DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-20 are pending.
Claims 1-20 are rejected.
Claims 1, 5-6, 11, and 15-16 are objected to.
Priority
Applicant’s claim for the benefit of a prior-filed application, U.S. Non-provisional App. No. 16/590,426 filed 02 Oct. 2019, under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 16/590,426, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application.
Claims 2 and 12, and dependent claims 3-6 and 13-16 recite “….determining, as a function of the causal gene data, one or more lifestyle factors”. However, the disclosure of App. No. ‘426 does not provide any support for determining life style factors as a function of the causal gene data, and there is no mention of lifestyle factors in App. No. ‘426.
Claims 8 and 18 recite “…generating the report…comprises generating the report…using a large language model”. However, the disclosure of App. No. ‘426 does not provide any support for using a large language model for any purpose, let alone for generating a report in App. No. ‘426.
Therefore, the effective filing date of claims 1, 7, 9-11, 17, and 19-20 are 02 Oct. 2019, and the effective filing date of claims 2-6, 8, 12-16, and 18 is 12 April 2023.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 18 July 2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the list of cited references was considered in full by the examiner.
Drawings
The drawings filed 12 April 2023 are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description:
#412 in FIG. 4
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claims 1, 5-6, 11, and 15-16 are objected to because of the following informalities:
Claims 1 and 11 recite “outputting…contains at least a genotypic node; and generating…including the at least a genotypic node”. To increase clarity and use consistent language, claim 1 should be amended to recite “outputting…contains at least a genotypic causal node; and generating…including the at least a genotypic causal node”.
Claims 5 and 15 recite “generating one or more positive lifestyle factor…”, which is a grammatical error and should recite “…one or more positive lifestyle factors…”.
Claims 6 and 16 recite “generating one or more negative lifestyle factor…”, which is a grammatical error and should recite “…one or more negative lifestyle factors…”.
Appropriate correction is required.
Claim Interpretation
Claims 5-6 and 15-16 recite “negative lifestyle factor” and “positive lifestyle factor”. Applicant’s specification at para. [0099] defines a “negative lifestyle factor” to be a lifestyle factor that negative impacts the health of a user, and “positive lifestyle factor” to be a lifestyle factor that positively impacts the health of a user. The terms will be interpreted accordingly.
Claim Rejections - 35 USC § 112(b)
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.
Claims 7-8 and 17-18 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention.
Claims 7-8 and 17-18 are indefinite for recitation of “the disease states”. Claims 1 and 11, from which claims 7-8 and 17-18 depend, recite “a plurality of genotypic causal nodes, wherein each genotypic causal node includes a disease state” (i.e. a plurality of disease states for the causal nodes), “a determined disease state” in the outputting step, and “disease states” that are part of the data structure in the final generating step. As a result, it is not clear if “the disease states” are referring to the disease states corresponding to the causal nodes, the determined disease state, and/or the disease states of the data structure of the causal model. Clarification is requested.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The Supreme Court has established a two-step framework for this analysis, wherein a claim does not satisfy § 101 if (1) it is “directed to” a patent-ineligible concept, i.e., a law of nature, natural phenomenon, or abstract idea, and (2), if so, the particular elements of the claim, considered “both individually and as an ordered combination,” do not add enough to “transform the nature of the claim into a patent-eligible application.” Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016) (quoting Alice, 134 S. Ct. at 2355). Applicant is also directed to MPEP 2106.
Step 1: The instantly claimed invention (claims 1 and 11 being representative) is directed to a system and method for generating a genotypic causal model of a disease state. Therefore, the instantly claimed invention falls into one of the four statutory categories. [Step 1: YES]
Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in in Prong Two if the recited judicial exception is integrated into a practical application of that exception.
Step 2A, Prong 1: Under the MPEP § 2106.04, the Step 2A (Prong 1) analysis requires determining whether a claim recites an abstract idea, law of nature, or natural phenomenon.
