Program and information provision method

Abstract

To provide a program and information providing method for supporting the selection of breeding pairs for obtaining offspring with a desired coat color in animal breeding, without depending on the presence or absence of genetic information on the parent generation used in mating or the experience level of the breeder. [Solution] In the information provision system 100, the program causes an information processing device, which is a server host computer for managing and operating the information provision service, to execute a step of outputting a prediction result for the coat color of offspring born from a candidate breeding individual pair arbitrarily selected from the database by executing a prediction model that outputs a prediction result for the coat color of offspring born from a breeding individual pair of the parent generation based on the correspondence between female animals of the parent generation, male animals, and the history of coat color occurrence of offspring born from mating between female and male animals, which are registered in the database.

Description

[Technical Field]

[0001] The present invention relates to a program and an information providing method. [Background technology]

[0002] Pets (companion animals) such as dogs, cats, and rabbits play an important role in human society, providing comfort and emotional connection within the home. These pets are primarily bred and raised by breeders, and their appearance, such as coat color, is a major factor in their popularity in the pet market.

[0003] Breeders consider mating combinations based on the genetic information of the parent animals in order to obtain offspring with popular coat colors. However, the selection of breeding pairs to obtain offspring with desirable coat colors relies heavily on the breeder's experience and is often not based on objective evidence. Furthermore, when parent animals are imported from outside or when artificial insemination is performed using only sperm, detailed genetic information of the parent animals may not be available, making it difficult to determine appropriate matings. In such situations, the accuracy of mating plans to obtain offspring with desirable coat colors may be reduced.

[0004] As an example of animal breeding technology, Patent Document 1 discloses a method for identifying breeding pairs of non-human animals capable of producing offspring animals with predetermined traits by checking a database system containing the results of previous genetic testing of multiple non-human animals. Patent Document 2 also discloses a method for estimating the kinship of companion pets using DNA information of the companion pets. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Special Publication No. 2023-540285 [Patent Document 2] Special Publication No. 2024-546366 Summary of the Invention [Problem to be solved by the invention]

[0006] The present disclosure aims to provide a technology that assists in the selection of breeding pairs in animal breeding to obtain offspring with desired coat colors, without relying on the presence or absence of genetic information from the parent generations used in mating or the experience level of the breeder. [Means for solving the problem]

[0007] In order to solve the above problems, the technology according to the present disclosure employs the following configuration. That is, the gist of the technology according to the present disclosure is as follows.

[0008] [1] A program that causes a computer to execute a step of executing a trained prediction model that can output a prediction result of the coat color of an offspring born from a breeding individual pair of the parent generation based on female animals of the parent generation registered in a database, male animals of the parent generation, and the history of coat color occurrence of offspring born from mating of the female animals and the male animals, and causing the prediction model to output a prediction result of the coat color of an offspring born from a candidate breeding individual pair arbitrarily selected from the database. [2] The program described in [1], wherein the predictive model is a trained large-scale language model, and in the step of outputting the predicted result of the offspring's coat color, a prompt including a data set registered in the database in which the female individuals, the male individuals, and the offspring's coat color occurrence history are associated, a designation of the candidate breeding individual pair, and an instruction to predict the coat color of the offspring to be born from the candidate breeding individual pair is input to the predictive model, thereby causing the predictive model to output the predicted result of the coat color of the offspring to be born from the candidate breeding individual pair. [3] A program that causes a computer to execute a step of executing a predictive model that can output predicted results of breeding individual pairs of parent generations that will produce offspring with a specific coat color based on female animals of the parent generation, male animals, and the history of coat color occurrence in offspring produced by mating between the female and male animals, all registered in a database, and causing the predictive model to output predicted results of candidate breeding individual pairs that can be selected from the database to obtain offspring with a specified coat color. [4] The program described in [3], wherein the predictive model is a trained large-scale language model, and in the step of outputting the predicted results of the candidate breeding individual pairs, the predictive model is caused to output the predicted results of the candidate breeding individual pairs by inputting a prompt to the predictive model that includes a dataset registered in the database in which the female individuals, the male individuals, and the coat color occurrence history of the offspring are associated, a specification of the offspring's coat color, and instructions to predict the candidate breeding individual pairs. [5] The program described in [4], wherein the prompt includes the specification of one of the female and male individuals that are candidates for the breeding individual pair, and in the step of outputting the prediction results for the candidate breeding individual pair, the prediction result for the other of the female and male individuals that are candidates for the breeding individual pair is output to the prediction model. [6] The program described in [2] or [4], wherein the dataset includes information about genetic diseases of at least one of the female individual, the male individual, and the offspring born from mating of the female individual and the male individual, which are registered in a database, and causes a computer to execute a step of causing the predictive model to output a prediction result of the risk of genetic disease in offspring born from the candidate breeding individual pair. [7] An information provision method executed by a computer, comprising the step of executing a trained prediction model capable of outputting a prediction result for the coat color of an offspring born from a breeding individual pair of the parent generation based on female animals of the parent generation, male animals of the parent generation, and the coat color occurrence history of offspring born from mating of the female animals and the male animals, all registered in a database, and having the prediction model output a prediction result for the coat color of an offspring born from a candidate breeding individual pair arbitrarily selected from the database. [8] An information provision method executed by a computer, comprising the steps of: executing a prediction model capable of outputting prediction results for a breeding individual pair of parent generation animals that will produce offspring with a specific coat color based on female animals of the parent generation, male animals, and the history of coat color occurrence in offspring produced by mating between the female animals and the male animals, all registered in a database; causing the prediction model to output prediction results for candidate breeding individual pairs that can be selected from the database to obtain offspring with a specified coat color; and presenting the prediction results of the candidate breeding individual pairs to a user. [Effects of the Invention]

