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 .
Notice to Applicant
This communication is in response to the amendment filed 06/08/2026. Claims 1-2, 4, 6, 7, 8, 10 -11, 13, 15 have been amended. Claims 3, 12 have been canceled. Claims 16-18 have been added. Claims 1-2, 4-11, 13-18 have been presented for examination.
Subject Matter Free of Prior Art
Claim(s) 1-2, 4-11, 13-18 are allowable over prior art because the prior art of record fail to expressly teach or suggest, either alone or in combination, the features found within the independent claims, in particular: “a connection between two nodes of the first graph is added if 80% of the health data and the health condition information of said two nodes are if 80% of the health data and the health condition information of said two nodes are of same values identical within a predetermined range, applying a node embedding algorithm to the first graph by encoding each node of the first graph as a low-dimensional vector comprising the health data and the health condition information of each node, a position of each node in the first graph, and data about one or more connections of each node defining a local first graph neighborhood structure, thereby obtaining a first set of low-dimensional vectors; and learning a neural network taking as input first set of low-dimensional vectors and configured to determine a future health condition of each individual of the set of individuals, the learning being performed using the first set of low-dimensional vectors as training data in an unsupervised training process comprising applying a similarity algorithm to identify, for each vector of the first set of low- dimensional vectors, similar vectors in the first set of low-dimensional vectors based on similarity of the health data and the health condition information of nodes of the first graph.” Because the prior art does not teach or disclose the above features in the specific manner and combinations recited in independent claims 1, 11, 13, claims 1, 11, 13 are hereby deemed to be allowable over prior art. Originally numbered dependent claims 2, 4-10, 14-18 incorporate the allowable features of originally numbered independent claims 1, 11, 13, through dependency, respectively.
However, the claims are still rejected under 101.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim(s) 1-2, 4-11, 13-18 is/are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 11, 13 recites "applying a node embedding algorithm to the first graph by encoding each node of the first graph as a low-dimensional vector comprising the health data and the health condition information of each node, a position of each node in the first graph, and data about one or more connections of each node defining a local first graph neighborhood structure , thereby obtaining a first set of low-dimensional vectors.” However, Applicant has provided no disclosure of how the node embedding algorithm actually works to obtain low-dimensional vectors.
The specification only mentions “The node embedding algorithm comprises an encoding of the nodes of the first graph. The encoding encodes each node as a low-dimensional vector… By applying at the step 40 a node embedding algorithm to the first graph, a first set of low-dimensional vectors is obtained” (¶ 0031) and does not further provide a specific description of the “node embedding algorithm.” Any rule, instruction, algorithm, or model associated with encoding could potentially read on the as-claimed invention. The claimed “node embedding algorithm” amounts to a black box into which information is inputted and a result is received; however, there is no disclosure as to what occurs in the box. As such, the claimed invention lacks adequate written description. See: MPEP § 2161.01.
The Examiner prospectively notes that this written description rejection is not based on whether one skilled in the art would know how to program a computer to perform any form of the node embedding algorithm (i.e., an enablement rejection), but rather is directed to the Applicant’s lack of specificity as to how the node embedding algorithm is specifically performed with respect to the Applicant’s claimed invention.
Claim(s) 2. 4-10,16 is/are rejected as being dependent on claim 1.
Claim(s) 17 is/are rejected as being dependent on claim 11.
Claim(s) 14-15, 18 is/are rejected as being dependent on claim 13.
Claims 1, 11, 13 recites "learning a neural network taking as input first set of low-dimensional vectors and configured to determine a future health condition of each individual of the set of individuals, the learning being performed using the first set of low-dimensional vectors as training data in an unsupervised training process comprising applying a similarity algorithm to identify, for each vector of the first set of low- dimensional vectors, similar vectors in the first set of low-dimensional vectors based on similarity of the health data and the health condition information of nodes of the first graph.” However, Applicant has provided no disclosure of how the unsupervised training process actually works to learn a neural network or how the similarity algorithm actually works to identify similar vectors in relation to the unsupervised training process.
