DETAILED ACTION
This action is responsive to the amendment filed on 02/12/2026. Claims 1-11 and 14-22 are pending in the case. Claims 1, 14, and 19 are independent claims.
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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4-8, 17-18, and 20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 4, the claim recites: “wherein generating the predictive classification comprises:”. It is unclear what this limitation is referring to as the parent claim does not recite a “generating” step. For examination purposes this limitation has been interpreted to mean “wherein processing the class network graph comprises:”.
Claims 5-6 are rejected as being dependent upon a rejected base claim without curing any of the deficiencies.
Regarding claim 7, the claim recites: “The computer-implemented method of claim 1, wherein identifying the class network graph comprises:”. It is unclear what this limitation is referring to as the parent claim does not recite an “identifying” step. For examination purposes this limitation has been interpreted to mean “The computer-implemented method of claim 1, further comprising identifying the class network graph by:”.
Claim 8 is rejected as being dependent upon a rejected base claim without curing any of the deficiencies.
Regarding claim 17, the claim recites: “wherein generating the predictive classification comprises:”. It is unclear what this limitation is referring to as the parent claim does not recite a “generating” step. For examination purposes this limitation has been interpreted to mean “wherein processing the class network graph comprises:”.
Claim 18 is rejected as being dependent upon a rejected base claim without curing any of the deficiencies.
Regarding claim 20, the claim recites: “The computer-implemented method of claim 1, wherein identifying the class network graph comprises:”. It is unclear what this limitation is referring to as the parent claim does not recite an “identifying” step. For examination purposes this limitation has been interpreted to mean “The computer-implemented method of claim 1, further comprising identifying the class network graph by:”.
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 1-11 and 14-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1:
Step 1 Statutory Category: Claim 1 is directed to a method, which falls under one of the four statutory categories.
Step 2A Prong 1 Judicial Exception: Claim 1 recites, in part, “processing, …, a class network graph, from the entity network graph, that corresponds to an entity class corresponding to the entity …, to output a predictive classification for the particular node based on a comparison between the class network graph and the entity network graph”. This limitation, under the broadest reasonable interpretation, covers the recitation of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion), in this case a judgment. See MPEP § 2106.04(a)(2)(III).
Step 2A Prong 2 Integration into a Practical Application: This judicial exception is not integrated into a practical application. In particular the claim recites that the method is “computer-implemented”. This limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §2106.05(f). Further, the claim recites: “receiving, …, an entity network graph for an entity based on a plurality of interaction data objects for the entity”. This limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g). Further, the claim recites: “by one or more processors” and “by the one or more processors”. These limitations are additional elements that amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §2106.05(f). Further, the claim recites the elements: “(i) an interaction data object of the plurality of interaction data objects comprises an interaction code”, “(ii) the entity network graph comprises a plurality of nodes and a plurality of edges”, “(iii) a particular node of the plurality of nodes corresponds to a particular interaction code of at least one of the plurality of interaction data objects”, “(iv) a particular edge of the plurality of edges connects a node pair of the plurality of nodes that is associated with a particular interaction data object”, “(v) the particular node is associated with a node weight, wherein the node weight is based on a code count identifying a number of the plurality of interaction data objects that comprise the particular interaction code”, and “(vi) the particular edge is associated with an edge weight, wherein the node pair corresponds to a first interaction data code and a second interaction code that are associated with the particular interaction data object, and the edge weight is based on a code pair count identifying a number of the plurality of interaction data objects that comprise the first interaction code and the second interaction code”. These limitations are additional elements that generally link the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h). Further, the claim recites: “…with a detection model…”. This limitation is an additional element that amounts to generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h).
Step 2B Significantly More: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements: that the method is “computer-implemented”, “by one or more processors”, and “by the one or more processors” amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. Elements that merely amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process cannot provide an inventive concept. Further, the additional element “receiving, …, an entity network graph for an entity based on a plurality of interaction data objects for the entity” amounts to adding insignificant extra-solution activity to the judicial exception. Further, the limitation is directed to receiving or transmitting data over a network which courts have recognized as well-understood, routine, and conventional when they are claimed in a generic manner, see MPEP §2106.05(d)(II). Further, the claim recites the additional elements: “(i) an interaction data object of the plurality of interaction data objects comprises an interaction code”, “(ii) the entity network graph comprises a plurality of nodes and a plurality of edges”, “(iii) a particular node of the plurality of nodes corresponds to a particular interaction code of at least one of the plurality of interaction data objects”, “(iv) a particular edge of the plurality of edges connects a node pair of the plurality of nodes that is associated with a particular interaction data object”, “(v) the particular node is associated with a node weight, wherein the node weight is based on a code count identifying a number of the plurality of interaction data objects that comprise the particular interaction code”, and “(vi) the particular edge is associated with an edge weight, wherein the node pair corresponds to a first interaction data code and a second interaction code that are associated with the particular interaction data object, and the edge weight is based on a code pair count identifying a number of the plurality of interaction data objects that comprise the first interaction code and the second interaction code” that generally link the use of the judicial exception to a particular technological environment or field of use. Elements that merely generally link the use of the judicial exception to a particular technological environment or field of use cannot provide an inventive concept. Further, the additional element “…with a detection model…” amounts to generally linking the use of the judicial exception to a particular technological environment or field of use. Elements that merely amount to generally linking the use of the judicial exception to a particular technological environment or field of use cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 2, the rejection of claim 1 is incorporated, and further, the claim recites: “wherein the predictive classification comprises an entity subclass label”. This limitation is a continuation of the “processing, …, a class network graph, from the entity network graph, that corresponds to an entity class corresponding to the entity …, to output a predictive classification for the particular node based on a comparison between the class network graph and the entity network graph” limitation identified as an abstract idea in the rejection of the parent claim. Further, the claim recites: “the computer-implemented method further comprises: assigning the entity subclass label to the entity”. This limitation recites mental processes in addition to those identified in the rejection of the parent claim, thus the claim recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 3, the rejection of claim 1 is incorporated, and further, the claim recites: “wherein the class network graph comprises a plurality of class nodes arranged into one or more node clusters, and wherein a particular node cluster comprises a subset of the plurality of class nodes”. This limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore, the additional element is well‐understood, routine, and conventional as taught by activity is supported under Berkheimer Option 2, Di Francescantoni et al., U.S. Patent Application No. 20230245357, Paragraph 0009, Lines 2-8, “The first one is the cluster calculation usually referred simply as clustering that consists, starting from a given network graph, in associating each node to a cluster. Several different methods are available for clustering. To ensure scalability for large network graph it is also common that clusters can be nested to each other arranged in a parent/child tree”). The claim is not patent eligible.
Regarding claim 4, the rejection of claim 3 is incorporated, and further, the claim recites: “determining one or more overlap scores between the class network graph and the entity network graph, wherein the one or more overlap scores comprise a respective overlap score for each of the one or more node clusters”. This limitation, under the broadest reasonable interpretation, covers the recitation of a mathematical concept, see MPEP §2106.04(a)(2)(I). Further, the claim recites: “identifying the particular node cluster from the one or more node clusters based on the one or more overlap scores, wherein a particular overlap score for the particular node cluster is a highest overlap score relative to the one or more overlap scores”. This limitation recites mental processes in addition to those identified in the rejection of the parent claim. Finally, the claim recites: “generating the predictive classification based on the particular node cluster”. This limitation recites mental processes in addition to those identified in the rejection of the parent claim. Thus, the claim recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 5, the rejection of claim 4 is incorporated, and further, the claim recites: “identifying one or more entity nodes from the entity network graph that correspond to the subset of the plurality of class nodes”. This limitation recites mental processes in addition to those identified in the rejection of the parent claim. Further, the claim recites: “determining the particular overlap score based on an aggregation of a respective node weight for each of the one or more entity nodes”. This limitation recites mathematical concepts in addition to those identified in the rejection of the parent claim. Thus, the claim recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 6, the rejection of claim 5 is incorporated, and further, the claim recites: “wherein the particular node cluster corresponds to the predictive classification”. This limitation is a continuation of the “identifying the particular node cluster from the one or more node clusters based on the one or more overlap scores, wherein a particular overlap score for the particular node cluster is a highest overlap score relative to the one or more overlap scores” limitation identified as an abstract idea in the rejection of the parent claim. Thus, the claim recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 7, the rejection of claim 1 is incorporated, and further, the claim recites: “identifying the class network graph corresponding to the entity class from the domain empirical taxonomy”. This limitation recites a mental process in addition to those identified in the rejection of the parent claim. Thus, the claim recites a judicial exception.
