CTNF 18/551,483 CTNF 93954 DETAILED ACTION 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. This action is responsive to the original application filed on 9/20/2023. Acknowledgment is made with respect to a claim of priority to PCT Application PCT/US2022/022298 filed on 3/29/2022 and Provisional Application 63/167,479 filed on 3/29/2021. Claim Objections 07-29-01 AIA Claim 5 is objected to because of the following informalities: Claim 5 recites the limitation “determining an objective node of the N entry nodes, the objective node corresponding to the classification ” (emphasis added) which should read as “determining an objective node of the N entry nodes, the objective node corresponding to the known classification ” (emphasis added) so that there is term consistency with the “known classification” of claim 1 to which this limitation in claim 5 refers . Appropriate correction is required. Claim Rejections - 35 USC § 112 07-30-02 AIA 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. 07-34-01 Claim 7 is 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. Claim 7 recites the limitation “generating a training vector by aggregating each of the K entity-specific entry vectors in response to the determination ” (emphasis added). It is not clear as to which determination “the determination” is referring to in this limitation. Is it the determination of a total number of fields or the determination that a total number of fields is above a field threshold? Please explain. For examination purposes, the limitation will be interpreted to mean “generating a training vector by aggregating each of the K entity-specific entry vectors in response to the determination that the total number of fields if above the field threshold ” (emphasis added). Appropriate correction is required. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-18 and 23-24 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”). When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself. Claim 1 Step 1 : The claim recites a method; therefore, it is directed to the statutory category of a process. Step 2A Prong 1 : The claim recites, inter alia: identifying N entry nodes of the M nodes that each match one of the plurality of fields, wherein N is less than M: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of identifying nodes in a graph that match a field, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can practically and mentally identify a node in a graph that contains specific field information. for each entry node of the N entry nodes, generating a propagated entry vector having M entry values, wherein each of the M entry values represents an importance of a corresponding node to the entry node: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating a vector that represents the importance of a node in a graph, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can practically and mentally create a vector of information that identifies the importance of a node in a graph. for each of the plurality of entities: identifying each of the fields of a set of corresponding entity records that matches one of the N entry nodes, thereby identifying K entity-specific entry nodes, wherein K is less than or equal to N: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of identifying a field that matches node information, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can practically and mentally identify similar information between a node and a field. identifying a set of K entity-specific entry vectors corresponding to the K entity-specific entry nodes: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of identifying vectors corresponding to node information, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can practically and mentally identify vectors that reflect or math node information. generating a training vector by aggregating each of the K entity-specific entry vectors: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of aggregating or combining vectors, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can practically and mentally combine vectors into one vector. Step 2A Prong 2 : The claim does not recite any additional limitations which integrate the abstract idea into a practical application. Specifically, the additional elements consist of “ storing a graph database comprising (1) M nodes of a plurality of node types and (2) a plurality of edges of a plurality of edge types ”, “ receiving, for a plurality of entities, a plurality of entity records, each with a plurality of fields and a known classification ”, and “ training the machine learning model using the training vectors and the known classifications ”. The additional element of “ training the machine learning model using the training vectors and the known classifications ” amounts to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the generic machine learning model is broadly trained using vectors and classifications. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer ( see MPEP § 2106.05(f)). The additional elements “ storing a graph database comprising (1) M nodes of a plurality of node types and (2) a plurality of edges of a plurality of edge types ” and “ receiving, for a plurality of entities, a plurality of entity records, each with a plurality of fields and a known classification ” insignificant extra-solution activities required for any uses of the abstract ideas ( see MPEP § 2106.05(g)). Thus, even when viewed individually and as an ordered combination, these additional elements do not integrate the abstract idea into a practical application and the claim is thus directed to the abstract idea. Step 2B : Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional element of “ training the machine learning model using the training vectors and the known classifications ” amounts to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the generic machine learning model is broadly trained using vectors and classifications. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer ( see MPEP § 2106.05(f)). The additional elements “ storing a graph database comprising (1) M nodes of a plurality of node types and (2) a plurality of edges of a plurality of edge types ” and “ receiving, for a plurality of entities, a plurality of entity records, each with a plurality of fields and a known classification ” insignificant extra-solution activities required for any uses of the abstract ideas ( see MPEP § 2106.05(g)), and are well-understood, routine, conventional activities (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network”, “Storing and retrieving information in memory”). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible. Claim 2 Step 1 : A process, as above. Step 2A Prong 1 : The claim recites, inter alia: for each entity record in the set of corresponding entity records: determining a corresponding entity record, of the set of corresponding entity records, and an entry node, of the N entry nodes, that are linked together in a terminology database: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining nodes and corresponding records in a database, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. Step 2A Prong 2, Step 2B : The claim does not recite any additional elements that are sufficient to integrate the judicial exceptions into a practical application or amount to significantly more than the judicial exception. As such, the claim is ineligible. Claim 3 Step 1 : A process, as above. Step 2A Prong 1 : The claim recites, inter alia: for each of the plurality of entities: determining a subset of the set of corresponding entity records, with a subset of fields, that were generated during a specified time period: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining subsets of entities and fields during a specified time period, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. identifying each of the subset of fields of the set of corresponding entity records that matches one of the N entry nodes, thereby identifying K entity-specific entry nodes, wherein K is less than or equal to N: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining matching nodes, fields, and entities, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. Step 2A Prong 2, Step 2B : The claim does not recite any additional elements that are sufficient to integrate the judicial exceptions into a practical application or amount to significantly more than the judicial exception. As such, the claim is ineligible. Claim 4 Step 1 : A process, as above. Step 2A Prong 1 : The claim recites, inter alia: for each of the plurality of entities: determining that a number of identified K entity-specific entry nodes is above an entry node threshold: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining that a number of nodes is above a threshold, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. generating a training vector by aggregating the K entity-specific entry vectors in response to the determination: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of aggregating vectors into a training vector, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. Step 2A Prong 2, Step 2B : The claim does not recite any additional elements that are sufficient to integrate the judicial exceptions into a practical application or amount to significantly more than the judicial exception. As such, the claim is ineligible. Claim 5 Step 1 : A process, as above. Step 2A Prong 1 : The claim recites, inter alia: determining an objective node of the N entry nodes, the objective node corresponding to the classification: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining an objective node that corresponds to a classification, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. for each of the plurality of entities: identifying a number of the fields of the set of corresponding entity records that matches the objective node: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining a number of fields that matches a node, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. determining if the identified number of fields that match is above an objective threshold: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining is a number of matching fields is above a threshold, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. generating a training vector by aggregating each of the K entity-specific entry vectors in response to the determination: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of aggregating vectors into a training vector, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. Step 2A Prong 2, Step 2B : The claim does not recite any additional elements that are sufficient to integrate the judicial exceptions into a practical application or amount to significantly more than the judicial exception. As such, the claim is ineligible. Claim 6 Step 1 : A process, as above. Step 2A Prong 1 : The claim recites, inter alia: for each of the plurality of entities: identifying a record source for each of the set of corresponding entity records: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of identifying a record source, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. identifying a subset of records, of the set of corresponding entity records, based on the record source: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of identifying a subset of records, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. identifying each of the fields of the subset of records that matches one of the N entry nodes, thereby identifying K entity-specific entry nodes, wherein K is less than or equal to N: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of identifying fields in records that match nodes, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. Step 2A