[0009] The technology disclosed herein makes it possible to assist in the selection of breeding pairs in animal breeding to obtain offspring with desired coat colors, without relying on the presence or absence of genetic information from the parent generations used in mating or the experience level of the breeder. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram showing an example of the configuration of an information providing system 100 according to the first embodiment. [Figure 2] FIG. 2 is a schematic diagram illustrating the information providing method according to the first embodiment. [Figure 3] FIG. 3 is a flowchart of the hair color prediction process according to the first embodiment. [Figure 4] FIG. 4 is a diagram showing a specific example of a prediction result presented to a user. [Figure 5]FIG. 5 is a diagram showing an example of the configuration of an information providing system 100A according to the second embodiment. [Figure 6] FIG. 6 is a schematic diagram illustrating an information providing method according to the second embodiment. [Figure 7] FIG. 7 is a flowchart of breeding individual pair prediction processing according to the second embodiment. [Figure 8] FIG. 8 is a diagram showing a specific example of a prediction result presented to the user. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. However, such descriptions should not be interpreted in a limiting sense, and do not limit the subject matter described in the claims. Furthermore, various changes, substitutions, and modifications can be made without departing from the spirit and scope of the present disclosure. Furthermore, different embodiments can be combined as appropriate.

[0012] The animals to which the technology according to the present disclosure is applicable are not particularly limited, but are preferably mammals (e.g., excluding humans). In a specific embodiment, the animals to which the technology is applicable are preferably companion animals kept as pets (hereinafter simply referred to as "pets"). Specific examples of pets include dogs, cats, rabbits, hamsters, and guinea pigs, with dogs and cats being preferred.

[0013] In this specification, the generation used for breeding is sometimes referred to as the "parent generation" or "P generation," the generation of offspring (first-child generation) born from the parent generation is sometimes referred to as the "F1 generation," and the generation one generation above the parent generation (grandparent generation) is sometimes referred to as the "F0 generation."

[0014] In this specification, the term "variety" is not limited to a specific variety, but also includes a cross (mix) of different varieties.

[0015] In the following, an embodiment in which the present invention is applied to dog breeding will be described.

[0016] First Embodiment [Overall configuration] FIG. 1 is a diagram showing an example of the configuration of an information provision system 100 according to the first embodiment. The information provision system 100 according to the first embodiment is a system for providing a service (hereinafter also referred to as an information provision service) that supports animal breeding. Specifically, the information provision system 100 according to the first embodiment supports the selection of a breeding pair to obtain offspring with a desired coat color by predicting the coat color of offspring that will be born when a female and male animal selected as parent candidates (candidates for a breeding pair) are mated in animal breeding.

[0017] As shown in Fig. 1, the information provision system 100 includes a terminal device 10, an information processing device 20, and an artificial intelligence system 30. Note that this configuration is an example, and one configuration may be combined with another configuration. Fig. 1 shows an example in which the information provision system 100 includes one terminal device 10, but the information provision system 100 may include two or more terminal devices 10.

[0018] As shown in Fig. 1, a terminal device 10, an information processing device 20, and an artificial intelligence system 30 are communicatively connected via a network 40. The network used for communication may be constructed using, for example, a public telephone network, a mobile phone network, a wireless communication network, Ethernet, the Internet, an intranet, or the like.

[0019] [Terminal device 10] The terminal device 10 is, for example, an information processing device used by a user who uses the information providing service. The user is, for example, a breeder who breeds animals. The terminal device 10 is, for example, a terminal such as a computer, a tablet terminal, or a smartphone managed by the user.