Further, the claim recites: “receiving a domain empirical taxonomy for a domain taxonomy associated with the entity, wherein: (i) the domain taxonomy comprises a plurality of entity classes, and (ii) the domain empirical taxonomy comprises a respective class taxonomy for each of the plurality of entity classes”. This limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g). Further, the limitation is directed to receiving or transmitting data over a network which courts have recognized as well-understood, routine, and conventional when they are claimed in a generic manner, see MPEP §2106.05(d)(II). The claim is not patent eligible.
Regarding claim 8, the rejection of claim 7 is incorporated, and further, the claim recites: “wherein the domain empirical taxonomy is previously generated by a third party”. This limitation is a continuation of the “identifying the class network graph corresponding to the entity class from the domain empirical taxonomy” limitation identified as an abstract idea in the rejection of the parent claim. Thus, the claim recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 9, the rejection of claim 1 is incorporated, and further, the claim recites: “selecting a machine learning model for the entity based on the predictive classification and one or more evaluation metrics for the machine learning model”. This limitation recites mental process in addition to those identified in the rejection of the parent claim, thus the claim recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 10, the rejection of claim 9 is incorporated, and further, the claim recites: “wherein the one or more evaluation metrics comprise an evaluation metric corresponding to each of one or more node clusters of the class network graph”. This limitation is a continuation of the “selecting a machine learning model for the entity based on the predictive classification and one or more evaluation metrics for the machine learning model” limitation identified as an abstract idea in the rejection of the parent claim, thus the claim recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 11, the rejection of claim 9 is incorporated, and further, the claim recites: “wherein the one or more evaluation metrics comprise a true positive rate for the machine learning model relative to a plurality of historical entities associated with the predictive classification”. This limitation is a continuation of the “selecting a machine learning model for the entity based on the predictive classification and one or more evaluation metrics for the machine learning model” limitation identified as an abstract idea in the rejection of the parent claim, thus the claim recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 14:
Step 1 Statutory Category: Claim 14 is directed to a machine, which falls under one of the four statutory categories.
Step 2A Prong 1 Judicial Exception: Claim 14 recites, in part, “process a class network graph, from the entity network graph, that corresponds to an entity class corresponding to the entity …, to output a predictive classification for the particular node based on a comparison between the class network graph and the entity network graph”. This limitation, under the broadest reasonable interpretation, covers the recitation of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion), in this case a judgment. See MPEP § 2106.04(a)(2)(III).
Step 2A Prong 2 Integration into a Practical Application: This judicial exception is not integrated into a practical application. In particular the claim recites: “A system comprising: one or more processors; and one or more memories storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations”. This limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §2106.05(f). Further, the claim recites: “receive an entity network graph for an entity based on a plurality of interaction data objects for the entity”. This limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g). Further, the claim recites the elements: “(i) an interaction data object of the plurality of interaction data objects comprises an interaction code”, “(ii) the entity network graph comprises a plurality of nodes and a plurality of edges”, “(iii) a particular node of the plurality of nodes corresponds to a particular interaction code of at least one of the plurality of interaction data objects”, “(iv) a particular edge of the plurality of edges connects a node pair of the plurality of nodes that is associated with a particular interaction data object”, “(v) the particular node is associated with a node weight, wherein the node weight is based on a code count identifying a number of the plurality of interaction data objects that comprise the particular interaction code”, and “(vi) the particular edge is associated with an edge weight, wherein the node pair corresponds to a first interaction code and a second interaction code that are associated with the particular interaction data object, and the edge weight is based on a code pair count identifying a number of the plurality of interaction data objects that comprise the first interaction code and the second interaction code”. These limitations are additional elements that generally link the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h). Further, the claim recites: “…with a detection model…”. This limitation is an additional element that amounts to generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h).
Step 2B Significantly More: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element: “A system comprising: one or more processors; and one or more memories storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations” amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. Elements that merely amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process cannot provide an inventive concept. Further, the additional element “receive an entity network graph for an entity based on a plurality of interaction data objects for the entity” amounts to adding insignificant extra-solution activity to the judicial exception. Further, this limitation is directed to receiving or transmitting data over a network which courts have recognized as well-understood, routine, and conventional when they are claimed in a generic manner, see MPEP §2106.05(d)(II). Further, the claim recites the additional elements: “(i) an interaction data object of the plurality of interaction data objects comprises an interaction code”, “(ii) the entity network graph comprises a plurality of nodes and a plurality of edges”, “(iii) a particular node of the plurality of nodes corresponds to a particular interaction code of at least one of the plurality of interaction data objects”, “(iv) a particular edge of the plurality of edges connects a node pair of the plurality of nodes that is associated with a particular interaction data object”, “(v) the particular node is associated with a node weight, wherein the node weight is based on a code count identifying a number of the plurality of interaction data objects that comprise the particular interaction code”, and “(vi) the particular edge is associated with an edge weight, wherein the node pair corresponds to a first interaction code and a second interaction code that are associated with the particular interaction data object, and the edge weight is based on a code pair count identifying a number of the plurality of interaction data objects that comprise the first interaction code and the second interaction code” that generally link the use of the judicial exception to a particular technological environment or field of use. Elements that merely generally link the use of the judicial exception to a particular technological environment or field of use cannot provide an inventive concept. Further, the additional element “…with a detection model…” amounts to generally linking the use of the judicial exception to a particular technological environment or field of use. Elements that merely amount to generally linking the use of the judicial exception to a particular technological environment or field of use cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 15, the rejection of claim 14 is incorporated, and further, claim 15 is substantially similar to claim 2 respectively, and is rejected in the same manner and reasoning applying.
Regarding claim 16, the rejection of claim 14 is incorporated, and further, claim 16 is substantially similar to claim 3 respectively, and is rejected in the same manner and reasoning applying.
Regarding claim 17, the rejection of claim 16 is incorporated, and further, claim 17 is substantially similar to claim 4 respectively, and is rejected in the same manner and reasoning applying.
Regarding claim 18, the rejection of claim 17 is incorporated, and further, claim 18 is substantially similar to claim 5 respectively, and is rejected in the same manner and reasoning applying.
Regarding claim 19:
Step 1 Statutory Category: Claim 19 is directed to a machine, which falls under one of the four statutory categories.
Step 2A Prong 1 Judicial Exception: Claim 19 recites, in part, “process a class network graph, from the entity network graph, that corresponds to an entity class corresponding to the entity …, to output a predictive classification for the particular node based on a comparison between the class network graph and the entity network graph”. This limitation, under the broadest reasonable interpretation, covers the recitation of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong 2 Integration into a Practical Application: This judicial exception is not integrated into a practical application. In particular the claim recites: “One or more non-transitory computer-readable media storing processor-executable instructions”. This limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §2106.05(f). Further, the claim recites: “one or more processors”. This limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §2106.05(f). Further, the claim recites: “receive an entity network graph for an entity based on a plurality of iteration data objects for the entity”. This limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g). Further, the claim recites the elements: “(i) an interaction data object of the plurality of interaction data objects comprises an interaction code”, “(ii) the entity network graph comprises a plurality of nodes and a plurality of edges”, “(iii) a particular node of the plurality of nodes corresponds to a particular interaction code of at least one of the plurality of interaction data objects”, “(iv) a particular edge of the plurality of edges connects a node pair of the plurality of nodes that is associated with a particular interaction data object”, “(v) the particular node is associated with a node weight, wherein the node weight is based on a code count identifying a number of the plurality of interaction data objects that comprise the particular interaction code”, and “(vi) the particular edge is associated with an edge weight, wherein the node pair corresponds to a first interaction code and a second interaction code that are associated with the particular interaction data object, and the edge weight is based on a code pair count identifying a number of the plurality of interaction data objects that comprise the first interaction code and the second interaction code”. These limitations are additional elements that generally link the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h). Further, the claim recites: “…with a detection model…”. This limitation is an additional element that amounts to generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h).