Claims 1 and 11 recite the following steps which fall under the mathematical concepts and/or mental processes groupings of abstract ideas:
generating a first machine learning model including a causal graph, wherein generating the machine learning model further comprises:
generating, using a first feature learning algorithm, a plurality of genotypic causal nodes, wherein each genotypic causal node includes a disease state and a gene combination correlated with the disease state;
outputting at least a path in the causal graph from inputs in the genetic sequence to a determined disease state, wherein the at least a path contains at least a genotypic node; and
generating a causal model, as a function of the at least a path in the causal graph including the at least a genotypic node, wherein the causal model comprises a data structure describing disease states and causal gene data.
The identified claim limitations falls into one of the groups of abstract ideas of mathematical concepts and/or mental processes for the following reasons. In this case, generating a machine learning model including a causal graph containing a plurality of causal nodes, wherein generating uses a first feature learning algorithm encompasses using machine learning models such as linear regression or k-means. As such, the steps of generating a machine learning model is recited at a high level of generality, and includes embodiments in which the training process only requires minimizing errors for output of a regression model and/or a clustering algorithm to determine associations between nodes of a causal graph, such that it could be practically performed in the mind. Outputting a path in the causal graph from inputs in the genetic sequence to a disease state can be practically performed in the mind by analyzing the causal graph to identify a path which links the input genetic sequence to an output of a disease. Last, generating a causal model comprising a data structure describing disease states and causal gene data, as a function of the at least a path in the causal graph involves analyzing the determined path in the graph to determine associations between disease states and gene data within the genotypic causal nodes of the path, and drawing a graph of the model via pen and paper. That is other, than reciting these steps are carried out by a computing device. Nothing in the claims precludes the steps from being practically performed in the mind. See MPEP 2106.04(a)(2) III.
The steps of generating a machine learning model including a causal graph, comprising generating, using a first feature learning algorithm, a plurality of genotypic causal nodes as claimed, and generating a causal model as a function of the at least path in the causal graph further recite a mathematical concept. As discussed above, generating a machine learning model including a causal graph using a first feature learning algorithm encompasses using a clustering algorithm (i.e. computing distances between points) or linear regression (i.e. weighted addition) to identify associations between genes and diseases, which amounts to a textual equivalent to performing mathematical calculations. Furthermore, generating the machine learning model comprising a causal graph and generating a causal model describing disease states and gene data as a function of the at least a path in the causal graph, encompasses generating mathematical correlations between disease states and genes as described in Applicant’s specification at para. [0028]-[0034], and thus further amount to a textual equivalent of a mathematical relationship. This is analogous to the mathematical concept of organizing information and manipulating information through mathematical correlations in Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). Therefore, these limitations recite a mathematical concept. See MPEP 2106.04(a)(2) I.
Dependent claims 2-8 and 12-18 further recite an abstract idea and/or further limit the abstract idea of claims 1 and 11 identified above. Claims 2 and 12 further recite the mental process of determining, as a function of the causal gene data, one or more lifestyle factors. Dependent claims 3 and 13 further recite the mental process and mathematical concept of using a lifestyle machine learning model to determine the one or more lifestyle factors, which encompasses using a linear regression model to determine the lifestyle factors correlated with gene data. Dependent claims 4-6 and 14-16 further recites the mathematical concept of training the lifestyle factor machine learning model using the lifestyle factor training data and generating one or more positive and negative lifestyle factors as a function of the lifestyle factor machine learning model. Dependent claims 7-8 and 17-18 further recite the mental process of generating a report describing the disease states and causal gene data. Therefore, claims 1-20 recite an abstract idea. [Step 2A, Prong 1: YES]
Step 2A: Prong 2: Under the MPEP § 2106.04, the Step 2A, Prong 2 analysis requires identifying whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluating those additional elements to determine whether they integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application for the following reasons.
Claims 2-3, 7, 12-13, and 17 do not recite any elements in addition to the judicial exception.