With regards to “learning a neural network” and “an unsupervised training process,” the specification only mentions “During the learning phase, an unsupervised neural network tries to mimic the data it's given and uses the error in its mimicked output to correct itself, by correcting its weights and biases. Sometimes the error is expressed as a low probability that the erroneous output occurs, or it might be expressed as an unstable high energy state in the network. One of the advantages of an unsupervised neural network is its ability to learn patterns from untagged data” (¶ 0006) and does not further provide a specific description of the “unsupervised training based on the first set of low-dimensional vectors.” Furthermore, with regards to “a similarity algorithm to identify, for each vector of the first set of low- dimensional vectors, similar vectors,” the specification only mentions “the second set of low-dimensional vectors is used to identify, for each vector of the first set of low-dimensional vectors, similar vectors in the first set of low-dimensional vectors. By using the second set of low-dimensional vectors, the learning is more efficient to identify, the similar vectors of the first set of low-dimensional vectors” (¶ 0034) and does not further provide a specific description of the “similarity algorithm [applied] to identify, for each vector of the first set of low-dimensional vectors, similar vectors in the first set of low-dimensional vectors.” Any rule, instruction, algorithm, or model associated with unsupervised training and similarities could potentially read on the as-claimed invention. The claimed “unsupervised training” comprising “a similarity algorithm” amounts to a black box into which information is inputted and a result is received; however, there is no disclosure as to what occurs in the box. As such, the claimed invention lacks adequate written description. See: MPEP § 2161.01.
The Examiner prospectively notes that this written description rejection is not based on whether one skilled in the art would know how to program a computer to perform any form of the unsupervised training or similarity algorithm (i.e., an enablement rejection), but rather is directed to the Applicant’s lack of specificity as to how the unsupervised training comprising the similarity algorithm is specifically performed with respect to the Applicant’s claimed invention.
Claim(s) 2. 4-10,16 is/are rejected as being dependent on claim 1.
Claim(s) 17 is/are rejected as being dependent on claim 11.
Claim(s) 14-15, 18 is/are rejected as being dependent on claim 13.
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 1-2, 4-11, 13-18 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. Based upon consideration of all of the relevant factors with respect to the claims as a whole, the claims are directed to non-statutory subject matter which do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the following analysis:
Claim 1 is drawn to a method which is within the four statutory categories (i.e., method). Claim 11 is drawn to a computer program which is interpreted to be within the four statutory categories (i.e., manufacture) for subject matter eligibility analysis purposes. Claim 13 is drawn to a system which is within the four statutory categories (i.e., machine).
Independent claim 13 (which is representative of independent claims 1, 11) recites… providing, for a set of individuals, health data that is one or more of unstructured and semi structured, said health data comprising pathology reports and at least one or more of family history health, genetic information, individual nature, body structure, face structure; obtaining, for each individual of the set of individuals, health condition information from the health data; generating a first graph based on the health data and the health condition information by converting the health data into a tree data structure having a root node corresponding to one individual of the set of individuals and at least one leaf substructure, each leaf substructure comprising one type of health data, and transforming the health data that is converted into a graph database format, wherein each node of the first graph comprises the health data and the health condition information of one individual of the set of individuals and is connected to at least one other node of the first graph, a connection between two nodes of the first graph if 80% of the health data and the health condition information of said two nodes are identical within a predetermined range, applying a node embedding algorithm to the first graph by encoding each node of the first graph as a low-dimensional vector comprising the health data and the health condition information of each node, a position of each node in the first graph, and data about one or more connections of each node defining a local first graph neighborhood structure, thereby obtaining a first set of low-dimensional vectors…
Under its broadest reasonable interpretation, the limitations noted above, as drafted, covers certain methods of organizing human activity (i.e., managing personal behavior or relationships or interactions between people…following rules or instructions), but for the recitation of generic computer components. The claims encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to collect data, analyze the collected data, and output relevant data based on the analysis accordingly in the manner described in the identified abstract idea, supra. The rules or instructions are the claimed steps as indicated supra. That is, other than reciting generic computer components (discussed infra), the claim amounts to managing personal behavior or relationships or interactions between people following rules or instructions. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Independent claim 13 (which is representative of independent claims 1, 11) further recites…learning a [model] taking as input the first set of low-dimensional vectors and configured to determine a future health condition of each individual of the set of individuals, the learning being performed using the first set of low-dimensional vectors as training data in an…process comprising applying a similarity algorithm to identify, for each vector of the first set of low-dimensional vectors, similar vectors in the first set of low-dimensional vectors based on similarity of the health data and the health condition information of nodes of the first graph.
