DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Claims 1, 7, 12, 14, 18, 20, 22, and 24 have been amended. Claims 2-4, 9-11, 15-16, 19, and 21 have not been modified. Claims 5-6, 8, 13, 17, 23, and 25 have been cancelled. Claims 26 and 27 have been added. Claims 1-4, 7, 9-12, 14-16, 18-22, 24, and 26-27 are pending and are provided to be examined upon their merits.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on November 19, 2025 has been entered.
Response to Arguments
Applicant’s arguments filed November 19, 2025 have been fully considered but they are not persuasive. A response is provided below.
Applicant argues 35 U.S.C. §101 Rejections, pg. 12 of Remarks:
Regarding Mathematical concepts, Applicant argues that the claims are only based on or involve a mathematical concept and does not directly recite a mathematical concept, as adjacency graphs are a data structure and the claims do not recite any mathematical manipulation of matrices. Examiner respectfully disagrees. As previously stated in the Final Rejection provided on August 20, 2025, an adjacency graph is simply a graphical representation of a matrix. Not explicitly reciting a matrix does not preclude the graph from representing a mathematical data structure, as a graph itself is a mathematical data structure ([0062] of Applicant specification, “In some instances, the data sets 145 are stored in a data structure comprising adjacency graphs, adjacency tables and/or adjacency lists that are similar to the matrix described above.”). Evidence is provided to demonstrate that graphs are mathematical structures.
Pg. 1 of Wikipedia, Graph (discrete mathematics), 10 Nov 2016, Wikipedia recites: “In mathematics, and more specifically in graph theory, a graph is a structure amounting to a set of objects in which some pairs of the objects are in some sense "related." The objects correspond to mathematical abstractions called vertices (also called nodes or points)and each of the related pairs of vertices is called an edge (also called an arc or line).[1] Typically, a graph is depicted in diagrammatic form as a set of dots for the vertices, joined by lines or curves for the edges. Graphs are one of the objects of study in discrete mathematics.”
Pg. 1 of Wikipedia, Graph theory, 21 Feb 2016, Wikipedia recites: “In mathematics and computer science, graph theory is the study of graphs, which are mathematical structures used to model pairwise relations between objects. A graph in this context is made up of vertices, nodes, or points which are connected by edges, arcs, or lines.”
As Examiner maintains that the adjacency graph is a mathematical data structure, Examiner respectfully disagrees that generating the graph is an additional element. Additionally, the claims also recite minimizing a loss function, which is a mathematical process.
Regarding Organizing Human Activity, Applicant argues that the Examiner oversimplifies the claims by focusing solely on the end use (aiding in diagnosis) and disregarding the specific technical means and processes recited in the claims. Examiner respectfully disagrees. Step 2A, Prong One is simply an identification of any abstract ideas and does not, by itself, represent a rejection. Any technical means and processes by which the additional elements are implemented by the claims are, and have been, analyzed under Step 2A, Prong Two. Thus, the Examiner maintains that the claims represent an abstract idea of certain methods of organizing human activities as managing personal behaviors as the generation of the model is for the purpose of aiding in animal clinical testing/diagnosis.
Regarding Examiner’s reliance on generic computer components, Applicant argues that performance of the claims on general-purpose computers does not, by itself, render the claims abstract or ineligible. Examiner agrees with Applicant and notes that the claims are analyzed according to the eligibility analysis as outlined in the MPEP.
Regarding technical improvement in computer functionality, Applicant argues that the claims transform how computers process and analyze animal health data by requiring the generation of adjacency graphs and usage of a multi-layer graph neural network model that go beyond the speed and accuracy of human review and can uncover complex relationships across time or between different types of observations. Examiner respectfully disagrees that such an application encompasses an improvement in computer functionality, as the multi-layer GNN model and adjacency graphs are applied to improve upon the abstract idea of clinical animal testing by automating the manual process of diagnosing animal condition (“the process remains slow, error-prone, and limited by human cognitive capacity” as noted in Remarks filed 11/19/2025).
As previously provided in the Final Rejection dated 8/20/2025, Examiner notes multi-layer GNN models for disease prognosis are known. Thus, the application of a multi-layer GNN in the instant application is analogous to applying known elements to the abstract idea of predicting animal health. See the following examples:
Sanchez-Lengeling; Benjamin, A Gentle Introduction to Graph Neural Networks, 2 Sep 2021, Distill: Pg. 3, “A way of visualizing the connectivity of a graph is through its adjacency matrix. We order the nodes, in this case each of 25 pixels in a simple 5x5 image of a smiley face, and fill a matrix of n nodes × n nodes n nodes × n nodes with an entry if two nodes share an edge.”
Sun; Zhenchao, Disease Prediction via Graph Neural Networks, 22 Jun 2020, IEEE Journal of Biomedical and Health Informatics: Pg. 818, “we introduce an innovative model based on Graph Neural Networks (GNNs) for disease prediction, which utilizes external knowledge bases to augment the insufficient EMR data, and learns highly representative node embeddings for patients, diseases and symptoms from the medical concept graph and patient record graph respectively constructed from the medical knowledge base and EMRs.”
Lu; Haohui, Disease Prediction Using Graph Machine Learning Based on Electronic Health Data: A Review of Approaches and Trends, 4 Apr 2023, MDPI healthcare: Pg. 1, “Herein, a review of graph ML methods and their applications in the disease prediction domain based on electronic health data is presented in this study from two levels: node classification and link prediction. Commonly used graph ML approaches for these two levels are shallow embedding and graph neural networks (GNN). This study performs comprehensive research to identify articles that applied or proposed graph ML models on disease prediction using electronic health data.”
Here, the instant claims seem more analogous to "apply it" (or an equivalent, such as implementing) with the judicial exception, or merely including instructions to implement an abstract idea on a computer via a multi-layer GNN model, or merely using a computer via a multi-layer GNN model as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
Regarding Applicant assertion that the claims substantially reduces the computational time and cost required for animal health monitoring, Examiner notes that animal health monitoring is an activity typically performed by veterinarians. Thus, any improvements of such represent an improvement to the abstract idea. Furthermore, the specified paragraph simply recites wherein “a GPU can provide better computing performance to further improve the computing cost and efficiency”, which only describes using a graphics processing unit without improvements to the underlying technology and no clear nexus to GNN models.
Regarding Applicant assertion that improvement to clinical animal testing through technical means is a technological improvement, Examiner respectfully disagrees. Examiner notes that the argued case law are specific to their respective fields (computer animation for McRO, Inc. v. Bandai Namco Games America Inc. and an improved cardiac monitoring device for CardioNet, LLC v. InfoBionic Inc.) and are not applicable to the instant application as the fact patterns of the respective cases do not align. Here, adjacency graphs and a multi-layer graph neural network model is used “to predict whether health of an animal subject is normal, veterinary attention for the animal subject is likely required in an upcoming time period, an unplanned death outcome for the animal subject is likely in the upcoming time period, or a treatment is likely to be administered to the animal subject in the upcoming time period”, which seeks to perform actions that could otherwise be performed by a veterinarian for their patients.
