Method of recommending companion animal product and device for performing same

Abstract

The present invention relates to a method of recommending a companion animal product and a device for performing the method. The method of recommending a companion animal product may comprise the steps of: receiving, by a product recommendation device, data for product recommendation for a companion animal; and recommending, by the product recommendation device, a product for the companion animal through preprocessing of the data, wherein the data includes genetic inspection data, companion animal data or health diary data, and product data.

Description

Method for recommending pet products and device for performing such method

[0001] The present invention relates to a method for recommending pet products and a device for performing such a method. More specifically, the present invention relates to a method for recommending pet products based on the results of a genetic test of a pet, and a device for performing such a method.

[0002] Pet tech, a portmanteau of "pet" and "technology," refers to products and services that incorporate cutting-edge information and communication technology (ICT), such as big data and artificial intelligence (AI), into pet-related products to aid in pet care. The scope of pet tech is extremely broad and diverse, ranging from CCTV products that monitor pets left alone at home, to wearable devices that track their locations, to smart toilets that automatically clean cat waste, to services that automatically produce and deliver customized, natural pet food using robots, and even genetically-based pet health management services.

[0003] Digital healthcare for pets refers to pet tech products and services directly related to pet health management. Examples include wearable devices that not only track pet locations but also monitor body temperature, heart rate, respiration rate, and pH levels, as well as genetically-based health management services.

[0004] In particular, pet genetic testing services allow customers to conveniently apply and receive results from home. They can also predict genetic diseases in their pets early, enabling disease prevention through personalized care, including improved diet and exercise. Furthermore, based on the results of pet genetic testing, recommendations for products tailored to the needs of your pet can be made.

[0005] Related technology includes Korean Publication Patent No. 10-2021-0156671.

[0006] The purpose of the present invention is to solve all of the above-described problems.

[0007] In addition, the present invention aims to recommend products necessary for a pet based on genetic test data for the pet.

[0008] In addition, the present invention aims to predict a disease caused by a genetic mutation in a companion animal based on genetic test data for the companion animal, and to recommend products necessary for the companion animal by considering whether the predicted disease has occurred.

[0009] A representative configuration of the present invention to achieve the above purpose is as follows.

[0010] According to one embodiment of the present invention, a method for recommending a pet product includes a step in which a product recommendation device receives data for recommending a product for a pet, and a step in which the product recommendation device recommends a product for the pet through preprocessing of the data, wherein the data may include genetic test data, pet data or health diary data, and product data.

[0011] Meanwhile, the above preprocessing can generate pet recommendation element data and product recommendation element data based on the above data.

[0012] Additionally, the recommendation of the above product can be performed based on the matching of the pet recommendation element data and the product recommendation element data.

[0013] According to another embodiment of the present invention, a product recommendation device for recommending pet products is implemented to receive data for recommending products for pets and recommend products for the pets through preprocessing of the data, wherein the data may include genetic test data, pet data or health diary data, and product data.

[0014] Meanwhile, the above preprocessing can generate pet recommendation element data and product recommendation element data based on the above data.

[0015] Additionally, the recommendation of the above product can be performed based on the matching of the pet recommendation element data and the product recommendation element data.

[0016] According to the present invention, products necessary for a pet can be recommended based on genetic test data for the pet.

[0017] In addition, according to the present invention, diseases caused by genetic mutations in companion animals can be predicted based on genetic test data for companion animals, and products necessary for companion animals can be recommended by considering whether or not the predicted disease occurs.

[0018] Figure 1 is a conceptual diagram illustrating a product recommendation device for companion animals according to an embodiment of the present invention.

[0019] Figure 2 is a conceptual diagram showing the operation of a data preprocessing unit according to an embodiment of the present invention.

[0020] Figure 3 is a conceptual diagram illustrating a preprocessing method for health diary data according to an embodiment of the present invention.

[0021] Figure 4 is a conceptual diagram illustrating the operation of a product recommendation unit according to an embodiment of the present invention.

[0022] Figure 5 is a conceptual diagram illustrating the operation of a product recommendation unit according to an embodiment of the present invention.

[0023] Figure 6 is a conceptual diagram illustrating the operation of a product recommendation unit according to an embodiment of the present invention.