The additional elements of claims 1 and 11 include:
a computing device; and
receiving a genetic sequence comprising a series of genes identified in a nucleotide sequence of a chromosomal nucleic acid of a human subject as input.
The additional elements of claims 4-6, 10, 14-16, and 20 include:
receiving lifestyle factor training data comprising a plurality of sets of causal gene data correlated to a plurality of sets of lifestyle factors (claims 4 and 14);
receiving lifestyle factor training data comprising a plurality of sets of causal gene data correlated to a plurality of sets of positive lifestyle factors (claims 5 and 15);
receiving lifestyle factor training data comprising a plurality of sets of causal gene data correlated to a plurality of sets of negative lifestyle factors (claims 6 and 16); and
receiving the genetic sequence from a user database (claims 10 and 20).
The additional elements of claims 8 and 18 include:
using a large language model.
The additional element of claims 9 and 19 include:
displaying the causal model to the user.
The additional elements of using a computing device to carry out the abstract idea, to receive data, and display data in claims 1, 4-6, 9-11, 14-16, and 19-20 only serve to use the computer as a tool to carry out the abstract idea and in its ordinary capacity to receive information. The courts have found the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
The additional elements of receiving data in claims 1, 4-6, 10-11, 14-16, and 20 and displaying the causal model in claims 9 and 19 only serve to collect data for use by the abstract idea and to output information generated by the abstract idea, respectively, which amounts to insignificant extra-solution activity that does not integrate the recited judicial exception into a practical application. See MPEP 2106.05(g).
Last, regarding the additional element of using a large language model to perform the abstract idea of generating a report in claims 8 and 18, the claims provide no details regarding how the “large language model” operates such that there is some improvement in deep learning algorithms. Applicant’s specification defines a “large language model” as a deep learning algorithm that can recognize, summarize, translate, predict and/or generate text, and that the deep learning algorithm may be implemented as a neural network. Therefore, the “large language model” only serves to generally link the abstract idea to the technological environment of deep learning or neural networks which does not integrate the recited judicial exception into a practical application. See MPEP 2106.05(h). Similarly, this additional element amounts to mere instructions to apply the exception using a computer as a tool. See MPEP 2106.05(f).
Therefore, the additionally recited elements merely invoke computers as a tool, generally link the abstract idea to a technological environment and/or amount to insignificant extra-solution activity and, as such, the claims as a whole do no integrate the abstract idea into practical application. Thus, claims 1-20 are directed to an abstract idea. [Step 2A, Prong 2: NO]
Step 2B: In the second step it is determined whether the claimed subject matter includes additional elements that amount to significantly more than the judicial exception. See MPEP § 2106.05.
The claims do not include any additional steps appended to the judicial exception that are sufficient to amount to significantly more than the judicial exception for the following reasons.
Claims 2-3, 7, 12-13, and 17 do not recite any elements in addition to the judicial exception. The additional elements of claims 1, 4-6, 8-11, 14-16,18-20 are outlined above.
The additional elements of using a computing device to carry out the abstract idea, to receive data, and display data in claims 1, 4-6, 9-11, 14-16, and 19-20, and additionally using a large language model (i.e. a deep learning model or neural network) in claims 8 and 18 only serve to use the computer as a tool to carry out the abstract idea and in its ordinary capacity to receive information. The courts have found the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
Further regarding the additional element in claims 8 and 18 of using a large language model to perform the abstract idea of generating a report, MPEP 2106.05(h) states a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application, citing
Therefore, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself. [Step 2B: NO]
Therefore, the instantly rejected claims are not drawn to eligible subject matter as they are directed to an abstract idea without significantly more. For additional guidance, applicant is directed generally to applicant is directed generally to the MPEP § 2106.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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.
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, 7, 9-11, 17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kramer (2017) in view of Gligorijevic (2016).
