DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Herein, “the previous Office action” refers to the Non-Final Rejection filed 1/3/2025.
Priority
As detailed on the Filing Receipt filed 11/29/2023, the instant application claims priority to as early as 10/26/2018. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 USC § 120 as follows:
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 USC § 112(a) 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, Provisional Application No. 62/751,233 (filed 10/26/2018), fails to provide adequate support or enablement in the manner provided by 35 USC § 112(a) for one or more claims of this application.
With respect to claims 1, 8, 15 and dependents thereof, the prior-filed application does not disclose at least the limitations of:
“having a first set of variants with a frequency below a predetermined threshold” (claim 1, lines 5-6; claim 8, lines 8-9; claim 15, lines 7-8);
“obtaining… self-reported data from the subject” (claim 1, line 21; claim 8, line 21; claim 15, line 20);
“determining… a set of self-reported characteristics of the subject based on the self-reported data” (claim 1, lines 22-23; claim 8, lines 22-23; claim 15, lines 21-22), wherein:
“the set… is determined by pre-processing the self-reported data” (claim 1, lines 24-25; claim 8, lines 24-25; claim 15, lines 23-24),
“the pre-processing includes image transformation on the self-reported data” (claim 1, line 26; claim 8, line 26; claim 15, line 25),
“the set of self-reported characteristics comprises personal information of the subject” (claim 1, line 27; claim 8, line 27; claim 15, line 26);
“obtaining a plurality of genomic routes… each… associated with an eligibility criteria and a routing criteria” (claim 1, lines 28-30; claim 8, lines 27-31; claim 15, lines 27-29);
“executing, by a query model… a first query on the eligibility criteria” (claim 1, lines 32-33; claim 8, lines 32-33; claim 15, line 31), wherein:
“the query model is a learning-to-rank model trained to predict that a genomic route is relevant to a query based on occurrences of terms in the first query in the routing criteria” (claim 1, lines 33-36; claim 8, lines 33-36; claim 15, lines 33-35);
“executing, by the query model, a second query on the routing criteria” (claim 1, lines 38-39; claim 8, line 37; claim 15, line 36);
“assigning, by a ranking model, a rank to each genomic route… based on a route-associated weight… [that] indicates a degree of relevancy… for the subject” (claim 1, lines 42-45; claim 8, lines 41-43; claim 15, lines 41-42);
“extracting other information associated with… selected genomic routes… [that] indicates at least one content personalized for the subject” (claim 1, lines 49-51; claim 8, lines 48-49; claim 15, lines 47-48).
With respect to claim 9, the prior-filed application does not disclose the limitation of:
“providing insight and/or content supporting a product, service, event or benefit associated with each of the selected one or more genomic routes to the subject” (lines 1-3).
With respect to claim 22, the prior-filed application does not disclose the limitation of:
“the first query is performed using a Boolean model” (lines 1-2).
Accordingly, claims 1-22 are not entitled to the benefit of the prior-filed applications.
Claims 1-22 are thus accorded the filing date of 10/28/2019.
Claim Status
Claims 1-22 are pending, and under examination.
Withdrawn Objections/Rejections
The objection to claim 1 is hereby withdrawn in view of Applicant’s amendment of the claim to resolve minor grammatical informalities.
Claim Objections
Claims 4, 8 and 15 are objected to because of the following informalities:
With respect to claim 4, the recited “for a second set loci” (line 3) should read, e.g., “for a second set of loci”.
Appropriate correction is required.
With respect to claims 8 and 15, the recited “sequencing data corresponding of a plurality” (claim 8, line 10; claim 15, line 9) should read, e.g., “corresponding sequencing data of a plurality” or “sequencing data corresponding to a plurality” as the recited language is grammatically incorrect.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 USC § 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.
Claim 21 is rejected under 35 USC § 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. This rejection is maintained from the previous Office action.
With respect to claim 21, there is uncertainty regarding scope of the recited term of “a ranking model” (line 10). It is unclear whether the recited ranking model is the same ranking model as recited in claim 1 (line 42) or a different ranking model. The Examiner suggests amendment to either “the ranking model” or “a second ranking model”, to clarify the nature of the model to which reference is made. For purposes of prosecution, the recited term is interpreted as “the ranking model”.
For the above reasons, the claim is indefinite. Appropriate amendment is required.
Response to Arguments - Claim Rejections Under 35 USC § 112
In the Remarks filed 6/7/2025, Applicant traverses the rejection under 35 USC § 112 and presents supporting arguments.
Applicant states that claim 21 has been amended, thereby obviating the rejection (pg. 13, para. 3). Claim 21 has not been amended, and the filed amendments do not obviate the rejection. Therefore, the rejection is maintained.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 USC §§ 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 USC § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 USC § 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 USC § 102(b)(2)(C) for any potential 35 USC § 102(a)(2) prior art against the later invention.