Under the broadest reasonable interpretation, the limitations noted above, as drafted, covers mathematical relationships, but for the recitation of generic computer components. When given its broadest reasonable interpretation in light of the disclosure, learning a model represents the creation of mathematical interrelationships between data. See Example 47, Claim 2. If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships, but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
For purposes of the following analysis, the aforementioned types of identified abstract ideas are considered together as a single abstract idea. See MPEP § 2106.04(II)(B).
Claim 1 recites additional elements (i.e., computer to implement the method; a neural network; unsupervised training). Claim 11 recites additional elements (i.e., A non-transitory data storage medium comprising computer program that comprises instructions; a computer; a neural network; unsupervised training). Claim 13 recites additional elements (i.e., A system comprising: a processor coupled to a memory, the memory having recorded thereon a non-transitory computer program comprising instructions; a computer; a neural network; unsupervised training). Looking to the specifications, a computer having a non-transitory data storage medium comprising computer program that comprises instructions, processor, memory is described at a high level of generality (¶ 0051), such that it amounts to no more than mere instructions to apply the exception using generic computer components. Also, “learning a neural network” and “unsupervised training” is described at a high level of generality and is only used to generally apply the abstract idea without placing any limits on how the unsupervised training functions and does not include details about how “learning a neural network” is accomplished (i.e., no description of the mechanism for accomplishing the result), such that learning a neural network via unsupervised training amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., machine learning), which does not impose meaningful limits on the scope of the claim. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea.
Reevaluated under step 2B, the additional elements noted above do not provide “significantly more” when taken either individually or as an ordered combination. The use of a general purpose computer or computers (i.e., a computer having a non-transitory data storage medium comprising computer program that comprises instructions, processor, memory) amounts to no more than mere instructions to apply the exception using generic computer components and does not impose any meaningful limitation on the computer implementation of the abstract idea, so it does not amount to significantly more than the abstract idea. Also, “learning a neural network” and “unsupervised training” is described at a high level of generality and is only used to generally apply the abstract idea without placing any limits on how the unsupervised training functions and does not include details about how “learning a neural network” is accomplished (i.e., no description of the mechanism for accomplishing the result), such that learning a neural network via unsupervised training amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., machine learning), which does not impose meaningful limits on the scope of the claim. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology and their collective functions merely provide a conventional computer implementation of the abstract idea. Furthermore, the additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generally linking the abstract idea to a particular technological environment or field of use, as the courts have found in Parker v. Flook; similarly, the current invention merely limits the claimed calculations to the healthcare industry which does not impose meaningful limits on the scope of the claim. Therefore, there are no limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception.
Dependent claims 2, 4-10, 14-18 include all the limitations of the parent claims and further elaborate on the abstract idea discussed above and incorporated herein.
Claims 6-10, 14-18 further define the analysis and organization of data for the performance of the abstract idea and do not recite any additional elements. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. Thus, the claims do not integrate the abstract idea into a practical application and do not provide “significantly more.”