An improvement to the field of clinical animal testing is an improvement to the abstract idea of clinical animal testing and diagnosis (see MPEP § 2106.05(a)(III) stating “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”).
Applicant argues 35 U.S.C. §102 Rejections, pg. 18 of Remarks:
Applicant argues that Vogler is insufficient to teach temporal graphs with edges representing temporal relationships. Examiner notes that Vogler suggests temporal graphs with edges representing temporal relationships.
[0089] of Vogler recites: “Thus, a graph in the sense of the present disclosure comprises a node representing the object of investigation (with its properties). The graph also includes an additional node for each additional object for which property data exists and which is related to the object of investigation. I2n other words, each further node of the graph represents a further object (with its properties) that is related to the object of investigation.”
[0100] of Vogler further recites: “A relation between two objects in a graph can be represented by an adjacency matrix.”
[0078] of Vogler further recites: “Typically (but not necessarily), the at least one object to which the object of investigation is related is of the same type (but preferably not the exact same) object as the object of investigation… If the object of investigation is a field, the at least one object is preferably also a field, preferably another field on which the same crops are grown or the same field at another time (e.g., at a time during a past vegetation period).”
As the data objects that comprise the nodes may refer to the same object but at different time points, the relation/edge that connects each node would result in a graph would be temporal in nature. Thus, it would be obvious to one of ordinary skill in the art to combine the capability of determining temporal relationships between crop fields with animal health data to generate temporal adjacency graphs for individual animal subjects based on their state at different time periods.
However, new art is applied to more explicitly teach the claim limitation. Please see the modified §103 rejection below.
Applicant argues 35 U.S.C. §103 Rejections, pg. 21 of Remarks:
Applicant argues that the prior cited art of record are insufficient to overcome the amended independent claims. Applicant argument is moot as the prior art applied to the dependent claims are not applied to teach temporal relationships between connected nodes.
Applicant further argues that Kong is a generic reference and is not from the same field of endeavor as the claimed invention, nor it is reasonably pertinent to the problem faced by the inventor as Kong is not directed towards veterinary clinical prediction. Examiner respectfully disagrees and notes that Vogler in view of Kong are considered analogous to the claimed invention because they are in the field of graph neural networks. As Kong is only applied to teach wherein adjacency graphs can be chronologically ordered (claims 22 and 24) and the claims only recites animal subjects as a field of use of the functionality of chronically ordered nodes, Examiner maintains that applying Kong to further define the organization of the graph is a valid combination.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4, 7, 9-12, 14-16, 18-22, 24, and 26-27 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Subject Matter Eligibility Criteria – Step 1:
The claims recite subject matter within a statutory category as a process and a machine
(claims 1-4, 7, 9-12, 14-16, 18-22, 24, and 26-27). Accordingly, claims 1-4, 7, 9-12, 14-16, 18-22, 24, and 26-27are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria – Step 2A – Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP §2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and /or c) mathematical concepts. MPEP §2106.04(a).
The Examiner has identified method claims 1 and 7 as the claim that represents the claimed invention for analysis, and is similar to system claims 14 and 18, respectively.
Claim 1:
A method comprising:
obtaining sets of data for a plurality of animal subjects over a time period, wherein the sets of data comprise: (i) clinical observation data, (ii) body weight measurement data, (iii) outcome status data, (iv) veterinary treatment record data, or (v) any combination thereof;
processing the sets of data into a training set of numerical values;
generating, for each animal subject of the plurality of animal subjects based on the training set of numerical values, an adjacency graph, wherein the adjacency graph is a temporal graph with each edge representing a temporal relationship between connected nodes;
training a multi-layer graph neural network (GNN) model on the adjacency graphs to predict whether health of an animal subject is normal, veterinary attention for the animal subject is likely required in an upcoming time period, an unplanned death outcome for the animal subject is likely in the upcoming time period, or a treatment is likely to be administered to the animal subject in the upcoming time period, wherein training the multi-layer GNN model comprises:
(a) aggregating information across nodes of the adjacency graphs;
(b) passing the aggregated information through the multi-layer GNN model; and
(c) updating learnable parameters of the multi-layer GNN model to minimize a loss function associated with predicting one or more of: a normal health status, a likelihood that veterinary attention is required in the upcoming time period, a likelihood of the unplanned death outcome in the upcoming time period, and a likelihood that the treatment is to be administered in the upcoming time period; and
outputting the multi-layer GNN model.
These claims recite an abstract idea of: mathematical processes. The claim recites processing the sets of data into a training set of numerical values, generating temporal adjacency graphs based on the numerical values, and training the multi-layer GNN model by updating learnable parameters by minimizing a loss function. Examiner notes that adjacency graphs are graphical representations of matrices, which are a mathematical structure. As recited in [0061] Applicant specification, “the data structure is a matrix of size m x n x p, and with m rows storing data of m animal subjects, and each row corresponding to one animal subject. The n columns of the matrix may correspond to an ordered list of entries, with each entry corresponding to an item in one of the Tables 1-4. The data stored in each cell of the matrix is a value of the corresponded item taken at a specific time. For each item, the animal subject can have p different values storing in the p dimension of the matrix.” [0062] of Applicant specification specifies that “In some instances, the data sets 145 are stored in a data structure comprising adjacency graphs, adjacency tables and/or adjacency lists that are similar to the matrix described above.” Further evidence is provided to demonstrate that graphs are mathematical structures.
Pg. 1 of Wikipedia, Graph (discrete mathematics), 10 Nov 2016, Wikipedia recites: “In mathematics, and more specifically in graph theory, a graph is a structure amounting to a set of objects in which some pairs of the objects are in some sense "related." The objects correspond to mathematical abstractions called vertices (also called nodes or points)and each of the related pairs of vertices is called an edge (also called an arc or line).[1] Typically, a graph is depicted in diagrammatic form as a set of dots for the vertices, joined by lines or curves for the edges. Graphs are one of the objects of study in discrete mathematics.”
Pg. 1 of Wikipedia, Graph theory, 21 Feb 2016, Wikipedia recites: “In mathematics and computer science, graph theory is the study of graphs, which are mathematical structures used to model pairwise relations between objects. A graph in this context is made up of vertices, nodes, or points which are connected by edges, arcs, or lines.”
These above limitations under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. The claim elements are directed towards obtaining sets of data for a plurality of animal subjects and training a multi-layer GNN model “to predict whether health of an animal subject is normal, veterinary attention for the animal subject is likely required in an upcoming time period, an unplanned death outcome for the animal subject is likely in the upcoming time period, or a treatment is likely to be administered to the animal subject in the upcoming time period”. These claim elements are directed towards aiding in patient diagnoses. Diagnosing an animal subject falls under the abstract concept of managing personal behaviors of people, as it is a human activity regularly performed by veterinary doctors for their patients. It is important to note that the examples provided by the MPEP such as social activities, teaching, and following rules or instructions are provided as examples and not an exclusive listing.
Accordingly, the claim recites at least one abstract idea.
Claim 14 is abstract for similar reasons.