Step 2B Significantly More: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements: “One or more non-transitory computer-readable media storing processor-executable instructions” and “one or more processors” amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. Elements that merely amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process cannot provide an inventive concept. Further, the additional element “receive an entity network graph for an entity based on a plurality of iteration data objects for the entity” amounts to adding insignificant extra-solution activity to the judicial exception, and further, is directed to receiving or transmitting data over a network which courts have recognized as well-understood, routine, and conventional when they are claimed in a generic manner, see MPEP §2106.05(d)(II). Further, the claim recites the additional elements: “(i) an interaction data object of the plurality of interaction data objects comprises an interaction code”, “(ii) the entity network graph comprises a plurality of nodes and a plurality of edges”, “(iii) a particular node of the plurality of nodes corresponds to a particular interaction code of at least one of the plurality of interaction data objects”, “(iv) a particular edge of the plurality of edges connects a node pair of the plurality of nodes that is associated with a particular interaction data object”, “(v) the particular node is associated with a node weight, wherein the node weight is based on a code count identifying a number of the plurality of interaction data objects that comprise the particular interaction code”, and “(vi) the particular edge is associated with an edge weight, wherein the node pair corresponds to a first interaction code and a second interaction code that are associated with the particular interaction data object, and the edge weight is based on a code pair count identifying a number of the plurality of interaction data objects that comprise the first interaction code and the second interaction code” that generally link the use of the judicial exception to a particular technological environment or field of use. Elements that merely generally link the use of the judicial exception to a particular technological environment or field of use cannot provide an inventive concept. Further, the additional element “…with a detection model…” generally links the use of the judicial exception to a particular technological environment or field of use. Elements that merely generally link the use of the judicial exception to a particular technological environment or field of use cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 20, the rejection of claim 19 is incorporated, and further, claim 20 is substantially similar to claim 7 respectively, and is rejected in the same manner and reasoning applying.
Regarding claim 21, the rejection of claim 19 is incorporated, and further, the claim recites: “generating a nodal community comprising the particular node and a plurality of community nodes, wherein a community node of the plurality of community nodes comprises the predictive classification of the particular node”. This limitation recites mental processes in addition to those identified in the rejection of the parent claim. Thus, the claim recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 22, the rejection of claim 19 is incorporated, and further, the claim recites: “wherein the detection model comprises a graph community detection algorithm”. This limitation is an additional element that amounts to generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h). Elements that merely amount to generally linking the use of the judicial exception to a particular technological environment or field of use cannot provide an inventive concept. The claim is not patent eligible.
Claim Rejections - 35 USC § 103
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.
Claims 1-3, 14-16, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over An et al., KnowAugNet: Multi-source medical knowledge augmented medication prediction network with multi-Level graph contrastive learning, 04/28/2022, https://arxiv.org/pdf/2204.11736, hereinafter referred to as “An” in view of Kirigin et al., Graph-Based Taxonomic Semantic Class Labeling, Future Internet 2022 14, 383., 12/19/2022, https://doi.org/10.3390/fi14120383, hereinafter referred to as “Kirigin”, in further view of Azmy et al., Matching Entities Across Different Knowledge Graphs with Graph Embeddings, 05/15/2019, https://arxiv.org/pdf/1903.06607, hereinafter referred to as “Azmy”.
Regarding claim 1, An teaches receiving, …, an entity network graph for an entity based on a plurality of interaction data objects for the entity (An, Page 6, Section 3.2.2, Paragraph 3, Lines 1-3, “Figure 4 shows the detailed process of the medical prior relation graph construction based on the history EMRs of patients”; An, Page 6, Section 3.2.2, Paragraph 3, Lines 10-11, “the visit-based medical record extracted from the patient’s history EMRs is represented as
R
i
”; the “patient” is considered to be the “entity” and the “medical record[s]” are considered to be the “plurality of interaction data objects”), wherein:
(i) an interaction data object of the plurality of interaction data objects comprises an interaction code (An, Page 6, Section 3.2.2, Paragraph 3, Lines 13-14 – Page 7, Line 1, “As shown in Figure 4, there are three historical records:
R
1
,
R
2
,
R
3
, and each record
R
i
includes diagnosis code
m
*
and medication code
d
*
”; see also An, Page 7, Figure 4; The “diagnosis code” and “medication code” are considered to be “an interaction code”),
(ii) the entity network graph comprises a plurality of nodes and a plurality of edges (An, Page 7, Figure 4(3), Red and purple nodes can be seen connected via solid and dashed edges; An, Page 7, Col 2, Paragraph 2, Lines 4-8, “The nodes in the medical prior relation graph are the set of medication codes and diagnosis codes, i.e.
V
r
=
C
. The medical code pairs obtained in Figure 4 (2) determine the edges between nodes in the relation graph”),
(iii) a particular node of the plurality of nodes corresponds to a particular interaction code of at least one of the plurality of interaction data objects (An, Page 7, Col 2, Paragraph 2, Lines 4-8, “The nodes in the medical prior relation graph are the set of medication codes and diagnosis codes, i.e.
V
r
=
C
. The medical code pairs obtained in Figure 4 (2) determine the edges between nodes in the relation graph”; see also An, Page 7, Figure 4; Each code corresponds to at least one of the medical records, which are considered to be the “interaction data objects”),
(iv) a particular edge of the plurality of edges connects a node pair of the plurality of nodes that is associated with a particular interaction data object (An, Page 7, Col 2, Paragraph 2, Lines 4-8, “The nodes in the medical prior relation graph are the set of medication codes and diagnosis codes, i.e.
V
r
=
C
. The medical code pairs obtained in Figure 4 (2) determine the edges between nodes in the relation graph”; see also An, Page 7, Figure 4; An, Page 7, Col 1, Lines 1-4, “Based on the visit records shown in Figure 4 (1), the corresponding medical code pairs in each visit record can be generated such as (
d
1
,
d
2
), (
d
1
,
m
1
), (
m
1
,
m
2
), …”),
(v) the particular node is associated with a node weight, wherein the node weight is based on a code count identifying a number of the plurality of interaction data objects that comprise the particular interaction code (An, Page 7, Figure 4 (2); An, Page 7, Col 1, Lines 4-6, “After mathematical statistics, the generated medical code pairs and the number of co-occurrence are shown in Figure4(2)”; The “number of co-occurrence” are considered to be the “node weight”, in accordance with the BRI and applicant’s specification paragraph 0062), and
(vi) the particular edge is associated with an edge weight (An, Page 7, Col 1, 9-15, “considering that the relations between medical codes are not only related to the number of co-occurrence but also related to the frequency of medical codes, the pointwise mutual information (PMI) [5] commonly used in natural language processing to measure the relevance of words is introduced to calculate the relation weights between medical codes”; see also An, Page 7, Equation 9), wherein the node pair corresponds to a first interaction code and a second interaction code that are associated with the particular interaction data object (An, Page 7, Col 1, Lines 1-9, “Based on the visit records shown in Figure 4 (1), the corresponding medical code pairs in each visit record can be generated such as (
d
1
,
d
2
), (
d
1
,
m
1
), (
m
1
,
m
2
), …. After mathematical statistics, the generated medical code pairs and the number of co-occurrence are shown in Figure 4 (2). There are three implicit relations: the concurrent relation between diseases d-d, the synergistic relation between medications m-m, and the therapeutic relation between diseases and medication d-m”; “medical code pairs” are generated for “each visit record”; An, Page 7, Figure 4(2)-4(3), The “weighted relational graph of medical codes” is generated from the “pairs of medical codes”, thus, the node pairs correspond to a pair of codes associated with a visit record, which is considered to be the “interaction data object”), and the edge weight is based on a code pair count identifying a number of the plurality of interaction data objects that comprise the first interaction code and the second interaction code (An, Page 7, Col 1, Lines 9-15, “Then, considering that the relations between medical codes are not only related to the number of co-occurrence but also related to the frequency of medical codes, the pointwise mutual information (PMI) [5] commonly used in natural language processing to measure the relevance of words is introduced to calculate the relation weights between medical codes”; An, Page 7, Col 2, Paragraph 2, Lines 8-10 and Equation 10, “Thus, the edge weights in adjacency matrix A of the medical prior relation graph
G
r
can be calculated as follows:
A
c
i
,
c
j
=
P
M
I
c
i
,
c
j
i
f
P
M
I
c
i
,
c
j
>
ζ
,
0
e
l
s
e
”; The edge weights are calculated based on “pointwise mutual information” which is calculated using the “number of co-occurrence” of the relations between medical codes, which is considered to be “a code pair count”); and
processing, …, a … network graph … with a detection model, to output a predictive classification for the particular node … (An, Page 10, Col 1, Lines 4-9 and Equation 18, “According to the comprehensive patient representation vector
O
P
, the current treatment medication
y
^
t
m
can be predicted as follows:
y
^
t
m
= softmax(
W
O
⋅
O
P
+
b
o
), (18) where
y
^
t
m
denotes the predicted multi-label medications set,
W
O
∈
R
|
C
m
|
×
d
e
and
b
o
∈
R
|
C
m
|
are the parameters to be learned” The “current treatment medication” is considered to be the “predictive classification”; and the softmax classifier is considered to be the “detection model”).