Claim 2 further recites the additional elements of “extracting the health condition information from at least one of the health data using one or more of optical character recognition, intelligent character recognition, natural language processing technologies.” Claim 4 further recites the additional elements of “extracting medical knowledges from at least a one of the at least one medical knowledge repository using one or more of optical character recognition, intelligent character recognition, natural language processing technologies.” Extracting data from “optical character recognition, intelligent character recognition, natural language processing technologies” only provides input data for the performance of the abstract idea, and as such, amounts to insignificant extrasolution activity (i.e., data gathering). Reevaluated under step 2B, electronically scanning or extracting data from a physical document has been recognized by the courts as well-understood, routine, and conventional elements/functions. See: MPEP § 2106.05(d)(II). Also, functional limitations further define the analysis and organization of data for the performance of the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. Thus, the claims as a whole do not integrate the abstract idea into a practical application and do not provide “significantly more.”
Claim 2 further recites the additional elements of “accessing and retrieving the health condition information from a healthcare organization database based on historical data or records.” Claim 4 further recites the additional elements of “obtaining at least one medical knowledge repository.” Receiving data from a database or repository only invokes the database and repository merely as a tool in its ordinary capacity to perform an existing process (i.e., receiving, storing, providing data), which does not impose meaningful limits on the scope of the claim and amounts to no more than a recitation of the words "apply it" (or an equivalent), and only provides the input data for the performance of the abstract idea, and as such, amounts to insignificant extrasolution activity (i.e., mere data gathering), which does not impose meaningful limits on the scope of the claim. Reevaluated under step 2B, the database and repository are invoked merely as a tool in its ordinary capacity to perform an existing process (i.e., receiving, storing, providing data), which does not impose meaningful limits on the scope of the claim and amounts to no more than a recitation of the words "apply it" (or an equivalent). Furthermore, receiving or transmitting data over a network has been recognized by the courts as well-understood, routine, and conventional elements/functions. See: MPEP § 2106.05(d)(II). Also, functional limitations further define the analysis and organization of data for the performance of the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. Thus, the claims as a whole do not integrate the abstract idea into a practical application and do not provide “significantly more.”
Claim 5 further recites the additional elements of “wherein additional health data that is one or more of structured and semi structured are obtained and used to update, at a predefined interval of time, the neural network that is learned.” The specification further describes the updating as: “steps 10 to 50 may be performed with the additional unstructured and/or semi structured health data” (¶ 0035), which have been analyzed supra to be part of the abstract idea, and only further define the analysis and organization of data for the performance of the abstract idea. Furthermore, see Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 12 (Fed. Cir. April 18, 2025) (finding that “[i]terative training using selected training material…are incident to the very nature of machine learning.”). Furthermore, the “neural network” is still described at a high level of generality, such that it amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., machine learning), which does not impose meaningful limits on the scope of the claim. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. Thus, the claims as a whole do not integrate the abstract idea into a practical application and do not provide “significantly more.”
Although the dependent claims add additional limitations, they only serve to further limit the abstract idea by reciting limitations on what the information is and how it is received and used. These information characteristics do not change the fundamental analogy to the abstract idea groupings and, when viewed individually or as a whole, they do not add anything substantial beyond the abstract idea. Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore, the claims when taken as a whole are ineligible for the same reasons as the independent claims.
Response to Arguments
Applicant's arguments filed 06/08/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 06/08/2026.
In the remarks, Applicant argues in substance that:
Regarding the 112(a) rejections, “the amended claim does not merely claim a desired result of "embedding." Rather, for example, it recites the specific information encoded into the vector representation: node data, node position, and node-connection/neighborhood structure… The specification, for example, expressly states that the neural network takes as input the low-dimensional vector and that learning is performed using the first set of low-dimensional vectors as training data. The specification further states, for example, that the learning comprises applying a similarity algorithm to identify, for each vector of the first set of low-dimensional vectors, similar vectors in the first set of low-dimensional vectors. The specification also describes, for example, the underlying similarity basis for the graph: edges may be added between nodes when the health data and health condition information of two nodes are similar, including when 80% of the health data and health condition information are identical within a predetermined range.”
