System and method for a three-dimensional digital pet expression platform

Abstract

A computer-implemented method for creating a three-dimensional pet avatar, the method including: receiving, by one or more processors, pet image data and at least one physical characteristic corresponding to at least one pet from a user device, the at least one physical characteristic including at least one pet breed; receiving, by the one or more processors, from the at least one service in response to sending a request for the additional characteristics to the at least one service, at least one additional pet characteristic corresponding to the at least one pet breed; generating, by the one or more processors, at least one pet avatar based on the pet image data, the at least one physical characteristic, and the at least one additional pet characteristic; and displaying, by the one or more processors, the at least one pet avatar on one or more user interfaces of the user device.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of priority to U.S. Provisional Patent Application No. 63 / 371,541, filed August 16, 2022, the entire contents of which are incorporated herein by reference. [Technical Field]

[0002] Various embodiments of the present disclosure generally relate to systems and methods for creating three-dimensional pet avatars, and in some embodiments, the present disclosure relates to systems and methods for training and using machine learning-based models to enable the use of three-dimensional pet avatars to assess the medical recovery of corresponding pets. [Background technology]

[0003] Pet owners desire the ability to condense their pet's appearance into a representation of their pet. While existing techniques have been developed to generate two-dimensional (2D) graphics of pets, these two-dimensional (2D) graphics fail to capture the pet's true dimensions. Furthermore, existing techniques have failed to provide owners with a true three-dimensional (3D) representation of their pet, incorporating both the pet's appearance and mannerisms. Therefore, there is a need for a three-dimensional representation of a pet's appearance and physical mannerisms that allows pet owners and third-party services (e.g., veterinarians, veterinary schools, insurance companies, grooming services, pet clothing designers, etc.) to infer additional information about the pet by interacting with the pet's representation. Summary of the Invention [Problem to be solved by the invention]

[0004] The present disclosure is intended to address the above-mentioned problems. The description of the background art in this section is intended to provide a brief overview of the background of the present disclosure. Unless otherwise specified in this specification, the content described in this section is not prior art to the claims of the present application. Therefore, the description in this section is not an admission that it is prior art or suggests prior art. [Means for solving the problem]

[0005] According to certain aspects of the present disclosure, a method and system for creating a three-dimensional pet avatar is disclosed.

[0006] In one aspect, an exemplary embodiment of a method for creating a three-dimensional pet avatar is disclosed. The method may include receiving, by one or more processors, pet image data and at least one physical characteristic corresponding to at least one pet from a user device, where the at least one physical characteristic may include at least one pet breed. The method may further include receiving, by the one or more processors, at least one additional pet characteristic corresponding to the at least one pet breed from the at least one service in response to sending a request for the additional characteristics to the at least one service. The method may also include generating, by the one or more processors, at least one pet avatar comprising a three-dimensional pet representation representing the at least one pet based on the pet image data, the at least one physical characteristic, and the at least one additional pet characteristic. The method may also include displaying, by the one or more processors, the at least one pet avatar on one or more user interfaces of the user device.

[0007] In a further aspect, an exemplary embodiment of a computer system for creating a three-dimensional pet avatar is disclosed. The computer system may include at least one memory storing instructions and at least one processor configured to perform operations by executing the instructions. The operations may include receiving pet image data and at least one physical characteristic corresponding to at least one pet from a user device, where the at least one physical characteristic may include at least one pet breed. The operations may further include receiving at least one additional pet characteristic corresponding to the at least one pet breed from the at least one service in response to sending a request for the additional characteristics to the at least one service. The operations may also include generating at least one pet avatar including a three-dimensional pet representation representing the at least one pet based on the pet image data, the at least one physical characteristic, and the at least one additional pet characteristic. The operations may also include displaying the at least one pet avatar on one or more user interfaces of the user device.

[0008] In a further aspect, a non-transitory computer-readable medium may contain instructions that, when executed by a processor, cause the processor to perform operations for creating a three-dimensional pet avatar. The operations may include receiving, from a user device, pet image data and at least one physical characteristic corresponding to at least one pet, where the at least one physical characteristic may include at least one pet breed. The operations may further include receiving, from the at least one service in response to sending a request for the additional characteristics to the at least one service, at least one additional pet characteristic corresponding to the at least one pet breed. The operations may also include generating at least one pet avatar including a three-dimensional pet representation representing the at least one pet based on the pet image data, the at least one physical characteristic, and the at least one additional pet characteristic. The operations may also include displaying the at least one pet avatar on one or more user interfaces of the user device.

[0009] It is to be understood that the foregoing general description and the following detailed description are merely exemplary of embodiments of the present disclosure as set forth in the claims, and are not intended to limit the disclosure of the embodiments of the present disclosure as set forth in the claims. [Brief explanation of the drawings]

[0010] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate various exemplary embodiments and, together with the following description, serve to explain the principles of embodiments of the present disclosure. [Figure 1] FIG. 1 illustrates an exemplary environment in which the techniques presented herein can be utilized, according to one or more embodiments. [Figure 2] 1 is a flowchart illustrating an exemplary method for creating a three-dimensional pet avatar, according to one or more embodiments. [Figure 3]1 is a flowchart illustrating an exemplary method for training a machine learning model to use a three-dimensional pet avatar to assess the medical recovery of a corresponding pet, according to one or more embodiments. [Figure 4] 1 is a flowchart illustrating an exemplary method for utilizing a three-dimensional pet avatar to assess the medical recovery of a corresponding pet by using a trained machine learning model, according to one or more embodiments. [Figure 5] FIG. 1 illustrates an example of a computing device capable of implementing the techniques presented herein, according to one or more embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0011] According to certain aspects of the present disclosure, methods and systems for creating three-dimensional pet avatars are disclosed. Previous techniques may not be suitable for this purpose because they do not take into account the behavior and appearance details (e.g., dimensions) of a pet. Furthermore, previous techniques are unable to dynamically adapt to modifications to a pet's appearance or behavior in real time. Therefore, there is a need for improved techniques for creating three-dimensional pet avatars representing pets.

[0012] Digital representations of pets can be very meaningful to pet owners. For example, a digital representation of a pet can be a permanent (or semi-permanent) record of that pet. Furthermore, as the metaverse becomes more popular, some pet owners are beginning to desire their pets to exist in the metaverse. For example, in the metaverse, pets can have a presence that persists beyond their lifespan and can interact with other pets and other users.