[0020] The terminal device 10 has a general computer hardware configuration and includes a processor (not shown) and a memory (not shown). The terminal device 10 also includes an input device 101 and an output device 102. The input device 101 is a device for receiving input operations by a user (for example, a touch panel, a touch pad, a pointing device such as a mouse, a keyboard, etc.). The output device 102 is a device for presenting information to a user (for example, a display, a speaker, etc.). In this embodiment, the terminal device 10 may include, for example, a touch panel in which the input device 101 and the output device 102 are integrated.

[0021] The terminal device 10, for example, receives an input operation by a user via the input device 101, thereby inputting the user's instructions to the information providing system 100. In addition, the terminal device 10, for example, outputs the coat color prediction result by the information providing system 100 from the output device 102, thereby presenting the coat color prediction result to the user.

[0022] [Information processing device 20] The information processing device 20 is, for example, a server host computer for managing and operating an information provision service. The information processing device 20 provides the information provision service by, for example, executing a program and performing a process for predicting the coat color of a foal (hereinafter also referred to as coat color prediction process).

[0023] 1, the information processing device 20 has, for example, the hardware configuration of a general computer. Specifically, the information processing device 20 has a CPU 201 which is a processor, a memory 202, a communication interface (communication IF) 203, and a storage medium 204. Each unit is connected to each other via a bus 205. Note that the information processing device 20 may be realized, for example, by multiple physical servers, by multiple virtual machines provided in one physical server, or by using a cloud server.

[0024] The storage medium 204 has, for example, a program storage area (not shown) that stores programs for realizing each function of the information processing device 20 in each of the above-mentioned processes. The storage medium 204 also has, for example, a storage area (not shown) that stores information used in each of the above-mentioned processes. The storage medium 204 may be, for example, a hard disk drive (HDD) or a solid state drive (SSD).

[0025] As shown in FIG. 1, the storage medium 204 stores, as an application program that causes the information processing device 20 to execute predetermined processes, for example, a program PG1 for performing a hair color prediction process.

[0026] The storage medium 204 also stores, as information used for various processes, a breeding database DB1, for example. Details of the breeding database DB1 will be described later.

[0027] The memory 202 is an area into which various programs stored in the storage medium 204 are loaded. The memory 202 may also be used as an area in which these programs store data.

[0028] The CPU 201 performs various processes by executing various programs, such as the program PG1, loaded from the storage medium 204 into the memory 202. The CPU 201 may also acquire and execute various programs from other devices, for example, via the communication network 40.

[0029] The communication IF 203 is a device that communicates with external devices via wired or wireless communication. For example, the communication IF 203 communicates with the terminal device 10 to acquire a user request input to the terminal device 10 via the input device 101 and present the coat color prediction result to the user. The communication IF 203 also acquires the coat color prediction result by the artificial intelligence system 30 by sending a prompt to the artificial intelligence system 30 instructing it to predict the coat color.

[0030] The electronic circuitry of the information processing device 20 may be, for example, a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC). The various processes executed by the information processing device 20 may be executed in, for example, the FPGA or the ASIC.

[0031] [Artificial Intelligence System 30] The artificial intelligence system 30 is an information processing device having a prediction model MD1. The prediction model MD1 is a so-called generative AI model, and is configured, for example, as a pre-trained large-scale language model (LLM). The large-scale language model constituting the prediction model MD1 may be a known model used in the field of natural language processing. Such a large-scale language model may be, for example, a recurrent neural network model, a long short-term memory network model, or a Transformer model. The pre-training of the large-scale language model is performed based on general-purpose data. In this embodiment, it is assumed that the large-scale language model constituting the prediction model MD1 has already learned the relationship between coat color and the genotype of a coat color-related gene locus. However, the prediction model according to the present disclosure is not limited to this.

[0032] The artificial intelligence system 30 inputs a prompt to the prediction model MD1, and outputs a response to the prompt. Specifically, the artificial intelligence system 30 has a dialogue (chat) function, and inputs any inquiry or command instruction to the prediction model MD1 as a text prompt, and outputs a response to the inquiry or command instruction. Examples of such an artificial intelligence system 30 that can be used include ChatGPT, OpenAI GPT, PerplexityAsk, and BingAI.

[0033] When the prompt output by the information processing device 20 is input, the prediction model MD1 outputs a prediction result for the coat color of the offspring to be born from a pair of breeding individuals of the parent generation, based on the female animal, the male animal, and the coat color occurrence history of the offspring born from the mating of the female animal and the male animal, which are registered in the mating database DB1.

