes music, a fortune-telling service can provide a music-related fortune. If a user likes sports, a fortune-telling service can provide a sports-related fortune. Furthermore, if a user likes traveling, the fortune-telling service can include travel destinations and travel advice in the fortune-telling results.

[0092] The fortune-telling service can estimate a user's emotions and adjust the fortune-telling results based on the estimated emotions. For example, if a user is feeling stressed, the fortune-telling results can include advice on how to relax. If a user is feeling happy, the fortune-telling results can provide a positive message to further enhance that emotion. Furthermore, if a user is feeling sad, the fortune-telling results can include a message of comfort or encouragement.

[0093] Fortune-telling services can also provide fortune-telling results that take into account a user's social connections. For example, they can analyze a user's social media data and predict their future based on their friendships and family relationships. They can also provide fortune-telling and advice for the workplace by taking into account the user's relationships at work. They can also predict their future based on the user's community activities.

[0094] The fortune-telling service can estimate the user's emotions and visualize the fortune-telling results based on the estimated emotions. For example, if the user is feeling happy, the fortune-telling results can be displayed using bright colors and positive images. If the user is feeling anxious, the fortune-telling results can be displayed using calming colors and relaxing images. Furthermore, if the user is excited, the fortune-telling results can be displayed using energetic colors and dynamic images.

[0095] The fortune-telling service can also provide fortune-telling results based on the user's life events. For example, if the user is about to get married, fortunes and advice regarding the marriage can be provided. If the user is considering changing jobs, fortunes and advice regarding the job change can also be provided. Furthermore, if the user is planning to move, fortunes and advice regarding the new residence can also be included in the fortune-telling results.

[0096] The fortune-telling service can estimate the user's emotions and provide the fortune-telling results in audio based on the estimated emotions. For example, if the user wants to relax, the fortune-telling results can be read out in a calm voice. If the user wants to cheer up, the fortune-telling results can be read out in an energetic voice. Furthermore, if the user wants to concentrate, the fortune-telling results can be read out in a calm voice.

[0097] The fortune-telling service can also incorporate a user's learning data and provide fortune-telling results related to academic achievement and skill improvement. For example, if the user is a student, the service can provide fortune-telling results and study advice based on their academic performance. If the user wants to learn a new skill, the service can provide fortune-telling results and study advice related to that skill. Furthermore, if the user is planning to take a qualification exam, the fortune-telling results can include fortune-telling results and advice on how to prepare for the exam.

[0098] The fortune-telling service can estimate the user's emotions and customize the fortune-telling results based on the estimated emotions. For example, if the user is feeling down, the fortune-telling results can include an encouraging message. If the user is feeling excited, the fortune-telling results can also include a positive message to further boost the user's emotions. Furthermore, if the user wants to relax, the fortune-telling results can include advice on how to relax.

[0099] Fortune-telling services can also incorporate a user's travel data and provide fortune-telling results related to travel destinations. For example, when a user is choosing a travel destination, the service can provide fortune-telling results and advice about that location. Fortune-telling services can also provide fortune-telling results for the places and activities the user will visit during their trip. Furthermore, when the user reflects on their experience after their trip, the fortune-telling results can include advice about the outcome of the trip and future fortunes.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The fortune-telling data learning unit learns multiple fortune-telling patterns. For example, it learns fortune-telling patterns such as tarot cards, horoscopes, palmistry, numerology, and feng shui. It can also learn based on past fortune-telling results and the knowledge of fortune-tellers. Step 2: The user input analysis unit analyzes the user's input. For example, if the user inputs "I want to know today's fortune," the unit analyzes that request. It can also analyze text input, voice input, and image input depending on the user's input format. Step 3: The fortune-telling result generator generates an appropriate fortune-telling result based on the user's input analyzed by the user input analyzer. For example, it analyzes the meaning of tarot cards and provides a fortune-telling result appropriate for the user. It can also generate a fortune-telling result by combining the results of horoscopes, palmistry, etc.

[0102] 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.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] 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.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0115] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0128] 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.

[0129] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0130] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0146] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a fortune-telling data learning unit that learns a plurality of fortune-telling patterns; a user input analysis unit that analyzes a user input; a fortune-telling result generating unit that generates an appropriate fortune-telling result based on the user input analyzed by the user input analyzing unit. A system characterized by:

2. The fortune-telling data learning unit Reflect user feedback data in real time 2. The system of claim 1.

3. The fortune-telling data learning unit Incorporating fortune-telling data from different cultural and historical backgrounds to provide fortune-telling results from a global perspective 2. The system of claim 1.

4. The user input analysis unit Refer to the user's past input history to provide more personalized fortune-telling results 2. The system of claim 1.

5. The fortune-telling result generating unit Explain the basis of the fortune-telling results in detail to deepen the user's understanding 2. The system of claim 1.

6. The fortune-telling result generating unit Analyze the correlation between different fortune-telling patterns to provide more consistent fortune-telling results 2. The system of claim 1.

7. The fortune-telling result generating unit Provides customizable output formats according to user preferences 2. The system of claim 1.

8. The fortune-telling data learning unit Using emotion estimation function, we learn the user's emotional response to fortune-telling results and generate fortune-telling results that are easy to empathize with emotionally.

2. The system of claim 1.

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