Claim 7:
A method comprising:
accessing a data table storing a set of data for an animal subject over a time period, the set of data including: (i) clinical observation data, (ii) body weight measurement data, (iii) outcome status data, (iv) veterinary treatment record data, or (v) any combination thereof;
generating an adjacency graph based on the set of data stored in the data table, wherein the adjacency graph is a temporal graph with each edge representing a temporal relationship between connected nodes;
inputting the adjacency graph into a trained multi-layer graph neural network (GNN) model, wherein the trained multi-layer GNN model was previously trained to predict a result for the animal subject comprising whether health of the animal subject is normal, veterinary attention for the animal subject is likely required in an upcoming time period, an unplanned death outcome for the animal subject is likely in the upcoming time period, or a treatment is likely to be administered to the animal subject in the upcoming time period using a plurality of training adjacency graphs generated based on data for a plurality of training animal subjects collected over a period of time;
predicting, using the trained multi-layer GNN model, the result for the animal subject; and
outputting a classification based on the result for the animal subject.
These claims recite an abstract idea of: mathematical processes. The claim recites generating an adjacency graph based on the set of data stored in the data table. Examiner notes that adjacency graphs are graphical representations of matrices, which are a mathematical structure. As recited in [0061] Applicant specification, “the data structure is a matrix of size m x n x p, and with m rows storing data of m animal subjects, and each row corresponding to one animal subject. The n columns of the matrix may correspond to an ordered list of entries, with each entry corresponding to an item in one of the Tables 1-4. The data stored in each cell of the matrix is a value of the corresponded item taken at a specific time. For each item, the animal subject can have p different values storing in the p dimension of the matrix.” [0062] of Applicant specification specifies that “In some instances, the data sets 145 are stored in a data structure comprising adjacency graphs, adjacency tables and/or adjacency lists that are similar to the matrix described above.” Further evidence is provided above (Wikipedia, Graph (discrete mathematics), 10 Nov 2016, Wikipedia and Wikipedia, Graph theory, 21 Feb 2016, Wikipedia) to demonstrate that graphs are mathematical structures.
These above limitations under their broadest reasonable interpretation, also cover performance of the limitation as certain methods of organizing human activity. The claim elements are directed towards obtaining sets of data for a plurality of animal subjects and training a multi-layer GNN model “to predict whether health of an animal subject is normal, veterinary attention for the animal subject is likely required in an upcoming time period, an unplanned death outcome for the animal subject is likely in the upcoming time period, or a treatment is likely to be administered to the animal subject in the upcoming time period”. These claim elements are directed towards aiding in patient diagnoses. Diagnosing an animal subject falls under the abstract concept of managing personal behaviors of people, as it is a human activity regularly performed by veterinary doctors for their patients.
Accordingly, the claim recites at least one abstract idea.
Claim 18 is abstract for similar reasons.
Subject Matter Eligibility Criteria – Step 2A – Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the idea into a practical application. As noted at MPEP §2106.04 (ID)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
Additional elements cited in the claims:
A multi-layer graph neural network model (1,7,12, 14,18,20); an embedding model (2,15); a graphical user interface (11,21); one or more data processors (14,16,18,20,21); a non-transitory computer readable storage medium (14,18)
Any computing devices and their components (processors and storage medium) that would be able to perform the method and the modules that are used within the computing environment are taught at a high level of generality such that the claim elements amounts to no more than mere instructions to apply the exception using any generic component capable of performing the claim limitations. [0104] of Applicant specification recites: “Systems, methods, and data structures described herein are operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of known computing systems, environments, and/or configurations that may be suitable include, but are not limited to, personal computers, server computers, thin clients, thick clients, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.” [0079] of Applicant specification recites: “hardware such as one or more processors (e.g., a CPU, GPU, TPU, FPGA, the like, or any combination thereof), memory, and storage that operates software or computer program instructions (e.g., TensorFlow, PyTorch, Keras, and the like)” No specific, technical improvements are being made to computing devices as generic devices with software modules are simply being used to perform the abstract idea.
Graph neural network models are also taught at a high level of generality. [0056] of Applicant specification recites: “Various types of graph neural networks exist, differing in the method used for aggregating information across connected observations. Examples include the graph convolutional neural network (GCN) and the graph attention network (GAT).” No specific, technical improvements are being made to the field of machine learning as various existing techniques are simply applied to perform the abstract idea.
Embedding models are also taught at a high level of generality. [0069] of Applicant specification recites: “Upon detecting the free text entry, the data preparation module 130 can apply an embedding model to the free text entry to generate a vector of the free text entry. For example, the embedding model may be Word2Vec, GloVe, or any other suitable word embedding model.” No specific, technical improvements are being made to embedding models as a variety of embedding models are simply applied to perform the abstract idea of data vectorization.
Graphical user interfaces are also taught at a high level of generality. [0018] of Applicant specification recites: “the computer-implement method further comprises providing the classification and/or the recommendation to a user through a graphical user interface (GUI).” [0097] of Applicant specification further recites: “The set of data may be input into the machine-learning model via a graphical user interface (GUI).” No specific, technical improvements are being made to graphical user interfaces as they are applied to perform the insignificant extra-solution activities of simply presenting and receiving data from a user.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole with the limitations reciting the at least one abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(IID)(A)(2).
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claims 2 and 15: These claims recite wherein processing the sets of data into the training set of numerical values comprises:(i) (a) determining a free text entry in the sets of data; (b) applying an embedding model to the free text entry to generate a vector of the free text entry; (c) reducing a size of the vector using a principal component analysis reduction method; and (d) including the vector in the training set; or (ii) (a) determining a categorical variable entry in the sets of data; (b) converting the categorical variable entry into a numerical value using a mapping between numerical values and categorical variable entries; and (c) including the numerical value in the training set; which teaches an abstract idea of mathematical processes as processing data into numerical values for machine learning input.
Claim 3: This claim recites the method further comprising, prior to training the multi-layer GNN model: labelling the numerical values of the training set with an unplanned death indicator, a veterinary request indicator, or a veterinary treatment indicator; which teaches an abstract idea of certain methods of organizing human activity and mental processes, as labeling data with indicators that inform the patient’s state can be performed mentally and encompasses managing personal behaviors as indicating a patient’s state based on data is regularly performed by veterinary staff for their patients.
Claim 4: This claim recites wherein labelling the numerical values of the training set comprises:(i) determining the clinical observation data for an animal subject of the plurality of animal subjects includes the veterinary request indicator and labelling the training set with the veterinary request indicator for the animal subject; (ii) determining the outcome status data for an animal subject of the plurality of animal subjects includes the unplanned death indicator and labelling the training set with the unplanned death indicator for the animal subject; (iii) determining the veterinary treatment record data for an animal subject of the plurality of animal subjects includes the veterinary treatment indicator and labelling the training set with the veterinary treatment indicator for the animal subject; or (iv) any combination of (i)-(iii); which teaches an abstract idea of certain methods of organizing human activity, as indicating a patient’s state based on data is regularly performed by veterinary staff for their patients.
Claim 16 is rejected for the same reasons as claims 3 and 4.