[0024] FIG. 7 is a conceptual diagram illustrating a symptom classification method based on a decision tree for more accurately predicting disease in companion animals according to an embodiment of the present invention.

[0025] The following detailed description of the present invention refers to the accompanying drawings, which illustrate specific embodiments in which the present invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the present invention. It should be understood that the various embodiments of the present invention, while different from each other, are not necessarily mutually exclusive. For example, specific shapes, structures, and characteristics described herein may be modified and implemented from one embodiment to another without departing from the spirit and scope of the present invention. Furthermore, it should be understood that the positions or arrangements of individual components within each embodiment may also be modified without departing from the spirit and scope of the present invention. Accordingly, the following detailed description is not to be taken in a limiting sense, and the scope of the present invention is to be construed to encompass the scope of the claims and all equivalents thereof. Like reference numerals in the drawings represent the same or similar elements throughout the several aspects.

[0026] Hereinafter, various preferred embodiments of the present invention will be described in detail with reference to the attached drawings so that a person having ordinary skill in the art to which the present invention pertains can easily practice the present invention.

[0027] Hereinafter, the term “gene” is used for convenience of explanation, but it can be interpreted to mean including genetic mutations based on genes.

[0028]

[0029] Figure 1 is a conceptual diagram illustrating a product recommendation device for companion animals according to an embodiment of the present invention.

[0030] In Fig. 1, a product recommendation device for recommending products for pets based on genetic test data of the pet is disclosed.

[0031] Referring to FIG. 1, the product recommendation device may include a data input unit (100), a data preprocessing unit (110), a product recommendation unit (120), and a processor (130).

[0032] The data input unit (100) may be implemented to receive data for recommending products for companion animals. For example, the data input unit (100) may receive genetic test data, companion animal data, health diary data, product data, etc. The genetic test data is data obtained by performing a genetic test on a companion animal. The companion animal data may include companion animal-related data that can be utilized for recommending products for companion animals, such as companion animal disease data, companion animal age data, and companion animal gender data. The health diary data may include records of daily life (eating, bowel movements, exercise, etc.) for managing companion animal diseases. The product data may include data regarding products to be recommended for companion animals. The products may include products recommended based on the companion animal's disease, such as nutritional supplements, food, utensils, equipment, flooring, lighting, and medicine.

[0033] The data preprocessing unit (110) may be implemented to perform data preprocessing for recommending products to companion animals. Genetic testing data, companion animal data, and health diary data may be generated as companion animal recommendation element data in the data preprocessing unit. Product data may be generated as companion animal recommendation element data in the data preprocessing unit. The companion animal recommendation element data and the companion animal recommendation element data may be matched and used to recommend specific products for specific companion animals.

[0034] A product recommendation unit (120) may be implemented to recommend specific products needed by a pet. The product recommendation unit (120) may recommend specific products needed by a specific pet by matching pet recommendation element data with product recommendation element data.

[0035] The processor (130) can be implemented to control the operations of the data input unit (100), the data preprocessing unit (110), and the product recommendation unit (120).

[0036]

[0037] Figure 2 is a conceptual diagram showing the operation of a data preprocessing unit according to an embodiment of the present invention.

[0038] In Fig. 2, an operation for generating pet recommendation element data and product recommendation element data in the data preprocessing unit is initiated.

[0039] Referring to FIG. 2, the data preprocessing unit can generate pet recommendation element data (200) based on genetic test data, pet data, and health diary data.

[0040] The pet recommendation element data (200) may include at least one of genetic data, genetic mutation data, predicted disease data, confirmed disease data, pet breed data, gender data, age data, health diary data, etc. as subordinate pet recommendation element data. The combined pet recommendation element data (220) may be a combination of at least one subordinate pet recommendation element data. Product recommendations may be made based on the combined pet recommendation element data (220) for each product.

[0041] Genetic data, genetic mutation data, predicted disease data, confirmed disease data, pet breed data, gender data, age data, and health diary data can each be coded data.

