System

Abstract

An object of a system according to an embodiment is to propose how to select an optimal horse, a buying target of a horse betting ticket, and a purchase amount by using quantitative data and subjective information.SOLUTION: A system includes an input unit, an analysis unit, and a proposal unit. The input unit inputs quantitative data and subjective information. The analysis unit analyzes the quantitative data and the subjective information input by the input unit. The proposal unit proposes how to select an optimal horse, a buying target of a horse betting ticket, and a purchase amount based on the information analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology predicts betting results using only quantitative data, making it difficult to make comprehensive predictions that also take subjective information into account.

[0005] The system according to the embodiment aims to propose the optimal way to select a horse, betting tips, and purchase price using quantitative data and subjective information. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, and a suggestion unit. The input unit inputs quantitative data and subjective information. The analysis unit analyzes the quantitative data and subjective information input by the input unit. The suggestion unit suggests optimal methods for selecting horses, betting tips, and purchase amounts based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can use quantitative data and subjective information to suggest the optimal way to select a horse, betting tips, and purchase amounts. [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 betting ticket purchase support system according to an embodiment of the present invention is a system in which a generation AI proposes the optimal method for selecting a horse, betting options, and purchase amount based on quantitative data and subjective information input by a user. This enables the betting ticket purchase support system to integrate the quantitative data and subjective information input by a user and use the generation AI to propose the optimal method for selecting a horse, betting options, and purchase amount.

[0029] A betting ticket purchase support system according to an embodiment includes an input unit, an analysis unit, and a suggestion unit. The input unit accepts quantitative data and subjective information input by a user. For example, quantitative data such as pedigree, past performance, and current day odds are input. Subjective information such as turf condition, track trends for the current day at the same racecourse, horse behavior in the paddock, and risk-return weighting can also be input in a conversational (chat) format. The analysis unit analyzes the quantitative data and subjective information input by the input unit. For example, the generation AI analyzes the information input by the user using a text generation AI (e.g., LLM). The generation AI can also analyze information such as images and audio using a multimodal generation AI. The generation AI can also analyze past race data using data mining technology. The suggestion unit suggests optimal horse selection methods, betting tips, and purchase amounts based on the information analyzed by the analysis unit. For example, the generation AI suggests optimal horse selection methods based on the information input by the user. The generation AI can also consider risk-return weighting to suggest optimal betting options. The generation AI can also consider the user's budget and risk-return weighting to suggest optimal purchase amounts. As a result, the betting ticket purchase support system according to the embodiment can suggest optimal methods for selecting horses, betting options, and purchase amounts based on the quantitative data and subjective information input by the user.

[0030] The analysis unit can compare data with past race data and prioritize analysis of highly reliable information. For example, when a user inputs turf conditions, the analysis unit has the generating AI analyze past race data and prioritize analysis of data from horses that have performed well on similar turf conditions. For example, if the turf is wet, the analysis unit prioritizes analysis of data from horses that have performed well on wet turf in the past. Furthermore, when a user inputs information about the track conditions for the day at the same racecourse, the generating AI compares past race data from the same racecourse and prioritizes analysis of highly reliable information. For example, if the track conditions at a particular racecourse are fast, the analysis unit prioritizes analysis of data from horses that fit that trend. Furthermore, when a user inputs information about the horse's condition in the paddock, the analysis unit compares past paddock conditions with race results and prioritizes analysis of highly reliable information. For example, if a horse that is calm in the paddock has performed well, the analysis unit prioritizes analysis of that information. This prioritizes analysis of highly reliable information, enabling more accurate predictions.

[0031] The analysis unit can provide real-time feedback on the user's input and automatically generate questions to elicit more specific information. For example, when a user inputs the condition of the turf, the generation AI automatically generates additional questions based on that information to elicit more specific information. For example, it asks, "Is the turf wet or dry?" Furthermore, when the analysis unit inputs information about the track conditions for the day at the same racecourse, the generation AI automatically generates additional questions based on that information to elicit more detailed information. For example, it asks, "Is the track condition fast or slow?" Furthermore, when the analysis unit inputs information about the condition of the horses in the paddock, the generation AI automatically generates additional questions based on that information to elicit more specific information. For example, it asks, "Is the horse calm or excited?" This elicits more specific information from the user, improving the accuracy of the analysis.

[0032] The input unit can input subjective information using voice input or image analysis and analyze the behavior of the horse in the paddock in real time. For example, when a user provides voice input about the behavior of the horse in the paddock, the generation AI analyzes the voice data and evaluates the horse's behavior in real time. For example, the input unit analyzes voice input such as "The horse is calm." Furthermore, when a user provides image data about the horse's behavior in the paddock, the generation AI analyzes the image data and evaluates the horse's behavior in real time. For example, the input unit analyzes the horse's posture and movements to determine whether it is calm. Furthermore, the input unit combines voice input and image analysis to analyze the behavior of the horse in the paddock provided by the user in more detail. For example, if the user provides voice input such as "The horse is excited," the image data is analyzed and confirmed. In this way, the behavior of the horse in the paddock can be analyzed in real time using voice input and image analysis.

[0033] The analysis unit can integrate data from different sports and gambling to build a system that can be applied to predictions other than horse racing. The analysis unit, for example, integrates data from bicycle racing and auto racing, and builds a system in which the generation AI makes predictions based on that data. For example, it analyzes the past results of bicycle racing athletes and their condition on the day. The analysis unit can also integrate data from different sports, and build a system in which the generation AI makes predictions based on that data. For example, it can analyze soccer match results and player performance data. The analysis unit can also integrate data other than gambling, and build a system in which the generation AI makes predictions based on that data. For example, it can analyze stock market data and economic indicators. In this way, by integrating data from different sports and gambling, it is possible to build a system that can be applied to predictions other than horse racing.

[0034] The analysis unit can analyze past race data and develop an algorithm that predicts horse performance under specific conditions. For example, the generation AI in the analysis unit analyzes past race data and develops an algorithm that predicts horse performance under specific turf conditions or weather conditions. For example, it identifies horses that have performed well in races in the rain. The analysis unit also analyzes past race data and develops an algorithm that predicts horse performance under specific racecourse or course conditions. For example, it identifies horses that have performed well at specific racecourses. The analysis unit also analyzes past race data and develops an algorithm that predicts horse performance in combination with specific jockeys or trainers. For example, it identifies horses that have performed well when ridden by specific jockeys. This allows for the development of an algorithm that predicts horse performance under specific conditions, enabling more accurate predictions.

[0035] The analysis unit can simulate multiple scenarios based on subjective information entered by the user and propose the optimal way to select a horse. For example, the analysis unit allows the generation AI to simulate multiple scenarios based on the turf condition and track trends entered by the user and propose the optimal way to select a horse. For example, it compares a scenario where the turf is wet with a scenario where the turf is dry. The analysis unit also allows the generation AI to simulate multiple scenarios based on the horse's appearance in the paddock entered by the user and propose the optimal way to select a horse. For example, it compares scenarios where the horse is calm with scenarios where the horse is excited. The analysis unit also allows the generation AI to simulate multiple scenarios based on the risk-return weighting entered by the user and propose the optimal way to select a horse. For example, it compares a high-risk, high-return scenario with a low-risk, low-return scenario. By simulating multiple scenarios, the analysis unit can propose the optimal way to select a horse.