Claims 9 and 19: These claims recite wherein the classification comprises comparing the result for the animal subject to a determined threshold and classifying, based on the comparison, the animal subject as having a normal health status, requiring veterinary attention in the upcoming time period, being likely to experience the unplanned death outcome in the upcoming time period, or likely to receive the treatment in the upcoming time period; which teaches an abstract idea of certain methods of organizing human activity, such as determining if an animal subject requires veterinary attention or is likely to receive a treatment, which could otherwise be performed by a veterinarian.
Claim 10: This claim recites further comprising providing a recommendation based on the classification of the animal subject; which teaches an abstract idea of certain methods of organizing human activity, as providing a recommendation is analogous to teaching.
Claims 11 and 21: These claims recite the method further comprising providing the classification and/or the recommendation to a user through a graphical user interface (GUI); which teaches a graphical user interface at a high level of generality.
Claims 12 and 20: These claims recite the method further comprising, prior to accessing the data table: obtaining sets of data for the plurality of training animal subjects over the period of time, wherein the sets of data comprise: (i) clinical observation data, (ii) body weight measurement data, (iii) outcome status data, (iv) veterinary treatment record data, or (v) any combination thereof; processing the sets of data into a training set of numerical values; generating, for each training animal subject of the plurality of training animal subjects based on the training set of numerical values, a training adjacency graph, wherein the training adjacency graph is a temporal graph; training the multi-layer GNN model on the training adjacency graphs to predict whether health of a training animal subject is normal, veterinary attention for the training animal subject is likely required in the upcoming time period, an unplanned death outcome for the training animal subject is likely in the upcoming time period, or a treatment is likely to be administered to the training animal subject in the upcoming time period, wherein training the multi-layer GNN model comprises:(a) aggregating information across nodes of the training adjacency graphs to update node representations;(b) passing the aggregated information through the multi-layer GNN model;(c) updating learnable parameters of the multi-layer GNN model to minimize a loss function associated with predicting one or more of: a normal health status, a likelihood that veterinary attention is required in the upcoming time period, a likelihood of the unplanned death outcome in the upcoming time period, and a likelihood that the treatment is to be administered in the upcoming time period; and outputting the multi-layer GNN model; which is abstract and does not provide a practical application for the same reasons as claim 1 above.
Claims 22 and 24: These claims recite wherein the adjacency graph is a chronologically ordered temporal graph, and wherein each node of the chronologically ordered temporal graph represents a data point at a given time of the time period and each edge of the chronologically ordered temporal graph represent a temporal or domain-specific relationship between the nodes; which only serves to further limit the adjacency graph.
Claims 26 and 27: These claims recite wherein the aggregating the information across the nodes of the training adjacency graphs is limited to nodes within a predetermined temporal window; which only serves to further limit the adjacency graph.
Subject Matter Eligibility Criteria – Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
These claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
Amount to elements that have been recognized as activities in particular fields (such as Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), MPEP §2106.05(d)(II)(i);storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv)).
Examiner notes multi-layer GNN models for disease prognosis are known. Thus, the application of a multi-layer GNN in the instant application is analogous to applying known elements to the abstract idea of predicting animal health. See the following examples:
Sanchez-Lengeling; Benjamin, A Gentle Introduction to Graph Neural Networks, 2 Sep 2021, Distill: Pg. 3, “A way of visualizing the connectivity of a graph is through its adjacency matrix. We order the nodes, in this case each of 25 pixels in a simple 5x5 image of a smiley face, and fill a matrix of n nodes × n nodes n nodes × n nodes with an entry if two nodes share an edge.”
Sun; Zhenchao, Disease Prediction via Graph Neural Networks, 22 Jun 2020, IEEE Journal of Biomedical and Health Informatics: Pg. 818, “we introduce an innovative model based on Graph Neural Networks (GNNs) for disease prediction, which utilizes external knowledge bases to augment the insufficient EMR data, and learns highly representative node embeddings for patients, diseases and symptoms from the medical concept graph and patient record graph respectively constructed from the medical knowledge base and EMRs.”
Lu; Haohui, Disease Prediction Using Graph Machine Learning Based on Electronic Health Data: A Review of Approaches and Trends, 4 Apr 2023, MDPI healthcare: Pg. 1, “Herein, a review of graph ML methods and their applications in the disease prediction domain based on electronic health data is presented in this study from two levels: node classification and link prediction. Commonly used graph ML approaches for these two levels are shallow embedding and graph neural networks (GNN). This study performs comprehensive research to identify articles that applied or proposed graph ML models on disease prediction using electronic health data.”
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2-4, 9-12, 15-16, 19-22, 24, and 26-27 additional limitations which amount to elements that have been recognized as activities in particular fields, claims 2-4, 9-12, 15-16, 19-22, 24, and 26-27, e.g., performing repetitive calculations, Flook, MPEP §2106.05(d)(II)(ii); claims 2-4, 9-12, 15-16, 19-22, 24, and 26-27, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-4, 7, 9-12, 14-16, 18-22, 24, and 26-27are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 7, 12, 14, 18, 20 and 26-27 are rejected under 35 U.S.C. 103 as being unpatentable over Vogler (US 20240347206) in view of Baranzini (US 20240303544).
Regarding claim 1, Vogler teaches a method comprising:
obtaining sets of data for a plurality of animal subjects over a time period, wherein the sets of data comprise: (i) clinical observation data, (ii) body weight measurement data, (iii) outcome status data, (iv) veterinary treatment record data, or (v) any combination thereof ([0064], “The object can be characterized by certain features. In the case of a person, these features include, for example, age, height, weight, body mass index (BMI), …, existing diseases and/or conditions, pre-existing diseases and/or conditions and/or similar properties.” [0066], “In addition to the property data already mentioned, the following additional property data can also provide information about the health status of a human or animal: history of previous illnesses, times when the diseases occurred, severity of the diseases that occurred, measures taken to cure and/or alleviate the diseases, current and/or past blood tests (e.g., blood sugar, oxygen saturation, red blood cell count, haemoglobin content, white blood cell count, platelet count, inflammatory values, blood lipids), liver values,...” [0127], “Training data can be obtained from clinical studies, patient records and/or publicly accessible databases”). Examiner notes that patient records implies a plurality of patients.
processing the sets of data into a training set of numerical values ([0083], “a numerical representation is created on the basis of the property data for the object of investigation and the relational data.”);
generating, for each animal subject of the plurality of animal subjects based on the training set of numerical values, an adjacency graph ([0089], “Thus, a graph in the sense of the present disclosure comprises a node representing the object of investigation (with its properties). The graph also includes an additional node for each additional object for which property data exists and which is related to the object of investigation. In other words, each further node of the graph represents a further object (with its properties) that is related to the object of investigation.” [0100], “A relation between two objects in a graph can be represented by an adjacency matrix. An adjacency matrix (also known as a neighbourhood matrix) specifies which nodes of the graph are connected by an edge. An adjacency matrix can have a row and a column for each node, which for n nodes results in an n.Math.n matrix. An entry in the i-th row and j-th column indicates whether an edge leads from the i-th to the j-th node.” [0066], “history of previous illnesses, times when the diseases occurred, …, current and/or past blood tests (e.g., blood sugar, oxygen saturation, red blood cell count, haemoglobin content, white blood cell count, platelet count, inflammatory values, blood lipids), liver values,...” [0078], “Typically (but not necessarily), the at least one object to which the object of investigation is related is of the same type (but preferably not the exact same) object as the object of investigation… If the object of investigation is a field, the at least one object is preferably also a field, preferably another field on which the same crops are grown or the same field at another time (e.g., at a time during a past vegetation period).”). Examiner notes that as the data objects may refer to the same object but at different time points, the resulting graph would be temporal in nature. Thus, it would be obvious to one of ordinary skill in the art to combine the capability of determining temporal relationships between crop fields with the animal health data to generate temporal adjacency graphs for individual animal subjects.