An also teaches that the method is computer-implemented and that the steps are performed by one or more processors (An, Page 11, Section 4.1.4, Lines 18-20, “All methods are trained on a Windows with 11GB memory and an Nvidia 2080Ti GPU using the deep learning computation platform Pytorch 1.6”).
An does not explicitly teach processing a class network graph, from an entity network graph, that corresponds to an entity class corresponding to the entity nor that the predictive classification is generated based on a comparison between the class network graph and the entity network graph.
Kirigin teaches a class network graph, from an entity network graph, that correspond to an entity class corresponding to the entity (Kirigin, Page 2, Paragraph 4, Lines 1-5, “The ConGraCNet coordination-based graph method for semantic association analysis [5] underlying the labeling task, reveals the polysemous nature of lexical concepts by using coordination and/or syntactic construction. The obtained graph of associative lexemes for a source lexeme is clustered into subgraphs that reveal the semantic classes of the source lexeme in a corpus”).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date to modify the entity network graph method of An to include associating classes with class network graphs as taught by Kirigin. The motivation to do so would have been that this method better demonstrates the taxonomical relationships between the entities (Kirigin, Page 2, Final Line – Page 3, Lines 1-3, “The proposed ConGraCNet multilayered method is an effective way of semantic analysis that reveals the polysemous nature of lexical concepts and provides a cognitive linguistic approach to understanding the semantic meanings of lexical concepts and their taxonomic relations”).
An in view of Kirigin does not explicitly teach that the predictive classification is generated based on a comparison between the class network graph and the entity network graph.
Azmy teaches a predictive classification generated based on a comparison between the class network graph and the entity network graph (Azmy, Page 1, Section 2, Lines 2-7, “Given a source knowledge graph S containing entities Es = {
e
1
s
,
e
2
s
,…,
e
m
s
}, where
e
i
is a Uniform Resource Identifier (URI), for each entity we wish find the entity in the target knowledge graph T containing entities Et = {
e
1
t
,
e
2
t
,…,
e
n
t
} that corresponds to the same real-world entity”; Azmy, Page 3, Paragraph 1, Lines 3-6, “we use a point-wise training strategy: a classifier is trained on each source–target entity pair and the probability of predicting a match is used for candidate ranking”; Azmy, Page 3, Paragraph 4, Lines 10-14, “Each pair of training example is associated with the ground truth from the datasets described in the previous section. We rank the candidates by the match probability for evaluation”).
It would have been obvious, to a person of ordinary skill in the art, before the effective filing date of the invention, to have modified the entity network graph method of the proposed combination to include generating a predictive classification based on a comparison between the class network graph and the entity network graph as taught by Azmy. The motivation for doing so would have been that using a comparison between graphs to make a prediction achieves high accuracy and requires only a small amount of training data and this method takes advantage of knowledge from the graphs such as connectivity between nodes (Azmy, Page 1, Abstract, Lines 10-16, “Using a classification-based approach, we find that a simple multilayered perceptron based on representations derived from RDF2VEC graph embeddings of entities in each knowledge graph is sufficient to achieve high accuracy, with only small amounts of training data”; Azmy, Page 5, Lines 2-5, “our approach can take advantage of the graph nature of RDF, including knowledge about connectivity between nodes and how they relate to one another”).
Regarding claim 2, the rejection of claim 1 is incorporated, and further, the proposed combination teaches wherein the predictive classification comprises an entity subclass label and the computer-implemented method further comprises: assigning the entity subclass label to the entity (An, Page 9, Col 2, Paragraph 4, Lines 4-7, “considering the importance of current diagnosis information for the medication prediction at the current timestamp, the current diagnosis code embedding vector
x
d
t
is also integrated into the patient representation”; An, Page 10, Col 1, Lines 4-9 and Equation 18, “According to the comprehensive patient representation vector
O
P
, the current treatment medication
y
^
t
m
can be predicted as follows:
y
^
t
m
= softmax(
W
O
⋅
O
P
+
b
o
), (18) where
y
^
t
m
denotes the predicted multi-label medications set,
W
O
∈
R
|
C
m
|
×
d
e
and
b
o
∈
R
|
C
m
|
are the parameters to be learned”; The “current treatment medication” is considered to be the “predictive classification” and is considered to be “an entity subclass label” because a treatment is used to treat a diagnosis, which would be considered the class).
Regarding claim 3, the rejection of claim 1 is incorporated, and further, the proposed combination teaches wherein the class network graph comprises a plurality of class nodes arranged into one or more node clusters, and wherein a particular node cluster comprises a subset of the plurality of class nodes (Kirigin, Page 6, Section 3.1.2, Paragraph 3, Lines 1-3, “For a selected polysemous source lexeme s, the ConGraCNet method first constructs a weighted undirected clustered graph FoF[and|or]s and computes the centrality of its lexeme nodes”; see also Kirigin, Page 7, Figure 1, The different clusters of the graph can be seen with matching central node colors; Each of the clusters are considered to be a subset of the plurality of class nodes; Kirigin, Page 8, Section 3.2.1).
Regarding claim 14, An teaches A system comprising: one or more processors; and one or more memories storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations, (An, Page 11, Section 4.1.4, Lines 18-20, “All methods are trained on a Windows with 11GB memory and an Nvidia 2080Ti GPU using the deep learning computation platform Pytorch 1.6”) comprising:
receive an entity network graph for an entity based on a plurality of interaction data objects for the entity (An, Page 6, Section 3.2.2, Paragraph 3, Lines 1-3, “Figure 4 shows the detailed process of the medical prior relation graph construction based on the history EMRs of patients”; An, Page 6, Section 3.2.2, Paragraph 3, Lines 10-11, “the visit-based medical record extracted from the patient’s history EMRs is represented as
R
i
”; the “patient” is considered to be the “entity” and the “medical record[s]” are considered to be the “plurality of interaction data objects”), wherein:
(i) an interaction data object of the plurality of interaction data objects comprises an interaction code (An, Page 6, Section 3.2.2, Paragraph 3, Lines 13-14 – Page 7, Line 1, “As shown in Figure 4, there are three historical records:
R
1
,
R
2
,
R
3
, and each record
R
i
includes diagnosis code
m
*
and medication code
d
*
”; see also An, Page 7, Figure 4; The “diagnosis code” and “medication code” are considered to be “an interaction code”),
(ii) the entity network graph comprises a plurality of nodes and a plurality of edges (An, Page 7, Figure 4(3), Red and purple nodes can be seen connected via solid and dashed edges; An, Page 7, Col 2, Paragraph 2, Lines 4-8, “The nodes in the medical prior relation graph are the set of medication codes and diagnosis codes, i.e.
V
r
=
C
. The medical code pairs obtained in Figure 4 (2) determine the edges between nodes in the relation graph”),
(iii) a particular node of the plurality of nodes corresponds to a particular interaction code of at least one of the plurality of interaction data objects (An, Page 7, Col 2, Paragraph 2, Lines 4-8, “The nodes in the medical prior relation graph are the set of medication codes and diagnosis codes, i.e.