[0036] The analysis unit can propose optimal horse selection methods for different racecourses and race conditions. For example, the generation AI analyzes data from different racecourses and proposes optimal horse selection methods for each racecourse. For example, it prioritizes the selection of horses that have performed well at specific racecourses. The analysis unit also proposes optimal horse selection methods for different race conditions (turf, dirt, distance, etc.). For example, it selects horses that have performed well in turf races. The analysis unit also proposes optimal horse selection methods for different weather conditions (sunny, rainy, snowy, etc.). For example, it selects horses that have performed well in races in the rain. This makes it possible to propose optimal horse selection methods for different racecourses and race conditions.

[0037] The analysis unit can develop selection algorithms that can be applied to other gambling (such as bicycle racing and auto racing). For example, the generation AI in the analysis unit analyzes bicycle racing data to develop an optimal selection algorithm based on the rider's past performance and the conditions on the day. For example, it analyzes the race conditions under which a specific rider achieved good results. The generation AI in the analysis unit also analyzes auto racing data to develop an optimal selection algorithm based on the vehicle's condition and weather conditions. For example, it analyzes the weather conditions under which a specific vehicle achieved good results. The generation AI in the analysis unit also integrates data from other gambling to develop a common selection algorithm. For example, it integrates data from horse racing, bicycle racing, and auto racing to propose the optimal selection method. This allows the development of selection algorithms that can be applied to other gambling, which is expected to be used in a wide range of fields.

[0038] The suggestion unit can analyze past betting data and propose betting patterns with the highest success rate. For example, the suggestion unit uses a generation AI to analyze past betting data and propose betting patterns with the highest success rate under specific conditions. For example, it analyzes successful patterns at specific racecourses and race conditions. The suggestion unit also uses a generation AI to analyze past betting data and propose successful patterns related to specific horses and jockeys. For example, it analyzes successful patterns when a specific jockey is riding. The suggestion unit also uses a generation AI to analyze past betting data and propose successful patterns based on specific odds and risk-return weighting. For example, it analyzes successful patterns for betting on horses with high odds. In this way, by analyzing past betting data, it is possible to propose betting patterns with the highest success rate.

[0039] The suggestion unit can simulate multiple betting scenarios based on information input by the user and suggest optimal bets. In the suggestion unit, for example, the generation AI simulates multiple betting scenarios based on the turf condition and track trends input by the user and suggests optimal bets. For example, it compares a scenario where the turf is wet with a scenario where the turf is dry. In addition, the suggestion unit simulates multiple betting scenarios based on the horse's condition in the paddock input by the user and suggests optimal bets. For example, it compares scenarios where the horse is calm with scenarios where the horse is excited. In addition, the suggestion unit simulates multiple betting scenarios based on the risk-return weighting input by the user and suggests optimal bets. For example, it compares a high-risk, high-return scenario with a low-risk, low-return scenario. In this way, optimal bets can be suggested by simulating multiple betting scenarios.

[0040] The proposal unit can develop betting algorithms that can be applied to different gambling (such as bicycle racing and auto racing). For example, the generation AI in the proposal unit analyzes bicycle racing data to develop an optimal betting algorithm based on the rider's past performance and the conditions on the day. For example, it analyzes the race conditions under which a specific rider achieved good results. The proposal unit also analyzes auto racing data to develop an optimal betting algorithm based on the vehicle's condition and weather conditions. For example, it analyzes the weather conditions under which a specific vehicle achieved good results. The proposal unit also integrates data from other gambling, and the generation AI develops a common betting algorithm. For example, it integrates data from horse racing, bicycle racing, and auto racing to propose optimal betting. This allows the development of betting algorithms that can be applied to other gambling, which is expected to be used in a wide range of fields.

[0041] The suggestion unit can analyze the user's past purchase history and suggest individually optimized bets. For example, the suggestion unit uses a generation AI to analyze the user's past betting history and suggest individually optimized bets. For example, the suggestion unit makes suggestions based on patterns of bets that have been successful in the past. The suggestion unit also uses the generation AI to analyze the user's past purchase history, identify successful patterns under specific conditions, and suggest bets based on those. For example, it analyzes successful patterns at specific racecourses or race conditions. The suggestion unit also uses the generation AI to analyze the user's past purchase history and suggest optimal bets based on risk-return weighting. For example, it suggests whether to choose a bet with high risk and high return, or a bet with low risk and low return. In this way, individually optimized bets can be suggested by analyzing the user's past purchase history.

[0042] The suggestion unit can develop an algorithm that analyzes a user's budget and past purchase history and suggests the optimal purchase price. For example, the suggestion unit develops an algorithm in which the generation AI analyzes a user's budget and past purchase history and suggests the optimal purchase price. For example, the suggestion unit suggests a purchase price based on past success patterns. The suggestion unit also analyzes the user's budget and past purchase history and suggests the optimal purchase price under specific conditions. For example, it analyzes success patterns at specific racecourses or racing conditions. The suggestion unit also analyzes the user's budget and past purchase history and suggests the optimal purchase price based on a risk-return weighting. For example, it determines whether to suggest a purchase price with a high risk and high return, or a purchase price with a low risk and low return. In this way, the optimal purchase price can be suggested by analyzing the user's budget and past purchase history.

[0043] The suggestion unit can simulate multiple purchase price scenarios based on the risk-return weighting input by the user and propose the optimal price. In the suggestion unit, for example, the generation AI simulates multiple purchase price scenarios based on the risk-return weighting input by the user and proposes the optimal price. For example, it compares a high-risk, high-return scenario with a low-risk, low-return scenario. In addition, the suggestion unit simulates multiple purchase price scenarios based on the budget input by the user and proposes the optimal price. For example, it compares a scenario when the budget is limited with a scenario when the budget is abundant. In addition, the suggestion unit simulates multiple purchase price scenarios based on the past purchase history input by the user and proposes the optimal price. For example, it proposes a purchase price based on past successful patterns. In this way, the optimal price can be proposed by simulating multiple purchase price scenarios.

[0044] The suggestion unit can develop a purchase amount algorithm that can be applied to different types of gambling. For example, the suggestion unit uses the generation AI to analyze bicycle racing data and develop an algorithm for the optimal purchase amount based on the rider's past performance and the conditions on the day. For example, it analyzes the race conditions under which a specific rider achieved good results. The suggestion unit also uses the generation AI to analyze auto racing data and develop an algorithm for the optimal purchase amount based on the vehicle's condition and weather conditions. For example, it analyzes the weather conditions under which a specific vehicle achieved good results. The suggestion unit also uses the generation AI to integrate data from other types of gambling and develop a common purchase amount algorithm. For example, it integrates data from horse racing, bicycle racing, and auto racing to propose the optimal purchase amount. This allows the development of a purchase amount algorithm that can be applied to other types of gambling, which is expected to be used in a wide range of fields.