Prong 2, Step 2B : The claim does not recite any additional elements that are sufficient to integrate the judicial exceptions into a practical application or amount to significantly more than the judicial exception. As such, the claim is ineligible. Claim 7 Step 1 : A process, as above. Step 2A Prong 1 : The claim recites, inter alia: for each of the plurality of entities: determining a total number of fields of the set of corresponding entity records: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining a total number of fields, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. determining that the total number of fields is above a field threshold: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining that a number of fields is above a threshold, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. generating a training vector by aggregating each of the K entity-specific entry vectors in response to the determination: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of aggregating vectors into a training vector, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. Step 2A Prong 2, Step 2B : The claim does not recite any additional elements that are sufficient to integrate the judicial exceptions into a practical application or amount to significantly more than the judicial exception. As such, the claim is ineligible. Claim 8 Step 1 : A process, as above. Step 2A Prong 1 : The claim recites, inter alia: wherein the K entity-specific entity vectors are aggregated by summing each of the entity-specific entry vectors of the K entity-specific entry vectors: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of aggregating vectors through summation, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper or is a mathematical concept. Step 2A Prong 2, Step 2B : The claim does not recite any additional elements that are sufficient to integrate the judicial exceptions into a practical application or amount to significantly more than the judicial exception. As such, the claim is ineligible. Claim 9 Step 1 : The claim recites a method; therefore, it is directed to the statutory category of a process. Step 2A Prong 1 : The claim recites, inter alia: identifying a set of the plurality of fields of the entity record that each matches one of the N entry nodes, thereby identifying K entity-specific entry nodes, wherein K is less than or equal to N;: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of identifying fields in records that match a node in a graph, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can practically and mentally identify a node in a graph that contains specific field information. identifying, from a plurality of entry vectors, K entity-specific entry vectors corresponding to the K entity-specific entry nodes, wherein each of the plurality of entry vectors includes M entry values, and wherein each of the M entry values for an entry vector represents an importance of a corresponding node to an entry node for the entry vector: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of identifying vectors that correspond to nodes, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can practically and mentally create a vector of information that identifies the importance of a node in a graph using entry vectors. generating an entity signature vector by aggregating the K entity-specific entry vectors: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of aggregating or combining vectors, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can practically and mentally combine vectors into one vector. Step 2A Prong 2 : The claim does not recite any additional limitations which integrate the abstract idea into a practical application. Specifically, the additional elements consist of “ storing database data comprising N entry nodes to a graph database that includes M nodes, wherein M is greater than N ”, “ receiving an entity record including a plurality of fields ”, “ inputting the entity signature vector into the machine learning model ”, and “ receiving an entity classification for the entity record as an output from the machine learning model ”. The additional element of “ receiving an entity classification for the entity record as an output from the machine learning model ” amounts to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the generic machine learning model is broadly used to output or generate a classification for the entity record. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer ( see MPEP § 2106.05(f)). The additional elements “ storing database data comprising N entry nodes to a graph database that includes M nodes, wherein M is greater than N ”, “ receiving an entity record including a plurality of fields ”, and “ inputting the entity signature vector into the machine learning model ” are insignificant extra-solution activities required for any uses of the abstract ideas ( see MPEP § 2106.05(g)). Thus, even when viewed individually and as an ordered combination, these additional elements do not integrate the abstract idea into a practical application and the claim is thus directed to the abstract idea. Step 2B : Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional element of “ receiving an entity classification for the entity record as an output from the machine learning model ” amounts to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the generic machine learning model is broadly used to output or generate a classification for the entity record. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer ( see MPEP § 2106.05(f)). The additional elements “ storing database data comprising N entry nodes to a graph database that includes M nodes, wherein M is greater than N ”, “ receiving an entity record including a plurality of fields ”, and “ inputting the entity signature vector into the machine learning model ” are insignificant extra-solution activities required for any uses of the abstract ideas ( see MPEP § 2106.05(g)), and are well-understood, routine, conventional activities (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network”, “Storing and retrieving information in memory”). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible. Claim 10 Step 1 : A process, as above. Step 2A Prong 1 : The claim recites, inter alia: classifying a compound, tissue, gene, phenotype, genotype, or disease as having an effect on one or more compounds, tissues, genes, phenotypes, genotypes, or diseases: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of classifying the effects of compounds, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. Step 2A Prong 2, Step 2B : The claim does not recite any additional elements that are sufficient to integrate the judicial exceptions into a practical application or amount to significantly more than the judicial exception. As such, the claim is ineligible. Claim 11 Step 1 : A process, as above. Step 2A Prong 1 : The claim recites, inter alia: for each of the plurality of fields: determining an entry node, of the N entry nodes, that matches a field of the plurality of fields: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining a node that matches a field, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. Step 2A Prong 2, Step 2B : The claim does not recite any additional elements that are sufficient to integrate the judicial exceptions into a practical application or amount to significantly more than the judicial exception. As such, the claim is ineligible. Claim 12 Step 1 : A process, as above. Step 2A Prong 1 : The claim recites, inter alia: identifying a first subset of database nodes and a second subset of the M nodes that are linked together in a terminology database: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of identifying linked nodes, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. merging the first subset of database nodes and the second subset of the M nodes: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of merging nodes, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. Step 2A Prong 2, Step 2B : The additional element of “ accessing a graph generated from a database comprising a plurality of database nodes of a plurality of database node types and a plurality of database edges of a plurality of database edge types ” is insignificant extra-solution activity required for any uses of the abstract ideas ( see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network”, “Storing and retrieving information in memory”). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible. Claim 13 Step 1 : A process, as above. Step 2A Prong 1 : The claim recites, inter alia: for each entity-specific entry vector: identifying an entity-specific entry vector, of the set of K entity-specific entry vectors, that was generated using an entity-specific entry node of the K entity-specific entry nodes: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of identifying vectors that were generated by a specific node, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. Step 2A Prong 2, Step 2B : The claim does not recite any additional elements that are sufficient to integrate the judicial exceptions into a practical application or amount to significantly more than the judicial exception. As such, the claim is ineligible. Claim 14 Step 1 : A process, as above. Step 2A Prong 1 : The claim recites the abstract ideas of the preceding claims from which it depends. Step 2A Prong 2, Step 2B : The additional element of “ providing a treatment to a subject associated with the entity record based on the entity classification ” is insignificant extra-solution activity required for any uses of the abstract ideas ( see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(i); “Immunizing a patient against a disease”). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible. Claim 15 Step 1 : A process, as above. Step 2A Prong 1 : The claim recites, inter alia: updating a treatment plan to a subject associated with the entity record based on the entity classification: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of updating a treatment plan, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. Step 2A Prong 2, Step 2B : The claim does not recite any additional elements that are sufficient to integrate the judicial exceptions into a practical application or amount to significantly more than the judicial exception. As such, the claim is ineligible. Claim 16 Step 1 : A process, as above. Step 2A Prong 1 : The claim recites the abstract ideas of the preceding claims from which it depends. Step 2A Prong 2, Step 2B : The additional element of “ performing tests on a subject associated with the entity record based on the entity classification ” is insignificant extra-solution activity required for any uses of the abstract ideas ( see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(i); “Detecting DNA or enzymes in a sample”). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible. Claim 17 Step 1 : A process, as above. Step 2A Prong 1 : The claim recites the abstract ideas of the preceding claims from which it depends. Step 2A Prong 2, Step 2B : The additional element of “ performing tests on an entity by: generating a test entity record to record the test results ” is insignificant extra- solution activity required for any uses of the abstract ideas ( see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(i); “Detecting DNA or enzymes in a sample”). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible. Claims 18, 23, and 24 Claims 18, 23, and 24 recite a computer program product (step 1: a manufacture) using a non-transitory computer-readable medium and a computer to perform the steps of claims 1-3, respectively, which by MPEP 2106.05(f) (“apply it”) cannot integrate an abstract idea into a practical application or provide significantly more than the abstract idea by itself, and are thus rejected for the same reasons set forth in the rejection of claims 1-3, respectively. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-15 AIA Claim s 1, 4-7, 9, 11, 13, and 18 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Rogynskyy et al. (US 20200372075 A1, hereinafter “Rogyn”) . Regarding claim 1 , Rogyn discloses [a] method for generating a machine learning model using a graph database, the method comprising : ([0036]; “The present disclosure relates to systems and methods for constructing a node graph based on electronic activity” ; and Abstract; and [0043]; “the system can identify trends and behaviors that may be determined through machine learning techniques otherwise not tracked by the managers, thereby providing reports that may otherwise not be generated by the managers” ; and [0310]; “It should be appreciated that the system 200 can be configured to generate, maintain, user or otherwise access keyword ontology or one or more machine learning models trained on keywords, clusters or test or other documents to build the master list of keywords” ) storing a graph database comprising (1) M nodes of a plurality of node types and (2) a plurality of edges of a plurality of edge types; ([0038]; “The node graph can include a plurality of nodes and a plurality of edges between the nodes indicating activity or relationships that are derived form a plurality of data sources that can include one or more types of electronic activities” ) receiving, for a plurality of entities, a plurality of entity records, each with a plurality of fields and a known classification; ([0041]; “As shown... the first tier, the system. aggregates electronic activities from one or more data source providers. At the second tier, the system extracts information from the aggregated electronic activities and one or more systems of record of one or more data source provides to construct and maintain a node graph including the plurality of nodes and edges indicating the connections between the nodes” ; and [0052]; “The node profile can include one or more field-value pairs that represent the node” ; and [0310]) identifying N entry nodes of the M nodes that each match one of the plurality of fields, wherein N is less than M; ([0171]; “In certain embodiments, the node pairing engine 240 can determine that a first node reports to a second node based on monitoring electronic activity exchanged between the two nodes as well as electronic activity that includes both nodes." The system can determine a first node, which may then be connected to a second, non-entry node. The nodes may be a record. ) for each entry node of the N entry nodes, ([0171]) generating a propagated entry vector having M entry values, ([0199]; “The feature vector can be an array of feature values that is associated with the electronic activity. The feature vector can include each of the features that were extracted or identified in the electronic activity by the feature extraction engine 310.' The system may create a feature vector for all activity data. Because the nodes correspond to the records retrieved from the source provides, the feature vectors may be equal to the number of total records, or nodes ) wherein each of the M entry values represents an importance of a corresponding node to the entry node; ([0520]; “The data source provider can assign the priority level, score, rank, or weight to each of the matching rules” ; and [0526]; “The electronic activity linking engine 250 can select the one or more record objects from the plurality of candidate record objects based on the priority level used to select or identify each of the of candidate record objects." ; and [0199]) for each of the plurality of entities: ([0041]) identifying each of the fields of a set of corresponding entity records that matches one of the N entry nodes, ([0041 and 0199]) thereby identifying K entity-specific entry nodes, wherein K is less than or equal to N; ([0665]; “The record object manager can add entries identifying record or object data structures of one or more systems of record that includes data identifying the same relationship between the first and the second entity." - There may be an entity associated with each record object ) identifying a set of K entity-specific entry vectors corresponding to the K entity-specific entry nodes; ([0201]; "The feature vector can be used to match electronic activities to record objects of one or more systems of record." The vectors may be used for record objects, which will also contain the entity data ; and [0199]) generating a training vector by aggregating each of the K entity-specific entry vectors; and ([0041]; “The system can be configured to receive and aggregate electronic activities identifying one or more nodes." ; and [0189]; “"The record object manager 255 can function as a systems of record object aggregator that is configured to aggregate data points from many systems of record, calculate the contribution score of each data point, and a timeline of the contribution score of each of those points." ; and [0462]; “An individual or member node can be an electronic representation of a user.. or any other entity that may have an account or an identifier that the data processing system can generate a node profile for." The entities contained in the nodes may generate a new node profile, which will have its own vector associated with it. ; and [0199]) training the machine learning model using the training vectors and the known classifications ([0319]; “The machine learning based filter can automatically establish, based on the training set, features, weights or other criteria that indicate whether or how an electronic activity should be tagged." ; and [0310]). Regarding claim 4 , the rejection of claim 1 is incorporated and Rogyn further discloses for each