V
r
=
C
. The medical code pairs obtained in Figure 4 (2) determine the edges between nodes in the relation graph”; see also An, Page 7, Figure 4; Each code corresponds to at least one of the medical records, which are considered to be the “interaction data objects”),
(iv) a particular edge of the plurality of edges connects a node pair of the plurality of nodes that is associated with a particular interaction data object (An, Page 7, Col 2, Paragraph 2, Lines 4-8, “The nodes in the medical prior relation graph are the set of medication codes and diagnosis codes, i.e.
V
r
=
C
. The medical code pairs obtained in Figure 4 (2) determine the edges between nodes in the relation graph”; see also An, Page 7, Figure 4; An, Page 7, Col 1, Lines 1-4, “Based on the visit records shown in Figure 4 (1), the corresponding medical code pairs in each visit record can be generated such as (
d
1
,
d
2
), (
d
1
,
m
1
), (
m
1
,
m
2
), …”)
(v) the particular node is associated with a node weight, wherein the node weight is based on a code count identifying a number of the plurality of interaction data objects that comprise the particular interaction code (An, Page 7, Figure 4 (2); An, Page 7, Col 1, Lines 4-6, “After mathematical statistics, the generated medical code pairs and the number of co-occurrence are shown in Figure4(2)”; The “number of co-occurrence” are considered to be the “node weight”, in accordance with the BRI and applicant’s specification paragraph 0062), and
(vi) the particular edge is associated with an edge weight (An, Page 7, Col 1, 9-15, “considering that the relations between medical codes are not only related to the number of co-occurrence but also related to the frequency of medical codes, the pointwise mutual information (PMI) [5] commonly used in natural language processing to measure the relevance of words is introduced to calculate the relation weights between medical codes”; see also An, Page 7, Equation 9), wherein the node pair corresponds to a first interaction code and a second interaction code that are associated with the particular interaction data object (An, Page 7, Col 1, Lines 1-9, “Based on the visit records shown in Figure 4 (1), the corresponding medical code pairs in each visit record can be generated such as (
d
1
,
d
2
), (
d
1
,
m
1
), (
m
1
,
m
2
), …. After mathematical statistics, the generated medical code pairs and the number of co-occurrence are shown in Figure 4 (2). There are three implicit relations: the concurrent relation between diseases d-d, the synergistic relation between medications m-m, and the therapeutic relation between diseases and medication d-m”; “medical code pairs” are generated for “each visit record”; An, Page 7, Figure 4(2)-4(3), The “weighted relational graph of medical codes” is generated from the “pairs of medical codes”, thus, the node pairs correspond to a pair of codes associated with a visit record, which is considered to be the “interaction data object”), and the edge weight is based on a code pair count identifying a number of the plurality of interaction data objects that comprise the first interaction code and the second interaction code (An, Page 7, Col 1, Lines 9-15, “Then, considering that the relations between medical codes are not only related to the number of co-occurrence but also related to the frequency of medical codes, the pointwise mutual information (PMI) [5] commonly used in natural language processing to measure the relevance of words is introduced to calculate the relation weights between medical codes”; An, Page 7, Col 2, Paragraph 2, Lines 8-10 and Equation 10, “Thus, the edge weights in adjacency matrix A of the medical prior relation graph
G
r
can be calculated as follows:
A
c
i
,
c
j
=
P
M
I
c
i
,
c
j
i
f
P
M
I
c
i
,
c
j
>
ζ
,
0
e
l
s
e
”; The edge weights are calculated based on “pointwise mutual information” which is calculated using the “number of co-occurrence” of the relations between medical codes, which is considered to be “a code pair count”); and
process a … network graph … with a detection model, to output a predictive classification for the particular node … (An, Page 10, Col 1, Lines 4-9 and Equation 18, “According to the comprehensive patient representation vector
O
P
, the current treatment medication
y
^
t
m
can be predicted as follows:
y
^
t
m
= softmax(
W
O
⋅
O
P
+
b
o
), (18) where
y
^
t
m
denotes the predicted multi-label medications set,
W
O
∈
R
|
C
m
|
×
d
e
and
b
o
∈
R
|
C
m
|
are the parameters to be learned” The “current treatment medication” is considered to be the “predictive classification”; and the softmax classifier is considered to be the “detection model”).
An does not explicitly teach processing a class network graph, from an entity network graph, that corresponds to an entity class corresponding to the entity nor that the predictive classification is generated based on a comparison between the class network graph and the entity network graph.
Kirigin teaches a class network graph, from an entity network graph, that correspond to an entity class corresponding to the entity (Kirigin, Page 2, Paragraph 4, Lines 1-5, “The ConGraCNet coordination-based graph method for semantic association analysis [5] underlying the labeling task, reveals the polysemous nature of lexical concepts by using coordination and/or syntactic construction. The obtained graph of associative lexemes for a source lexeme is clustered into subgraphs that reveal the semantic classes of the source lexeme in a corpus”).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date to modify the entity network graph method of An to include associating classes with class network graphs as taught by Kirigin. The motivation to do so would have been that this method better demonstrates the taxonomical relationships between the entities (Kirigin, Page 2, Final Line – Page 3, Lines 1-3, “The proposed ConGraCNet multilayered method is an effective way of semantic analysis that reveals the polysemous nature of lexical concepts and provides a cognitive linguistic approach to understanding the semantic meanings of lexical concepts and their taxonomic relations”).
An in view of Kirigin does not explicitly teach that the predictive classification is generated based on a comparison between the class network graph and the entity network graph.
Azmy teaches a predictive classification generated based on a comparison between the class network graph and the entity network graph (Azmy, Page 1, Section 2, Lines 2-7, “Given a source knowledge graph S containing entities Es = {
e
1
s
,
e
2
s
,…,
e
m
s
}, where
e
i
is a Uniform Resource Identifier (URI), for each entity we wish find the entity in the target knowledge graph T containing entities Et = {
e
1
t
,
e
2
t
,…,
e
n
t
} that corresponds to the same real-world entity”; Azmy, Page 3, Paragraph 1, Lines 3-6, “we use a point-wise training strategy: a classifier is trained on each source–target entity pair and the probability of predicting a match is used for candidate ranking”; Azmy, Page 3, Paragraph 4, Lines 10-14, “Each pair of training example is associated with the ground truth from the datasets described in the previous section. We rank the candidates by the match probability for evaluation”).
It would have been obvious, to a person of ordinary skill in the art, before the effective filing date of the invention, to have modified the entity network graph method of the proposed combination to include generating a predictive classification based on a comparison between the class network graph and the entity network graph as taught by Azmy. The motivation for doing so would have been that using a comparison between graphs to make a prediction achieves high accuracy and requires only a small amount of training data and this method takes advantage of knowledge from the graphs such as connectivity between nodes (Azmy, Page 1, Abstract, Lines 10-16, “Using a classification-based approach, we find that a simple multilayered perceptron based on representations derived from RDF2VEC graph embeddings of entities in each knowledge graph is sufficient to achieve high accuracy, with only small amounts of training data”; Azmy, Page 5, Lines 2-5, “our approach can take advantage of the graph nature of RDF, including knowledge about connectivity between nodes and how they relate to one another”).
Regarding claim 15, the rejection of claim 14 is incorporated, and further, the proposed combination teaches wherein the predictive classification comprises an entity subclass label and wherein the one or more processors are further configured to: assign the entity subclass label to the entity (An, Page 9, Col 2, Paragraph 4, Lines 4-7, “considering the importance of current diagnosis information for the medication prediction at the current timestamp, the current diagnosis code embedding vector
x
d
t
is also integrated into the patient representation”; An, Page 10, Col 1, Lines 4-9 and Equation 18, “According to the comprehensive patient representation vector
O
P
, the current treatment medication
y
^
t
m
can be predicted as follows:
y
^
t
m
= softmax(
W
O
⋅
O
P
+
b
o
), (18) where
y
^
t
m
denotes the predicted multi-label medications set,
W
O
∈
R
|
C
m
|
×
d
e
and
b
o
∈
R
|
C
m
|
are the parameters to be learned”; The “current treatment medication” is considered to be the “predictive classification” and is considered to be “an entity subclass label” because a treatment is used to treat a diagnosis, which would be considered the class).