[0045] The suggestion unit can analyze the user's past purchase history and suggest an individually optimized purchase amount. For example, the suggestion unit uses a generation AI to analyze the user's past betting history and suggest an individually optimized purchase amount. For example, the suggestion unit makes a suggestion based on patterns of past successful purchase amounts. The suggestion unit also uses the generation AI to analyze the user's past purchase history, identify successful patterns under specific conditions, and suggest a purchase amount based on that. For example, it analyzes successful patterns at specific racecourses or race conditions. The suggestion unit also uses the generation AI to analyze the user's past purchase history and suggest an optimal purchase amount based on a risk-return weighting. For example, it determines whether to suggest a purchase amount with a high risk and high return, or a purchase amount with a low risk and low return. In this way, an individually optimized purchase amount can be suggested by analyzing the user's past purchase history.

[0046] The suggestion unit can analyze data from bicycle racing or auto racing to suggest optimal betting methods and purchase amounts. For example, the generation AI analyzes bicycle racing data to suggest optimal betting methods and purchase amounts based on the rider's past performance and the conditions on the day. For example, it analyzes the race conditions under which a specific rider achieved good results. The suggestion unit also analyzes auto racing data to suggest optimal betting methods and purchase amounts based on the condition of the vehicle and weather conditions. For example, it analyzes the weather conditions under which a specific vehicle achieved good results. The suggestion unit also integrates data from other gambling activities and develops common betting methods and purchase amount algorithms. For example, it integrates data from horse racing, bicycle racing, and auto racing to suggest optimal betting methods and purchase amounts. In this way, it is possible to suggest optimal betting methods and purchase amounts by analyzing bicycle racing and auto racing data.

[0047] The suggestion unit can integrate data on different gambling types and develop a common selection method and purchase amount algorithm. For example, the generation AI integrates data on horse racing, bicycle racing, and auto racing to develop a common selection method and purchase amount algorithm. For example, the generation AI analyzes the data on each gambling type and proposes the optimal selection method and purchase amount. The suggestion unit also integrates data on different gambling types and develops a common selection method and purchase amount algorithm. For example, the generation AI integrates data on horse racing and bicycle racing to propose the optimal selection method and purchase amount. The suggestion unit also integrates data on different gambling types and develops a common selection method and purchase amount algorithm. For example, the generation AI integrates data on horse racing and auto racing to propose the optimal selection method and purchase amount. In this way, by integrating data on different gambling types, a common selection method and purchase amount algorithm can be developed.

[0048] The suggestion unit can analyze data on different gambling types and suggest optimal betting methods and purchase amounts. For example, the suggestion unit uses the generation AI to analyze bicycle racing data and suggest optimal betting methods and purchase amounts based on the rider's past performance and the conditions on the day. For example, the suggestion unit analyzes the race conditions under which a specific rider achieved good results. The suggestion unit also uses the generation AI to analyze auto racing data and suggest optimal betting methods and purchase amounts based on the condition of the vehicle and weather conditions. For example, the suggestion unit analyzes the weather conditions under which a specific vehicle achieved good results. The suggestion unit also uses the generation AI to integrate data on other gambling types and develop common betting methods and purchase amount algorithms. For example, data on horse racing, bicycle racing, and auto racing is integrated to suggest optimal betting methods and purchase amounts. This makes it possible to suggest optimal betting methods and purchase amounts by analyzing data on different gambling types.

[0049] The suggestion unit can integrate data on different gambling types and develop a common selection method and purchase amount algorithm. For example, the generation AI integrates data on horse racing, bicycle racing, and auto racing to develop a common selection method and purchase amount algorithm. For example, the generation AI analyzes the data on each gambling type and proposes the optimal selection method and purchase amount. The suggestion unit also integrates data on different gambling types and develops a common selection method and purchase amount algorithm. For example, the generation AI integrates data on horse racing and bicycle racing to propose the optimal selection method and purchase amount. The suggestion unit also integrates data on different gambling types and develops a common selection method and purchase amount algorithm. For example, the generation AI integrates data on horse racing and auto racing to propose the optimal selection method and purchase amount. In this way, by integrating data on different gambling types, a common selection method and purchase amount algorithm can be developed.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The analysis unit can suggest an individually optimized method for selecting a horse based on the user's past purchase history. For example, the suggestion can be made based on patterns of horse selection that have been successful in the past. The analysis unit can also analyze the user's past purchase history to identify patterns of success under specific conditions and suggest a method for selecting a horse based on that. For example, it can analyze patterns of success at specific racecourses or race conditions. The analysis unit can also analyze the user's past purchase history to suggest an optimal method for selecting a horse based on risk-return weighting. For example, it can suggest whether to select a horse with high risk and high return, or a horse with low risk and low return. In this way, by analyzing the user's past purchase history, an individually optimized method for selecting a horse can be suggested.

[0052] The analysis unit can integrate data from different sports and gambling sources to build a system that can be applied to predictions other than horse racing. For example, data from bicycle racing and auto racing can be integrated to build a system in which the generation AI makes predictions based on that data. For example, it can analyze the past performance of bicycle racing athletes and their condition on the day. The analysis unit can also integrate data from different sports to build a system in which the generation AI makes predictions based on that data. For example, it can analyze soccer match results and player performance data. The analysis unit can also integrate data other than gambling data to build a system in which the generation AI makes predictions based on that data. For example, it can analyze stock market data and economic indicators. In this way, by integrating data from different sports and gambling sources, it is possible to build a system that can be applied to predictions other than horse racing.

[0053] The analysis unit can provide real-time feedback on user input and automatically generate questions to elicit more specific information. For example, when a user inputs the condition of the turf, the generation AI automatically generates additional questions based on that information to elicit more specific information. For example, it asks, "Is the turf wet or dry?" Furthermore, when the analysis unit inputs information about the track conditions for the day at the same racecourse, the generation AI automatically generates additional questions based on that information to elicit more detailed information. For example, it asks, "Is the track condition fast or slow?" Furthermore, when the analysis unit inputs information about the condition of the horses in the paddock, the generation AI automatically generates additional questions based on that information to elicit more specific information. For example, it asks, "Is the horse calm or excited?" This improves the accuracy of the analysis by eliciting more specific information from the user.

[0054] The analysis unit can analyze past race data and develop an algorithm that predicts horse performance under specific conditions. For example, the generation AI analyzes past race data and develops an algorithm that predicts horse performance under specific turf conditions or weather conditions. For example, it identifies horses that have performed well in races in the rain. The analysis unit also analyzes past race data and develops an algorithm that predicts horse performance under specific racecourse or course conditions. For example, it identifies horses that have performed well at specific racecourses. The analysis unit also analyzes past race data and develops an algorithm that predicts horse performance in combination with specific jockeys or trainers. For example, it identifies horses that have performed well when ridden by specific jockeys. This allows for the development of an algorithm that predicts horse performance under specific conditions, enabling more accurate predictions.

[0055] The analysis unit can simulate multiple scenarios based on subjective information entered by the user and suggest the optimal way to select a horse. For example, the generation AI simulates multiple scenarios based on the turf condition and track trends entered by the user and suggests the optimal way to select a horse. For example, it compares a scenario where the turf is wet with a scenario where the turf is dry. The analysis unit also simulates multiple scenarios based on the horse's appearance in the paddock entered by the user and suggests the optimal way to select a horse. For example, it compares scenarios where the horse is calm with scenarios where it is excited. The analysis unit also simulates multiple scenarios based on the risk-return weighting entered by the user and suggests the optimal way to select a horse. For example, it compares a high-risk, high-return scenario with a low-risk, low-return scenario. In this way, by simulating multiple scenarios, it is possible to suggest the optimal way to select a horse.