Cited references:
Kramer et al., Leveraging network analytics to infer patient syndrome and identify causal genes in rare disease cases, 2017, BMC Genomics, 18(Suppl 5):551, pg. 1-23 (cited in IDS filed 18 July 2023); and
Gligorijevic et al., Large-Scale Discovery of Disease-Disease and Disease-Gene Associations, 2016, Scientific Reports, 6:32404, pg. 1-12.
Regarding claims 1 and 11, Kramer discloses a method for identifying causal genes of diseases using network analytics (Abstract), comprising the following steps:
Kramer discloses generating a large-scale phenotype-disease-gene network in the form of a directed acyclic graph (i.e. a causal graph) based on literature-curated findings, databases, and curated clinical information (pg. 16, col. 2, para. 2). Kramer discloses generating disease nodes including a disease state and one or more genes correlated with the disease state (i.e. a plurality of genotypic causal nodes) (pg. 16, col. 2, para. 2, e.g. gene-disease (GD) edges, each gene associated with 6.8 diseases on average; FIG. 2; pg. 19, col. 1, para. 1).
Kramer discloses receiving next-generation sequencing data of a subject supplied as a VCF file to trim down a gene list and obtain variant-affected genes (i.e. the sequence data comprises genes) (pg. 16, col. 1, para. 2; pg. 21, col. 2, para. 3; Fig. 1, e.g. “variant-affected genes).
Kramer discloses taking the set of genes, in addition to a set of phenotypes, as input and determining a path in the network from the input genes to a set of diseases D correlated or caused by the input genes (pg. 18, col. 1, para. 3; Fig. 3).
Kramer discloses generating a causal model, based on the identified path in the causal graph (Fig. 2; pg. 18, col. 1, para. 3 to col. 2, para. 1, e.g. result of algorithm, which identifies paths, is a list of diseases from D with one or more causally associated genes from G), wherein the causal model comprises a data stricture describing disease states and causal gene data (Fig. 2, e.g. see Fig. 2A, e.g. graph causal model with disease and associated gene and FIG. 2B, e.g. see table data structure with disease and associated genes and variants).
Further regarding independent claims 1 and 11¸ Kramer does not disclose the following:
First, regarding claim 1, Kramer does not explicitly disclose a system comprising a computing device configured to perform the steps of the method. However, the courts have held that broadly providing an automatic or mechanical means to replace a manual activity which accomplished the same result is not sufficient to distinguish over the prior art. See MPEP 2144.04 III.
Further regarding claims 1 and 11, Kramer does not disclose the causal graph is a machine learning model that was generated using a first feature learning algorithm. Instead, Kramer discloses the causal graph, including diseases with correlated genes, was generated using literature-curated findings, databases, and curated clinical information (pg. 16, col. 2, para. 2), as discussed above.
However, Gligorijevic discloses a method for discovering disease-gene associations (Abstract), which comprises using a DiseaseAndGens2Diseases model, to learn disease and gene vector representations simultaneously using a training data set (pg. 4, para. 3; pg. 5, para. 3-5; Figure 2), and representing diseases and genes as nodes with edges crated based on disease-gene associations (i.e. the causal graph created using machine learning) (pg. 10, para. 3). Gligorijevic further discloses machine learning allows for learning associations in an unsupervised manner, without the need for expensive labeling and annotation efforts (pg. 4, para. 1-3).
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the method of Kramer to have generated the causal graph as a machine learning model using a first feature learning algorithm according to the method of Gligorijevic, discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Kramer with Gligorijevic in order to identify associations between diseases and genes without using expensive labeling and annotation efforts, as shown by Gligorijevic (pg. 4, para. 1-3), thus reducing the time and costs to generating a causal graph in Kramer, which relies on expert curated findings (i.e. expensive annotation efforts) (Kramer: pg. 16, col. 2, para. 2). This modification would have had a reasonable expectation of success given Kramer generates a causal graph with disease gene associations, such that the machine learning method of Gligorijevic for identifying disease gene associations and a causal graph is applicable to the method of Kramer.