[0034] [Mating Database DB1] Mating database DB1 stores performance data on the actual mating of multiple breeding individual pairs. Specifically, mating database DB1 stores a mating data set consisting of multiple mating data in which information on female individuals from the P generation (parent generation) (hereinafter also referred to as female individual information), information on male individuals from the P generation (hereinafter also referred to as male individual information), and information on offspring from the F1 generation (offspring generation) produced by mating between the female individuals and the male individuals (hereinafter also referred to as offspring information) are associated with each other. Mating database DB1 is configured as a relational database that stores data in table format.

[0035] The female individual information and male individual information include at least identification information for identifying the individual. The identification information is, for example, an identification ID (identifier) ​​unique to each individual. The identification information may also be an individual name unique to each individual, as long as it is unique. The female individual information and male individual information may also include information indicating the individual's breed (e.g., toy poodle, pomeranian, chihuahua, etc.), information indicating the individual's coat color (e.g., white, black, cream, red, orange, yellow, black tan, etc.), and information on genetic diseases the individual suffers from (including developing) (e.g., in the case of dogs, progressive retinal atrophy (PRA), hereditary cataracts, collie eye anomaly (CEA), von Willebrand's disease (vWD), ivermectin intoxication (MDR1 gene mutation), osteogenesis imperfecta, X-linked muscular dystrophy, ceroid lipofuscinosis (CL), GM1 gangliosidosis, cystinuria, etc.).

[0036] The offspring information includes at least information indicating the coat color of the offspring as mating performance data of the breeding individual pair. The offspring information may also include information regarding any genetic diseases the offspring may suffer from.

[0037] In this embodiment, the breeding database DB1 is stored in the information processing device 20, but it may also be stored in an external server device and acquired by the information processing device 20 via a communication network. The breeding database DB1 may be managed in the form of a database server, on a cloud server, or as a distributed database.

[0038] [Information providing method of the first embodiment] Next, an information provision method by the information provision system 100 according to the first embodiment will be described. Fig. 2 is a schematic diagram illustrating the information provision method according to the first embodiment. In the information provision method according to the first embodiment, the CPU 201 of the information processing device 20 executes a program PG1 to perform a coat color prediction process. Fig. 3 is a flowchart of the coat color prediction process according to the first embodiment.

[0039] As shown in FIG. 3, first, in step S1, the information processing device 20 accepts, via the terminal device 10, the designation of a combination of a female individual and a male individual selected by the user from the breeding database DB1 as a candidate for a breeding individual pair (hereinafter also referred to as a breeding individual pair candidate) (S1 in FIG. 3). In detail, the breeding individual pair candidate designated here is a combination of one female individual (hereinafter also referred to as a mother candidate) and one male individual (hereinafter also referred to as a father candidate) arbitrarily selected by the user from a plurality of female individuals and a plurality of male individuals whose mating data are registered in the breeding database DB1. The user designates a candidate for a breeding individual pair from the individuals registered in the breeding database DB1, for example, by performing an input operation on the terminal device 101.

[0040] After step S1, in step S2, the information processing device 20 generates a prompt to cause the prediction model MD1 to output a prediction result of the coat color of the offspring that will be produced when breeding individual pair candidates designated by the user from the breeding database DB1 are mated (S2 in FIG. 3). Specifically, the information processing device 20 generates a prompt that includes a breeding dataset in which female individuals, male individuals, and offspring coat colors are associated with each other and that is registered in the breeding database DB1, a designation of the female individuals and male individuals selected by the user as candidates for the breeding individual pair, and an instruction to predict the coat color of the offspring.

[0041] In this embodiment, the information processing device 20 generates a prompt based on the user's designation of a breeding individual pair candidate, but the prompt may also be input to the information processing device 20 by the user.

[0042] Following step S2, in step S3, the information processing device 20 executes the prediction model MD1 of the artificial intelligence system 30, causing the prediction model MD1 to output a prediction result of the coat color of the offspring born from the candidate breeding individual pair (S3 in FIG. 3). Specifically, the information processing device 20 inputs the above-mentioned prompt into the prediction model MD1 of the artificial intelligence system 30, causing the prediction model MD1 to perform inference and output a prediction result of the coat color. The information processing device 20 obtains the prediction result output by the prediction model MD1 from the artificial intelligence system 30.

[0043] Following step S3, in step S4, the information processing device 20 presents the prediction result of the offspring's coat color output by the prediction model MD1 to the user (S4 in FIG. 3). Specifically, the information processing device 20 transmits the prediction result of the offspring's coat color to the terminal device 10, which then outputs it from the output device 102 of the terminal device 10. FIG. 4 is a diagram showing a specific example of the prediction result presented to the user. FIG. 4 shows an example of the prediction result when the user selects "Hanako" (a candidate mother) and "Taro" (a candidate father) as candidates for a breeding individual pair.