Regarding the 101 rejections,
“Amended claim 1 as amended herein, for example, now recites specific technical data- processing operations… wherein these operations are not steps a person could practically perform mentally, nor are they merely "organizing human activity." Rather, for example, they are computer-implemented transformations of heterogeneous medical data into graph database structures and graph-neighborhood-aware vector representations”;
“the claim does not merely say "apply machine learning to health data." Instead, for example, it recites a specific technological architecture for preparing and representing unstructured/semi-structured health data for machine learning. For example, the claimed process transforms irregular health data into a graph database and then into graph-topology-aware low-dimensional vectors used for neural-network training… the amended claim improves the technical processing of heterogeneous health data… Applicant is not merely claiming the field-of-use application of generic machine learning to healthcare. Rather, for example, the claim recites a particular graph-based architecture for transforming heterogeneous health data into graph database structures and graph-neighborhood-aware low- dimensional vectors. Applicant respectfully notes, for example, wherein the claimed node embeddings are not generic inputs… Those technical limitations, for example, define how the data are represented and processed before and during learning. Therefore, for example, the amended claim does not merely invoke machine learning as a black box… the claimed limitations remain novel over prior art”; and
“The additional elements, for example, are not merely generic computer components. Rather, for example, they include a specific ordered combination of operations... Viewed as an ordered combination, for example, the claim imposes meaningful technical limits and does not preempt all uses of machine learning for predicting health conditions. Applicant respectfully notes, for example, wherein the claim is limited to a particular graph- based representation and training pipeline… Viewed as an ordered combination, for example, Applicant respectfully notes wherein these limitations recite a specific graph-based machine-learning architecture for processing heterogeneous medical data and are not directed merely to an abstract idea implemented on generic computers.”
It is respectfully submitted that Examiner has considered Applicant’s arguments and does not find them persuasive. Examiner has attempted to address all of the arguments presented by Applicant; however, any arguments inadvertently not addressed are not persuasive for at least the following reasons:
In response to Applicant’s argument that (a) regarding the 112(a) rejections, “the amended claim does not merely claim a desired result of "embedding." Rather, for example, it recites the specific information encoded into the vector representation: node data, node position, and node-connection/neighborhood structure… The specification, for example, expressly states that the neural network takes as input the low-dimensional vector and that learning is performed using the first set of low-dimensional vectors as training data. The specification further states, for example, that the learning comprises applying a similarity algorithm to identify, for each vector of the first set of low-dimensional vectors, similar vectors in the first set of low-dimensional vectors. The specification also describes, for example, the underlying similarity basis for the graph: edges may be added between nodes when the health data and health condition information of two nodes are similar, including when 80% of the health data and health condition information are identical within a predetermined range”:
It is respectfully submitted that Applicant argues “the amended claim does not merely claim a desired result of "embedding." Rather, for example, it recites the specific information encoded into the vector representation: node data, node position, and node-connection/neighborhood structure.” However, the rejection is not directed to the Applicant’s lack of specificity as to what is being encoded, but how the node embedding algorithm or encoding is specifically performed with respect to the Applicant’s claimed invention. For example, how does the node embedding algorithm transform a node of a graph as a vector? As stated in Office Action dated 03/26/2026 and above, the specification only mentions “The node embedding algorithm comprises an encoding of the nodes of the first graph. The encoding encodes each node as a low-dimensional vector… By applying at the step 40 a node embedding algorithm to the first graph, a first set of low-dimensional vectors is obtained” (¶ 0031) and does not further provide a specific description of the “node embedding algorithm.” Any rule, instruction, algorithm, or model associated with encoding could potentially read on the as-claimed invention. The claimed “node embedding algorithm” amounts to a black box into which information is inputted and a result is received; however, there is no disclosure as to what occurs in the box. As such, the claimed invention lacks adequate written description. See: MPEP § 2161.01.