[0013] There is a demand for 3D pet avatars that capture various pet characteristics, particularly their appearance and mannerisms. Such 3D pet avatars can be implemented in various environments, such as the Metaverse. Furthermore, pet owners or third parties can modify the 3D pet avatar to reflect changes in the pet's appearance or mannerisms. For example, 3D pet avatars can be used to diagnose medical conditions, benchmark recovery from injuries, provide product or service recommendations, display relationships with other 3D pet avatars based on the pet's genetic data, pedigree data, and pet owner registration data, and manipulate the 3D pet representation to reflect potential changes in the pet's appearance. For example, a pet owner can modify the 3D pet avatar to reflect a medical condition, such as a lame leg. Veterinarians, veterinary students, or machine learning models can then analyze the 3D pet avatar to benchmark recovery from injuries and determine optimal treatment options. For example, a pet groomer can modify the appearance of a 3D pet avatar to reflect how the pet will look after being groomed. For example, a pet clothing designer can use a 3D pet avatar to take measurements of a pet and create custom clothing. The pet clothing designer can also reflect in the 3D pet avatar how the pet will look when dressed in the clothing. Insurance companies can use 3D pet avatars to determine the types of insurance and premium rates available for pets.

[0014] As described in more detail below, various embodiments describe systems and methods for creating a three-dimensional pet avatar. The systems and methods can receive pet image data and at least one physical characteristic corresponding to at least one pet from a user device, where the at least one physical characteristic can include at least one pet breed. The systems and methods can then receive at least one additional pet characteristic corresponding to the at least one pet breed from the at least one service in response to sending a request for the additional characteristics to the at least one service. The systems and methods can then generate at least one pet avatar including a three-dimensional pet representation representing the at least one pet based on the pet image data, the at least one physical characteristic, and the at least one additional pet characteristic. The systems and methods can then display the at least one pet avatar on one or more user interfaces of the user device.

[0015] As described in more detail below, various embodiments describe systems and methods for utilizing three-dimensional pet avatars to assess the medical recovery of corresponding pets using a machine learning model. The machine learning model can be trained (e.g., trained using supervised or semi-supervised learning) to learn associations between one or more pet avatars, one or more benchmark pet avatars, one or more pet illnesses, pet recovery data for one or more pets, and one or more recovery levels, such that the trained machine learning model can be used to predict one or more recovery levels.

[0016] As described in more detail below, machine learning techniques adapted to predict one or more recovery levels of a pet may include one or more aspects of the present disclosure, such as a particular technique for selecting training data, a particular process for training a machine learning model, the operation of a particular device suitable for use in combination with a trained machine learning model, the operation of a machine learning model in conjunction with particular data, a technique for modifying such particular data by a machine learning model, other aspects that would be apparent to one of ordinary skill in the art based on the present disclosure, or any combination thereof.

[0017] One or more embodiments of the subject matter of this disclosure may be implemented in a metaverse. The metaverse, also known as the spatial internet, represents a virtual space where users can create and explore together with others who are not in physical contact with or near the user. The metaverse potentially spans a variety of virtual shared collective spaces created by blending virtually augmented physical reality with physically persistent virtual spaces, and may include the entirety of all virtual worlds, augmented reality, and internet products and services. Thus, any aspect of the subject matter of this disclosure, where applicable, may be implemented in a metaverse.

[0018] It should be noted that the technology disclosed in this application is not limited to three-dimensional pet avatars, and can be applied to one-dimensional pet avatar embodiments, two-dimensional pet avatar embodiments, three-dimensional pet avatar embodiments, four-dimensional pet avatar embodiments, etc.

[0019] Example Environment 1 illustrates an exemplary environment 100 in which the technology presented herein can be utilized. One or more user devices 105, one or more external systems 110, and one or more server systems 115 can communicate over a network 101. As described in more detail below, the one or more server systems 115 can communicate with one or more other components of the environment 100 over the network 101. The one or more user devices 105 can be associated with a user, e.g., associated with one or more of the following: generating a three-dimensional pet avatar; training / tuning a machine learning model to enable the three-dimensional pet avatar.

[0020] In some embodiments, components of environment 100 are associated with a public entity, such as, for example, a metaverse, a veterinarian, a clinic, an animal specialist, a research center, or a university. In some embodiments, one or more of the components of environment 100 are associated with a different entity than other components. The systems and devices of environment 100 can communicate in any configuration. As described below, the systems and devices of environment 100 can communicate to, among other things, create three-dimensional pet avatars and / or create, train, and use machine learning models for generating and / or using three-dimensional pet avatars.

[0021] User device 105 may be configured to allow a user to access and interact with other systems within environment 100 through user device 105. For example, user device 105 may be a computer system, such as an augmented reality device, a virtual reality device, a device for interacting with the metaverse, a desktop computer, a mobile device, a tablet, etc. In some embodiments, user device 105 may include one or more electronic applications (e.g., programs, plug-ins, browser extensions, etc.) installed on the memory of user device 105.

[0022] The user device 105 may include a display / user interface (UI) 105A, a processor 105B, a memory 105C, a network interface 105D, or any combination thereof. The processor 105B enables the user device 105 to execute an operating system (O / S) and at least one electronic application, all of which are stored in the memory 105C. The electronic application may be a desktop program, a browser program, a web client, a mobile application program (which may be a browser program in a mobile O / S), a proprietary program developed by the applicant, system control software, system monitoring software, a software development tool, or the like. For example, the environment 100 may extend the information of a web client accessible through a web browser. In some embodiments, the electronic application may be associated with one or more of the other components in the environment 100. The application may manage the memory 105C, such as a database, and send streaming data to the network 101. The display / UI 105A may be a touchscreen or a display with other input systems (e.g., a mouse, keyboard, headset, etc.) that allow a user to interact with the application and the O / S. Network interface 105D may be a TCP / IP network interface (e.g., for Ethernet or wireless communication with network 101). Processor 105B may generate data, receive user input from display / UI 105A, send or receive messages from server system 115, or any combination thereof, in parallel with executing applications, and may perform one or more further steps before providing output to network 101.

[0023] External system 110 can be, for example, one or more third party and / or auxiliary systems that can be integrated into or communicate with server system 115 to perform various three-dimensional pet avatar tasks. External system 110 can communicate with other devices or systems in environment 100 via one or more networks 101. For example, external system 110 can communicate with server system 115 on one or more networks 101 via API (Application Programming Interface) access and with user devices 105 on one or more networks 101 via web browser access.

[0024] In various embodiments, network 101 may be a wide area network ("WAN"), a local area network ("LAN"), a personal area network ("PAN"), or the like. In some embodiments, network 101 includes the Internet, and the provision of information and data between various systems occurs online. The term "online" can refer to connecting to or accessing a source of data or information from a location separate from other devices or networks connected to the Internet. Alternatively, the term "online" may refer to connecting to or accessing a network (wired or wireless) via a mobile communication network or device. The Internet is a global system of computer networks, a web of networks that allows parties at networked computers or other devices to obtain information from other computers and communicate with parties at other computers or devices. The most widely used part of the Internet is the World Wide Web (often abbreviated "WWW" or simply referred to as the "Web"). The term "website page" broadly encompasses any location or data store that is made accessible online, for example, by being hosted or operated by a computer system, and that may contain data that is configured to cause a program, such as a web browser, to perform steps such as sending and receiving data, processing, displaying visually, or generating an interactive interface.