[0056] The analysis unit can propose optimal horse selection methods for different racecourses and race conditions. For example, the generation AI analyzes data from different racecourses and proposes optimal horse selection methods for each racecourse. For example, horses that have performed well at a particular racecourse are selected preferentially. The analysis unit also proposes optimal horse selection methods for the generation AI for different race conditions (turf, dirt, distance, etc.). For example, horses that have performed well in turf races are selected. The analysis unit also proposes optimal horse selection methods for the generation AI for different weather conditions (sunny, rainy, snowy, etc.). For example, horses that have performed well in races in the rain are selected. This makes it possible to propose optimal horse selection methods for different racecourses and race conditions.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The input unit accepts quantitative data and subjective information entered by the user. For example, quantitative data such as pedigree, past performance, and the current day's odds can be entered. Subjective information such as the condition of the turf, the current day's track conditions at the same racecourse, the state of the horses in the paddock, and risk-return weighting can also be entered in a conversational (chat) format. Step 2: The analysis unit analyzes the quantitative data and subjective information input by the input unit. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the information input by the user. The generation AI can also use a multimodal generation AI to analyze information such as images and audio. The generation AI can also use data mining technology to analyze past race data. Step 3: The suggestion unit suggests the optimal way to select a horse, betting options, and purchase price based on the information analyzed by the analysis unit. For example, the generation AI suggests the optimal way to select a horse based on the information entered by the user. The generation AI can also consider risk-return weighting to suggest the optimal betting options. The generation AI can also consider the user's budget and risk-return weighting to suggest the optimal purchase price.

[0059] (Example 2) A betting ticket purchase support system according to an embodiment of the present invention is a system in which a generation AI proposes the optimal method for selecting a horse, betting options, and purchase amount based on quantitative data and subjective information input by a user. This enables the betting ticket purchase support system to integrate the quantitative data and subjective information input by a user and use the generation AI to propose the optimal method for selecting a horse, betting options, and purchase amount.

[0060] A betting ticket purchase support system according to an embodiment includes an input unit, an analysis unit, and a suggestion unit. The input unit accepts quantitative data and subjective information input by a user. For example, quantitative data such as pedigree, past performance, and current day odds are input. Subjective information such as turf condition, track trends for the current day at the same racecourse, horse behavior in the paddock, and risk-return weighting can also be input in a conversational (chat) format. The analysis unit analyzes the quantitative data and subjective information input by the input unit. For example, the generation AI analyzes the information input by the user using a text generation AI (e.g., LLM). The generation AI can also analyze information such as images and audio using a multimodal generation AI. The generation AI can also analyze past race data using data mining technology. The suggestion unit suggests optimal horse selection methods, betting tips, and purchase amounts based on the information analyzed by the analysis unit. For example, the generation AI suggests optimal horse selection methods based on the information input by the user. The generation AI can also consider risk-return weighting to suggest optimal betting options. The generation AI can also consider the user's budget and risk-return weighting to suggest optimal purchase amounts. As a result, the betting ticket purchase support system according to the embodiment can suggest optimal methods for selecting horses, betting options, and purchase amounts based on the quantitative data and subjective information input by the user.

[0061] The analysis unit can compare data with past race data and prioritize analysis of highly reliable information. For example, when a user inputs turf conditions, the analysis unit has the generating AI analyze past race data and prioritize analysis of data from horses that have performed well on similar turf conditions. For example, if the turf is wet, the analysis unit prioritizes analysis of data from horses that have performed well on wet turf in the past. Furthermore, when a user inputs information about the track conditions for the day at the same racecourse, the generating AI compares past race data from the same racecourse and prioritizes analysis of highly reliable information. For example, if the track conditions at a particular racecourse are fast, the analysis unit prioritizes analysis of data from horses that fit that trend. Furthermore, when a user inputs information about the horse's condition in the paddock, the analysis unit compares past paddock conditions with race results and prioritizes analysis of highly reliable information. For example, if a horse that is calm in the paddock has performed well, the analysis unit prioritizes analysis of that information. This prioritizes analysis of highly reliable information, enabling more accurate predictions.

[0062] The analysis unit can provide real-time feedback on the user's input and automatically generate questions to elicit more specific information. For example, when a user inputs the condition of the turf, the generation AI automatically generates additional questions based on that information to elicit more specific information. For example, it asks, "Is the turf wet or dry?" Furthermore, when the analysis unit inputs information about the track conditions for the day at the same racecourse, the generation AI automatically generates additional questions based on that information to elicit more detailed information. For example, it asks, "Is the track condition fast or slow?" Furthermore, when the analysis unit inputs information about the condition of the horses in the paddock, the generation AI automatically generates additional questions based on that information to elicit more specific information. For example, it asks, "Is the horse calm or excited?" This elicits more specific information from the user, improving the accuracy of the analysis.

[0063] The analysis unit can use the emotion estimation function to analyze the emotional nuances of subjective information input by the user and prioritize processing information with positive emotions. For example, when a user inputs the condition of the turf, the analysis unit uses the emotion estimation function to analyze the emotional nuances of the input content and prioritize processing information with positive emotions. For example, if a user inputs "The condition of the turf is very good," the analysis unit prioritizes analyzing that information. Furthermore, when inputting information about the track condition for the day at the same racecourse, the analysis unit uses the emotion estimation function to analyze the emotional nuances of the input content and prioritize processing information with positive emotions. For example, if a user inputs "The track condition is very fast," the analysis unit prioritizes analyzing that information. Furthermore, when inputting information about the condition of horses in the paddock, the analysis unit uses the emotion estimation function to analyze the emotional nuances of the input content and prioritize processing information with positive emotions. For example, if a user inputs "The horses are very calm," the analysis unit prioritizes analyzing that information. This prioritizes processing information with positive emotions, thereby improving user satisfaction.

[0064] The input unit can input subjective information using voice input or image analysis and analyze the behavior of the horse in the paddock in real time. For example, when a user provides voice input about the behavior of the horse in the paddock, the generation AI analyzes the voice data and evaluates the horse's behavior in real time. For example, the input unit analyzes voice input such as "The horse is calm." Furthermore, when a user provides image data about the horse's behavior in the paddock, the generation AI analyzes the image data and evaluates the horse's behavior in real time. For example, the input unit analyzes the horse's posture and movements to determine whether it is calm. Furthermore, the input unit combines voice input and image analysis to analyze the behavior of the horse in the paddock provided by the user in more detail. For example, if the user provides voice input such as "The horse is excited," the image data is analyzed and confirmed. In this way, the behavior of the horse in the paddock can be analyzed in real time using voice input and image analysis.

[0065] The analysis unit can integrate data from different sports and gambling to build a system that can be applied to predictions other than horse racing. The analysis unit, for example, integrates data from bicycle racing and auto racing, and builds a system in which the generation AI makes predictions based on that data. For example, it analyzes the past results of bicycle racing athletes and their condition on the day. The analysis unit can also integrate data from different sports, and build a system in which the generation AI makes predictions based on that data. For example, it can analyze soccer match results and player performance data. The analysis unit can also integrate data other than gambling, and build a system in which the generation AI makes predictions based on that data. For example, it can analyze stock market data and economic indicators. In this way, by integrating data from different sports and gambling, it is possible to build a system that can be applied to predictions other than horse racing.