[0044] In this way, the coat color prediction process is completed. The user can refer to the coat color prediction results presented by the information providing system 100 to select a breeding individual pair to obtain a desired coat color.

[0045] The information processing device 20 may input a breeding data set including breeding data of the F0 generation, which is the generation immediately above the P generation (parent generation), into the prediction model MD1. This can further improve the accuracy of genotype estimation by the prediction model MD1, and as a result, can further improve the accuracy of coat color prediction.

[0046] [Effects of the first embodiment] As described above, the program PG1 according to the first embodiment executes the prediction model MD1, causing the information processing device 20 (computer) to execute a step of outputting a prediction result of the coat color of an offspring born from a candidate breeding individual pair consisting of a female individual and a male individual arbitrarily selected from the breeding database DB1. The prediction model MD1 outputs a prediction result of the coat color of an offspring born from a breeding individual pair of parent generations based on the female animal, the male animal, and the coat color occurrence history of offspring born from the mating of the female individual and the male individual, which are registered in the breeding database DB1.

[0047] That is, in the first embodiment, prediction model MD1 is used to predict the coat color of offspring from a candidate breeding pair randomly selected from the breeding database DB1, based on the coat color occurrence record of offspring from breeding pairs registered in the breeding database DB1. This allows the offspring's coat color to be predicted without using genetic information on the female and male individuals who are candidate breeding pairs, eliminating the need for genetic testing and allowing the offspring's coat color to be predicted easily. Furthermore, prediction model MD1 allows the offspring's coat color to be predicted based on objective data, such as the coat color occurrence record, allowing the offspring's coat color to be predicted with high accuracy without relying on empirical data. As described above, according to the first embodiment, it is possible to select a breeding pair to obtain offspring with a desired coat color, without relying on the presence or absence of genetic information on the parent generations used in the mating or the breeder's empirical data.

[0048] In the first embodiment, the prediction model MD1 is a trained large-scale language model, and in the step of causing the prediction model MD1 to output a coat color prediction result, a prompt including a mating data set registered in the mating database DB1 in which female individuals, male individuals, and offspring coat colors are associated, a designation of female individuals and male individuals selected as candidates for breeding individual pairs, and an instruction to predict the offspring coat color is input to the prediction model MD1, causing the prediction model MD1 to output the offspring coat color prediction result. This allows the use of a trained large-scale language model to easily predict the offspring coat color with high accuracy.

[0049] [Modification of the first embodiment] In the first embodiment, the mating data set input to the prediction model MD1 may include the coat colors of the female and / or male individuals of the parent generation, in addition to the coat color occurrence record of the offspring. This allows the prediction model MD1 to predict the coat color of the offspring born from a candidate breeding individual pair based on the coat color of the candidate mother and / or the coat color of the father, and the coat color occurrence record of the coat color of the offspring born to the candidate mother and / or the coat color occurrence record of the offspring born to the candidate father. As a result, the accuracy of predicting the offspring's coat color can be further improved.

[0050] Furthermore, in the first embodiment, the breeding dataset input to the prediction model MD1 may include information registered in the database regarding the genetic disease incidence of at least one of female individuals, male individuals, and offspring produced by mating between female individuals and male individuals. The program PG1 may then cause the information processing device 20 (computer) to execute a step of causing the prediction model MD1 to output a predicted result of the genetic disease incidence risk of offspring produced by a candidate breeding individual pair. In this case, the information processing device 20 may cause the prediction model MD1 to output a predicted result of the offspring's coat color and genetic disease incidence risk by inputting a breeding dataset in which female individuals, male individuals, and offspring coat colors are associated, a designation of the candidate breeding individual pair, and a prompt including instructions to predict the offspring's coat color and genetic disease incidence risk to the prediction model MD1. This allows the user to refer to the predicted results of coat color and genetic disease incidence risk presented by the information providing system 100 when selecting a breeding individual pair. As a result, the user can select breeding pairs that have a lower risk of genetic disease and can produce the desired coat color.

[0051] Furthermore, in the first embodiment, the prediction model MD1 may be configured as a non-generative AI type pre-trained machine learning model. In this case, the prediction model MD1 may be, for example, a machine learning model trained using the coat color of a breeding individual pair as an explanatory variable and the coat color of offspring born from the breeding individual pair as a target variable, and when the coat colors of a candidate breeding individual pair are input, the prediction model may output a probability distribution of the coat colors of offspring born from the candidate breeding individual pair as a prediction result. Furthermore, the information processing device 20 may input the coat colors (coat colors of the female individual and the male individual) of the candidate breeding individual pair specified by the user into the prediction model MD1, causing the prediction model MD1 to perform inference and output a probability distribution of the offspring's coat colors as a prediction result.