[0025] Server system 115 may comprise an electronic data system, and may comprise computer-readable memory, such as a hard drive, flash drive, disk, etc. In some embodiments, server system 115 provides and / or interacts with application programming interfaces for exchanging data with other systems (e.g., one or more of the other components in the environment).

[0026] The server system 115 may include a database 115A and at least one server 115B. The server system 115 may be a computer, a computer system (e.g., a rack server), a cloud service computer system, or any combination thereof. The server system may store or access the database 115A (e.g., hosted on a third-party server or in memory 115E). The server may include a display / UI 115C, a processor 115D, memory 115E, a network interface 115F, or any combination thereof. The display / UI 115C may be a display with a touchscreen or other input system (e.g., a mouse, keyboard, headset, etc.) that allows a person operating the server 115B to control the functions of the server 115B. The server system 115 may execute an operating system (O / S) and at least one instance of a servlet program (both of which are stored in memory 115E) via the processor 115D.

[0027] The server system 115 may be responsible for generating, storing, modifying, or any combination thereof, the three-dimensional pet avatars. The server system 115 may include machine learning models, instructions associated with the machine learning models, or both, such as instructions for generating the machine learning models, instructions for training the machine learning models, instructions for using the machine learning models, etc. The server system 115 may also include instructions for modifying the three-dimensional pet avatars (e.g., based on the output of the machine learning models), instructions for operating the display 115C to output three-dimensional pet avatar data (e.g., adjusted based on the machine learning models), or both. The server system 115 may also include training data, such as one or more pet avatars, one or more reference pet avatars, one or more pet illnesses, pet recovery data for one or more pets, one or more recovery levels, or any combination thereof.

[0028] In some embodiments, a system or device other than server system 115 is used to generate and / or train the machine learning model. For example, such a system may include instructions for generating the machine learning model, training data, and ground truth, instructions for training the machine learning model, or both. The resulting trained machine learning model may then be provided to server system 115.

[0029] Generally, a machine learning model includes a set of variables (e.g., variables for nodes, neurons, filters, etc.), and these variables are adjusted to different values (e.g., by applying weights and biases to these variables) by applying training data. In supervised learning, for example, if the ground truth of the provided training data is known, training can begin by inputting training data samples into the machine learning model with the machine learning model's variables set to default values (e.g., default values set randomly based on Gaussian noise or a pre-trained model). The output can then be compared with the ground truth to identify errors, which can then be back-propagated to the machine learning model to adjust the values of the variables.

[0030] Training may be performed in any suitable manner (e.g., batch) and may include any suitable training method (e.g., stochastic gradient descent, non-stochastic gradient descent, gradient boosting, random forests, etc.). In some embodiments, a portion of the training data may be set aside during training, and this portion of data may be used to validate the trained machine learning model, e.g., to evaluate the accuracy of the trained model by comparing the output of the trained model for that portion of the training data with its ground truth. Training the machine learning model may be configured to train the machine learning model to learn associations among one or more pet avatars, one or more reference pet avatars, one or more pet illnesses, pet recovery data for one or more pets, and one or more recovery levels. Thus, the trained machine learning model will be configured to identify a pet's recovery level based on the associations learned during training.

[0031] In various embodiments, the variables of the machine learning model may be correlated with one another in any suitable relationship to generate an output of the machine learning model. For example, in some embodiments, the machine learning model may comprise a signal processing architecture configured to identify, isolate, or extract features, patterns, or structures contained in text. For example, the machine learning model may comprise one or more convolutional neural networks ("CNNs") configured to identify features within document information data, and may further comprise additional architectures (e.g., connection layers, neural networks, etc.) configured to identify relationships between the identified features to assess the pet's medical recovery.

[0032] 1 depicts the components of environment 100 as separate components, it should be understood that in some embodiments, a component or portion of a component of environment 100 may be integrated with or incorporated into one or more other components. For example, a portion of display 115C may be integrated into user device 105, etc. In some embodiments, the operations or aspects of one or more components described above may be distributed among one or more other components. Any suitable arrangement and integration of the various systems and devices of environment 100 may be used.

[0033] Further aspects of how the machine learning model creates the three-dimensional pet avatar and uses the three-dimensional pet avatar to assess the pet's medical recovery are described in more detail below in the description of the method. It should be noted that in the description of the method below, components shown in FIG. 1 (e.g., server system 115, user device 105, or components therein) may be described as performing various operations. However, it should be understood that in various embodiments, various components of environment 100 described below may execute instructions or perform operations, including those described above. When a device performs an operation, it should be understood that a processor, actuator, etc. associated with the device may be considered to perform the operation. It should also be understood that various steps may be added, omitted, or reordered in any suitable manner in various embodiments.

[0034] In general, any processes or operations described herein that are understood to be computer-implementable, such as the processes illustrated in FIGS. 2-4, may be performed by one or more processors of a computer system (e.g., any of the systems or devices of environment 100 illustrated in FIG. 1), as described above. Processes or processing steps performed by one or more processors may also be referred to as operations. One or more processors may be configured to perform such operations by accessing instructions (e.g., software or computer-readable code) that, when executed by the one or more processors, cause the one or more processors to perform such operations. These instructions may be stored in the memory of the computer system. The processor may be a central processing unit (CPU), a graphics processing unit (GPU), or any suitable type of processing device.

[0035] A computer system (e.g., a system or device that performs the processes or steps illustrated in the above examples) may include one or more computing devices (e.g., one or more of the systems or devices illustrated in FIG. 1). One or more processors of a computer system may be included in a single computing device or may be distributed across multiple computing devices. Additionally, the memory of the computer system may include memory of each of the multiple computing devices.

[0036] An exemplary process for creating a 3D pet avatar 2 illustrates an exemplary process 200 for creating a three-dimensional pet avatar, according to one or more embodiments. Note that method 200 may be performed by one or more processors of a server in communication with one or more user devices and other external systems over a network. However, it should be noted that method 200 may also be performed by any one or more of the server, one or more user devices, and other external systems.