Regarding the dependent claims:
Regarding claims 7 and 17, Kramer discloses generating a report describing the disease states and causal gene data (Fig. 2b, e.g. see table with “Disease”, “Gene”, “Classification”, and “Causal” columns indicating which genes are causal to which disease).
Regarding claims 9 and 19, Kramer further discloses a user interface that displays the causal model to the user (Fig. 2, e.g. User interface showring map and results after running PDR algorithm).
Regarding claims 10 and 20¸ Kramer further discloses receiving the sequencing data of genes in the form of a VCF file (Fig. 1; pg. 16, col. 1, para. 2).
Kramer does not disclose the sequencing data is received from a user database.
However, Gligorijevic further discloses knowledge on genes for understanding the onset and path of disease progression is hidden in vast electronic health record databases of patients (pg. 3, para. 1), and that EHRs store information concerning all stages of patient care, including past medical history, medications, procedures, immunizations, and diagnostic findings (pg. 1, para. 1). Gligorijevic further discloses discharge records in EHRs of a patient include genes associated with diseases of the patient (pg. 6, para. 6-10).
It would have been prima facie obvious, to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the method of Kramer to have received genetic sequence information from an EHR of a patient (i.e. a user database) as shown by Gligorijevic above. One of ordinary skill in the art would have been motivated to further combine the methods of Kramer and Gligorijevic in order to provide information on genes useful for understanding the onset and progression of diseases of patients, as shown by Gligorijevic (pg. 3, para. 1), thus facilitating the generation of causal model for a given patient in Kramer. This modification would have had a reasonable expectation of success given EHRs contain gene information and diagnostic findings, and thus genetic sequence information in EHRs can be used in the method of Kramer, which receives a genetic sequence as input.
Therefore, the invention is prima facie obvious.
Claims 2-6, 8, 12-16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Neumann (2021) in view of Kenneth (2021).
Cited references:
Neumann US 2021/0104330 A1; and
Neumann (referred to as Kenneth), US 2021/0295977 A1
Regarding claims 2 and 12, Neumann discloses a system comprising a computer device, configured to perform steps for generating a genotypic causal model of a disease state (Abstract; claim 1) and a method comprising the steps for generating a genotypic causal model of a disease state (Abstract; claim 11), wherein the steps include the following:
Neumann discloses generating a machine-learning model including a causal graph (claim 1; [0003]-[0004]), wherein generating the machine learning model comprises: generating, using a first feature learning algorithm, a plurality of genotypic causal nodes, wherein each genotypic causal node includes a disease state and a gene combination correlated with the disease state (claim 1; [0004]).
Neumann discloses receiving a genetic sequence comprising a series of genes identified in a nucleotide sequence of chromosomal nucleic acid of a human subject as input (claim 1; [0003]).
Neumann discloses outputting at least a path in the causal graph from inputs in the genetic sequence to outputs at a determined first symptomatic node, which includes a disease state (i.e. a path from inputs in the genetic sequence to a determined disease state) (claim 1), wherein the at least a path contains at least a genotypic node (claim 1; [0004]).
Neumann discloses generating a causal model as a function of the at least a path in the causal graph including the at least a genotypic node and the at least a linked symptomatic node (claim 1), wherein the causal model comprises a description of one or more possible disease states and genetic causes (i.e. causal gene data) as represented by nodes (i.e. a graph data structure) ([0069]).
Further regarding claims 2-6 and 12-16, Neumann does not disclose the following limitations:
Regarding claims 2-3 and 12-13, Neumann does not disclose determining, as a function of the causal gene data, one or more lifestyle factors using a lifestyle factor machine-learning model
Regarding claims 4-6 and 14-16, Neumann does not disclose receiving lifestyle factor training data comprising a plurality of sets of causal gene data correlated to a plurality of sets of positive and negative lifestyle factors; training the lifestyle factor machine learning model using the lifestyle factor training data; and generating one or more positive and negative lifestyle factors as a function of the lifestyle factor machine learning model.