Applicant argues “The specification, for example, expressly states that the neural network takes as input the low-dimensional vector and that learning is performed using the first set of low-dimensional vectors as training data. The specification further states, for example, that the learning comprises applying a similarity algorithm to identify, for each vector of the first set of low-dimensional vectors, similar vectors in the first set of low-dimensional vectors. The specification also describes, for example, the underlying similarity basis for the graph: edges may be added between nodes when the health data and health condition information of two nodes are similar, including when 80% of the health data and health condition information are identical within a predetermined range.” However, the rejection is not directed to the Applicant’s lack of specificity as to what is being input and output, but how the learning or similarity algorithm is specifically performed with respect to the Applicant’s claimed invention. For example, how does the similarity algorithm calculate similarity of the health data and the health condition information of nodes of the first graph and identify similarity vectors? Applicant points to support in the specification for determining similarity “when 80% of the health data and health condition information are identical within a predetermined range,” but this seems to describe how to generate the graph and not how to “[learn] a neural network taking as input first set of low-dimensional vectors and configured to determine a future health condition of each individual of the set of individuals, the learning being performed using the first set of low-dimensional vectors as training data in an unsupervised training process comprising applying a similarity algorithm to identify, for each vector of the first set of low- dimensional vectors, similar vectors in the first set of low-dimensional vectors based on similarity of the health data and the health condition information of nodes of the first graph.” As stated in Office Action dated 03/26/2026 and above, any rule, instruction, algorithm, or model associated with unsupervised training and similarities could potentially read on the as-claimed invention. The claimed “unsupervised training” comprising “a similarity algorithm” amounts to a black box into which information is inputted and a result is received; however, there is no disclosure as to what occurs in the box. As such, the claimed invention lacks adequate written description. See: MPEP § 2161.01.
Thus, Examiner maintains the 112(a) rejections of claims 1-2, 4-11, 13-18 of Office Action dated 03/26/2026, which have been updated to address Applicant’s amendments and remarks.
In response to Applicant’s argument that (b) regarding the 101 rejections,
“Amended claim 1 as amended herein, for example, now recites specific technical data- processing operations… wherein these operations are not steps a person could practically perform mentally, nor are they merely "organizing human activity." Rather, for example, they are computer-implemented transformations of heterogeneous medical data into graph database structures and graph-neighborhood-aware vector representations”:
It is respectfully submitted that Applicant argues “Amended claim 1 as amended herein, for example, now recites specific technical data- processing operations… wherein these operations are not steps a person could practically perform mentally, nor are they merely "organizing human activity." Rather, for example, they are computer-implemented transformations of heterogeneous medical data into graph database structures and graph-neighborhood-aware vector representations.” However, Applicant fails to specify how the “specific technical data- processing operations” and “transformations of heterogeneous medical data into graph database structures and graph-neighborhood-aware vector representations” “are not steps a person could practically perform mentally, nor are they merely "organizing human activity."” Regardless, the claim limitations to which Applicant seem to refer as “converting heterogeneous health data into a tree data structure; transforming the converted health data into a graph database format; generating graph nodes representing individuals; adding graph connections based on an 80% similarity threshold; encoding graph nodes into low-dimensional vectors that include node data, node position, and local graph-neighborhood connection data; and” are encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to collect data, analyze the collected data, and output relevant data based on the analysis accordingly in the manner described in the identified abstract idea, supra, which amounts to managing personal behavior or relationships or interactions between people following rules or instructions within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, and not a concept performed in the human mind in the “Mental Processes” grouping, as Applicant now argues. Furthermore, the claim limitations to which Applicant seem to refer as “training a neural network using the resulting graph-derived vectors” represents the creation of mathematical interrelationships between data, which covers mathematical relationships within the “Mathematical Concepts” grouping of abstract ideas, and not a concept performed in the human mind in the “Mental Processes” grouping, as Applicant now argues.