[0052] Second Embodiment The second embodiment will be described below with reference to the drawings. The following description will focus on differences from the information provision system 100 according to the first embodiment, and the same components as those in the information provision system 100 will be denoted by the same reference numerals and will not be described in detail.

[0053] [Overall configuration] 5 is a diagram showing an example of the configuration of an information provision system 100A according to the second embodiment. In animal breeding, the information provision system 100A according to the second embodiment predicts combinations of female and male individuals that are candidates for breeding pairs to obtain offspring with a specified coat color, thereby assisting in the selection of breeding pairs to obtain offspring with a desired coat color.

[0054] In the second embodiment, the prediction model MD1, which is a generation AI model, outputs a prediction result of a breeding individual pair of parent generations that will produce offspring with a specific coat color, based on the female animal, the male animal, and the coat color occurrence history of offspring produced by mating between the female animal and the male animal, which are registered in the breeding database DB1, when a prompt output by the information processing device 20A is input.

[0055] [Information providing method according to the second embodiment] Next, we will explain the information provision method by the information provision system 100A according to the second embodiment. In the information provision method according to the second embodiment, the CPU 201 of the information processing device 20A executes the program PG2 to perform a process of predicting candidates for breeding individual pairs for obtaining offspring with a specified coat color (hereinafter also referred to as breeding individual pair prediction process).

[0056] Fig. 6 is a schematic diagram illustrating an information providing method according to the second embodiment. Fig. 7 is a flowchart of breeding individual pair prediction processing according to the second embodiment.

[0057] As shown in FIG. 7, first, in step S1A, information processing device 20A accepts a user's designation of the offspring's breed and coat color via terminal device 10 (S1A in FIG. 7). The user designates the desired breed and coat color by, for example, performing an input operation on terminal device 10 using input device 101. If the desired breed is a mix of multiple different breeds, the multiple different breeds are designated. At this time, information processing device 20A may present candidate breeds and coat colors via terminal device 10.

[0058] After step S1A, in step S2A, information processing device 20A generates a prompt for causing prediction model MD1 to output prediction results of breeding individual pair candidates for obtaining offspring with a coat color designated by the user (S2A in FIG. 7). Specifically, information processing device 20A generates a prompt including a mating data set in which female individuals, male individuals, and offspring coat colors are associated with each other and registered in mating database DB1, a user's designation of the offspring breed, a user's designation of the offspring coat color, and an instruction to predict candidate breeding individual pairs.

[0059] In this embodiment, the information processing device 20A generates a prompt based on the coat color designated by the user, but the prompt may also be input to the information processing device 20A by the user.

[0060] Following step S2A, in step S3A, the information processing device 20A executes the prediction model MD1 of the artificial intelligence system 30, causing the prediction model MD1 to output a prediction result of a candidate breeding individual pair for obtaining an offspring having a specified coat color (S3A in FIG. 7). Specifically, the information processing device 20A inputs the above-mentioned prompt into the prediction model MD1 of the artificial intelligence system 30, causing the prediction model MD1 to perform inference and output a prediction result of a candidate breeding individual pair. The information processing device 20A obtains the prediction result output by the prediction model MD1 from the artificial intelligence system 30.

[0061] After step S3A, in step S4A, the information processing device 20A presents the prediction results of the breeding individual pair candidates output by the prediction model MD1 to the user (S4A in FIG. 7). Specifically, the information processing device 20A transmits the prediction results of the breeding individual pair candidates to the terminal device 10, and causes the output device 102 of the terminal device 10 to output them. FIG. 8 is a diagram showing a specific example of the prediction results presented to the user.

[0062] For example, when multiple breeding individual pair candidates are output as prediction results, as shown in FIG. 8, the information processing device 20A may preferentially present breeding individual pair candidates that are more likely to produce offspring with the specified coat color. For example, the information processing device 20A may present the breeding individual pair candidates in order starting with the breeding individual pair candidate with the highest probability of producing offspring with the specified coat color. Furthermore, for example, the information processing device 20A may narrow down the breeding individual pair candidates that are presented to those with a probability of producing offspring with the specified coat color that is equal to or greater than a predetermined threshold. Note that the above-mentioned presented content, which takes into account the probability of coat color occurrence, may be output by the prediction model MD1.

[0063] In this way, the coat color prediction process is completed. The user can refer to the prediction results of the breeding individual pair candidates presented by the information provision system 100A to select a breeding individual pair to obtain a desired coat color.

[0064] For example, if the user does not specify the child variety, the information processing device 20A does not need to include the specification of the variety in the prompt to be input to the prediction model MD1 in step S3A.