[0066] The analysis unit can use the emotion estimation function to analyze the emotion of the user when entering information and provide advice according to the input content. For example, when the user enters the condition of the turf, the analysis unit uses the emotion estimation function to analyze the emotion in real time and provide advice according to the input content. For example, if the user enters "the condition of the turf is poor," the analysis unit provides appropriate advice based on that information. Furthermore, when the user enters information about the track condition for the day at the same racecourse, the analysis unit uses the emotion estimation function to analyze the emotion in real time and provide advice according to the input content. For example, if the user enters "the track condition is slow," the analysis unit provides appropriate advice based on that information. Furthermore, when the user enters information about the condition of the horses in the paddock, the analysis unit uses the emotion estimation function to analyze the emotion in real time and provide advice according to the input content. For example, if the user enters "the horses are excited," the analysis unit provides appropriate advice based on that information. This provides advice according to the user's emotion, thereby improving user satisfaction.

[0067] The analysis unit can analyze past race data and develop an algorithm that predicts horse performance under specific conditions. For example, the generation AI in the analysis unit analyzes past race data and develops an algorithm that predicts horse performance under specific turf conditions or weather conditions. For example, it identifies horses that have performed well in races in the rain. The analysis unit also analyzes past race data and develops an algorithm that predicts horse performance under specific racecourse or course conditions. For example, it identifies horses that have performed well at specific racecourses. The analysis unit also analyzes past race data and develops an algorithm that predicts horse performance in combination with specific jockeys or trainers. For example, it identifies horses that have performed well when ridden by specific jockeys. This allows for the development of an algorithm that predicts horse performance under specific conditions, enabling more accurate predictions.

[0068] The analysis unit can simulate multiple scenarios based on subjective information entered by the user and propose the optimal way to select a horse. For example, the analysis unit allows the generation AI to simulate multiple scenarios based on the turf condition and track trends entered by the user and propose the optimal way to select a horse. For example, it compares a scenario where the turf is wet with a scenario where the turf is dry. The analysis unit also allows the generation AI to simulate multiple scenarios based on the horse's appearance in the paddock entered by the user and propose the optimal way to select a horse. For example, it compares scenarios where the horse is calm with scenarios where the horse is excited. The analysis unit also allows the generation AI to simulate multiple scenarios based on the risk-return weighting entered by the user and propose the optimal way to select a horse. For example, it compares a high-risk, high-return scenario with a low-risk, low-return scenario. By simulating multiple scenarios, the analysis unit can propose the optimal way to select a horse.

[0069] The analysis unit uses the emotion estimation function to suggest a way to select a horse based on the user's emotions, encouraging the user to make an emotionally satisfying choice. The analysis unit, for example, uses the emotion estimation function to analyze the emotions the user is feeling when inputting information and suggests a way to select a horse based on that emotion. For example, it prioritizes analysis of information with positive emotions and suggests options. The analysis unit also analyzes the user's emotional response in real time and adjusts the way to select a horse based on the results. For example, if the user is excited, it suggests a high-risk option. The analysis unit also builds a system that suggests a way to select a horse based on the user's emotions based on the emotion estimation data. For example, if the user is relaxed, it suggests a low-risk option. In this way, by suggesting a way to select a horse based on the user's emotions, it is possible to encourage the user to make an emotionally satisfying choice.

[0070] The analysis unit can propose optimal horse selection methods for different racecourses and race conditions. For example, the generation AI analyzes data from different racecourses and proposes optimal horse selection methods for each racecourse. For example, it prioritizes the selection of horses that have performed well at specific racecourses. The analysis unit also proposes optimal horse selection methods for different race conditions (turf, dirt, distance, etc.). For example, it selects horses that have performed well in turf races. The analysis unit also proposes optimal horse selection methods for different weather conditions (sunny, rainy, snowy, etc.). For example, it selects horses that have performed well in races in the rain. This makes it possible to propose optimal horse selection methods for different racecourses and race conditions.

[0071] The analysis unit can develop selection algorithms that can be applied to other gambling (such as bicycle racing and auto racing). For example, the generation AI in the analysis unit analyzes bicycle racing data to develop an optimal selection algorithm based on the rider's past performance and the conditions on the day. For example, it analyzes the race conditions under which a specific rider achieved good results. The generation AI in the analysis unit also analyzes auto racing data to develop an optimal selection algorithm based on the vehicle's condition and weather conditions. For example, it analyzes the weather conditions under which a specific vehicle achieved good results. The generation AI in the analysis unit also integrates data from other gambling to develop a common selection algorithm. For example, it integrates data from horse racing, bicycle racing, and auto racing to propose the optimal selection method. This allows the development of selection algorithms that can be applied to other gambling, which is expected to be used in a wide range of fields.

[0072] The analysis unit uses the emotion estimation function to adjust the horse selection method in real time according to the user's emotions, thereby providing the optimal selection. The analysis unit, for example, uses the emotion estimation function to analyze the user's emotions in real time and adjust the horse selection method in accordance with the emotions. For example, if the user is excited, a high-risk option is suggested. The analysis unit also monitors the user's emotional reactions in real time and adjusts the horse selection method based on the results. For example, if the user is relaxed, a low-risk option is suggested. The analysis unit also builds a system that adjusts the horse selection method in real time according to the user's emotions based on the emotion estimation data. For example, it prioritizes analysis of information that the user has positive emotions and suggests options. This makes it possible to provide the optimal selection by adjusting the horse selection method in real time according to the user's emotions.

[0073] The suggestion unit can analyze past betting data and propose betting patterns with the highest success rate. For example, the suggestion unit uses a generation AI to analyze past betting data and propose betting patterns with the highest success rate under specific conditions. For example, it analyzes successful patterns at specific racecourses and race conditions. The suggestion unit also uses a generation AI to analyze past betting data and propose successful patterns related to specific horses and jockeys. For example, it analyzes successful patterns when a specific jockey is riding. The suggestion unit also uses a generation AI to analyze past betting data and propose successful patterns based on specific odds and risk-return weighting. For example, it analyzes successful patterns for betting on horses with high odds. In this way, by analyzing past betting data, it is possible to propose betting patterns with the highest success rate.

[0074] The suggestion unit can simulate multiple betting scenarios based on information input by the user and suggest optimal bets. In the suggestion unit, for example, the generation AI simulates multiple betting scenarios based on the turf condition and track trends input by the user and suggests optimal bets. For example, it compares a scenario where the turf is wet with a scenario where the turf is dry. In addition, the suggestion unit simulates multiple betting scenarios based on the horse's condition in the paddock input by the user and suggests optimal bets. For example, it compares scenarios where the horse is calm with scenarios where the horse is excited. In addition, the suggestion unit simulates multiple betting scenarios based on the risk-return weighting input by the user and suggests optimal bets. For example, it compares a high-risk, high-return scenario with a low-risk, low-return scenario. In this way, optimal bets can be suggested by simulating multiple betting scenarios.