Thus, the claims are directed to an abstract idea.
“the claim does not merely say "apply machine learning to health data." Instead, for example, it recites a specific technological architecture for preparing and representing unstructured/semi-structured health data for machine learning. For example, the claimed process transforms irregular health data into a graph database and then into graph-topology-aware low-dimensional vectors used for neural-network training… the amended claim improves the technical processing of heterogeneous health data… Applicant is not merely claiming the field-of-use application of generic machine learning to healthcare. Rather, for example, the claim recites a particular graph-based architecture for transforming heterogeneous health data into graph database structures and graph-neighborhood-aware low- dimensional vectors. Applicant respectfully notes, for example, wherein the claimed node embeddings are not generic inputs… Those technical limitations, for example, define how the data are represented and processed before and during learning. Therefore, for example, the amended claim does not merely invoke machine learning as a black box… the claimed limitations remain novel over prior art”:
Applicant argues “the claim does not merely say "apply machine learning to health data." Instead, for example, it recites a specific technological architecture for preparing and representing unstructured/semi-structured health data for machine learning. For example, the claimed process transforms irregular health data into a graph database and then into graph-topology-aware low-dimensional vectors used for neural-network training… the amended claim improves the technical processing of heterogeneous health data… Applicant is not merely claiming the field-of-use application of generic machine learning to healthcare. Rather, for example, the claim recites a particular graph-based architecture for transforming heterogeneous health data into graph database structures and graph-neighborhood-aware low- dimensional vectors. Applicant respectfully notes, for example, wherein the claimed node embeddings are not generic inputs… Those technical limitations, for example, define how the data are represented and processed before and during learning. Therefore, for example, the amended claim does not merely invoke machine learning as a black box.” However, “preparing and representing unstructured/semi-structured health data for machine learning,” “transforms irregular health data into a graph database and then into graph-topology-aware low-dimensional vectors used for neural-network training,” and “processing of heterogeneous health data” does not address a technical problem to any specific devices, technology (i.e., machine learning), or computers for that matter, and thus, the claims do not provide a technical solution. Examiner notes that even a technical solution to a nontechnical problem does not integrate the judicial exception into a practical application. Even if the claims provide the aforementioned alleged improvements, these alleged benefits are at best, an improvement to the abstract idea. However, an improved abstract idea is still an abstract idea.
Examiner cannot find any problem caused by the technological environment to which the claims are confined, which per broadest reasonable interpretation of the claim in light of the specification, is a well-known, general purpose computer. The computing system did not cause the argued problem and thus it is not a technical problem caused by the technological environment to which the claims are confined. While the specification need not explicitly set forth the improvement, the disclosure does not provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing any technical improvement or any physical improvement to the computer. See MPEP § 2106.04(d)(1) and 2106.05(a).
Furthermore, the claim limitations to which Applicant seem to refer (i.e., as “a particular graph-based architecture for transforming heterogeneous health data into graph database structures and graph-neighborhood-aware low- dimensional vectors,” “node embeddings,” “how the data are represented and processed before and during learning”) are interpreted as rules or instructions for a person or persons to follow, with or without the aid of a computer, to collect data, analyze the collected data, and output relevant data based on the analysis accordingly in the manner described in the identified abstract idea, supra, which amounts to managing personal behavior or relationships or interactions between people following rules or instructions within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas; or the creation of mathematical interrelationships between data, which covers mathematical relationships within the “Mathematical Concepts” grouping of abstract ideas, and not additional elements to be interpreted in Step 2A, Prong Two. Also, “learning a neural network” and “unsupervised training” is described at a high level of generality and is only used to generally apply the abstract idea without placing any limits on how the unsupervised training functions and does not include details about how “learning a neural network” is accomplished (i.e., no description of the mechanism for accomplishing the result), such that learning a neural network via unsupervised training amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., machine learning), which does not impose meaningful limits on the scope of the claim. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually.