training a multi-layer graph neural network (GNN) model on the training set adjacency graphs to predict whether health of an animal subject is normal, veterinary attention for the animal subject is likely required in an upcoming time period, an unplanned death outcome for the animal subject is likely in the upcoming time period, or a treatment is likely to be administered to the animal subject in the upcoming time period ([0014], “the trained machine learning model was trained on the basis of training data to determine an expected value for the presence and/or occurrence and/or incidence of a condition and/or event” [0059], “In the case of a plant, an animal or human being, the condition and/or event may be an existing or occurring disease.” [0007], “Examples of such diseases are diabetes, asthma, allergies, atopic dermatitis, migraine, rheumatism, hypertension, depression, Alzheimer's, dementia, Parkinson's disease, cancer, chronic bowel inflammatory disease, endometriosis, age-related macular degeneration, etc.” [0128], “the machine learning model used to calculate the expected value is a graph network (graph neural network), or GNN) or it comprises one. Graph neural networks form a class of artificial neural networks for processing data represented as graphs.”). Examiner interprets prediction of a disease in an animal to be a prediction wherein veterinary attention for the animal subject is likely required in an upcoming time period, as the average pet owner would be unable to treat or manage diseases such as cancer or chronic bowel inflammatory disease without veterinary oversight. Additionally, a prediction that an animal does not have a disease encompasses a prediction that the health of the animal subject is normal.
wherein training the multi-layer GNN model comprises:
(a) aggregating information across nodes of the adjacency graphs ([0116], “Preferably, in the case of a graph as input data, the machine learning model is trained to perform a task at the level of the nodes, particularly preferably a task for the object of investigation.” [0124], “The training data comprises, for each reference object of the multiplicity of reference objects, i) property data for the reference object and ii) relational data comprising property data for other objects that are related to the reference object, in the form of a numerical representation (for example in the form of a graph). This data acts as input data.” [0089], “Thus, a graph in the sense of the present disclosure comprises a node representing the object of investigation (with its properties). The graph also includes an additional node for each additional object for which property data exists and which is related to the object of investigation. In other words, each further node of the graph represents a further object (with its properties) that is related to the object of investigation.”). As each graph encompasses aggregates of the nodes, creating a training data set that comprises a graph representing each reference object encompasses aggregating information across nodes of the adjacency graphs.
(b) passing the aggregated information through the multi-layer GNN model ([0125], “During training, the input data for the individual reference objects is fed (sequentially) to the machine learning model. The model is configured to generate an expected value for the reference object on the basis of the input data and model parameters.”); and
(c) updating learnable parameters of the multi-layer GNN model to minimize a loss function associated with predicting one or more of: a normal health status, a likelihood that veterinary attention is required in the upcoming time period, a likelihood of the unplanned death outcome in the upcoming time period, and a likelihood that the treatment is to be administered in the upcoming time period ([0126], “The calculated expected value is compared with the target data (reference expected value) in order to identify deviations. The deviations between the expected value and the target data (reference expected value) can be quantified using a loss function. In an optimization procedure (e.g. a gradient procedure), the deviations can be reduced by modifying the model parameters. If the deviations have reached a defined minimum, the training can be terminated.” [0014], “the trained machine learning model was trained on the basis of training data to determine an expected value for the presence and/or occurrence and/or incidence of a condition and/or event” [0059], “In the case of a plant, an animal or human being, the condition and/or event may be an existing or occurring disease.”); and
outputting the multi-layer GNN model ([0167], “outputting the trained machine learning model ”).
Although Vogler suggests wherein the adjacency graph is a temporal graph with each edge representing a temporal relationship between connected nodes ([0078], “Typically (but not necessarily), the at least one object to which the object of investigation is related is of the same type (but preferably not the exact same) object as the object of investigation… If the object of investigation is a field, the at least one object is preferably also a field, preferably another field on which the same crops are grown or the same field at another time (e.g., at a time during a past vegetation period).”), Vogler does not explicitly recite wherein the adjacency graph is a temporal graph with each edge representing a temporal relationship between connected nodes.
However, Baranzini does teach wherein the adjacency graph is a temporal graph with each edge representing a temporal relationship between connected nodes ([0133], “Training vectors could be made for different entities, the same entity over different time periods or at different points in disease progression (e.g. before clinical presentation).” [0025], “Graph elements (nodes) in SPOKE are linked to one another through edges that represent the node's relationships in the corresponding database” [0074], “A SPOKEsig can also be created for a limited portion of a patient's medical history. Instead of selecting every SEP associated with a patient, only the SEPs from EHRs that were generated over a given time period can be chosen.” [0068], “an adjacency matrix is created using the edges in SPOKE to initialize the SPOKE transition probability matrix (TPM) in which each column sums to 1. The adjacency matrix is an square matrix with a column for each node and a row for each node.”). It would be obvious to one of ordinary skill in the art that relationships between nodes generated from data of the same entity over different time periods would be temporal in nature.
Vogler in view of Baranzini are considered analogous to the claimed invention because they are in the field of machine learning for patient diagnosis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Vogler with Baranzini for the advantage of “encode[ing] each […] node's importance for a particular patient over a given time period” (Baranzini; [0076]).
Regarding claim 7, Vogler teaches the method comprising:
accessing a data table storing a set of data for an animal subject over a time period, the set of data including: (i) clinical observation data, (ii) body weight measurement data, (iii) outcome status data, (iv) veterinary treatment record data, or (v) any combination thereof ([0064], “The object can be characterized by certain features. In the case of a person, these features include, for example, age, height, weight, body mass index (BMI), …, existing diseases and/or conditions, pre-existing diseases and/or conditions and/or similar properties.” [0066], “In addition to the property data already mentioned, the following additional property data can also provide information about the health status of a human or animal: history of previous illnesses, times when the diseases occurred, severity of the diseases that occurred, measures taken to cure and/or alleviate the diseases, current and/or past blood tests (e.g., blood sugar, oxygen saturation, red blood cell count, haemoglobin content, white blood cell count, platelet count, inflammatory values, blood lipids), liver values,...” [0016], “receiving relational data, wherein the relational data comprises property data for one or more objects that are related to the object of investigation”). Examiner notes that relational data is data structured in tables with rows and columns.