Regarding claim 16, the rejection of claim 14 is incorporated, and further, the proposed combination teaches wherein the class network graph comprises a plurality of class nodes arranged into one or more node clusters, and wherein a particular node cluster comprises a subset of the plurality of class nodes (Kirigin, Page 6, Section 3.1.2, Paragraph 3, Lines 1-3, “For a selected polysemous source lexeme s, the ConGraCNet method first constructs a weighted undirected clustered graph FoF[and|or]s and computes the centrality of its lexeme nodes”; see also Kirigin, Page 7, Figure 1, The different clusters of the graph can be seen with matching central node colors; Kirigin, Page 8, Section 3.2.1).
Regarding claim 19, An teaches One or more non-transitory computer-readable media storing processor-executable instructions that, when executed by one or more processors, cause the one or more processors (An, Page 11, Section 4.1.4, Lines 18-20, “All methods are trained on a Windows with 11GB memory and an Nvidia 2080Ti GPU using the deep learning computation platform Pytorch 1.6”) to perform operations comprising:
receive an entity network graph for an entity based on a plurality of interaction data objects for the entity (An, Page 6, Section 3.2.2, Paragraph 3, Lines 1-3, “Figure 4 shows the detailed process of the medical prior relation graph construction based on the history EMRs of patients”; An, Page 6, Section 3.2.2, Paragraph 3, Lines 10-11, “the visit-based medical record extracted from the patient’s history EMRs is represented as
R
i
”; the “patient” is considered to be the “entity” and the “medical record[s]” are considered to be the “plurality of interaction data objects”), wherein:
(i) an interaction data object of the plurality of interaction data objects comprises an interaction code (An, Page 6, Section 3.2.2, Paragraph 3, Lines 13-14 – Page 7, Line 1, “As shown in Figure 4, there are three historical records:
R
1
,
R
2
,
R
3
, and each record
R
i
includes diagnosis code
m
*
and medication code
d
*
”; see also An, Page 7, Figure 4; The “diagnosis code” and “medication code” are considered to be “one or more interaction codes”),
(ii) the entity network graph comprises a plurality of nodes and a plurality of edges (An, Page 7, Figure 4(3), Red and purple nodes can be seen connected via solid and dashed edges; An, Page 7, Col 2, Paragraph 2, Lines 4-8, “The nodes in the medical prior relation graph are the set of medication codes and diagnosis codes, i.e.
V
r
=
C
. The medical code pairs obtained in Figure 4 (2) determine the edges between nodes in the relation graph”),
(iii) a particular node of the plurality of nodes corresponds to a particular interaction code of at least one of the plurality of interaction data objects (An, Page 7, Col 2, Paragraph 2, Lines 4-8, “The nodes in the medical prior relation graph are the set of medication codes and diagnosis codes, i.e.
V
r
=
C
. The medical code pairs obtained in Figure 4 (2) determine the edges between nodes in the relation graph”; see also An, Page 7, Figure 4; Each code corresponds to at least one of the medical records, which are considered to be the “interaction data objects”),
(iv) a particular edge of the plurality of edges connects a node pair of the plurality of nodes that is associated with a particular interaction data object (An, Page 7, Col 2, Paragraph 2, Lines 4-8, “The nodes in the medical prior relation graph are the set of medication codes and diagnosis codes, i.e.
V
r
=
C
. The medical code pairs obtained in Figure 4 (2) determine the edges between nodes in the relation graph”; see also An, Page 7, Figure 4; An, Page 7, Col 1, Lines 1-4, “Based on the visit records shown in Figure 4 (1), the corresponding medical code pairs in each visit record can be generated such as (
d
1
,
d
2
), (
d
1
,
m
1
), (
m
1
,
m
2
), …”)
(v) the particular node is associated with a node weight, wherein the node weight is based on a code count identifying a number of the plurality of interaction data objects that comprise the particular interaction code (An, Page 7, Figure 4 (2); An, Page 7, Col 1, Lines 4-6, “After mathematical statistics, the generated medical code pairs and the number of co-occurrence are shown in Figure4(2)”; The “number of co-occurrence” are considered to be the “node weight”, in accordance with the BRI and applicant’s specification paragraph 0062), and
(vi) the particular edge is associated with an edge weight (An, Page 7, Col 1, 9-15, “considering that the relations between medical codes are not only related to the number of co-occurrence but also related to the frequency of medical codes, the pointwise mutual information (PMI) [5] commonly used in natural language processing to measure the relevance of words is introduced to calculate the relation weights between medical codes”; see also An, Page 7, Equation 9), wherein the node pair corresponds to a first interaction code and a second interaction code that are associated with the particular interaction data object (An, Page 7, Col 1, Lines 1-9, “Based on the visit records shown in Figure 4 (1), the corresponding medical code pairs in each visit record can be generated such as (
d
1
,
d
2
), (
d
1
,
m
1
), (
m
1
,
m
2
), …. After mathematical statistics, the generated medical code pairs and the number of co-occurrence are shown in Figure 4 (2). There are three implicit relations: the concurrent relation between diseases d-d, the synergistic relation between medications m-m, and the therapeutic relation between diseases and medication d-m”; “medical code pairs” are generated for “each visit record”; An, Page 7, Figure 4(2)-4(3), The “weighted relational graph of medical codes” is generated from the “pairs of medical codes”, thus, the node pairs correspond to a pair of codes associated with a visit record, which is considered to be the “interaction data object”), and the edge weight is based on a code pair count identifying a number of the plurality of interaction data objects that comprise the first interaction code and the second interaction code (An, Page 7, Col 1, Lines 9-15, “Then, considering that the relations between medical codes are not only related to the number of co-occurrence but also related to the frequency of medical codes, the pointwise mutual information (PMI) [5] commonly used in natural language processing to measure the relevance of words is introduced to calculate the relation weights between medical codes”; An, Page 7, Col 2, Paragraph 2, Lines 8-10 and Equation 10, “Thus, the edge weights in adjacency matrix A of the medical prior relation graph
G
r
can be calculated as follows:
A
c
i
,
c
j
=
P
M
I
c
i
,
c
j
i
f
P
M
I
c
i
,
c
j
>
ζ
,
0
e
l
s
e
”; The edge weights are calculated based on “pointwise mutual information” which is calculated using the “number of co-occurrence” of the relations between medical codes, which is considered to be “a code pair count”); and
process a … network graph … with a detection model, to output a predictive classification for the particular node … (An, Page 10, Col 1, Lines 4-9 and Equation 18, “According to the comprehensive patient representation vector
O
P
, the current treatment medication
y
^
t
m
can be predicted as follows:
y
^
t
m
= softmax(
W
O
⋅
O
P
+
b
o
), (18) where
y
^
t
m
denotes the predicted multi-label medications set,
W
O
∈
R
|
C
m
|
×
d
e
and
b
o
∈
R
|
C
m
|
are the parameters to be learned” The “current treatment medication” is considered to be the “predictive classification”; and the softmax classifier is considered to be the “detection model”).
An does not explicitly teach processing a class network graph, from an entity network graph, that corresponds to an entity class corresponding to the entity nor that the predictive classification is generated based on a comparison between the class network graph and the entity network graph.
Kirigin teaches a class network graph, from an entity network graph, that correspond to an entity class corresponding to the entity (Kirigin, Page 2, Paragraph 4, Lines 1-5, “The ConGraCNet coordination-based graph method for semantic association analysis [5] underlying the labeling task, reveals the polysemous nature of lexical concepts by using coordination and/or syntactic construction. The obtained graph of associative lexemes for a source lexeme is clustered into subgraphs that reveal the semantic classes of the source lexeme in a corpus”).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date to modify the entity network graph method of An to include associating classes with class network graphs as taught by Kirigin. The motivation to do so would have been that this method better demonstrates the taxonomical relationships between the entities (Kirigin, Page 2, Final Line – Page 3, Lines 1-3, “The proposed ConGraCNet multilayered method is an effective way of semantic analysis that reveals the polysemous nature of lexical concepts and provides a cognitive linguistic approach to understanding the semantic meanings of lexical concepts and their taxonomic relations”).
An in view of Kirigin does not explicitly teach that the predictive classification is generated based on a comparison between the class network graph and the entity network graph.