[0065] Furthermore, the information processing device 20A may input a mating data set including mating data of the F0 generation, which is the generation immediately above the P generation (parent generation), to the prediction model MD1. This can further improve the accuracy of genotype estimation by the prediction model MD1, and as a result, can further improve the prediction accuracy of breeding individual pair candidates.

[0066] Furthermore, in step S1A, the information processing device 20A may accept from the user a designation of one of the female and male individuals that will be candidates for the breeding pair from among the female and male individuals registered in the breeding database DB1. In this case, the information processing device 20A may include the designation of one of the female and male individuals that will be candidates for the breeding pair in the prompt that is input to the prediction model MD1 in step S3A. Then, the information processing device 20A may cause the prediction model MD1 to output a prediction result for the other of the female and male individuals that will be candidates for the breeding pair.

[0067] [Effects of the second embodiment] As described above, the program PG2 according to the second embodiment executes the prediction model MD1, causing the information processing device 20A (computer) to execute the following steps: acquiring prediction results for breeding pair candidates that can be selected from the breeding database DB1 to obtain offspring with a specified coat color; and presenting the prediction results for the breeding pair candidates to the user. The prediction model MD1 outputs prediction results for breeding pair candidates of the parent generation that will produce offspring with a specific coat color, based on the female animals, male animals, and coat color occurrence records of offspring produced by mating between the female animals and the male animals, all registered in the breeding database DB1.

[0068] That is, in the second embodiment, prediction model MD1 is used to predict candidate breeding pairs from female and male individuals registered in the breeding database DB1 to obtain offspring with a specified coat color, based on the coat color occurrence history of offspring born from breeding pairs registered in the breeding database DB1. This allows candidate breeding pairs to be predicted without using genetic information on the female and male individuals, eliminating the need for genetic testing and allowing candidate breeding pairs to be predicted easily. Furthermore, prediction model MD1 can be used to predict candidate breeding pairs based on objective data, such as the coat color occurrence history, allowing candidate breeding pairs to be predicted with high accuracy without relying on empirical data. As described above, according to the second embodiment, it is possible to select breeding pairs to obtain offspring with a desired coat color, without relying on the presence or absence of genetic information on the parent generations used in the mating or the breeder's empirical data.

[0069] In addition, in the second embodiment, the prediction model MD1 is a trained large-scale language model, and in the step of obtaining the predicted coat color results, a prompt including a mating data set registered in the mating database DB1 in which female individuals, male individuals, and offspring coat colors are associated, a specification of the offspring coat color, and an instruction to predict candidate breeding individual pairs is input to the prediction model MD1, causing the prediction model MD1 to output the predicted breeding individual pair candidate results.

[0070] According to this, by using a trained large-scale language model, it is possible to predict breeding pair candidates with high accuracy and ease. Furthermore, by including the mating dataset input to the prediction model MD1 with the record of occurrence of offspring coat color, the prediction model MD1 can estimate the genotypes of coat color-related loci of female and male individuals included in the mating dataset. As a result, it is possible to predict breeding pair candidates with high accuracy compared to when the record of occurrence of offspring coat color is not used.

[0071] [Modification of the second embodiment] In the second embodiment, the mating data set input to the prediction model MD1 may include the coat colors of the female individuals and / or male individuals of the parent generation, in addition to the coat color occurrence record of the offspring. This allows the prediction model MD1 to predict candidate breeding individual pairs for obtaining offspring with a specified coat color based on the coat colors of the candidate mother and / or the coat color of the father, and the coat color occurrence record of the coat color of offspring born to the candidate mother and / or the coat color occurrence record of the coat color of offspring born to the candidate father. As a result, the prediction accuracy of candidate breeding individual pairs can be further improved.

[0072] Furthermore, in the second embodiment, the breeding dataset input to the prediction model MD1 may include information registered in the database regarding the genetic disease incidence history of at least one of female individuals, male individuals, and offspring produced by mating between female individuals and male individuals. The program PG1 may cause the information processing device 20A (computer) to execute the following steps: outputting a prediction result of the genetic disease incidence risk of offspring born from candidate breeding individual pairs to the prediction model MD1; and preferentially presenting to the user candidate breeding individual pairs that have a higher probability of producing offspring with a specified coat color and a lower risk of the offspring suffering from the genetic disease. In this case, the information processing device 20 may input to the prediction model MD1 a prompt including the breeding dataset, a user's designation of the offspring's breed, a user's designation of the offspring's coat color, and an instruction to predict candidate breeding individual pairs for producing offspring with the specified coat color and the genetic disease incidence risk of offspring born from the candidate breeding individual pairs, thereby causing the prediction model MD1 to output a prediction result of the genetic disease incidence risk of the offspring born from the candidate breeding individual pairs. This allows the user to refer to the predicted results of the breeding pair candidates presented by information provision system 100A and the predicted results of the genetic disease risk of the offspring of the breeding pair when selecting a breeding pair, thereby enabling the user to select a breeding pair that will have a lower genetic disease risk and will produce a desired coat color.