generating an adjacency graph based on the set of data stored in the data table ([0089], “Thus, a graph in the sense of the present disclosure comprises a node representing the object of investigation (with its properties). The graph also includes an additional node for each additional object for which property data exists and which is related to the object of investigation. In other words, each further node of the graph represents a further object (with its properties) that is related to the object of investigation.” [0100], “A relation between two objects in a graph can be represented by an adjacency matrix. An adjacency matrix (also known as a neighbourhood matrix) specifies which nodes of the graph are connected by an edge. An adjacency matrix can have a row and a column for each node, which for n nodes results in an n.Math.n matrix. An entry in the i-th row and j-th column indicates whether an edge leads from the i-th to the j-th node.”);
inputting the adjacency graph into a trained multi-layer graph neural network (GNN) model, wherein the trained multi-layer GNN model was previously trained to predict a result for the animal subject comprising whether health of the animal subject is normal, veterinary attention for the animal subject is likely required in an upcoming time period, an unplanned death outcome for the animal subject is likely in the upcoming time period, or a treatment is likely to be administered to the animal subject in the upcoming time period using a plurality of training adjacency graphs generated based on data for a plurality of training animal subjects collected over a period of time ([0014], “the trained machine learning model was trained on the basis of training data to determine an expected value for the presence and/or occurrence and/or incidence of a condition and/or event” [0059], “In the case of a plant, an animal or human being, the condition and/or event may be an existing or occurring disease.” [0007], “Examples of such diseases are diabetes, asthma, allergies, atopic dermatitis, migraine, rheumatism, hypertension, depression, Alzheimer's, dementia, Parkinson's disease, cancer, chronic bowel inflammatory disease, endometriosis, age-related macular degeneration, etc.” [0128], “the machine learning model used to calculate the expected value is a graph network (graph neural network), or GNN) or it comprises one. Graph neural networks form a class of artificial neural networks for processing data represented as graphs.”). Examiner interprets prediction of a disease in an animal to be a prediction wherein veterinary attention for the animal subject is likely required in an upcoming time period, as the average pet owner would be unable to treat or manage diseases such as cancer or chronic bowel inflammatory disease without veterinary oversight. Additionally, a prediction that an animal does not have a disease encompasses a prediction that the health of the animal subject is normal.
predicting, using the trained multi-layer GNN model, the result for the animal subject ([0137], “Once the machine learning model training is complete, it can be used for prediction. The trained model can be supplied with a numerical representation of a new object of investigation in relation to other objects, and the trained model calculates an expected value for the new object of investigation based on the new numerical representation.” [0004], “Predictions can help people prepare for conditions and/or events that may occur or arise in the future.” [0059], “In the case of a plant, an animal or human being, the condition and/or event may be an existing or occurring disease.”); and
outputting a classification based on the result for the animal subject ([0138], “The calculated expected value can be output, i.e. displayed on a screen, printed out on a printer, stored in a data storage medium and/or transmitted to a separate computer system (e.g. via a network).” [0119], “The expected value can specify a class to which the (investigation) object is assigned. For example, a class can represent objects in which a defined condition and/or a defined event occurs. Another class can represent objects in which the defined condition does not occur and/or the defined event does not occur.”).
Although Vogler suggests wherein the adjacency graph is a temporal graph with each edge representing a temporal relationship between connected nodes ([0078], “Typically (but not necessarily), the at least one object to which the object of investigation is related is of the same type (but preferably not the exact same) object as the object of investigation… If the object of investigation is a field, the at least one object is preferably also a field, preferably another field on which the same crops are grown or the same field at another time (e.g., at a time during a past vegetation period).”), Vogler does not explicitly recite wherein the adjacency graph is a temporal graph with each edge representing a temporal relationship between connected nodes.
However, Baranzini does teach wherein the adjacency graph is a temporal graph with each edge representing a temporal relationship between connected nodes ([0133], “Training vectors could be made for different entities, the same entity over different time periods or at different points in disease progression (e.g. before clinical presentation).” [0025], “Graph elements (nodes) in SPOKE are linked to one another through edges that represent the node's relationships in the corresponding database” [0074], “A SPOKEsig can also be created for a limited portion of a patient's medical history. Instead of selecting every SEP associated with a patient, only the SEPs from EHRs that were generated over a given time period can be chosen.” [0068], “an adjacency matrix is created using the edges in SPOKE to initialize the SPOKE transition probability matrix (TPM) in which each column sums to 1. The adjacency matrix is an square matrix with a column for each node and a row for each node.”). It would be obvious to one of ordinary skill in the art that relationships between nodes generated from data of the same entity over different time periods would be temporal in nature.
Vogler in view of Baranzini are considered analogous to the claimed invention because they are in the field of machine learning for patient diagnosis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Vogler with Baranzini for the advantage of “encode[ing] each […] node's importance for a particular patient over a given time period” (Baranzini; [0076]).
Regarding claim 12, Vogler teaches the method of claim 7, as described above. This claim is rejected for the same reasons as claim 1.
Regarding claim 14, this claim is rejected for the same reasons as claim 1. Vogler further teaches one or more data processors; and a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform the method (Claim 11, “A non-transitory computer-readable storage medium storing software commands that, when executed by a processor of a computer system, cause the computer system.”).
Regarding claims 18 and 20, this claim is rejected for the same reasons as claims 7 and 12, respectively. Vogler further teaches one or more data processors; and a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform the method ([0067], “the present disclosure provides a non-transitory computer readable medium, storing instructions that, when executed by a processor, cause a computer system to execute the steps of any one of the methods disclosed herein.”).
Regarding claim 26, Vogler teaches the method of claim 1, as described above. Vogler does not teach wherein the aggregating the information across the nodes of the training adjacency graphs is limited to nodes within a predetermined temporal window.
However, Baranzini does teach wherein the aggregating the information across the nodes of the training adjacency graphs is limited to nodes within a predetermined temporal window ([0074], “Instead of selecting every SEP associated with a patient, only the SEPs from EHRs that were generated over a given time period can be chosen.” [0076], “SPOKEsigs (training vectors) encode each SPOKE node's importance for a particular patient over a given time period. SPOKEsigs are an example of training vectors according to the present disclosure, and SPOKEsigs are inputs used to train the machine learning model.” [0082], “Example entities include patients, drugs, and animal subjects. Entity records can be excluded if the entity records were created outside of a specified time period” [0068], “an adjacency matrix is created using the edges in SPOKE to initialize the SPOKE transition probability matrix (TPM) in which each column sums to 1.”).
Vogler in view of Baranzini are considered analogous to the claimed invention because they are in the field of machine learning for patient diagnosis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Vogler with Baranzini for the advantage of “include[ing] records generated over a limited time period” (Baranzini; [0099]).
Regarding claim 27, this claim is rejected for the same reasons as claim 26, as described above.
Claims 2 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Vogler (US 20240347206) in view of Baranzini (US 20240303544) further in view of Hirsch (US 20190096526).
Regarding claim 2, Vogler in view of Baranzini teaches the method of claim 1, as described above. Vogler in view of Baranzini does not explicitly teach wherein processing the sets of data into the training set of numerical values comprises: (i) (a) determining a free text entry in the sets of data; (b)applying an embedding model to the free text entry to generate a vector of the free text entry; (c) reducing a size of the vector using a principal component analysis reduction method; and (d) including the vector in the training set; or (ii) (a) determining a categorical variable entry in the sets of data; (b) converting the categorical variable entry into a numerical value using a mapping between numerical values and categorical variable entries; and (c) including the numerical value in the training set.