Azmy teaches a predictive classification generated based on a comparison between the class network graph and the entity network graph (Azmy, Page 1, Section 2, Lines 2-7, “Given a source knowledge graph S containing entities Es = {
e
1
s
,
e
2
s
,…,
e
m
s
}, where
e
i
is a Uniform Resource Identifier (URI), for each entity we wish find the entity in the target knowledge graph T containing entities Et = {
e
1
t
,
e
2
t
,…,
e
n
t
} that corresponds to the same real-world entity”; Azmy, Page 3, Paragraph 1, Lines 3-6, “we use a point-wise training strategy: a classifier is trained on each source–target entity pair and the probability of predicting a match is used for candidate ranking”; Azmy, Page 3, Paragraph 4, Lines 10-14, “Each pair of training example is associated with the ground truth from the datasets described in the previous section. We rank the candidates by the match probability for evaluation”).
It would have been obvious, to a person of ordinary skill in the art, before the effective filing date of the invention, to have modified the entity network graph method of the proposed combination to include generating a predictive classification based on a comparison between the class network graph and the entity network graph as taught by Azmy. The motivation for doing so would have been that using a comparison between graphs to make a prediction achieves high accuracy and requires only a small amount of training data and this method takes advantage of knowledge from the graphs such as connectivity between nodes (Azmy, Page 1, Abstract, Lines 10-16, “Using a classification-based approach, we find that a simple multilayered perceptron based on representations derived from RDF2VEC graph embeddings of entities in each knowledge graph is sufficient to achieve high accuracy, with only small amounts of training data”; Azmy, Page 5, Lines 2-5, “our approach can take advantage of the graph nature of RDF, including knowledge about connectivity between nodes and how they relate to one another”).
Regarding claim 20, the rejection of claim 19 is incorporated, and further, the proposed combination teaches wherein identifying the class network graph comprises:
receiving a domain empirical taxonomy for a domain taxonomy associated with the entity (Cronin, Page 1862, Taxonomy, Lines 1-2, “Our research team developed a taxonomy of consumer health information needs and communications shown in Figure 1”), wherein:
(i) the domain taxonomy comprises a plurality of entity classes (Cronin, Page 1862, Taxonomy, Paragraph 2, Lines 1-2, “Our taxonomy divides information needs and communications into five main categories: clinical information, medical, logistical, social, and other”), and
(ii) the domain empirical taxonomy comprises a respective class taxonomy for each of the plurality of entity classes (Cronin, Page 1863, Figure 1, Under each of the entity classes there are a plurality of further categories, which are considered to make up the “respective class taxonomy”); and
identifying the class … corresponding to the entity class from the domain empirical taxonomy (Cronin, Page 1864, Automated Classifiers, Lines 1-2, “Automated classifiers utilized message contents to learn the major categories of consumer health information needs present using the taxonomy described above”).
Kirigin teaches associating classes with a class network graph (Kirigin, Page 2, Paragraph 4, Lines 1-5, “The ConGraCNet coordination-based graph method for semantic association analysis [5] underlying the labeling task, reveals the polysemous nature of lexical concepts by using coordination and/or syntactic construction. The obtained graph of associative lexemes for a source lexeme is clustered into subgraphs that reveal the semantic classes of the source lexeme in a corpus”).
Claims 7 and 8 are rejected as being unpatentable over An in view of Kirigin in view of Azmy in further view of Cronin, Robert M et al. “Automated Classification of Consumer Health Information Needs in Patient Portal Messages.” AMIA ... Annual Symposium proceedings. AMIA Symposium vol. 2015 1861-70. 5 Nov. 2015, https://pmc.ncbi.nlm.nih.gov/articles/PMC4765690/, hereinafter referred to as “Cronin”.
Regarding claim 7, the rejection of claim 1 is incorporated, and further, Kirigin teaches associating classes with a class network graph (Kirigin, Page 2, Paragraph 4, Lines 1-5, “The ConGraCNet coordination-based graph method for semantic association analysis [5] underlying the labeling task, reveals the polysemous nature of lexical concepts by using coordination and/or syntactic construction. The obtained graph of associative lexemes for a source lexeme is clustered into subgraphs that reveal the semantic classes of the source lexeme in a corpus”).
The proposed combination thus far does not explicitly teach wherein identifying the class network graph comprises: receiving a domain empirical taxonomy for a domain taxonomy associated with the entity, wherein: (i) the domain taxonomy comprises a plurality of entity classes, and (ii) the domain empirical taxonomy comprises a respective class taxonomy for each of the plurality of entity classes; and identifying the class … corresponding to the entity class from the domain empirical taxonomy.
Cronin teaches wherein identifying the class network graph comprises: receiving a domain empirical taxonomy for a domain taxonomy associated with the entity (Cronin, Page 1862, Taxonomy, Lines 1-2, “Our research team developed a taxonomy of consumer health information needs and communications shown in Figure 1”), wherein:
(i) the domain taxonomy comprises a plurality of entity classes (Cronin, Page 1862, Taxonomy, Paragraph 2, Lines 1-2, “Our taxonomy divides information needs and communications into five main categories: clinical information, medical, logistical, social, and other”), and
(ii) the domain empirical taxonomy comprises a respective class taxonomy for each of the plurality of entity classes (Cronin, Page 1863, Figure 1, Under each of the entity classes there are a plurality of further categories, which are considered to make up the “respective class taxonomy”); and
identifying the class … corresponding to the entity class from the domain empirical taxonomy (Cronin, Page 1864, Automated Classifiers, Lines 1-2, “Automated classifiers utilized message contents to learn the major categories of consumer health information needs present using the taxonomy described above”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention, to have modified the entity network graph method of the proposed combination to include identifying a class for the entity as taught by Cronin. The motivation for doing so would have been that automatically classifying information from medical health records would ease healthcare providers' burden (Cronin, Page 1861, Abstract, Lines 2-4, "As patient portal adoption increases, growing volumes of secure messages may burden healthcare providers. Automated classification could expedite portal message triage and answering") and a person of ordinary skill in the art would recognize that automatically identifying a class network graph would be more time efficient that using a domain expert.
Regarding claim 8, the rejection of claim 7 is incorporated, and further, the proposed combination teaches wherein the domain empirical taxonomy is previously generated by a third party (Cronin, Page 1862, Taxonomy, Lines 1-2, “Our research team developed a taxonomy of consumer health information needs and communications shown in Figure 1”; The “research team” is considered to be a “third party” as they are identified as a separate group to the group developing the automated classifiers).
Claims 9 and 10 are rejected as being unpatentable over An in view of Kirigin in view of Azmy in further view of Alstad et al., U.S. Patent No. 10474792, hereinafter referred to as “Alstad”.
Regarding claim 9, the rejection of claim 1 is incorporated.
The proposed combination does not explicitly teach selecting a machine learning model for the entity based on the predictive classification and one or more evaluation metrics for the machine learning model.
Alstad teaches selecting a machine learning model for the entity based on the predictive classification and one or more evaluation metrics for the machine learning model (Alstad, Col 13, Lines 58-66, “ the method has compressed the original claims database into an abstract graph where each node in the graph represents a group of claims in the original database and there is an edge between two nodes if the claim groups overlap in anyway and the method used graph invariants and techniques in node partitioning to group the nodes into neighborhoods. The method may use a “bucket of models” approach to train a classifier for each node neighborhood”; Alstad, Col 14, Lines 9-15, “For each classification algorithm in the bucket (SVM, RandomForest, LogisticRegression in this example), the method trains the algorithm on the training set and then uses the cross validation data set to calculate the root mean squared error of the classifier. The method then chooses the classifier for Ni that has the lowest root mean squared error”; Since the “predictive classification” is an entity sub-class, it is considered equivalent to the “neighborhoods” and since the classifiers are trained per neighborhood, the machine learning model is selected “based on the predictive classification” and the “root mean squared error” is considered to be the “evaluation metric” for the machine learning model).