[0037] The method may include receiving, by one or more processors, pet image data and at least one physical characteristic corresponding to at least one pet from a user device (step 202). The at least one physical characteristic may include at least one pet breed. The at least one pet breed may include only one pet breed (e.g., in the case of a purebred pet) or multiple pet breeds (e.g., in the case of a mixed breed pet). The pet image data may include at least one photograph, at least one short video clip, at least one X-ray, at least one sketch, at least one image corresponding to the at least one pet created via an application, or any combination thereof. In some embodiments, the pet image data may also include other pet data, such as pet clinical data, pet genetic data, pet pedigree data, pet owner registration data, Internet of Things (IoT) data, or pet diagnostic data. In some embodiments, the pet image data may be received from one or more user devices. Additionally, a user of the user device may create the pet image data. For example, a user may use the user device to create a sketch of the at least one pet. The user can then submit this sketch as pet image data. The at least one physical characteristic can include at least one breed of the at least one pet. The at least one physical characteristic can also include a diameter of an element of the at least one pet, a circumference of an element of the at least one pet, a height, a weight, at least one coat color, at least one eye color, a tail type, at least one ear length, a coat length, at least one eye size, an age, or a coat type. For example, the diameter of an element of the at least one pet can include the diameter of the pet's legs, body, neck, tail, etc. For example, the circumference of an element of the at least one pet can include the circumference of the pet's legs, body, neck, tail, etc.Also, for example, the coat color may correspond to the color(s) of the pet. The tail type may include whether the tail is short or long, curly or straight, etc. The length of the at least one ear may include the length of one or more ears of the pet. The coat type may include whether the coat is straight or curly, thick or thin, etc. In some embodiments, a prompt requesting pet image data and at least one physical characteristic may be displayed on the user device. And, for example, in response to the displayed prompt, the user may input pet image data, at least one physical characteristic, or both of at least one pet into the user device.

[0038] The method may also include receiving, by one or more processors, at least one habit of the at least one pet. The at least one habit may include at least one sitting behavior of the pet, at least one physical behavior of the pet, or at least one physical habit of the pet. The at least one sitting behavior of the pet may include a way in which the pet sits, a way in which the pet stands, or both. The at least one physical behavior of the pet may include a way in which the pet walks, runs, jumps, plays, etc. The at least one physical habit of the pet may include at least one special habit or trick of the pet, such as a lame leg, a seizure, a trick taught to the pet by the owner, etc. In some embodiments, the at least one habit may be received from one or more user devices. Further, the at least one habit may be illustrated by at least one photograph, at least one short video, at least one sketch, or at least one image corresponding to the at least one pet created via the application. For example, a user may select a short video of the at least one pet running through a field. A prompt may be displayed on the user device requesting the user to input at least one habit, and, for example, in response to the displayed prompt, the user may input at least one habit for at least one pet.

[0039] The method may also include receiving, by the one or more processors, at least one additional pet feature corresponding to the at least one pet breed from the at least one service in response to sending the request for the additional features to the at least one service (step 204). The request for the additional features may include the received at least one pet breed, but may also include additional information, such as pet image data, at least one physical feature, at least one quirk, or any combination thereof. The request for the additional features may be sent to the at least one service. For example, the at least one service may include an internal service or an external service that includes at least one database.

[0040] In some embodiments, the method may also include receiving, by one or more processors, a request for additional features. The request for additional features may include at least one pet breed. The method may further include searching, by the one or more processors, at least one database of the at least one service for at least one database record matching the at least one pet breed. The at least one database record may include at least one default three-dimensional pet visual representation, at least one stored pet breed, at least one additional physical characteristic, at least one growth indicator, or at least one body condition. The at least one database of the at least one service may store at least one database record. The at least one database record may include default information corresponding to a pet breed. For example, the default information may include at least one default three-dimensional pet representation, at least one pet breed, at least one additional pet characteristic, at least one growth indicator, at least one body condition, or any combination thereof. The default three-dimensional pet representation may correspond to a standard three-dimensional pet representation representing the at least one pet breed. The at least one pet breed may include a single breed or a mixed breed (e.g., multiple pet breeds). The at least one additional pet characteristic may correspond to pet characteristic data of the pet breed, such as a diameter of an element of the at least one pet breed, a circumference of an element of the at least one pet breed, or a height, weight, at least one coat color, at least one eye color, tail type, at least one ear length, coat length, at least one eye size, age, or coat type of the at least one pet breed. The at least one growth indicator may correspond to a growth rate of the at least one breed. For example, the growth rate may be associated with a particular age of the at least one breed.The at least one body type may correspond to a general size of the at least one breed. For example, the at least one body type may include a stocky body type, an elongated body type, etc. Further, the database may include at least one growth chart or at least one body type for the at least one pet breed. The at least one growth chart may provide data regarding the growth rate of the at least one breed.

[0041] The method may further include generating, by the one or more processors, at least one pet avatar (step 206) including a three-dimensional pet representation representing the at least one pet based on the pet image data, the at least one physical characteristic, and the at least one additional pet characteristic. In some embodiments, the method may also include modifying the default three-dimensional pet representation based on the pet image data, the at least one physical characteristic, the at least one additional pet characteristic, or any combination thereof. The at least one pet avatar may include a three-dimensional pet representation representing the at least one pet.

[0042] The method may also include displaying, by the one or more processors, the at least one pet avatar on one or more user interfaces of the user device (step 208). For example, the at least one pet avatar may be displayed on one or more user interfaces of the user device. In some embodiments, the at least one pet avatar may be displayed as part of a photo gallery, showcase, or profile(s) on one or more platforms or publishers. In some embodiments, the at least one pet avatar may be displayed within one or more virtual worlds, such as the metaverse. Furthermore, a user may interact with the at least one pet avatar in the metaverse using virtual or augmented reality technology (e.g., a headset). In some examples, additional data associated with the pet (e.g., data received in step 202, step 204, or both) may be displayed in association with the pet avatar in the metaverse. To cite one specific example, when a pet avatar is located within a predetermined proximity range of one or more other pet avatars with the same or similar pet genetic data, pet pedigree data, or both, some relationship indicator (e.g., an indication that the pets are siblings, parent and child, etc.) may be displayed to visually indicate the relationship between the pets represented by those pet avatars. Similar indicators may also be used to indicate the relationship between pets that they were adopted from the same shelter (e.g., a relationship based on pet owner registration data). Additionally or alternatively, additional pet data may be associated with the pet avatar and visually displayed, regardless of proximity to other pet avatars with similar characteristics.

[0043] The method may also include receiving, by the one or more processors, at least one modification from the user device in response to the displaying step. For example, a user may indicate modification of one or more physical characteristics of the at least one pet avatar (e.g., coat color, leg length, tail length, etc.). In some embodiments, the user may communicate the at least one modification using virtual reality or augmented reality technology. In some embodiments, the at least one modification may include a modification of at least one mannerism. For example, a user may indicate that the pet limps when walking.

[0044] The method may also include updating, by the one or more processors, the at least one pet avatar based on the at least one modification. The one or more processors may update the at least one pet avatar upon receiving the at least one modification. In some embodiments, upon updating the at least one pet avatar, the one or more processors may display the updated at least one pet avatar.

[0045] The method may also include storing, by the one or more processors, the at least one pet avatar. For example, the at least one pet avatar may be stored in one or more data stores (e.g., one or more databases) for future use (e.g., modification / update). In some embodiments, the at least one pet avatar may be stored in the one or more data stores along with corresponding pet avatar information. The pet avatar information may include pet image data, at least one physical characteristic, at least one user identifier associated with the user, or any combination thereof.