However, Hirsch does teach wherein processing the sets of data into the training set of numerical values comprises:
(i) (a) determining a free text entry in the sets of data ([0052], “formulate the entity by performing preprocessing, text processing, data compression, data reduction, dimensional reduction, etc. from the retrieved dataset… Examples of data types can include, but are not limited to, text, alphanumeric, biological sequences,…”);
(b)applying an embedding model to the free text entry to generate a vector of the free text entry ([0086], “The system processor 128 can derive a set of conformed, that is mathematically well behaved, feature vectors in an N-dimensional Euclidean space, which can be viewed as a Hilbert Space, called the embedding space, from the input data in Tables 1 through 9, and any subset and/or combination thereof.” [0098], “the linear and/or nonlinear clustering can be mapped (that is, embedded) onto at least one of linear manifold and nonlinear manifold with the clustering modules 212 and 222.” [0084], “The linear manifold (LM) clustering can use locally linear and/or locally nonlinear high-dimensional spaces that are embedded on a linear manifold. The nonlinear manifold (NLM) clustering can use locally linear and/or locally nonlinear high-dimensional spaces that are embedded on a nonlinear manifold.”);
(c) reducing a size of the vector using a principal component analysis reduction method ([0063], “Dimensionally of the vector space can be reduced/increased using information loss/gain as a controlling factor. The information loss using Principle Component Analysis (PCA), Singular Value Decomposition (SVD) and/or State Vector Machine (SVM).”); and
(d) including the vector in the training set ([Table 11], “5 Based on the expected loss and associated utility function and entity/attribute vector freeze the methods in the module under test and vary other modules at least one other module and observe the change in the expected loss. 6 Use global adjudication to select best processing branch. Repeat processes 1 to 5 as required continuing to observe the expect loss processing performed in the math model module 7 Rate of convergence/divergence, determination of cluster inclusion, bias, etc. as a tool to signal off ramp 8 Domain Expert can control training process, initial data set and dimensionality, maximum data set and dimensionality, method of expansion of data set and dimensionality, class of cost functions (e.g. cubic, quadratic, etc.), update coefficients for cost functions, thresholds, bias of data, weights and masking, collaborative filtering, etc.”);
or (ii) (a) determining a categorical variable entry in the sets of data;(b) converting the categorical variable entry into a numerical value using a mapping between numerical values and categorical variable entries; and (c) including the numerical value in the training set.
Vogler in view of Baranzini further in view of Hirsch are considered analogous to the claimed invention because they are in the field of machine learning for patient diagnosis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Vogler in view of Baranzini with Hirsch for the advantage of “generat[ing] or modify[ing] term vectors formed in the data conditioning modules 210 and 220, with the TF.IDF being a metric that assigns numerical values to unstructured text” (Hirsch; [0065]).
Regarding claim 15, this claim is rejected for the same reasons as claim 2, as described above.
Claims 3-4 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Vogler (US 20240347206) in view of Baranzini (US 20240303544) further in view of Ward (US 20200160998).
Regarding claim 3, Vogler in view of Baranzini teaches the method of claim 1, as described above. Vogler in view of Baranzini does not explicitly teach the method further comprising, prior to training the multi-layer GNN model:
labelling the numerical values of the training set with an unplanned death indicator, a veterinary request indicator, or a veterinary treatment indicator.
However, the combination of Vogler in view of Ward does teach the method further comprising, prior to training the multi-layer GNN model (Vogler, [0128], “the machine learning model used to calculate the expected value is a graph network (graph neural network), or GNN) or it comprises one”):
labelling the numerical values of the training set with an unplanned death indicator, a veterinary request indicator, or a veterinary treatment indicator (Ward, [0065], “The 250 method may further include breaking up the EHR data into windowed intervals, as discussed above (block 258), labeling the EHR data using a plurality of outcome labels (e.g., ICU transfer, death, etc.) (block 260), and imputing missing values (block 262). The pipeline method 250 may include training a model (block 264). Training the model may include training an ML model using one or more years of the windowed EHR data (block 266), tuning parameters on a subset of the EHR data (block 268), evaluating the EHR data at the window level (block 270), and evaluating the data at the encounter level (block 272).”).
Vogler in view of Baranzini further in view of Ward are considered analogous to the claimed invention because they are in the field of machine learning for patient diagnosis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Vogler in view of Baranzini with Ward for the advantage of “labeling the EHR data using a plurality of outcome labels (e.g., ICU transfer, death, etc.)” (Ward; [0065]).
Regarding claim 4, Vogler in view of Baranzini further in view of Ward teaches the method of claims 1 and 3, as described above. Vogler in view of Baranzini does not explicitly teach the method teaches wherein labelling the numerical values of the training set comprises: (i) determining the clinical observation data for an animal subject of the plurality of animal subjects includes the veterinary request indicator and labelling the training set with the veterinary request indicator for the animal subject; (ii) determining the outcome status data for an animal subject of the plurality of animal subjects includes the unplanned death indicator and labelling the training set with the unplanned death indicator for the animal subject; (iii) determining the veterinary treatment record data for an animal subject of the plurality of animal subjects includes the veterinary treatment indicator and labelling the training set with the veterinary treatment indicator for the animal subject; or (iv) any combination of (i)-(iii).
However, Ward does teach the method teaches wherein labelling the numerical values of the training set comprises:
(i) determining the clinical observation data for an animal subject of the plurality of animal subjects includes the veterinary request indicator and labelling the training set with the veterinary request indicator for the animal subject;
(ii) determining the outcome status data for an animal subject of the plurality of animal subjects includes the unplanned death indicator and labelling the training set with the unplanned death indicator for the animal subject ([0065], “The 250 method may further include breaking up the EHR data into windowed intervals, as discussed above (block 258), labeling the EHR data using a plurality of outcome labels (e.g., ICU transfer, death, etc.) (block 260), and imputing missing values (block 262). The pipeline method 250 may include training a model (block 264).” [0087], “of 79,955 total patient encounters in the training, validation and test cohorts used for training and testing the models developed using the present techniques, only 8509 ICU transfers and 480 deaths were reported.”). It would be obvious to one of ordinary skill in the art that humans are also animals.
(iii) determining the veterinary treatment record data for an animal subject of the plurality of animal subjects includes the veterinary treatment indicator and labelling the training set with the veterinary treatment indicator for the animal subject; or
(iv) any combination of (i)-(iii).
Vogler in view of Baranzini further in view of Ward are considered analogous to the claimed invention because they are in the field of machine learning for patient diagnosis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Vogler in view of Baranzini with Ward for the advantage of “labeling the EHR data using a plurality of outcome labels (e.g., ICU transfer, death, etc.)” (Ward; [0065]).
Regarding claim 16, this claim is rejected for the same reasons as claims 3 and 4, as described above.
Claims 9-11, 19, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Vogler (US 20240347206) in view of Baranzini (US 20240303544) further in view of Bradley (US 20190096526).