[0075] The suggestion unit uses the emotion estimation function to suggest bets based on the user's emotions, and can encourage emotionally satisfying choices. The suggestion unit, for example, uses the emotion estimation function to analyze the emotions when the user inputs information and suggests bets based on those emotions. For example, it prioritizes analysis of information with positive emotions and suggests options. The suggestion unit also analyzes the user's emotional response in real time and adjusts bets based on the results. For example, if the user is excited, it suggests a high-risk option. The suggestion unit also builds a system that suggests bets according to the user's emotions based on the emotion estimation data. For example, if the user is relaxed, it suggests a low-risk option. In this way, by suggesting bets based on the user's emotions, it is possible to encourage emotionally satisfying choices.

[0076] The proposal unit can develop betting algorithms that can be applied to different gambling (such as bicycle racing and auto racing). For example, the generation AI in the proposal unit analyzes bicycle racing data to develop an optimal betting algorithm based on the rider's past performance and the conditions on the day. For example, it analyzes the race conditions under which a specific rider achieved good results. The proposal unit also analyzes auto racing data to develop an optimal betting algorithm based on the vehicle's condition and weather conditions. For example, it analyzes the weather conditions under which a specific vehicle achieved good results. The proposal unit also integrates data from other gambling, and the generation AI develops a common betting algorithm. For example, it integrates data from horse racing, bicycle racing, and auto racing to propose optimal betting. This allows the development of betting algorithms that can be applied to other gambling, which is expected to be used in a wide range of fields.

[0077] The suggestion unit can analyze the user's past purchase history and suggest individually optimized bets. For example, the suggestion unit uses a generation AI to analyze the user's past betting history and suggest individually optimized bets. For example, the suggestion unit makes suggestions based on patterns of bets that have been successful in the past. The suggestion unit also uses the generation AI to analyze the user's past purchase history, identify successful patterns under specific conditions, and suggest bets based on those. For example, it analyzes successful patterns at specific racecourses or race conditions. The suggestion unit also uses the generation AI to analyze the user's past purchase history and suggest optimal bets based on risk-return weighting. For example, it suggests whether to choose a bet with high risk and high return, or a bet with low risk and low return. In this way, individually optimized bets can be suggested by analyzing the user's past purchase history.

[0078] The suggestion unit can use the emotion estimation function to adjust bets in real time according to the user's emotions and provide optimal selections. The suggestion unit, for example, uses the emotion estimation function to analyze the user's emotions in real time and adjust bets according to the emotions. For example, if the user is excited, it suggests high-risk options. The suggestion unit also monitors the user's emotional reactions in real time and adjusts bets based on the results. For example, if the user is relaxed, it suggests low-risk options. The suggestion unit also builds a system that adjusts bets in real time according to the user's emotions based on the emotion estimation data. For example, it prioritizes analysis of information that the user has positive emotions about and suggests options. This makes it possible to provide optimal selections by adjusting bets in real time according to the user's emotions.

[0079] The suggestion unit can develop an algorithm that analyzes a user's budget and past purchase history and suggests the optimal purchase price. For example, the suggestion unit develops an algorithm in which the generation AI analyzes a user's budget and past purchase history and suggests the optimal purchase price. For example, the suggestion unit suggests a purchase price based on past success patterns. The suggestion unit also analyzes the user's budget and past purchase history and suggests the optimal purchase price under specific conditions. For example, it analyzes success patterns at specific racecourses or racing conditions. The suggestion unit also analyzes the user's budget and past purchase history and suggests the optimal purchase price based on a risk-return weighting. For example, it determines whether to suggest a purchase price with a high risk and high return, or a purchase price with a low risk and low return. In this way, the optimal purchase price can be suggested by analyzing the user's budget and past purchase history.

[0080] The suggestion unit can simulate multiple purchase price scenarios based on the risk-return weighting input by the user and propose the optimal price. In the suggestion unit, for example, the generation AI simulates multiple purchase price scenarios based on the risk-return weighting input by the user and proposes the optimal price. For example, it compares a high-risk, high-return scenario with a low-risk, low-return scenario. In addition, the suggestion unit simulates multiple purchase price scenarios based on the budget input by the user and proposes the optimal price. For example, it compares a scenario when the budget is limited with a scenario when the budget is abundant. In addition, the suggestion unit simulates multiple purchase price scenarios based on the past purchase history input by the user and proposes the optimal price. For example, it proposes a purchase price based on past successful patterns. In this way, the optimal price can be proposed by simulating multiple purchase price scenarios.

[0081] The suggestion unit uses the emotion estimation function to suggest a purchase price based on the user's emotions, encouraging a choice that is emotionally satisfying. For example, the suggestion unit uses the emotion estimation function to analyze the emotions of the user when inputting information and suggests a purchase price based on the emotions. For example, it prioritizes analysis of information with positive emotions and suggests a purchase price. The suggestion unit also analyzes the user's emotional response in real time and adjusts the purchase price based on the results. For example, if the user is excited, it suggests a purchase price with a high risk. The suggestion unit also builds a system that suggests a purchase price according to the user's emotions based on the emotion estimation data. For example, if the user is relaxed, it suggests a purchase price with a low risk. In this way, by suggesting a purchase price based on the user's emotions, it is possible to encourage a choice that is emotionally satisfying.

[0082] The suggestion unit can develop a purchase amount algorithm that can be applied to different types of gambling. For example, the suggestion unit uses the generation AI to analyze bicycle racing data and develop an algorithm for the optimal purchase amount based on the rider's past performance and the conditions on the day. For example, it analyzes the race conditions under which a specific rider achieved good results. The suggestion unit also uses the generation AI to analyze auto racing data and develop an algorithm for the optimal purchase amount based on the vehicle's condition and weather conditions. For example, it analyzes the weather conditions under which a specific vehicle achieved good results. The suggestion unit also uses the generation AI to integrate data from other types of gambling and develop a common purchase amount algorithm. For example, it integrates data from horse racing, bicycle racing, and auto racing to propose the optimal purchase amount. This allows the development of a purchase amount algorithm that can be applied to other types of gambling, which is expected to be used in a wide range of fields.

[0083] The suggestion unit can analyze the user's past purchase history and suggest an individually optimized purchase amount. For example, the suggestion unit uses a generation AI to analyze the user's past betting history and suggest an individually optimized purchase amount. For example, the suggestion unit makes a suggestion based on patterns of past successful purchase amounts. The suggestion unit also uses the generation AI to analyze the user's past purchase history, identify successful patterns under specific conditions, and suggest a purchase amount based on that. For example, it analyzes successful patterns at specific racecourses or race conditions. The suggestion unit also uses the generation AI to analyze the user's past purchase history and suggest an optimal purchase amount based on a risk-return weighting. For example, it determines whether to suggest a purchase amount with a high risk and high return, or a purchase amount with a low risk and low return. In this way, an individually optimized purchase amount can be suggested by analyzing the user's past purchase history.

[0084] The suggestion unit uses the emotion estimation function to adjust the purchase price in real time according to the user's emotion, thereby providing an optimal choice. The suggestion unit, for example, uses the emotion estimation function to analyze the user's emotion in real time and adjust the purchase price according to the emotion. For example, if the user is excited, it suggests a high-risk option. The suggestion unit also monitors the user's emotional response in real time and adjusts the purchase price based on the results. For example, if the user is relaxed, it suggests a low-risk option. The suggestion unit also builds a system that adjusts the purchase price in real time according to the user's emotion based on the emotion estimation data. For example, it prioritizes analysis of information that the user has positive emotion and suggests options. This makes it possible to provide an optimal choice by adjusting the purchase price in real time according to the user's emotion.