[0042] Genetic data may be data on disease-causing genes that may cause diseases in companion animals. Genetic variant data may be data on genetic variations in disease-causing genes in companion animals. Predicted disease data may be data on diseases predicted based on disease-causing genes and genetic variants, as well as the predicted timing of disease onset. Confirmed disease data may be data on diseases already determined to be confirmed. Pet breed data may be data on the breed of the companion animal. Gender data may be data on the gender of the companion animal. Age data may be data on the age of the companion animal. Health diary data may be preprocessed and generated as health diary analysis result data using health diary analysis basic information determined based on genetic test data, companion animal data, and / or companion animal disease data. The preprocessing method for health diary data is described below.

[0043] The data preprocessing unit may generate pet recommendation element data (200) based on lower-level pet recommendation element data, and may also generate combined pet recommendation element data (220) based on a combination of lower-level pet recommendation element data.

[0044] Additionally, the data preprocessing unit can generate product recommendation element data (250) based on product data. The product recommendation element data (250) can include lower-level product recommendation element data to correspond to lower-level pet recommendation element data related to the product.

[0045] The plurality of sub-product recommendation element data may include genetic data (product), genetic mutation data (product), predicted disease data (product), confirmed disease data (product), pet breed data (product), gender data (product), age data (product), and health diary data (product). Combination product recommendation element data (260) may be determined by combining at least one of the plurality of sub-product recommendation element data. A specific product may have only some of the sub-product recommendation element data among the plurality of sub-product recommendation element data, and in such a case, the product data of the specific product may be preprocessed to generate combination product recommendation element data (260).

[0046] Genetic data (products) may be a recommendation element for sub-products related to the genetic data. For example, Product A may be a product needed by a pet with gene X, and in this case, the genetic data (product) of Product A may include data about gene X.

[0047] Genetic variant data (products) can be a recommendation element for sub-products related to genetic variant data. For example, Product A may be a product needed by a pet with genetic variant N. In this case, Product A's genetic variant data (product) may include data on genetic variant N.

[0048] Predicted disease data (products) can be a sub-product recommendation element related to the predicted disease data. For example, Product A may be a product needed by a pet with predicted disease Y. In this case, Product A's predicted disease data (products) may include data about predicted disease Y.

[0049] Disease diagnosis data (products) can be a recommendation element for sub-products related to the disease diagnosis data. For example, Product A may be a product needed by a pet diagnosed with Disease K. In this case, Product A's disease diagnosis data (products) may include data related to the confirmed Disease K.

[0050] Pet breed data (products) can be a recommendation element for sub-products related to a pet breed. For example, Product A may be a product needed by a pet of breed R. In this case, Product A's pet breed data (products) may include data about breed R.

[0051] Gender data (product) can be a sub-product recommendation element related to the pet's gender. For example, Product A may be a product for a pet of gender M. In this case, Product A's gender data (product) may include data about gender M.

[0052] Age data (product) can be a sub-product recommendation element related to a pet's age. For example, Product A may be a product for pets over a certain age. In this case, Product A's age data (product) could include data about the age of the pet requiring the product.

[0053] Health diary data (products) can be a recommendation element for sub-products related to health diaries. For example, Product A may be a product needed by a pet with specific health diary analysis results. In this case, Product A's health diary data (product) may include health diary analysis results.

[0054] Based on the pet recommendation element data / combined pet recommendation element data and product recommendation element data / combined product recommendation element data preprocessed and extracted as above, recommendations for specific products for specific pets can be made.

[0055] The sub-pet recommendation element data and sub-product recommendation element data disclosed in FIG. 2 are just one example, and various other data may be used as the sub-pet recommendation element data and sub-product recommendation element data, and such embodiments may also be included in the scope of the present invention.

[0056]

[0057] Figure 3 is a conceptual diagram illustrating a preprocessing method for health diary data according to an embodiment of the present invention.

[0058] In Fig. 3, an operation for preprocessing health diary data by considering the predicted disease and illness of a companion animal and generating health diary analysis result data is initiated.

[0059] Referring to FIG. 3, the preprocessing unit can preprocess health diary data based on genetic test data and pet data to generate health diary analysis result data.

[0060] The preprocessing unit can determine target words (300) and target sentences (320) based on genetic test data and pet data. The target words (300) and target sentences (320) can include words and sentences that serve as targets for performing health diary analysis on pets.

[0061] For example, if a pet has a gene related to obesity, the target word (300) and target sentence (320) may be set as words and sentences that can extract information about dietary habits, amount of exercise, etc. As another example, if a pet has a gene related to patellar dislocation, the target word (300) and target sentence (320) may be set as words and sentences that can extract information about behavioral habits related to patellar dislocation.