of the plurality of entities: determining that a number of identified K entity-specific entry nodes is above an entry node threshold; and ([0581]; “If a predetermined number or threshold of values have a frequency count that satisfies a predetermined threshold, the field can have a lower rarity score than another field into which none of the values have a frequency count that exceeds the predetermined threshold." - The system may determine if the field is rare or not based on a threshold ) generating a training vector by aggregating the K entity-specific entry vectors in response to the determination ([0041], [0189] and [0462]). Regarding claim 5 , the rejection of claim 1 is incorporated and Rogyn further discloses determining an objective node of the N entry nodes, the objective node corresponding to the classification; ([0216]; “The matching model 340 can use natural networks, nearest neighbor classification or other modeling approaches to classify the electronic activity based on the feature vector." ; and [0199]) for each of the plurality of entities: ([0041]) identifying a number of the fields of the set of corresponding entity records that matches the objective node; ([0454]; “"It should be appreciated that matching processes described above can be based on objective data that is parsed from electronic activities” ) determining if the identified number of fields that match is above an objective threshold; and ([0580]; “The node graph generation system 200 can compare each match score between the electronic activity and the node profile to a match score threshold to determine whether the electronic activity is to be matches to the node profile” ) generating a training vector by aggregating each of the K entity-specific entry vectors in response to the determination ([0041], [0189] and [0462]). Regarding claim 6 , the rejection of claim 1 is incorporated and Rogyn further discloses for each of the plurality of entities: ([0041]) identifying a record source for each of the set of corresponding entity records; ([0195]; "The electronic activity linking engine 250 can use metadata to identify a data source provider associated with an ingested electronic activity and identify corresponding system of record.") identifying a subset of records, of the set of corresponding entity records, based on the record source; and ([0510]; “The data processing system 9300 can include semi-global rules that are applied to electronic activities from a subset of the data source providers.” ; and [0171]) identifying each of the fields of the subset of records that matches one of the N entry nodes, ([0171]]) thereby identifying K entity-specific entry nodes, wherein K is less than or equal to N ([0665]). Regarding claim 7 , the rejection of claim 1 is incorporated and Rogyn further discloses for each of the plurality of entities: ([0041]) determining a total number of fields of the set of corresponding entity records; ([0287]; “In some embodiments, the completeness of the system of record ca be based on the ratio of the total number of populated standard fields to the total number of unpopulated standard fields” ) determining that the total number of fields is above a field threshold; and ([0107]; “In some embodiments, the threshold number can be based on a percentage of another value, such as a total number of nodes belonging to the same domain and also having the phone number beginning with the same number of digits." ; and [0288]; “In some embodiments, the health score can be based on the total count of the fields that are populated or just the total count of the standard fields that are populated.” ) generating a training vector by aggregating each of the K entity-specific entry vectors in response to the determination ([0041], [0189] and [0462]). Regarding claim 9 , Rogyn discloses [a] method for categorizing entity records using a machine learning model, the method comprising: ([0036]; and [0310]) storing database data comprising N entry nodes to a graph database that includes M nodes, wherein M is greater than N; ([0171]) receiving an entity record including a plurality of fields; ([0041], [0052] and [0310]) identifying a set of the plurality of fields of the entity record that each matches one of the N entry nodes, ([0041]) thereby identifying K entity-specific entry nodes, wherein K is less than or equal to N; ([0665]) identifying, from a plurality of entry vectors, ([0199]) K entity-specific entry vectors corresponding to the K entity-specific entry nodes, ([0665]) wherein each of the plurality of entry vectors includes M entry values, ([0171]) and wherein each of the M entry values for an entry vector represents an importance of a corresponding node to an entry node for the entry vector; ([0199], [0520] and [0526]) generating an entity signature vector by aggregating the K entity-specific entry vectors; ([0041], [0189] and [0462]) inputting the entity signature vector into the machine learning model; and ([0310]; and [0391]) receiving an entity classification for the entity record as an output from the machine learning model ([0236]; “"In some embodiments, the output array 1208 can include one or more record objects that can be possible matches for the electronic activity” ; and [0664]; “The present disclosure relations to systems and methods of maintaining confidence scores of entity associates derived from systems of record” ; and [0668]; “In some embodiments, the record object manager can identify an entity associated with each record object” ). Regarding claim 11 , the rejection of claim 9 is incorporated and Rogyn discloses wherein identifying the N entry nodes that match one of the plurality of fields comprises: ([0041]) for each of the plurality of fields: ([0041]) determining an entry node, ([0171]) of the N entry nodes, ([0171]) that matches a field of the plurality of fields ([0041]). Regarding claim 13 , the