[0085] The suggestion unit can analyze data from bicycle racing or auto racing to suggest optimal betting methods and purchase amounts. For example, the generation AI analyzes bicycle racing data to suggest optimal betting methods and purchase amounts based on the rider's past performance and the conditions on the day. For example, it analyzes the race conditions under which a specific rider achieved good results. The suggestion unit also analyzes auto racing data to suggest optimal betting methods and purchase amounts based on the condition of the vehicle and weather conditions. For example, it analyzes the weather conditions under which a specific vehicle achieved good results. The suggestion unit also integrates data from other gambling activities and develops common betting methods and purchase amount algorithms. For example, it integrates data from horse racing, bicycle racing, and auto racing to suggest optimal betting methods and purchase amounts. In this way, it is possible to suggest optimal betting methods and purchase amounts by analyzing bicycle racing and auto racing data.

[0086] The suggestion unit can integrate data on different gambling types and develop a common selection method and purchase amount algorithm. For example, the generation AI integrates data on horse racing, bicycle racing, and auto racing to develop a common selection method and purchase amount algorithm. For example, the generation AI analyzes the data on each gambling type and proposes the optimal selection method and purchase amount. The suggestion unit also integrates data on different gambling types and develops a common selection method and purchase amount algorithm. For example, the generation AI integrates data on horse racing and bicycle racing to propose the optimal selection method and purchase amount. The suggestion unit also integrates data on different gambling types and develops a common selection method and purchase amount algorithm. For example, the generation AI integrates data on horse racing and auto racing to propose the optimal selection method and purchase amount. In this way, by integrating data on different gambling types, a common selection method and purchase amount algorithm can be developed.

[0087] The suggestion unit uses the emotion estimation function to suggest a selection method and purchase amount based on the user's emotions, thereby encouraging an emotionally satisfying choice. For example, the suggestion unit uses the emotion estimation function to analyze the emotions of the user when inputting information and suggests a selection method and purchase amount based on the emotions. For example, the suggestion unit prioritizes analysis of information with positive emotions and suggests options. The suggestion unit also analyzes the user's emotional response in real time and adjusts the selection method and purchase amount based on the results. For example, if the user is excited, it suggests a high-risk option. The suggestion unit also builds a system that suggests a selection method and purchase amount according to the user's emotions based on the emotion estimation data. For example, if the user is relaxed, it suggests a low-risk option. In this way, by suggesting a selection method and purchase amount based on the user's emotions, it is possible to encourage an emotionally satisfying choice.

[0088] The suggestion unit can analyze data on different gambling types and suggest optimal betting methods and purchase amounts. For example, the suggestion unit uses the generation AI to analyze bicycle racing data and suggest optimal betting methods and purchase amounts based on the rider's past performance and the conditions on the day. For example, the suggestion unit analyzes the race conditions under which a specific rider achieved good results. The suggestion unit also uses the generation AI to analyze auto racing data and suggest optimal betting methods and purchase amounts based on the condition of the vehicle and weather conditions. For example, the suggestion unit analyzes the weather conditions under which a specific vehicle achieved good results. The suggestion unit also uses the generation AI to integrate data on other gambling types and develop common betting methods and purchase amount algorithms. For example, data on horse racing, bicycle racing, and auto racing is integrated to suggest optimal betting methods and purchase amounts. This makes it possible to suggest optimal betting methods and purchase amounts by analyzing data on different gambling types.

[0089] The suggestion unit can integrate data on different gambling types and develop a common selection method and purchase amount algorithm. For example, the generation AI integrates data on horse racing, bicycle racing, and auto racing to develop a common selection method and purchase amount algorithm. For example, the generation AI analyzes the data on each gambling type and proposes the optimal selection method and purchase amount. The suggestion unit also integrates data on different gambling types and develops a common selection method and purchase amount algorithm. For example, the generation AI integrates data on horse racing and bicycle racing to propose the optimal selection method and purchase amount. The suggestion unit also integrates data on different gambling types and develops a common selection method and purchase amount algorithm. For example, the generation AI integrates data on horse racing and auto racing to propose the optimal selection method and purchase amount. In this way, by integrating data on different gambling types, a common selection method and purchase amount algorithm can be developed.

[0090] The suggestion unit uses the emotion estimation function to suggest a selection method and purchase amount based on the user's emotions, thereby encouraging an emotionally satisfying choice. For example, the suggestion unit uses the emotion estimation function to analyze the emotions of the user when inputting information and suggests a selection method and purchase amount based on the emotions. For example, the suggestion unit prioritizes analysis of information with positive emotions and suggests options. The suggestion unit also analyzes the user's emotional response in real time and adjusts the selection method and purchase amount based on the results. For example, if the user is excited, it suggests a high-risk option. The suggestion unit also builds a system that suggests a selection method and purchase amount according to the user's emotions based on the emotion estimation data. For example, if the user is relaxed, it suggests a low-risk option. In this way, by suggesting a selection method and purchase amount based on the user's emotions, it is possible to encourage an emotionally satisfying choice.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The analysis unit can suggest an individually optimized method for selecting a horse based on the user's past purchase history. For example, the suggestion can be made based on patterns of horse selection that have been successful in the past. The analysis unit can also analyze the user's past purchase history to identify patterns of success under specific conditions and suggest a method for selecting a horse based on that. For example, it can analyze patterns of success at specific racecourses or race conditions. The analysis unit can also analyze the user's past purchase history to suggest an optimal method for selecting a horse based on risk-return weighting. For example, it can suggest whether to select a horse with high risk and high return, or a horse with low risk and low return. In this way, by analyzing the user's past purchase history, an individually optimized method for selecting a horse can be suggested.

[0093] The analysis unit can integrate data from different sports and gambling sources to build a system that can be applied to predictions other than horse racing. For example, data from bicycle racing and auto racing can be integrated to build a system in which the generation AI makes predictions based on that data. For example, it can analyze the past performance of bicycle racing athletes and their condition on the day. The analysis unit can also integrate data from different sports to build a system in which the generation AI makes predictions based on that data. For example, it can analyze soccer match results and player performance data. The analysis unit can also integrate data other than gambling data to build a system in which the generation AI makes predictions based on that data. For example, it can analyze stock market data and economic indicators. In this way, by integrating data from different sports and gambling sources, it is possible to build a system that can be applied to predictions other than horse racing.

[0094] The analysis unit can use the emotion estimation function to analyze the emotional nuances of subjective information input by the user and prioritize processing information with positive emotions. For example, when a user inputs the condition of the turf, the emotion estimation function is used to analyze the emotional nuances of the input content and prioritize processing information with positive emotions. For example, if a user inputs "The condition of the turf is very good," the analysis unit prioritizes analyzing that information. Furthermore, when inputting information about the track condition for the day at the same racecourse, the analysis unit uses the emotion estimation function to analyze the emotional nuances of the input content and prioritize processing information with positive emotions. For example, if a user inputs "The track condition is very fast," the analysis unit prioritizes analyzing that information. Furthermore, when inputting information about the condition of horses in the paddock, the analysis unit uses the emotion estimation function to analyze the emotional nuances of the input content and prioritize processing information with positive emotions. For example, if a user inputs "The horses are very calm," the analysis unit prioritizes analyzing that information. This prioritizes processing information with positive emotions, thereby improving user satisfaction.