[0062] Based on target words (300) and target sentences (320), health diary data can be analyzed to generate health diary analysis basic information (340). The health diary analysis basic information (340) can be information that serves as the basis for generating health diary analysis results. For example, based on target words (300) and target sentences (320) set for a pet with an obesity-related gene, target sentences related to obesity, such as "the amount of food eaten has increased significantly compared to before" and "the pet constantly acts as if it is hungry," can be extracted as health diary analysis basic information (340).

[0063] That is, by excluding words and sentences with low relevance in the health diary, information for analyzing the health of a pet in the health diary can be determined as basic information for health diary analysis (340).

[0064] A health diary analysis result (360) can be generated based on the health diary analysis basic information (340). The health diary analysis result (360) can be data for recommending products to companion animals based on the health diary analysis basic information (340).

[0065]

[0066] Figure 4 is a conceptual diagram illustrating the operation of a product recommendation unit according to an embodiment of the present invention.

[0067] In Fig. 4, a product recommendation method of a product recommendation unit based on pet recommendation element data and product recommendation element data is disclosed.

[0068] Referring to FIG. 4, the product recommendation unit can recommend products to pets based on pet recommendation element data and product recommendation element data.

[0069] First, the product recommendation unit can extract target combination pet recommendation element data (420) to be used to recommend products to pets from among multiple combination pet recommendation element data (400).

[0070] For example, the first target combination pet recommendation element data may be data for recommending products based on the pet's current illness. The second target combination pet recommendation element data may be data for recommending products based on the pet's predicted illness. The third target combination pet recommendation element data may be data for recommending products based on the pet's gender and age.

[0071] The product recommendation unit may set a primary priority (430) of multiple target combination pet recommendation element data (420) considering the urgency of product recommendations. For example, the priority of the target combination pet recommendation element data (420) may be determined first so that recommendations for products immediately needed, such as in the order of current disease, predicted disease, gender, and age, can be made first.

[0072] Next, a secondary priority (440) can be set based on the existing purchase history. For example, it can be assumed that the user has already purchased a product related to the pet's disease based on the first target combination pet recommendation element data, and that a critical period of time has not passed since the purchase of the product. In this case, product recommendations (450) can be made based on the remaining target combination pet recommendation element data, excluding the first target combination pet recommendation element data.

[0073] Product recommendation (450) can be made by extracting products having combination product recommendation element data that matches the target combination pet recommendation element data.

[0074] At this time, if there are multiple products that have combination product recommendation element data that matches the target combination pet recommendation element data, the priority for providing information on the multiple products can be determined based on other users' purchase history data (ratings, reviews, sales volume, etc.).

[0075]

[0076] Figure 5 is a conceptual diagram illustrating the operation of a product recommendation unit according to an embodiment of the present invention.

[0077] In Fig. 5, a method for recommending a product in a product recommendation section is disclosed when there is no sub-product recommendation element data of the product.

[0078] Referring to Figure 5, for product recommendations for products for which product data does not exist, inferences can be made regarding sub-product recommendation element data for the product. For example, if users of pets with Disease X purchase a specific product, the disease diagnosis data (product) for that product can be inferred to be Disease X. In this way, inferences can be made regarding sub-product recommendation element data for a product based on pet data and user purchase data.

[0079] The sub-product recommendation element data estimated in this way can be expressed as sub-product recommendation element data (estimated) (500).

[0080] The product recommendation unit may perform product recommendations based on the sub-product recommendation element data (estimation) (500) by considering the estimated reliability (520) of the sub-product recommendation element data (estimation) (500). The estimated reliability (520) may be determined based on product purchase frequency, product purchase rate, product purchase amount, product purchase review data, etc. of users with specific pet data.

[0081] The estimated confidence threshold for using the sub-product recommendation element data (estimate) (500) for product recommendations may be set differently for each sub-product recommendation element data. If the sub-product recommendation element data is disease confirmation data (product), recommendations require more careful consideration, and therefore a relatively high estimated confidence threshold may be set to utilize the sub-product recommendation element data (estimate) (500) for product recommendations.