rejection of claim 9 is incorporated and Rogyn discloses further comprising identifying the set of K entity-specific entry vectors corresponding to the K entity-specific entry nodes by: ([0202]) for each entity-specific entry vector: ([0041], [0189]; and [0462]) identifying an entity-specific entry vector, of the set of K entity-specific entry vectors, that was generated using an entity-specific entry node of the K entity-specific entry nodes ([0114]; “in some embodiments, the node graph generation system 200 can be configured to generate, maintain and update an array of domain names that belong to the same company or entity.” ; and [0229]; “Each value in the matched record object array 1202 can include an indication of one of the record objects that was matched using the matching strategies. For example, the matched record object arrays1202 can include an array UIDs associated with each of the record objects that were matched by the record object identification engine 315..." The arrays may be identified using the UIDs” ). Regarding claim 18 , it is a computer program product claim corresponding to the steps of claim 1, and is rejected for the same reasons as claim 1 . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-aia AIA 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 2, 3, 8, 10, 12, 14-17, 23, and 24 are rejected under 35 U.S.C. § 103 as being obvious over Rogyn in view of Ozeran et al. (US 20200381087 A1, hereinafter “Ozeran”). Regarding claims 2 and 23 , the rejection of claims 1 and 18 are incorporated and Rogyn further discloses identifying a set of the corresponding entity records that matches one of the N entry nodes by: ([0041]; and [0199]) for each entity record in the set of corresponding entity records: ([0085]; “The system 200 can match the third electronic activity to the node profile corresponding to the node profile representation 662” ; and [0041]) determining a corresponding entity record, of the set of corresponding entity records, ([0041]) and an entry node, of the N entry nodes, that are linked together ([0335]; “Furthermore, the node graph generation system 200 can further establish links, connections or relationships between member node profiles based on electronic activities These established links, connections or relationships and the corresponding node profiles from the node graph generated by the node graph generation system 200.” ; and [0310]). Rogyn fails to explicitly disclose but Ozeran discloses a terminology database ([0271]; “FIG. 10 is an exemplary ontological graph database 122 for viewing links between different dictionaries (databases of concepts) that may be interlinked through a universal dictionary lookup in order to carry out the normalizing stage 70 in FIG. 5” and [0299]; “The data fields in the data field portion 3404 can follow a standardized terminology and/or standardized formatting system, which can allow the clinical trial to be search and/or matched to a patient” ). Rogyn and Ozeran are analogous art because both are concerned with lexical analysis in databases. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in lexical analysis to combine the terminology database of Ozeran with the method of Rogyn to yield to the predictable result of determining a corresponding entity record, of the set of corresponding entity records, and an entry node, of the N entry nodes, that are linked together in a terminology database . The motivation for doing so would be to allow the system to be linked together by the terminology in the data fields of the records (Ozeran; [0271 and 0279]). Regarding claims 3 and 24 , the rejection of claims 1 and 18 are incorporated and Rogyn further discloses identifying K entity-specific entry nodes by: for each of the plurality of entities ([0041]; and [0199]) determining a subset of the set of corresponding entity records ([0564]; “The data processing system 9300 can identify a first subset of record objects based on one or more tags assigned to the electronic activity” ) with a subset of fields ([0564]) identifying each of the subset of fields of the set of corresponding entity records that matches one of the N entry nodes, ([0552]; “The system can further determine a confidence score for the tag classifying the two node profiles based on how confident the system is in the prediction that the two node profiles have a personal relationship” ; and [0564]) thereby identifying K entity-specific entry nodes, wherein K is less than or equal to N ([0665]). Rogyn fails to explicitly disclose but Ozeran discloses that were generated during a specified time period ([0294]; “In some embodiments, the flow 3200 can include generating the report 2382 at predetermine time point and/or as new information about a patient or trial becomes available.” ). The motivation to combine Rogyn and Ozeran is the same as discussed above with respect to claim 2. Regarding claim 8 , the rejection of claim 1 is incorporated and Rogyn discloses wherein the K entity-specific entity vectors are aggregated ([0041], [0189] and [0462]) each of the entity-specific entry vectors of the K entity-specific entry vectors ([0041], [0189] and [0462]). Rogyn fails to explicitly disclose but Ozeran discloses summing ([0336]; “For patient-level classification, the flow500 can include accumulating the similarities per document using a linear sum of the similarities, which can gather evidence per organization per document." ). The motivation to combine Rogyn and Ozeran is the same as discussed above with respect to claim 2. Regarding claim 10 , the rejection of claim 9 is incorporated and Rogyn discloses wherein the classifications performed by the machine learning model include classifying ([0041], [0310]). Rogyn fails to explicitly disclose but Ozeran discloses classifying a compound, tissue, gene, phenotype, genotype, or disease as having an effect on one or more