[0095] The analysis unit can provide real-time feedback on user input and automatically generate questions to elicit more specific information. For example, when a user inputs the condition of the turf, the generation AI automatically generates additional questions based on that information to elicit more specific information. For example, it asks, "Is the turf wet or dry?" Furthermore, when the analysis unit inputs information about the track conditions for the day at the same racecourse, the generation AI automatically generates additional questions based on that information to elicit more detailed information. For example, it asks, "Is the track condition fast or slow?" Furthermore, when the analysis unit inputs information about the condition of the horses in the paddock, the generation AI automatically generates additional questions based on that information to elicit more specific information. For example, it asks, "Is the horse calm or excited?" This improves the accuracy of the analysis by eliciting more specific information from the user.

[0096] The analysis unit can use the emotion estimation function to analyze the emotion of a user when entering information and provide advice according to the input content. For example, when a user enters the condition of the turf, the emotion estimation function can be used to analyze the emotion in real time and provide advice according to the input content. For example, if a user enters "the condition of the turf is poor," appropriate advice can be provided based on that information. Furthermore, when information regarding the track condition for the day at the same racecourse is entered, the analysis unit can use the emotion estimation function to analyze the emotion in real time and provide advice according to the input content. For example, if a user enters "the track condition is slow," appropriate advice can be provided based on that information. Furthermore, when information regarding the condition of a horse in the paddock is entered, the analysis unit can use the emotion estimation function to analyze the emotion in real time and provide advice according to the input content. For example, if a user enters "the horse is excited," appropriate advice can be provided based on that information. This provides advice according to the user's emotion, thereby improving user satisfaction.

[0097] The analysis unit can analyze past race data and develop an algorithm that predicts horse performance under specific conditions. For example, the generation AI analyzes past race data and develops an algorithm that predicts horse performance under specific turf conditions or weather conditions. For example, it identifies horses that have performed well in races in the rain. The analysis unit also analyzes past race data and develops an algorithm that predicts horse performance under specific racecourse or course conditions. For example, it identifies horses that have performed well at specific racecourses. The analysis unit also analyzes past race data and develops an algorithm that predicts horse performance in combination with specific jockeys or trainers. For example, it identifies horses that have performed well when ridden by specific jockeys. This allows for the development of an algorithm that predicts horse performance under specific conditions, enabling more accurate predictions.

[0098] The analysis unit can simulate multiple scenarios based on subjective information entered by the user and suggest the optimal way to select a horse. For example, the generation AI simulates multiple scenarios based on the turf condition and track trends entered by the user and suggests the optimal way to select a horse. For example, it compares a scenario where the turf is wet with a scenario where the turf is dry. The analysis unit also simulates multiple scenarios based on the horse's appearance in the paddock entered by the user and suggests the optimal way to select a horse. For example, it compares scenarios where the horse is calm with scenarios where it is excited. The analysis unit also simulates multiple scenarios based on the risk-return weighting entered by the user and suggests the optimal way to select a horse. For example, it compares a high-risk, high-return scenario with a low-risk, low-return scenario. In this way, by simulating multiple scenarios, it is possible to suggest the optimal way to select a horse.

[0099] The analysis unit uses the emotion estimation function to suggest a way to select a horse based on the user's emotions, encouraging the user to make an emotionally satisfying choice. For example, the emotion estimation function is used to analyze the emotions the user is feeling when inputting information, and suggests a way to select a horse based on that emotion. For example, information with positive emotions is prioritized in the analysis and options are suggested. The analysis unit also analyzes the user's emotional responses in real time and adjusts the way to select a horse based on the results. For example, if the user is excited, an option with a high risk is suggested. The analysis unit also builds a system that suggests a way to select a horse based on the user's emotions, based on the emotion estimation data. For example, an option with a low risk is suggested if the user is relaxed. In this way, by suggesting a way to select a horse based on the user's emotions, it is possible to encourage the user to make an emotionally satisfying choice.

[0100] The analysis unit can propose optimal horse selection methods for different racecourses and race conditions. For example, the generation AI analyzes data from different racecourses and proposes optimal horse selection methods for each racecourse. For example, horses that have performed well at a particular racecourse are selected preferentially. The analysis unit also proposes optimal horse selection methods for the generation AI for different race conditions (turf, dirt, distance, etc.). For example, horses that have performed well in turf races are selected. The analysis unit also proposes optimal horse selection methods for the generation AI for different weather conditions (sunny, rainy, snowy, etc.). For example, horses that have performed well in races in the rain are selected. This makes it possible to propose optimal horse selection methods for different racecourses and race conditions.

[0101] The analysis unit can use the emotion estimation function to adjust the horse selection method in real time according to the user's emotions and provide the optimal selection. For example, the emotion estimation function is used to analyze the user's emotions in real time and adjust the horse selection method according to those emotions. For example, if the user is excited, a high-risk option is suggested. The analysis unit also monitors the user's emotional reactions in real time and adjusts the horse selection method based on the results. For example, if the user is relaxed, a low-risk option is suggested. The analysis unit also builds a system that adjusts the horse selection method in real time according to the user's emotions based on the emotion estimation data. For example, it prioritizes analysis of information that the user has positive emotions and suggests options. This makes it possible to provide the optimal selection by adjusting the horse selection method in real time according to the user's emotions.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The input unit accepts quantitative data and subjective information entered by the user. For example, quantitative data such as pedigree, past performance, and the current day's odds can be entered. Subjective information such as the condition of the turf, the current day's track conditions at the same racecourse, the state of the horses in the paddock, and risk-return weighting can also be entered in a conversational (chat) format. Step 2: The analysis unit analyzes the quantitative data and subjective information input by the input unit. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the information input by the user. The generation AI can also use a multimodal generation AI to analyze information such as images and audio. The generation AI can also use data mining technology to analyze past race data. Step 3: The suggestion unit suggests the optimal way to select a horse, betting options, and purchase price based on the information analyzed by the analysis unit. For example, the generation AI suggests the optimal way to select a horse based on the information entered by the user. The generation AI can also consider risk-return weighting to suggest the optimal betting options. The generation AI can also consider the user's budget and risk-return weighting to suggest the optimal purchase price.

[0104] 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.

[0105] 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.

[0106] 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.

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

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input unit for inputting quantitative data and subjective information; an analysis unit that analyzes the quantitative data and the subjective information input by the input unit; and a suggestion unit that suggests the optimal way to select a horse, betting options, and purchase amount based on the information analyzed by the analysis unit. A system characterized by:

2. The analysis unit Compare with past race data and prioritize analysis of highly reliable information 2. The system of claim 1.

3. The analysis unit It provides real-time feedback on user input and automatically generates questions to elicit more specific information.

2. The system of claim 1.

4. The analysis unit Analyzing the emotional nuances of the subjective information entered by the user and preferentially processing information with positive emotions 2. The system of claim 1.

5. The input unit The subjective information is input using voice input or image analysis, and the behavior of the horse in the paddock is analyzed in real time.

2. The system of claim 1.

6. The analysis unit By integrating data from different sports and gambling, we will develop a system that can be applied to predictions other than horse racing.

2. The system of claim 1.

Images