[0082]

[0083] Figure 6 is a conceptual diagram illustrating the operation of a product recommendation unit according to an embodiment of the present invention.

[0084] In Fig. 6, a product recommendation method of a product recommendation unit based on pet recommendation element data and product recommendation element data is disclosed.

[0085] Referring to FIG. 6, a method for recommending products for companion animals based on a determination of disease diagnosis data is disclosed.

[0086] If a user clearly inputs information about a disease their pet has, the disease will be included in the pet's disease diagnosis data, and product recommendations based on the disease can be made.

[0087] In the present invention, if a user does not clearly input information about a disease of a companion animal, disease diagnosis data (estimation) (640) is generated, and a product can be recommended based on the disease diagnosis data (estimation) (640).

[0088] Disease confirmation data (estimation) (640) may be based on predicted disease data (610) and health diary data (620). As described above, predicted diseases may be determined based on the companion animal's genes / genetic mutations. Disease confirmation data (estimation) (640) may be determined based on health diary analysis result data based on predicted disease data and health diary data. If symptoms matching symptoms 1 to n of the predicted disease are included in the health diary analysis result data, disease confirmation data (estimation) (640) may be determined.

[0089] At this time, weights for each symptom of the predicted disease are set, and the reliability of the disease diagnosis data (estimation) (640) can be determined by considering the periodic frequency of the symptom. The weights can be set higher for symptoms that are unique to a specific predicted disease, and the reliability of the disease diagnosis data (estimation) (640) can be determined higher as the periodic frequency set for each symptom is relatively high.

[0090] If the reliability of the disease diagnosis data (estimation) (640) is higher than the threshold reliability, product recommendations can be made based on the disease diagnosis data (estimation) (640). The threshold reliability value may be set differently for each disease. While certain diseases may have clear symptoms, others may not. For example, in the case of patellar dislocation, the symptoms exhibited by the pet are clearly evident, while in the case of heart disease, the symptoms exhibited by the pet may not be clear. Therefore, the more clearly the symptoms of a disease are present, the lower the threshold reliability can be set, and the less clearly the symptoms are present, the higher the threshold reliability can be set.

[0091] In addition, the disease diagnosis data (estimation) (640) may be additionally estimated based on the user purchase data (630). For example, if a user purchases a specific product and the disease diagnosis data (product) of the product is disease X, the disease diagnosis data (estimation) (640) may include disease X. At this time, the reliability of the disease diagnosis data (estimation) (640) may be determined by considering the user's product purchase frequency, product purchase cycle, etc. As the user's product purchase frequency and product purchase cycle relatively increase, the reliability of the disease diagnosis data (estimation) (640) for product recommendation based on disease X included in the disease diagnosis data (estimation) (640) may increase.

[0092]

[0093] FIG. 7 is a conceptual diagram illustrating a symptom classification method based on a decision tree for more accurately predicting disease in companion animals according to an embodiment of the present invention.

[0094] In Fig. 7, a method for creating and expanding a decision tree for judging symptoms in more detail for each disease is disclosed.

[0095] Referring to Figure 7, a decision tree can be used to more specifically classify symptoms associated with genes. For example, even in the case of anorexia, a variety of anorexia symptoms can occur. Anorexia can be accompanied by a variety of symptoms, including simple anorexia, anorexia that restricts eating to a specific food, anorexia accompanied by lethargy, and anorexia accompanied by diarrhea and vomiting. By further specifying the symptoms of a companion animal, a decision tree can enable more specific reports on the symptoms of genetically related diseases, which can then be utilized to improve the predictive performance of genetically related diseases.

[0096] A decision tree can be created through a symptom-based level differentiation step and a genetic data-based symptom linkage step.

[0097] (1) Symptom-based level differentiation stage (S700)

[0098] The decision tree can be initially created at the first level, based on symptoms. Level 1 can be the most comprehensive symptom type. For example, Level 1 can be defined as the most comprehensive term for each symptom, such as "loss of appetite," "cough," or "movement disorder."

[0099] The n levels after Level 1 may be defined by the companion animal disease management device based on clinical data, or may be generated based on health diary data entered by users.