It would have been obvious, to a person of ordinary skill in the art, before the effective filing date of the invention to have modified the entity network graph method of the proposed combination to include selecting a machine learning model for the entity as taught by Alstad. The motivation for doing so would have been that this method allows for the best classifier to be chosen for each specific entity, and a person of ordinary skill in the art would recognize that this would allow for the method to be used on very different entities (Alstad, Col 14, Lines 37-41, “The method then looks up the best trained classifier for Neighborhoodi, which lets assume is RandomForesti in this example. Since RandomForesti is a trained classifier for Neighborhoodi, we can use it to predict the class of the claim vector”).
Regarding claim 10, the rejection of claim 9 is incorporated, and further, the proposed combination teaches wherein the one or more evaluation metrics comprise an evaluation metric corresponding to each of one or more node clusters of the class network graph (Alstad, Col 14, Lines 15-26, “In one embodiment, a method for selecting the best classifier for each neighborhood may be: For each
N
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: Let data=all claim vectors associated with all nodes in
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For each
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: Do c times: (Where c is some constant) Randomly divide the data into two datasets: A, and B. Train
C
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with the A Test
C
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with B Select the classifier or combination thereof that obtains the highest average score” Each “neighborhood” is considered to be equivalent to “one or more node clusters”).
Claim 11 is rejected as being unpatentable over An in view of Kirigin in view of Azmy in view of Alstad in further view of Lever, J., Krzywinski, M. & Altman, N. Classification evaluation. Nat Methods 13, 603–604 (2016). https://doi.org/10.1038/nmeth.3945, hereinafter referred to as “Lever”.
Regarding claim 11, the rejection of claim 9 is incorporated.
The proposed combination does not explicitly teach wherein the one or more evaluation metrics comprise a true positive rate for the machine learning model relative to a plurality of historical entities associated with the predictive classification.
Lever teaches wherein the one or more evaluation metrics comprise a true positive rate for the machine learning model relative to a plurality of historical entities associated with the predictive classification (Lever, Page 603, Col 2, Paragraph 2, Lines 7-13, “The most popular is the Fβ score, which uses the parameter β to control the balance of recall and precision and is defined as Fβ = (1 + β2)(Precision × Recall)/(β2 × Precision + Recall). As β decreases, precision is given greater weight. With β = 1, we have the commonly used F1 score, which balances recall and precision equally and reduces to the simpler equation 2TP/(2TP + FP + FN)”).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to have modified the entity network graph method of the proposed combination to include using an evaluation metric that include a true positive rate for the machine learning model as taught by Lever. The motivation for doing so would have been that for the context of medical treatment prediction, it is important to have low FNs and FPs (Lever, Page 603, Col 2, Lines 1-5, “Ideally a medical test should have very low numbers of both FNs and FPs. Individuals who do not have the disease should not be given unnecessary treatment or be burdened with the stress of a positive result, and those who do have the disease should not be given false optimism about being disease free”).
Claims 21 and 22 are rejected as being unpatentable over An in view of Kirigin in view of Azmy in further view of Harris et al., U.S. Patent Application Publication No. 20240112042, hereinafter referred to as “Harris”.
Regarding claim 21, the rejection of claim 19 is incorporated.
The proposed combination does not explicitly teach generating a nodal community comprising the particular node and a plurality of community nodes, wherein a community node of the plurality of community nodes comprises the predictive classification of the particular node (Harris, Paragraph 0032, Lines 8-13, “Graph community detection is used to infer any edges (e.g., linking a news source to a personality that was not linked in the survey). Accordingly, the machine learning device may generate new sub-graphs using the inferred edge and original edges for each personality binning”; The “edge” is considered to be “the predictive classification of the particular node” and the other node that is connected via the edge is considered to be the “community node”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to have modified the entity network graph method of the proposed combination to include generating a nodal community to make a predictive classification as taught by Harris. The motivation to do so would have been that using community detection allows community structures to be stored which can then be merged to create new network graphs (Harris, Paragraph 0031; Harris, Paragraph 0043).
Regarding claim 22, the rejection of claim 19 is incorporated.
The proposed combination thus far does not explicitly teach wherein the detection model comprises a graph community detection algorithm.
Harris teaches wherein the detection model comprises a graph community detection algorithm (Harris, Paragraph 0032, Lines 8-13, “Graph community detection is used to infer any edges (e.g., linking a news source to a personality that was not linked in the survey). Accordingly, the machine learning device may generate new sub-graphs using the inferred edge and original edges for each personality binning”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to have modified the entity network graph method of the proposed combination to include generating a nodal community to make a predictive classification as taught by Harris. The motivation to do so would have been that using community detection allows community structures to be stored which can then be merged to create new network graphs (Harris, Paragraph 0031; Harris, Paragraph 0043).
Response to Arguments
Applicant’s arguments regarding the 35 U.S.C. 101 rejections of the claims have been fully considered but are unpersuasive.
Applicant first argues, in page 11, paragraph 2 of the response, that each of the abstract ideas identified in the 35 U.S.C. 101 rejection have been removed from the claims, and as such, the claims no longer recite an abstract idea. Examiner respectfully disagrees. While applicant amended to remove the “generating” and “identifying” steps identified as abstract ideas, the currently recited “processing” limitation still recites a mental process. The broadest reasonable interpretation of “processing a class network graph … to output a predictive classification for the particular node based on a comparison between the class network graph and the entity network graph” includes a person thinking about a class network graph, comparing the class network graph with the entity network graph in their head, and making a prediction.
Applicant next argues, on page 12, paragraph 1 of the response, that a human mind cannot practically process the entity network graph using a detection model, as these steps require the use of a computer. Examiner respectfully disagrees. Mere physical or tangible implementation of an exception is not in itself an inventive concept and does not guarantee eligibility, see MPEP 2106.05(I)(A).
Applicant next argues, on page 13, paragraphs 1-2 of the response, that claim 1 is integrated into a practical application by reflecting an improvement in processing the graph data structure to output node-level predictions and enhancing the accuracy of predictions within the predictive domain. Examiner respectfully disagrees. An improvement to making predictions may be an improvement in an abstract idea, but not an improvement in the functioning of a computer, as a computer. 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, see MPEP 2106.05(a)(II). With regard to an “improved graph data structure”, applicant has not made a specific argument as to how the graph data structure is an improvement, there are no clear details of how the graph data structure is improved in the specification or reflected in the claims.
Applicant's arguments regarding the remainder of the claims rely upon the arguments asserted with respect to the independent claims, and are thus unpersuasive.
Applicant’s arguments regarding the 35 U.S.C. 103 rejections of the claims have been fully considered but are unpersuasive.
Applicant argues, on page 15 – page 16 of the response, that the cited references do not disclose, teach, or suggest “the particular node is associated with a node weight, wherein the node weight is based on a code count identifying a number of the plurality of interaction data objects that comprise the particular interaction code”. Examiner respectfully disagrees. Under the broadest reasonable interpretation of “node weight”, and according to applicant’s specification paragraph 0062, includes the “number of co-occurrence” values associated with nodes as taught by An, see the updated 35 U.S.C. 103 rejection above.
Applicant's arguments regarding the remainder of the claims rely upon the arguments asserted with respect to the independent claims, and are thus unpersuasive.
Conclusion
Claims 4-6 and 17-18 have been rejected under 35 U.S.C. 112(b) and 35 U.S.C. 101 only. A complete prior art search was performed for these claims; however, no prior art was uncovered that disclose or fairly suggest the following claimed features:
After detailed search, the cited arts, neither alone nor in combination, teach the claimed subject matter of claims 4 and 17, “wherein the one or more overlap scores comprise a respective overlap score for each of the one or more node clusters; identifying the particular node cluster from the one or more node clusters based on the one or more overlap scores, wherein a particular overlap score for the particular node cluster is a highest overlap score relative to the one or more overlap scores; and generating the predictive classification based on the particular node cluster”.
Pertinent art (Azmy et al., Matching Entities Across Different Knowledge Graphs with Graph Embeddings, 05/15/2019, https://arxiv.org/pdf/1903.06607) discloses determining what could be considered an overlap score between two different network graphs but does not disclose calculating an overlap score for each cluster of a network graph and identifying the highest one in order to make a prediction, and thus does not specifically disclose the claimed subject matter of claims 4 and 17.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
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/M.C.S./ Examiner, Art Unit 2122
/KAKALI CHAKI/ Supervisory Patent Examiner, Art Unit 2122
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