compounds, tissues, genes, phenotypes, genotypes, or diseases ([0133]; “The analytics module 136 can, in general, use available data to indicate a diagnosis, predict progression, predict treatment outcomes, and/or suggest or select an optimized treatment plan... based on the specific disease state, clinical data, and/or molecular data of each patient” ; and [0247]). The motivation to combine Rogyn and Ozeran is the same as discussed above with respect to claim 2. Regarding claim 12 , the rejection of claim 9 is incorporated and Rogyn discloses adding a new database to the graph database by: accessing a graph generated from a database comprising a plurality of database nodes of a plurality of database node types and a plurality of database edges of a plurality of database edge types; identifying a first subset of database nodes and a second subset of the M nodes that are linked together [[in a terminology database]] ; and merging the first subset of database nodes and the second subset of the M nodes ([0038], [0355]; and [0280]). Rogyn fails to explicitly disclose but Ozeran discloses a terminology database ([0271]; “FIG. 10 is an exemplary ontological graph database 122 for viewing links between different dictionaries (databases of concepts) that may be interlinked through a universal dictionary lookup in order to carry out the normalizing stage 70 in FIG. 5” and [0299]; “The data fields in the data field portion 3404 can follow a standardized terminology and/or standardized formatting system, which can allow the clinical trial to be search and/or matched to a patient” ). The motivation to combine Rogyn and Ozeran is the same as discussed above with respect to claim 2. Regarding claim 14 , the rejection of claim 9 is incorporated and Rogyn discloses the entity record based on the entity classification ([0114], [0229] and [0310]). Rogyn fails to explicitly disclose but Ozeran discloses providing a treatment to a subject associated with the entity record ([0133]). The motivation to combine Rogyn and Ozeran is the same as discussed above with respect to claim 2. Regarding claim 15 , the rejection of claim 9 is incorporated and Rogyn fails to explicitly disclose but Ozeran discloses updating a treatment plan to a subject associated with the entity record based on the entity classification ([0145]; “Trial metadata 210 can be used to view, update, and sort data corresponding to clinical trials” ; and [0133]; and [0247]). The motivation to combine Rogyn and Ozeran is the same as discussed above with respect to claim 2. Regarding claim 16 , the rejection of claim 9 is incorporated and Rogyn discloses the entity record based on the entity classification ([0114], [0229] and [0310]). Rogyn fails to explicitly disclose but Ozeran discloses performing tests on a subject ([0187]; and [0284]). The motivation to combine Rogyn and Ozeran is the same as discussed above with respect to claim 2. Regarding claim 17 , the rejection of claims 9 and 16 are incorporated and Rogyn fails to explicitly disclose but Ozeran discloses generating a test entity record to record the test results ([0187]; and [0184]). The motivation to combine Rogyn and Ozeran is the same as discussed above with respect to claim 2. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to Brent Hoover whose telephone number is (303)297-4403. The examiner can normally be reached Monday - Friday 9-5 MST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Kawsar can be reached at 571-270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /BRENT JOHNSTON HOOVER/Primary Examiner, Art Unit 2127 Application/Control Number: 18/551,483 Page 2 Art Unit: 2127 Application/Control Number: 18/551,483 Page 3 Art Unit: 2127 Application/Control Number: 18/551,483 Page 4 Art Unit: 2127 Application/Control Number: 18/551,483 Page 5 Art Unit: 2127 Application/Control Number: 18/551,483 Page 6 Art Unit: 2127 Application/Control Number: 18/551,483 Page 7 Art Unit: 2127 Application/Control Number: 18/551,483 Page 8 Art Unit: 2127 Application/Control Number: 18/551,483 Page 9 Art Unit: 2127 Application/Control Number: 18/551,483 Page 10 Art Unit: 2127 Application/Control Number: 18/551,483 Page 11 Art Unit: 2127 Application/Control Number: 18/551,483 Page 12 Art Unit: 2127 Application/Control Number: 18/551,483 Page 13 Art Unit: 2127 Application/Control Number: 18/551,483 Page 14 Art Unit: 2127 Application/Control Number: 18/551,483 Page 15 Art Unit: 2127 Application/Control Number: 18/551,483 Page 16 Art Unit: 2127 Application/Control Number: 18/551,483 Page 17 Art Unit: 2127 Application/Control Number: 18/551,483 Page 18 Art Unit: 2127 Application/Control Number: 18/551,483 Page 19 Art Unit: 2127 Application/Control Number: 18/551,483 Page 20 Art Unit: 2127 Application/Control Number: 18/551,483 Page 21 Art Unit: 2127 Application/Control Number: 18/551,483 Page 22 Art Unit: 2127 Application/Control Number: 18/551,483 Page 23 Art Unit: 2127 Application/Control Number: 18/551,483 Page 24 Art Unit: 2127 Application/Control Number: 18/551,483 Page 25 Art Unit: 2127 Application/Control Number: 18/551,483 Page 26 Art Unit: 2127 Application/Control Number: 18/551,483 Page 27 Art Unit: 2127 Application/Control Number: 18/551,483 Page 28 Art Unit: 2127 Application/Control Number: 18/551,483 Page 29 Art Unit: 2127 Application/Control Number: 18/551,483 Page 30 Art Unit: 2127 Application/Control Number: 18/551,483 Page 31 Art Unit: 2127 Application/Control Number: 18/551,483 Page 32 Art Unit: 2127 Application/Control Number: 18/551,483 Page 33 Art Unit: 2127 Application/Control Number: 18/551,483 Page 34 Art Unit: 2127 Application/Control Number: 18/551,483 Page 35 Art Unit: 2127 Application/Control Number: 18/551,483 Page 36 Art Unit: 2127 Application/Control Number: 18/551,483 Page 37 Art Unit: 2127 Application/Control Number: 18/551,483 Page 38 Art Unit: 2127
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