[0073] Furthermore, in the second embodiment, the prediction model MD1 may be configured as a non-generative AI type pre-trained machine learning model. In this case, the prediction model MD1 may be, for example, a machine learning model trained using the coat color of a breeding individual pair as an explanatory variable and the coat color of offspring born from the breeding individual pair as a target variable, and when the coat colors of candidate breeding individual pairs are input, the prediction model may output a probability distribution of the coat colors of offspring born from the candidate breeding individual pair as a prediction result. Furthermore, the information processing device 20 may execute the prediction model MD1 to output a probability distribution of the coat colors of offspring for each breeding individual pair, and select candidate breeding individual pairs to be proposed to the user based on the probability distribution.

[0074] <Other> The programs PG1 and PG2, which describe the various processes performed in the above-described embodiments, can be recorded on a computer-readable recording medium. Examples of the computer-readable recording medium include a magnetic recording device, an optical disk, a magneto-optical recording medium, and a semiconductor memory. The programs may or may not be pre-stored in the information processing device 20 or 20A. For example, the programs may be stored in an external server device and then loaded onto the information processing device 20 or 20A via a communication network.

[0075] The programs PG1 and PG2 may be configured to include multiple programs (modules). Furthermore, the information processing devices 20 and 20A may omit some of the functions realized by executing the programs PG1 and PG2 by assigning those functions to another device that can communicate with the information processing devices 20 and 20A.

[0076] Furthermore, the programs PG1 and PG2 may be installed as application software in the terminal device 10 and executed by the terminal device 10. That is, the terminal device 10 may perform the coat color prediction process and the breeding individual pair prediction process.

[0077] Furthermore, although the above describes an example in which the prediction model MD1 is stored in the artificial intelligence system 30, the prediction model MD1 may be stored in the information processing device 20, 20A, or in the terminal device 10. [Explanation of symbols]

[0078] 10: Terminal device 20, 20A: Information processing device 30: Artificial Intelligence System 100, 100A: Information provision system PG1, PG2: Program MD1: Prediction model DB1: Breeding database

Claims

1. a step of causing a computer to execute a prediction model capable of outputting a prediction result of a breeding individual pair of parent generations that will produce offspring with a specific coat color, based on individual information registered in a database, the individual information including at least identification information of female individuals of parent generations of animals, the individual information including at least identification information of male individuals of said animals, and the occurrence record of coat colors of offspring produced by mating of said female individuals with said male individuals, thereby causing the prediction model to output a prediction result of candidate breeding individual pairs that can be selected from said database to obtain offspring with a specified coat color; program.

2. the predictive model is a trained large-scale language model; In the step of outputting the predicted results of the candidates for the breeding individual pair, a prompt including a data set registered in the database in which individual information including at least identification information of the female individual and individual information including at least identification information of the male individual and the occurrence record of the offspring's coat color are associated, a designation of the offspring's coat color, and an instruction to predict the candidates for the breeding individual pair is input to the prediction model, thereby causing the prediction model to output the predicted results of the candidates for the breeding individual pair. The program according to claim 1.

3. the prompt includes designating one of the female and the male as a candidate for the breeding pair; In the step of outputting the prediction results of the breeding individual pair candidates, the prediction result of the other of the female individual and the male individual that are the breeding individual pair candidates is output to the prediction model. The program according to claim 2.

4. The data set is Information about genetic diseases of the female individual, whose individual information including at least identification information is registered in the database; Information about genetic diseases of the male individual whose individual information including at least identification information is registered in the database; and Among the information about genetic diseases of offspring born from mating of the female individual with the male individual, whose individual information including at least identification information is registered in the database, At least one of causing the computer to execute a step of causing the prediction model to output a prediction result of the risk of genetic disease in offspring born from the candidate breeding individual pair; The program according to claim 2.

5. 1. A computer-implemented method for providing information, comprising: a step of executing a prediction model capable of outputting prediction results of breeding individual pairs of parent generations that will produce offspring with a specific coat color based on individual information registered in a database, the individual information including at least identification information of female individuals of parent generation animals, the individual information including at least identification information of male individuals of said animals, and the occurrence record of coat colors of offspring produced by mating of said female individuals with said male individuals, thereby causing the prediction model to output prediction results of candidate breeding individual pairs that can be selected from said database to obtain offspring with a specified coat color; and presenting the predicted results of the breeding individual pair candidates to a user. Information provision method.

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