[0046] The method may also include receiving, by one or more processors, a description of at least one medical condition. The description of the at least one medical condition may include a lame leg, a seizure, a physical defect, a wound, etc. In some embodiments, a user may input the description of the at least one medical condition via the user device, for example, using words, a photograph, a video, an X-ray, etc. Additionally or alternatively, one or more medical condition options may be displayed on the user device for selection by the user. The method may also include modifying, by the one or more processors, the at least one pet avatar to reflect the description of the at least one medical condition. For example, if the description of the at least one medical condition indicates a lame right front leg, the at least one pet avatar may be modified to have a lame right front leg.

[0047] In some embodiments, the method may include analyzing, by one or more processors, at least one pet avatar to determine a medical condition. For example, analyzing the at least one pet avatar may determine that the at least one pet avatar indicates a lame left front leg. In some embodiments, the analyzing may include comparing the at least one pet avatar to previously stored pet avatars. The method may also include displaying, by the one or more processors, an alert notification on the user device indicating the medical condition. For example, the alert notification may indicate that the pet's left front leg may be lame and that treatment may be required.

[0048] It should be noted that while Figure 2 illustrates exemplary blocks of the exemplary method 200, in some implementations, the exemplary method 200 may include additional blocks, fewer blocks, different blocks, or a different order of blocks than those illustrated in Figure 2. Additionally or alternatively, two or more blocks of the exemplary method 200 may be performed simultaneously.

[0049] Training a machine learning model to assess pet medical recovery 3 illustrates a method 300 for training a machine learning model to assess the medical recovery of a corresponding pet using a three-dimensional pet avatar, according to one or more embodiments. It should be noted that method 300 may be performed by one or more processors of a server in communication with one or more user devices and other external systems over a network. However, it should be noted that method 300 may also be performed by any one or more of the server, one or more user devices, and other external systems.

[0050] The method may include receiving, by one or more processors, one or more pet avatars, each including a three-dimensional representation of one or more pets, pet data, one or more pet ailments, pet recovery data for one or more pets, one or more recovery levels, or any combination thereof (step 302). The data received in step 302 may be used to train a machine learning model. The one or more pet avatars may be previously created using the process illustrated in FIG. 2. Additionally or alternatively, the one or more pet avatars may have been previously stored in one or more data stores. The one or more pet avatars may include one or more three-dimensional representations, each representing one or more pets. The one or more pet ailments may include one or more visually indicated ailments or one or more descriptively indicated ailments. The one or more visually indicated ailments may be represented by a video, a photograph, an x-ray, a sketch, or the like. In some embodiments, the one or more visually indicated ailments may correspond to a superficial ailment of the pet, such as a lame leg, a wound, or the like. Meanwhile, the one or more descriptively indicated ailments may be represented in a narrative format using keywords, or the like. In some embodiments, the one or more descriptively indicated ailments may correspond to an internal ailment of the pet, such as kidney failure, heart disease, and the like.

[0051] The pet recovery data may include one or more of pet feeding data, pet watering data, pet movement data, pet defecation data, and pet sleep data. The pet recovery data may correspond to the behavior of a pet recovering from an illness (e.g., surgery). For example, the pet feeding data may include the frequency and amount of food the pet eats. The pet watering data may include the frequency and amount of water the pet drinks. The pet movement data may include the frequency, type of movement (e.g., walking, running, etc.), and manner of movement (e.g., limping, not limping, etc.) of the pet. The pet defecation data may correspond to the pet's defecation, such as the type and frequency of the pet's defecation. The pet sleep data may include the duration of sleep, the location of sleep, etc. The pet recovery data may also include data corresponding to the pet (e.g., at least one of breed, age, weight, etc.).

[0052] The one or more recovery levels may include one or more of an "improving" recovery level, a "stagnating" recovery level, or a "regressing" recovery level. An "improving" recovery level may indicate that the pet's recovery is improving. A "stagnating" recovery level may indicate that the pet's recovery is stagnating (e.g., not improving or regressing). A "regressing" recovery level may indicate that the pet's recovery is regressing. The one or more recovery levels received in step 302 may be considered one or more "labels" in the model training data.

[0053] In some embodiments, the three-dimensional pet representation can include at least one pet characteristic and at least one pet quirk. The at least one pet quirk can include at least one of at least one pet sitting behavior, at least one pet physical behavior, and at least one pet physical habit. The at least one pet sitting behavior can include a way the pet sits, a way the pet stands, or both. The at least one pet physical behavior can include a way the pet walks, a way the pet runs, a way the pet jumps, etc. The at least one pet physical habit can include at least one special quirk of the pet, such as a lame leg or foot, any kind of seizure, etc.

[0054] The method may also include, in response to the receiving step, training, by one or more processors, a machine learning model to predict one or more recovery levels for one or more pet avatars (step 304), as described in more detail below in the description of steps 306 and 308.

[0055] The method may also include obtaining one or more reference pet avatars corresponding to the one or more pet avatars (step 306). The one or more reference pet avatars may include at least one previously stored pet avatar. The at least one previously stored pet avatar may indicate how a pet with particular illness or recovery data is likely to behave. In this sense, the previously stored pet avatar may serve as a reference for comparison with the one or more pet avatars. The obtaining step may include identifying the one or more reference pet avatars as having similar illness or pet recovery data as each of the one or more pet avatars.

[0056] The method may include analyzing one or more reference pet avatars, one or more pet avatars, one or more pet illnesses, pet recovery data, one or more recovery levels, or any combination thereof, for one or more pets to identify one or more predicted recovery levels (step 308). In some embodiments, each pet avatar of the one or more pet avatars may be compared to its corresponding reference pet avatar to determine whether each pet avatar is performing better, worse, or the same as its corresponding reference pet avatar. For example, the one or more predicted recovery levels may include one or more of an "improving" predicted recovery level, a "stagnating" predicted recovery level, and a "regressing" predicted recovery level. An "improving" predicted recovery level may indicate that the pet's recovery is progressing. A "stagnating" predicted recovery level may indicate that the pet's recovery is stagnating (e.g., not progressing or regressing). A "regressing" predicted recovery level may indicate that the pet's recovery is regressing.

[0057] It should be noted that while Figure 3 illustrates exemplary blocks of the exemplary method 300, in some implementations, the exemplary method 300 may include additional blocks, fewer blocks, modified blocks, or reordered blocks compared to those illustrated in Figure 3. Additionally or alternatively, two or more blocks of the exemplary method 300 may be performed simultaneously.

[0058] Assessing pet medical recovery using trained machine learning models 4 illustrates an exemplary process for utilizing a three-dimensional pet avatar to assess the medical recovery of a corresponding pet using a machine learning model, according to one or more embodiments. It should be noted that method 400 may be performed by one or more processors of a server in communication with one or more user devices and other external systems over a network. However, it should be noted that method 400 may also be performed by any one or more of the server, one or more user devices, and other external systems.