However, regarding claims 2-6 and 12-16, Kenneth discloses a method for generating lifestyle change recommendations based on biological extractions comprising genetic changes of genes that contribute to disease (Abstract; [0017], e.g. biological extraction is an element of physiological data [0027]; [0050]-[0051], e.g. physiological data includes genetic changes). Kenneth discloses generating a lifestyle intervention (i.e. lifestyle factor) as a function of the biological extraction comprising gene changes using a machine learning model (i.e. a lifestyle factor machine-learning model (Abstract; [0110]; FIG. 5).
Kenneth further discloses receiving training data for the lifestyle factor machine-learning model, wherein the training data correlates biological extraction/physiological data (i.e. the causal gene data) to lifestyle intervention combinations and behaviors (i.e. causal gene data correlated to a plurality of sets of lifestyle factors) ([0075]; [0088]). Kenneth further discloses the statistical correlations may indicative a positive and/or negative association, and additionally using training data including biological extraction to negative lifestyle behaviors ([0068]; [0088], e.g. training data with negative lifestyle behaviors), demonstrating the causal gene data may be negatively and/or positively correlated to the lifestyle factors in the training data (i.e. positive and negative lifestyle factors). Kenneth further discloses training the lifestyle factor machine learning algorithm using the training data ([0073], e.g. machine learning uses training data to generate an algorithm; [0074]-[0075]; FIG. 1). Kenneth further discloses using the trained machine learning model to generate negative lifestyle behaviors ([0088]) and using the trained machine learning model to generate lifestyle intervention combinations that alleviate negative lifestyle behaviors (i.e. positive lifestyle factors) ([0093]). Kenneth further discloses that significant constitutional improvements in a subject can result from lifestyle modifications ([0022]).
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method and system of Neumann to have trained a lifestyle factor machine learning model using the claimed lifestyle factor training data, and determined one or more positive or negative lifestyle factors as a function of the causal gene data, using a lifestyle factor machine learning model as shown by Kenneth above. One of ordinary skill in the art would have been motivated to combine the methods of Neumann and Kenneth in order to generate lifestyle modifications that result in significant constitutional improvements in the subject, as shown by Kenneth ([0022]). This modification would have had a reasonable expectation of success given Kenneth uses causal gene data as input into the machine learning model, and Neumann identifies causal gene data associated with a subject, such that the machine learning model of Kenneth is applicable to the data generated by Neumann.
Regarding claims 8 and 18, Neumann further discloses generating the causal model comprises generating a report describing one or more possible disease states and genetic causes ([0069]).
Further regarding claims 8 and 18, Neumann does not disclose the report is generated using a large language model.
However, Kenneth further discloses a language processing module that uses a natural language processing classification algorithm that derives statistical relationships between input and output terms and detects correlated values in data, such as between categories of physiological data and prognostic labels ([0069]; [0075]).
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the method of Neumann to have generated the report describing disease states and genetic causes to have used a natural language processing algorithm as shown by Kenneth ([0069]; [0075]), based on the application of the known technique of natural language processing shown by Kenneth to the known method of generating a report between disease states and genetic causes of Neumann. One of ordinary skill in the art would recognize that the natural language processing algorithm of Kenneth is applicable to the generation of the report describing disease states and genetic causes, given Kenneth discloses natural language processing Is used to generate relationships between inputs and outputs such as physiological data (i.e. genetic data as discussed above) and prognostic labels (e.g. a disease labels). Furthermore, one of ordinary skill in the art would have recognized applying the natural language processing algorithm of Kenneth to the generation of the report of Neumann would have resulted in an improved method of generating a report by automating the process.
Therefore, the invention is prima facie obvious.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1 and 11 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-18 of U.S. Patent No. 11,636,951 B2.