Applicant argues “the claimed limitations remain novel over prior art.” However, per MPEP § 2106.05(I): “the search for an inventive concept should not be confused with a novelty or non-obviousness determination…As made clear by the courts, the "‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter…a claim for a new abstract idea is still an abstract idea. The search for a § 101 inventive concept is thus distinct from demonstrating § 102 novelty…Because [novelty and obviousness] are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101.”
Thus, the claim as a whole does not integrate the recited judicial exception into a practical application.
“The additional elements, for example, are not merely generic computer components. Rather, for example, they include a specific ordered combination of operations... Viewed as an ordered combination, for example, the claim imposes meaningful technical limits and does not preempt all uses of machine learning for predicting health conditions. Applicant respectfully notes, for example, wherein the claim is limited to a particular graph- based representation and training pipeline… Viewed as an ordered combination, for example, Applicant respectfully notes wherein these limitations recite a specific graph-based machine-learning architecture for processing heterogeneous medical data and are not directed merely to an abstract idea implemented on generic computers”:
Applicant argues “The additional elements, for example, are not merely generic computer components. Rather, for example, they include a specific ordered combination of operations... Viewed as an ordered combination, for example, the claim imposes meaningful technical limits and does not preempt all uses of machine learning for predicting health conditions. Applicant respectfully notes, for example, wherein the claim is limited to a particular graph- based representation and training pipeline… Viewed as an ordered combination, for example, Applicant respectfully notes wherein these limitations recite a specific graph-based machine-learning architecture for processing heterogeneous medical data and are not directed merely to an abstract idea implemented on generic computers.” However, preemption "is not a standalone test for determining eligibility. Instead, questions of preemption are inherent in and resolved by the two-part framework from Alice Corp. and Mayo (the Alice/Mayo test referred to by the Office as Steps 2A and 2B). It is necessary to evaluate eligibility using the Alice/Mayo test, because while a preemptive claim may be ineligible, the absence of complete preemption does not demonstrate that a claim is eligible." See MPEP § 2106.04(1). Therefore, the claims can still be patent ineligible (and they are patent ineligible) based on the above analysis even in the event that they do not preempt, or “tie up,” the judicial exception. Furthermore, the claim limitations to which Applicant seem to refer as “converting heterogeneous health data into a tree data structure; transforming the converted health data into a graph database format; generating graph nodes representing individuals; adding graph connections based on an 80% similarity threshold; encoding graph nodes into low-dimensional vectors that include node data, node position, and local graph-neighborhood connection data; and” are encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to collect data, analyze the collected data, and output relevant data based on the analysis accordingly in the manner described in the identified abstract idea, supra, which amounts to managing personal behavior or relationships or interactions between people following rules or instructions within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, and not additional elements to be interpreted in Step 2B. Furthermore, the claim limitations to which Applicant seem to refer as “training a neural network using the resulting graph-derived vectors” represents the creation of mathematical interrelationships between data, which covers mathematical relationships within the “Mathematical Concepts” grouping of abstract ideas, and not additional elements to be interpreted in Step 2B. Also, “learning a neural network” and “unsupervised training” is described at a high level of generality and is only used to generally apply the abstract idea without placing any limits on how the unsupervised training functions and does not include details about how “learning a neural network” is accomplished (i.e., no description of the mechanism for accomplishing the result), such that learning a neural network via unsupervised training amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., machine learning), which does not impose meaningful limits on the scope of the claim. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually.
Thus, Examiner maintains the 101 rejections of claims 1-2, 4-11, 13-18, which have been updated to address Applicant’s remarks and to comply with the 2019 Revised Patent Subject Matter Eligibility Guidance and the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence in the above Office Action.
Conclusion
THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/EMILY HUYNH/Primary Examiner, Art Unit 3683
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