[0059] The method may include receiving, by one or more processors, from a user device (step 402), at least one pet avatar including at least one three-dimensional representation representing at least one pet, at least one pet illness for the at least one pet, and pet recovery data for the at least one pet. The at least one pet avatar may have been previously created by the process illustrated in FIG. 2. Additionally or alternatively, the at least one pet avatar may have been previously stored in one or more data stores. The at least one pet avatar may include at least one three-dimensional representation representing the at least one pet.

[0060] The at least one condition may include at least one visually indicated condition or at least one descriptively indicated condition. The at least one visually indicated condition may be represented by a video, photograph, x-ray, sketch, etc. In some embodiments, the at least one visually indicated condition may be a condition that is external to the pet, such as a lame leg, a wound, etc., while the at least one descriptively indicated condition may be a narrative condition using keywords, etc. In some embodiments, the at least one descriptively indicated condition may be a condition that is internal to the pet, such as kidney failure, heart disease, etc.

[0061] The pet recovery data may include at least one of pet feeding data, pet watering data, pet movement data, pet bowel movement data, and pet sleep data. The pet recovery data may correspond to the behavior of a pet recovering from an illness (e.g., surgery). For example, the pet feeding data may include the frequency and amount of food the pet eats. The pet watering data may include the frequency and amount of water the pet drinks. The pet movement data may include the frequency, type of movement (e.g., walking, running, etc.), and manner of movement (e.g., limping, not limping, etc.) of the pet. The pet bowel movement data may correspond to the pet's bowel movements, such as the type and frequency of the pet's bowel movements. The pet sleep data may include the duration of sleep, the location of sleep, etc. In some embodiments, the pet recovery data may include at least one breed of at least one pet.

[0062] In some embodiments, the three-dimensional pet representation can include at least one pet characteristic and at least one pet quirk. The at least one pet quirk can include at least one of at least one pet sitting behavior, at least one pet physical behavior, and at least one pet physical habit. The at least one pet sitting behavior can include a way the pet sits, a way the pet stands, or both. The at least one pet physical behavior can include a way the pet walks, a way the pet runs, a way the pet jumps, etc. The at least one pet physical habit can include at least one special quirk of the pet, such as a lame leg or foot, any kind of seizure, etc.

[0063] The method may include using a trained machine learning model to identify, by one or more processors, at least one recovery level for the at least one pet based on the at least one pet avatar, the at least one pet illness, and the pet recovery data (step 404). For example, the trained machine learning model may have been trained according to the process shown in FIG.

[0064] The method may also include determining, by one or more processors, at least one product or service recommendation based on the at least one recovery level (step 406). In some embodiments, a request for recommendations may be sent to a content service. The request may include request information. For example, the request information may include at least one recovery level, at least one pet illness, pet recovery data, or any combination thereof. In response to receiving the request for recommendations, the content service may search one or more data stores for at least one product or service recommendation based on the request information. The method may also include receiving at least one product or service recommendation from the content service. The at least one product or service recommendation may include at least one of pet food, pet equipment, pet toys, recommended treatments, recommended veterinarians, etc.

[0065] The method may also include displaying, by the one or more processors, the at least one recovery level and the at least one product or service recommendation on one or more user interfaces of the user device (step 408). For example, the at least one recovery level and the at least one product or service recommendation may be displayed on one or more user interfaces of the user device. In some embodiments, the at least one recovery level, the at least one product or service recommendation, or both may be displayed in one or more virtual worlds, such as the Metaverse. Furthermore, a user may interact with the at least one recovery level and the at least one product or service recommendation in the Metaverse using virtual reality or augmented reality technology (e.g., a headset).

[0066] The method can include providing, by one or more processors, a plurality of visual outputs to one or more interfaces of a user device. The plurality of visual outputs can include three-dimensional representations of similar pets having at least one condition but with varying degrees of the condition. For example, if the at least one condition indicates that the pet has a lame right hind leg, the plurality of visual outputs can include one or more three-dimensional representations of similar pets with a lame right hind leg. Further, the severity of the lameness can vary among the three-dimensional representations.

[0067] The method may further include receiving, by the one or more processors, in response to providing the plurality of visual outputs, at least one user selection designating at least one of the plurality of visual outputs. For example, a user may select one of the plurality of visual outputs that most closely resembles the illness of the user's pet.

[0068] The method may also include identifying, by the one or more processors, the at least one resilience level using a trained machine learning model based on the at least one user selection. In some embodiments, the trained machine learning model may identify the at least one resilience level based on the at least one user selection specifying at least one of the plurality of visual outputs.

[0069] It should be noted that while Figure 4 illustrates exemplary blocks of the exemplary method 400, in some implementations, the exemplary method 400 may include additional blocks, fewer blocks, modified blocks, or reordered blocks compared to those illustrated in Figure 4. Additionally or alternatively, two or more blocks of the exemplary method 400 may be performed simultaneously.

[0070] Exemplary Devices FIG. 5 is a simplified functional block diagram of a computer 500 that can be configured as a device for performing the methods shown in FIGS. 2-4 according to an exemplary embodiment of the present disclosure. For example, the device 500 can include a central processing unit (CPU) 520. The CPU 520 can be any type of processor device, such as any type of special-purpose or general-purpose microprocessor device. As will be appreciated by those skilled in the art, the CPU 520 can also be one processor in a multi-core / multi-processor system operating alone or as a cluster of multiple computing devices operating as a cluster or server farm. The CPU 520 can also be connected to a data communications infrastructure 510 (e.g., a bus, message queue, network, or multi-core message passing scheme).

[0071] The device 500 may also include a main memory 540 (e.g., random access memory (RAM)) and may further include a secondary memory 530. The secondary memory 530 (e.g., read-only memory (ROM)) may be, for example, a hard disk drive or a removable storage drive. Such a removable storage drive may be, for example, a floppy disk drive, a magnetic tape drive, an optical disk drive, or a flash memory. The removable storage drive in this example reads and writes to a removable storage unit in a well-known manner. The removable storage unit may be a floppy disk, magnetic tape, optical disk, or the like, which may be read and written by the removable storage drive. As will be appreciated by those skilled in the art, such a removable storage unit typically includes a computer-usable storage medium having computer software, data, or both stored thereon.

[0072] In alternative embodiments, secondary memory 530 may comprise other similar means by which computer programs or other instructions can be loaded into device 500. Examples of such means include a program cartridge and cartridge interface (e.g., as found in video game devices) or a combination of a removable storage unit and interface that allows software and data to be transferred from a removable storage unit to device 500, such as a removable memory chip and its corresponding socket (e.g., EPROM or PROM).

[0073] Device 500 may also include a communications interface ("COM") 560. Communications interface 560 allows software and data to be transferred between device 500 and external devices. Communications interface 560 may include a modem, a network interface (e.g., an Ethernet card), a communications port, a PCMCIA slot and card, etc. The software and data transferred through communications interface 560 may be in the form of signals receivable by communications interface 560, such as electronic, electromagnetic, or optical signals. These signals may be provided to communications interface 560 via a communications path in device 500. Such a communications path may be implemented using, for example, a communications channel such as wire or cable, optical fiber, a telephone line, a cellular phone link, or an RF link.