[0100] For example, if anorexia is the first level, it can be defined as simple anorexia, anorexia that does not eat only a specific food, anorexia that occurs with lethargy, anorexia that occurs with diarrhea and vomiting, etc. on the nth level based on veterinary clinical data. Alternatively, if a new symptom of anorexia is recorded by users at a threshold rate or higher based on the symptoms of anorexia recorded by users, it can be added to the nth level. The threshold rate can be set differently for each symptom, and the more common the symptom, the higher the threshold rate can be set.

[0101] In this way, a decision tree can be generated by segmenting it into n levels, and a specific level or final level of the decision tree can be used to determine the pet's symptoms. Symptoms located at a specific level can be referred to as "specific level symptoms" (e.g., "third level symptoms"), and symptoms located at the final level of the decision tree can be referred to as "final level symptoms."

[0102] (2) Genetic data-based symptom linkage step (S720)

[0103] The genetic data-based symptom linking step (S720) may be a step for linking genetic data with symptoms located in a decision tree divided into n levels. For example, if heart disease exists due to genetic factors, anorexia accompanied by lethargy due to heart disease may occur. Therefore, genetic data related to heart disease may be linked to anorexia accompanied by lethargy in the decision tree. In this manner, specific or final-level symptoms can be designated as genetically linked symptoms (750, 760).

[0104] Gene-linked symptoms (750, 760) can be determined based on veterinary clinical data and health diary data of companion animals with specific genes. Symptoms identified as being linked to specific genes based on veterinary clinical data can be determined as gene-linked symptoms (750, 760). In addition, if a specific symptom occurs at a threshold rate or higher based on genetic test data and health diary data of companion animals with specific genes, the symptom can also be determined as a gene-linked symptom (750, 760). The threshold rate for determining a gene-linked symptom can be set differently for each symptom and gene.

[0105] The decision tree generated in this manner can be reflected in a health diary, allowing users to enter symptoms without entering text. For example, if the symptom of anorexia is selected, the final symptom level is presented as a choice to the user, and the health diary can be created by selecting the symptom most closely resembling the pet's symptoms.

[0106] Based on this health diary data, it may be possible to more accurately predict pet diseases, and the reliability of disease diagnosis data (estimation) and product recommendations based on disease diagnosis data (estimation) may also become more accurate.

[0107]

[0108] The embodiments of the present invention described above may be implemented in the form of program commands that can be executed through various computer components and recorded on a computer-readable recording medium. The computer-readable recording medium may include program commands, data files, data structures, etc., either singly or in combination. The program commands recorded on the computer-readable recording medium may be specially designed and configured for the present invention or may be known and available to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and hardware devices specifically configured to store and execute program commands, such as ROMs, RAMs, and flash memories. Examples of program commands include not only machine language codes generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter, etc. Hardware devices may be changed into one or more software modules to perform processing according to the present invention, and vice versa.

[0109] Although the present invention has been described above with specific details such as specific components and limited examples and drawings, these are provided only to help a more general understanding of the present invention, and the present invention is not limited to the above examples, and those with ordinary knowledge in the technical field to which the present invention pertains can make various modifications and changes based on this description.

[0110] Therefore, the idea of ​​the present invention should not be limited to the embodiments described above, and not only the scope of the patent claims described below but also all scopes equivalent to or equivalently modified from the scope of the patent claims are considered to fall within the scope of the idea of ​​the present invention.

Claims

1. How to recommend pet products: A step of the product recommendation device receiving data for product recommendation for pets; and The above product recommendation device includes a step of recommending a product for the pet through preprocessing of the data, A method characterized in that the above data includes genetic test data, pet data, health diary data, or product data.

2. In paragraph 1, A method characterized in that the above preprocessing generates pet recommendation element data and product recommendation element data based on the above data.

3. In paragraph 2, A method characterized in that the recommendation of the above product is performed based on the matching of the pet recommendation element data and the product recommendation element data.

4. Product recommendation device for recommending pet products, Receive data for product recommendations for pets, It is implemented to recommend products for the pet through preprocessing of the above data, A product recommendation device characterized in that the above data includes genetic test data, pet data, health diary data, and product data.

5. In paragraph 4, A product recommendation device characterized in that the above preprocessing generates pet recommendation element data and product recommendation element data based on the above data.

6. In paragraph 5, A product recommendation device characterized in that the recommendation of the above product is performed based on matching of the pet recommendation element data and the product recommendation element data.

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