Regarding claim 9, Vogler in view of Baranzini teaches the method of claim 7, as described above. Vogler in view of Baranzini does not teach wherein the classification comprises comparing the result for the animal subject to a determined threshold and classifying, based on the comparison, the animal subject as having a normal health status, requiring veterinary attention in the upcoming time period, being likely to experience the unplanned death outcome the upcoming time period, or likely to receive the treatment in the upcoming time period comparison.
However, Bradley does teach wherein the classification comprises comparing the result for the animal subject to a determined threshold and classifying, based on the comparison, the animal subject as having a normal health status, requiring veterinary attention in the upcoming time period, being likely to experience the unplanned death outcome the upcoming time period, or likely to receive the treatment in the upcoming time period comparison ([0024], “determining the risk of developing CKD by comparing the score with a threshold value; ” [0104], “A score of between 0 and 5 suggests that the cat will not likely develop CKD within the next 2 years. A score of between 6 and 25 suggests insufficient certainty to predict CKD in the cat, and a veterinary visit within 6 months is recommended. A score of between 26 and 49 suggests insufficient certainty to predict CKD in the cat, and a veterinary visit within 3 months is recommended.”).
Vogler in view of Baranzini further in view of Bradley are considered analogous to the claimed invention because they are in the field of machine learning for patient diagnosis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Vogler in view of Baranzini with Bradley for the advantage of using a “decision threshold to classify an individual as predicted or non-ill patient” (Bradley; [0346]).
Regarding claim 10, Vogler in view of Baranzini further in view of Bradley teaches the method of claims 7 and 9, as described above. Vogler in view of Baranzini does not teach the method further comprising providing a recommendation based on the classification of the animal subject.
However, Bradley does teach the method further comprising providing a recommendation based on the classification of the animal subject ([0009], “determining or categorizing, based on the output, whether the feline is at risk of developing CKD; and determining a customized recommendation based on the determining or categorizing.”).
Vogler in view of Baranzini further in view of Bradley are considered analogous to the claimed invention because they are in the field of machine learning for patient diagnosis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Vogler in view of Baranzini with Bradley for the advantage of “determining a customized recommendation based on the categorizing” (Bradley; [0049]).
Regarding claim 11, Vogler in view of Baranzini further in view of Bradley teaches the method of claims 7 and 9-10, as described above. Vogler further teaches the method further comprising providing the classification and/or the recommendation to a user through a graphical user interface (GUI) ([0199], “The processing unit (20) may be connected not just to the storage medium (50), but also to one or more interfaces (11, 12, 30, 41, 42) in order to display, transmit and/or receive information. The interfaces can comprise one or more communication interfaces (41, 42) and/or one or more user interfaces (11, 12, 30).” [0200], “The user interfaces (11, 12, 30) can comprise a display (30). A display (30) may be configured to display information to a user.” [0117], “the machine learning model is preferably trained to determine an expected value for the object of investigation. ” [0118], “The expected value indicates a probability that the condition and/or event is present and/or will occur in the (investigation) object.” [0119], “The expected value can specify a class to which the (investigation) object is assigned. For example, a class can represent objects in which a defined condition and/or a defined event occurs. Another class can represent objects in which the defined condition does not occur and/or the defined event does not occur.” [0138], “The calculated expected value can be output, i.e. displayed on a screen”).
Regarding claims 19 and 21, these claims are rejected for the same reasons as claims 9 and 11, respectively.
Claims 22 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Vogler (US 20240347206) in view of Baranzini (US 20240303544) further in view of Kong (US 20230306489).
Regarding claim 22, Vogler in view of Baranzini teaches the method of claim 1, as described above. Vogler in view of Baranzini does not teach wherein the adjacency graph is a chronologically ordered temporal graph for a specific animal subject of the plurality of animal subject, and wherein each node of the chronologically ordered temporal graph represents a data point at a given time of the time period; each edge of the chronologically ordered temporal graph represent a temporal relationship connecting nodes corresponding to consecutive time points for the same animal subject; and the chronologically ordered temporal graph does not include edges connecting nodes for different animal subjects than the specific animal subject.
However, the combination of Vogler in view of Kong does teach wherein the adjacency graph is a chronologically ordered temporal graph (Kong, [0008], “A data analysis apparatus according to the present invention is provided with a graph data generation unit configured to generate, in chronological order, a plurality of items of graph data configured by combining a plurality of nodes representing attributes for each element”)
for a specific animal subject of the plurality of animal subject (Vogler, [0058], “If the object of investigation is an animal or a human being, the object of investigation is also referred to as an individual or a patient in this description. A human being is also referred to as a person in this description.”[0125], “During training, the input data for the individual reference objects is fed (sequentially) to the machine learning model.” [0078], “Typically (but not necessarily), the at least one object to which the object of investigation is related is of the same type (but preferably not the exact same) object as the object of investigation… If the object of investigation is a field, the at least one object is preferably also a field, preferably another field on which the same crops are grown or the same field at another time (e.g., at a time during a past vegetation period).”). Examiner notes that as the data objects may refer to the same object but at different time points, and Vogler teaches wherein the object of investigation can be a single animal, the resulting graph would be for the specific animal subject in question.
and wherein each node of the chronologically ordered temporal graph represents a data point at a given time of the time period (Kong, [0049], “A spatiotemporal feature vector calculated by the spatiotemporal feature vector calculation unit 110 numerically expresses temporal and spatial features for each item of graph data generated for each predetermined time section At with respect to time-series video data in the graph data generation unit 20, and is calculated for each node included in each item of graph data.”);
each edge of the chronologically ordered temporal graph represent a temporal relationship connecting nodes corresponding to consecutive time points for the same animal subject (Kong, [0049], “the spatiotemporal feature vector calculation unit 110 performs convolution processing that is to be applied by individually weighting a feature vector for another node in an adjacent relation with the respective node and a feature vector for an edge set between these adjacent nodes, in each of a space direction and a time direction.”); and
the chronologically ordered temporal graph does not include edges connecting nodes for different animal subjects than the specific animal subject (Vogler, [0058], “If the object of investigation is an animal or a human being, the object of investigation is also referred to as an individual or a patient in this description. A human being is also referred to as a person in this description.”[0125], “During training, the input data for the individual reference objects is fed (sequentially) to the machine learning model.” [0078], “Typically (but not necessarily), the at least one object to which the object of investigation is related is of the same type (but preferably not the exact same) object as the object of investigation… If the object of investigation is a field, the at least one object is preferably also a field, preferably another field on which the same crops are grown or the same field at another time (e.g., at a time during a past vegetation period).”). Examiner notes that a graph that contains only data objects referring to an animal at different moments of time would not include edges connecting nodes for different animal subjects.
Vogler in view of Baranzini further in view of Kong are considered analogous to the claimed invention because they are in the field of graph neural networks. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Vogler in view of Baranzini with Kong for the advantage of including “graph data changes in accordance with the passage of time” (Kong; [0007]).
Regarding claim 24, this claim is rejected for the same reasons as claim 22, as described above.
Conclusion
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/D.C./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684