[0074] The hardware elements, operating systems, and programming languages of such equipment are conventional in nature and are presumed to be sufficiently familiar to those skilled in the art. Device 500 may also include input / output ports 550 for connecting input / output devices such as a keyboard, mouse, touch panel, monitor, display, etc. Of course, various server functions may be distributed across multiple similar platforms to distribute the processing load. Alternatively, the server may be implemented by appropriate programming of a single computer hardware platform.

[0075] The program aspects of the present technology may be considered "products" or "articles of manufacture," typically in the form of executable code and / or associated data carried or embodied by any type of machine-readable medium. The "storage" type of medium includes any or all of the tangible memory of a computer, processor, or the like, or its associated modules (e.g., various semiconductor memories, tape drives, disk drives, etc.), capable of providing non-transitory storage for software programming at any time. All or portions of the software may also be transmitted over various communications networks, such as the Internet. Such communications may, for example, allow software to be loaded from one computer or processor to another, such as from a management server or host computer in a mobile communications network to load software onto a server computer platform, or from a server to load software onto a mobile device. Accordingly, other types of media that may carry software elements include optical waves, radio waves, and electromagnetic waves (e.g., those used in physical interfaces between local devices, wired and optical fixed-line networks, and various air links). Additionally, the physical elements that carry such waves (e.g., wired or wireless links, optical links, etc.) may also be considered media carrying the software. As used herein, except when limited to non-transitory, tangible "storage" media, terms such as computer "readable medium" and machine "readable medium" refer to any medium that participates in providing instructions to a processor for execution.

[0076] Where reference is made in this disclosure to a particular activity, such description is for convenience only and is not intended to limit the disclosure. Those skilled in the art will recognize that the concepts underlying the disclosed devices and methods can be applied to any activity suitable therefor. This disclosure can also be understood by reference to the above description and the accompanying drawings, in which like elements are designated by the same reference numerals.

[0077] The terms used in the above description should be interpreted in the broadest reasonable sense, even if they are used to describe in detail a particular embodiment of the present disclosure. Indeed, while this point may be emphasized for some terms in the above description, if a term is intended to be interpreted in a restrictive manner, the term will be clearly and specifically so defined in the "Detailed Description of the Invention" section above. Both the general description and the detailed description are merely illustrative of the present disclosure and are not intended to limit the features set forth in the claims.

[0078] In this disclosure, the phrase "based on" means "based at least in part on." The singular forms "a," "an," and "the" are intended to include the corresponding plural references unless the context clearly dictates otherwise. Additionally, the word "exemplary" is used in its "example" sense, not its "ideal" sense. The terms "comprise," "comprising," "include," "including," and other variations thereof are intended to encompass a non-exclusive inclusion. Thus, when a process, method, or article "comprises" ("includes" or "has") enumerated elements, it does not necessarily include only the enumerated elements but may also include other elements not expressly enumerated and elements inherent to such process, method, article, or apparatus. Additionally, the term "or" is used disjunctively, i.e., the phrase "at least one of A or B" includes (A), (B), (A and A), (A and B), etc. Relative terms such as "substantially" and "generally" are used to indicate that there may be a ±10% variation from the stated or implied value.

[0079] As used herein, terms such as "user" broadly encompass one or more pet parents. Terms such as "pet" broadly encompass a user's pet, and the term "pet" can encompass multiple pets. Examples of pets include dogs, cats, birds, horses, and turtles.

[0080] As used herein, the term "machine-learning model" broadly encompasses instructions, data, models, or any combination thereof, configured to weight, bias, classify, and / or analyze received inputs to generate outputs. Such outputs may include any suitable type of output, such as a classification of the inputs, an analysis based on the inputs, or a design, process, inference, or recommendation related to the inputs. Typically, machine-learning models / systems are trained using training data (e.g., empirical data, samples of input data, or both), and one or more aspects of the model (e.g., weights, biases, classification or clustering criteria, etc.) are set, adjusted, or modified by inputting the training data into the model. Aspects of the machine-learning model may operate on the inputs in any suitable configuration, such as linearly, in parallel, via a network (e.g., a neural network), or the like.

[0081] Execution of a machine learning model may include deploying one or more machine learning techniques, such as linear regression, logistic regression, random forest, gradient boosted machine (GBM), decision tree, gradient boosting of decision trees, deep learning, deep neural network, or any combination thereof. Supervised training, unsupervised training, or both may be performed. For example, supervised learning may include providing training data and multiple labels corresponding to the training data (e.g., as ground truth). Meanwhile, unsupervised approaches may include clustering, classification, and the like. K-means clustering (unsupervised) or K-nearest neighbors (supervised) may also be used. K-nearest neighbors and unsupervised clustering techniques may also be used in combination. Furthermore, any suitable type of training may be employed, such as stochastic methods, gradient boosting, random seeding, recursive methods, epoch-based, or batch-based methods.

[0082] In the foregoing description of exemplary embodiments of the present invention, various features of the present invention may be grouped together in a single embodiment, figure, or description thereof. It should be understood that this is done for the purpose of streamlining the disclosure and facilitating understanding of one or more of the various aspects of the present invention. However, this method of disclosure should not be interpreted as reflecting an intention that the present invention requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, the number of features conferring inventive step on the present invention is fewer than all of the features shown in the single embodiment disclosed above. Accordingly, the claims following this Detailed Description of the Invention are expressly incorporated into the Detailed Description of the Invention, and each claim herein stands on its own as an embodiment of the present invention.

[0083] Furthermore, while some embodiments described herein may include some features of other embodiments but not others, those skilled in the art will recognize that combinations of features from different embodiments are also within the scope of the present invention, and that such combinations are intended to form a variety of different embodiments. For example, in the following claims, any of the embodiments defined by the claims may be used in any combination.

[0084] Thus, while particular embodiments have been described, those skilled in the art will recognize that other and further modifications may be made without departing from the spirit of the claims, and that all such modifications and variations are intended to be included within the scope of the present invention. For example, functions shown in block diagrams may be added or deleted, steps may be interchanged between functional blocks, etc. Steps may also be added or deleted to methods described within the scope of the present invention.