Although the claims at issue are not identical, they are not patentably distinct from each other because
Reference claims 1 and 10 recite the limitations of instant claims 1 and 11. Although the limitations are not identical the limitation of reference claims 1 and 10 are narrower than those of the instant claims, and thus anticipate the instant claims.
Claims 7, 9, 17, and 19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-18 of U.S. Patent No. 11,636,951 B2, as applied to claims 1 and 11 above, further in view of Kramer (2017).
Kramer et al., Leveraging network analytics to infer patient syndrome and identify causal genes in rare disease cases, 2017, BMC Genomics, 18(Suppl 5):551, pg. 1-23 (cited on IDS filed 18 July 2023)
Regarding instant claims 7, 9, 17, and 19, the reference claims disclose the limitations of instant claims 1 and 11, as applied above.
However, the reference claims do not disclose the limitations of instant claims 7, 9, 17, and 19. Instead, Kramer discloses these limitations.
Regarding instant claims 7 and 17, Kramer discloses generating a report describing the disease states and causal gene data (Fig. 2b, e.g. see table with “Disease”, “Gene”, “Classification”, and “Causal” columns indicating which genes are causal to which disease).
Regarding claims 9 and 19, Kramer further discloses a user interface that displays the causal model to the user (Fig. 2, e.g. User interface showring map and results after running PDR algorithm).
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the method of the reference claims to have generated a report describing the disease states and causal gene data and display the causal model via a user interface, as shown by Kramer above. One of ordinary skill in the art would have been motivated to combine the methods of the reference claims with Kramer in order to provide rank-ordered information of diseases and disease-gene-variation combinations, as shown by Kramer (Fig. 2), thus facilitating interpretation of results. This modification would have had a reasonable expectation of success given the reference claims similarly generate a causal model with a data structure describing disease states and genetic causes, which may be provided in the form of a report on a user interface according to Kramer.
Claims 10 and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-18 of U.S. Patent No. 11,636,951 B2, as applied to claims 1 and 11 above, further in view of Gligorijevic (2016).
Gligorijevic et al., Large-Scale Discovery of Disease-Disease and Disease-Gene Associations, 2016, Scientific Reports, 6:32404, pg. 1-12.
Regarding instant claims 10 and 20, the reference claims disclose the limitations of instant claims 1 and 11, as applied above.
Further regarding instant claims 10 and 20, the reference claims do not disclose the genetic sequence is received from a user database.
However, Gligorijevic further discloses knowledge on genes for understanding the onset and path of disease progression is hidden in vast electronic health record databases of patients (pg. 3, para. 1), and that EHRs store information concerning all stages of patient care, including past medical history, medications, procedures, immunizations, and diagnostic findings (pg. 1, para. 1). Gligorijevic further discloses discharge records in EHRs of a patient include genes associated with diseases of the patient (pg. 6, para. 6-10).
It would have been prima facie obvious, to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the method of the reference claims to have received genetic sequence information from an EHR of a patient (i.e. a user database) as shown by Gligorijevic above. One of ordinary skill in the art would have been motivated to further combine the methods of the reference claims and Gligorijevic in order to provide information on genes useful for understanding the onset and progression of diseases of patients, as shown by Gligorijevic (pg. 3, para. 1), thus facilitating the generation of causal model for a given patient in the reference claims. This modification would have had a reasonable expectation of success given EHRs contain gene information and diagnostic findings, and thus genetic sequence information in EHRs can be used in the method of the reference claims, which receives a genetic sequence as input.
Claims 2-6, 8, 12-16, and 18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-18 of U.S. Patent No. US 11,636,951 B2, as applied to claims 1 and 11 above, further in view of Kenneth (2021).
Cited reference: Neumann (referred to as Kenneth), US 2021/0295977 A1
Regarding claims 2-6, 8, 12-16, and 18, the reference claims disclose the method and system of instant claims 1 and 11 as applied above.