[0085] The subject matter of the present disclosure has been described above, but should be considered illustrative and not limiting, and the appended claims are intended to encompass all modifications, extensions, and other embodiments falling within the true spirit and scope of the present disclosure. Accordingly, the scope of the present disclosure should be determined to the fullest extent permitted by law by interpreting the following claims and their equivalents in the broadest possible sense, and should not be limited or constrained by the above detailed description of the invention. While various embodiments of the present disclosure have been described above, it will be apparent to those skilled in the art that many more embodiments are possible within the scope of the present disclosure. Accordingly, the present disclosure is not to be limited except as limited by the claims and their equivalents. [Explanation of symbols]

[0086] 100 Environment 101 Network 105 User Devices 105A User Device Display / User Interface (UI) 105B Processor of User Device 105C User Device Memory 105D User Device Network Interface 110 External Systems 115 Server System 115A Server System Database 115B Server of the server system 115C Server System Display / User Interface (UI) 115D Server System Processor 115E Server System Memory 115F Server System Network Interface 500 devices (computers) 510 Data Communication Infrastructure 520 Central Processing Unit (CPU) 530 Auxiliary Memory 540 main memory 550 input / output ports 560 Communication Interface

Claims

1. 1. A computer-implemented method for creating a three-dimensional pet avatar, comprising: receiving, by one or more processors, pet image data and at least one physical characteristic corresponding to at least one pet from a user device, the at least one physical characteristic including at least one pet breed; receiving, by the one or more processors, from the at least one service at least one additional pet characteristic corresponding to the at least one pet breed in response to sending a request for additional characteristics to the at least one service; generating, by the one or more processors, at least one pet avatar comprising a three-dimensional pet representation representing the at least one pet based on the pet image data, the at least one physical characteristic, and the at least one additional pet characteristic; displaying, by the one or more processors, the at least one pet avatar on one or more user interfaces of the user device; A computer-implemented method comprising:

2. receiving, by the one or more processors, at least one modification from the user device in response to the displaying step; updating, by the one or more processors, the at least one pet avatar based on the at least one modification; The computer-implemented method of claim 1 further comprising:

3. 2. The computer-implemented method of claim 1, wherein the at least one physical characteristic comprises a diameter of an element of the at least one pet, a circumference of an element of the at least one pet, a height, a weight, at least one coat color, at least one eye color, a tail type, at least one ear length, a coat length, at least one eye size, an age, or a coat type.

4. 10. The computer-implemented method of claim 1, wherein the pet image data includes at least one of at least one photograph, at least one X-ray, at least one short video, at least one sketch, and at least one image created via an application.

5. receiving the at least one additional pet characteristic; receiving, by the one or more processors, a request for additional features, wherein the request for additional features includes the at least one pet breed; searching, with the one or more processors, at least one database of the at least one service for at least one database record matching the at least one pet breed; Including, 2. The computer-implemented method of claim 1, wherein the at least one database record includes at least one default three-dimensional pet visual representation, at least one stored pet breed, at least one additional physical characteristic, at least one growth indicator, or at least one body shape.

6. The computer-implemented method of claim 5 , wherein the at least one database includes at least one growth chart for the at least one pet breed.

7. receiving, by the one or more processors, a description of at least one medical disorder; modifying, by the one or more processors, the at least one pet avatar to reflect the at least one medical condition; The computer-implemented method of claim 1 further comprising:

8. the method further comprising receiving, by the one or more processors, at least one habit of the at least one pet; The computer-implemented method of claim 1 , wherein the at least one habit comprises at least one pet sitting behavior, at least one pet physical behavior, or at least one pet physical habit.

9. at least one memory for storing instructions; at least one processor configured to perform operations by executing the instructions; A computer system for creating a three-dimensional pet avatar, comprising: The operation is receiving pet image data and at least one physical characteristic corresponding to at least one pet from a user device, the at least one physical characteristic including at least one pet breed; receiving at least one additional pet characteristic corresponding to the at least one pet breed from the at least one service in response to the step of sending a request for the additional characteristic to the at least one service; generating at least one pet avatar comprising a three-dimensional pet representation representing the at least one pet based on the pet image data, the at least one physical characteristic, and the at least one additional pet characteristic; displaying the at least one pet avatar on one or more user interfaces of the user device; 2. A computer system comprising:

10. The operation is receiving at least one correction from the user device in response to the displaying step; updating the at least one pet avatar based on the at least one modification; 10. The computer system of claim 9, further comprising:

11. 10. The computer system of claim 9, wherein the at least one physical characteristic comprises a diameter of an element of the at least one pet, a circumference of an element of the at least one pet, a height, a weight, at least one coat color, at least one eye color, a tail type, at least one ear length, a coat length, at least one eye size, an age, or a coat type.

12. 10. The computer system of claim 9, wherein the pet image data includes at least one of at least one photograph, at least one short video, at least one X-ray, at least one sketch, and at least one image created via an application.

13. receiving the at least one additional pet characteristic; receiving a request for additional features, wherein the request for additional features includes the at least one pet breed; searching at least one database of said at least one service for at least one database record matching said at least one pet breed; Including, 10. The computer system of claim 9, wherein the at least one database record includes at least one default three-dimensional pet visual representation, at least one stored pet breed, at least one additional physical characteristic, at least one growth indicator, or at least one body type.

14. 14. The computer system of claim 13, wherein the at least one database includes at least one growth chart for the at least one pet breed.

15. The operation is receiving at least one medical condition description; modifying the at least one pet avatar to reflect the at least one medical condition; 10. The computer system of claim 9, further comprising:

16. the operations further include receiving at least one habit of the at least one pet; 10. The computer system of claim 9, wherein the at least one habit comprises at least one pet sitting behavior, at least one pet physical behavior, or at least one pet physical habit.

17. A non-transitory computer-readable medium containing instructions, comprising: the instructions, when executed by a processor, cause the processor to perform an operation to create a three-dimensional pet avatar; The operation is receiving pet image data and at least one physical characteristic corresponding to at least one pet from a user device, the at least one physical characteristic including at least one pet breed; receiving at least one additional pet characteristic corresponding to the at least one pet breed from the at least one service in response to the step of sending a request for the additional characteristic to the at least one service; generating at least one pet avatar comprising a three-dimensional pet representation representing the at least one pet based on the pet image data, the at least one physical characteristic, and the at least one additional pet characteristic; displaying the at least one pet avatar on one or more user interfaces of the user device; 1. A non-transitory computer-readable medium, comprising:

18. 20. The non-transitory computer-readable medium of claim 17, wherein the at least one physical characteristic comprises a diameter of an element of the at least one pet, a circumference of an element of the at least one pet, a height, a weight, at least one coat color, at least one eye color, a tail type, at least one ear length, a coat length, at least one eye size, an age, or a coat type.

19. receiving the at least one additional pet characteristic; receiving a request for additional features, wherein the request for additional features includes the at least one pet breed; searching at least one database of said at least one service for at least one database record matching said at least one pet breed; Including, 20. The non-transitory computer-readable medium of claim 17, wherein the at least one database record includes at least one default three-dimensional pet visual representation, at least one stored pet breed, at least one additional physical characteristic, at least one growth indicator, or at least one body shape.

20. 20. The non-transitory computer-readable medium of claim 19, wherein the at least one database includes at least one growth chart for the at least one pet breed.

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