However, the reference claims do not disclose the limitations of instant claims 2-6 and 12-16.
However, regarding claims 2-6 and 12-16, Kenneth discloses a method for generating lifestyle change recommendations based on biological extractions comprising genetic changes of genes that contribute to disease (Abstract; [0017], e.g. biological extraction is an element of physiological data [0027]; [0050]-[0051], e.g. physiological data includes genetic changes). Kenneth discloses generating a lifestyle intervention (i.e. lifestyle factor) as a function of the biological extraction comprising gene changes using a machine learning model (i.e. a lifestyle factor machine-learning model (Abstract; [0110]; FIG. 5).
Kenneth further discloses receiving training data for the lifestyle factor machine-learning model, wherein the training data correlates biological extraction/physiological data (i.e. the causal gene data) to lifestyle intervention combinations and behaviors (i.e. causal gene data correlated to a plurality of sets of lifestyle factors) ([0075]; [0088]). Kenneth further discloses the statistical correlations may indicative a positive and/or negative association, and additionally using training data including biological extraction to negative lifestyle behaviors ([0068]; [0088], e.g. training data with negative lifestyle behaviors), demonstrating the causal gene data may be negatively and/or positively correlated to the lifestyle factors in the training data (i.e. positive and negative lifestyle factors). Kenneth further discloses training the lifestyle factor machine learning algorithm using the training data ([0073], e.g. machine learning uses training data to generate an algorithm; [0074]-[0075]; FIG. 1). Kenneth further discloses using the trained machine learning model to generate negative lifestyle behaviors ([0088]) and using the trained machine learning model to generate lifestyle intervention combinations that alleviate negative lifestyle behaviors (i.e. positive lifestyle factors) ([0093]). Kenneth further discloses that significant constitutional improvements in a subject can result from lifestyle modifications ([0022]).
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method and system of the reference claims to have trained a lifestyle factor machine learning model using the claimed lifestyle factor training data, and determined one or more positive or negative lifestyle factors as a function of the causal gene data, using a lifestyle factor machine learning model as shown by Kenneth above. One of ordinary skill in the art would have been motivated to combine the methods of the reference claims and Kenneth in order to generate lifestyle modifications that result in significant constitutional improvements in the subject, as shown by Kenneth ([0022]). This modification would have had a reasonable expectation of success given Kenneth uses causal gene data as input into the machine learning model, and the reference claims identifies causal gene data associated with a subject, such that the machine learning model of Kenneth is applicable to the data generated by Neumann.
Further regarding instant claims 8 and 18, the reference claims do not explicitly disclose generating a report describing the disease states and causal gene data, and instead the reference claims only generate a data structure describing disease states and causal gene data. The reference claims further do not disclose the report is generated using a large language model.
However, Kenneth further discloses a language processing module that uses a natural language processing classification algorithm that derives statistical relationships between input and output terms and detects correlated values in data, such as between categories of physiological data and prognostic labels ([0069]; [0075]). Kenneth further discloses providing information about a user in the form of a report ([0060]).
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the method of the reference claims to have generated a report describing disease states and genetic causes using natural language processing algorithm as shown by Kenneth ([0069]; [0075]), based on the application of the known technique of generating a report and natural language processing shown by Kenneth to the known method of generating a data structure between disease states and genetic causes of Neumann. One of ordinary skill in the art would recognize that the report and natural language processing algorithm of Kenneth is applicable to the generation of the data structure describing disease states and genetic causes, given Kenneth discloses natural language processing Is used to generate relationships between inputs and outputs such as physiological data (i.e. genetic data as discussed above) and prognostic labels (e.g. a disease labels) and information may be provided in a report. Furthermore, one of ordinary skill in the art would have recognized applying the natural language processing algorithm of Kenneth to the generation of the report of Neumann would have resulted in an improved method of generating a report by automating the process.
Conclusion
No claims are allowed.
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/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685
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