Claims 1-22 are rejected under 35 USC § 103 as being unpatentable over Kenedy et al (US 8,209,319; published 6/26/2012; previously cited), in view of Downs (WO 2016/040909; published 9/11/2015; previously cited), Pasaniuc et al (Nature Genetics 44(6): 631-635; published 5/20/2012; previously cited), Matsuguchi et al (US 2018/0089373; published 3/29/2018; previously cited), and Lashkari et al (Proceedings of the 2009 International Conference on Information Management and Engineering, pp. 385-389, IEEE Xplore; published 6/19/2009; previously cited). This rejection is maintained from the previous Office action and has been revised to address the amended claims (filed 6/7/2025).
Herein, italic text paraphrases relevant instant claim limitations for reference. Additionally, where instant claim limitations are indexed using a lettering and/or numbering scheme, relevant teachings of applied references are subsequently presented using a correspondent scheme. Hence, the limitations of claim 1 are indexed wherein the recited step of “performing a low-coverage whole genome sequencing” is labeled “b)”, and the teachings of Kenedy are subsequently indexed wherein teachings of particular relevance to this recited step are also labeled “b)”.
Claim 1 recites a method, comprising processing system-implemented steps of:
a) receiving a request for sequencing analysis of a biological sample of a subject;
b) performing a low coverage whole genome sequencing of the biological sample to obtain a set of reads, wherein:
1. the sequencing is performed on a first set of loci having a first set of variants with frequency below a predetermined threshold;
c) retrieving corresponding sequencing data of a plurality of biological samples associated with a plurality of subjects;
d) identifying, for each locus of the first set of loci, a first set of samples from the plurality of samples having a second variant with a same genetic sequence as at least one of the first set of variants located at the locus;
e) evaluating coverage of the set of reads;
f) performing statistical association for the first set of loci with the first set of biological samples of the set of reads having an observation;
g) determining an inference of a phenotype from the set of reads based on the statistical association;
h) identifying a set of genomic-data characteristics using at least part of the set of reads;
i) obtaining self-reported data from the subject;
j) determining self-reported characteristics, based on the self-reported data, wherein:
1. the set of self-reported characteristics are determined by pre-processing the self-reported data,
2. the pre-processing includes image transformation on the self-reported data, and
3. the set of self-reported characteristics comprises personal information of the subject;
k) obtaining a plurality of genomic routes from a data structure communicatively connected to the processing system, wherein:
1. each genomic route is associated with an eligibility criterion and a routing criterion for selection of a corresponding genomic route;
l) executing, by a query model, a first query on the eligibility criteria to obtain a set of genomic routes that satisfy the first query, wherein:
1. the first query includes the phenotype, and
2. the query model is a learning-to-rank model trained to predict that a genomic route is relevant to a query based on occurrences of query terms in the routing criteria;
m) executing, by the query model, a second query on the routing criteria for the set of genomic routes to obtain a subset of genomic routes that satisfy the second query, wherein:
1. the second query includes the phenotype, at least part of the self- reported characteristics, and at least part of the genomic-data characteristics;
n) assigning, by a ranking model, a rank to each genomic route of the subset based on a route-associated weight, wherein:
1. the weight is based on the first and second queries,
2. the rank indicates relevancy of each genomic route in the subset for the subject, and
3. the ranking model is trained to predict a probability of a genomic route ranking over another genomic route;
o) selecting one or more genomic routes from the subset based on the rank of each;
p) extracting other information associated with the one or more selected genomic routes, wherein:
1. the other information indicates at least one content personalized for the subject, and
2. the at least one content includes healthcare recommendation; and
q) providing the other information in response to the request for sequencing analysis.
With respect to claim 1, Kenedy discloses “a bioinformatics method… to generate… bioattribute combinations that co-associate with [a] query attribute” (Abstract), comprising:
b) “Complete… sequencing of an individual’s genome” (col. 3, line 38, Figure 8; see col. 54, lines 10-12) and “accessing a first set of genetic attributes associated with… [an] individual… comprising a first nucleotide sequence” (col. 44, lines 2-4) i.e., performing a whole genome sequencing of a sample to obtain a set of reads;
c) “accessing a second set of genetic attributes associated with… [a] group of individuals… comprising a second nucleotide sequence” (col. 44, lines 6-9), i.e., retrieving sequencing data corresponding to a plurality of biological samples associated with a plurality of subjects;
d) “identifying whether the first nucleotide sequence and the second nucleotide sequence are equivalent based on an equivalence rule… generating a determination indicating that the first set of genetic attributes… is identical to the second set… [and] storing the determination” (col. 44, lines 13-23), i.e., identifying samples from the plurality having a variant with a same genetic sequence as the individual sample;
f) “determin[ing] which attributes and combinations of attributes… are statistically related to [a] query attribute” (col. 11, lines 56-62; see Figure 8), wherein attributes may be “genetic attribute[s]: any… chromosome locus… [or] gene locus” (col. 5, lines 56-59);
g) “attribute expansion can be used to derive a set of lower resolution genetic attributes… As an example, 19 different nucleotide mutations have been identified… each of which can… result[] in clinical diagnosis of cystic fibrosis disease… presence of any… can be the basis for deriving a single lower resolution attribute of ‘CFCR gene with cystic fibrosis mutation’”, i.e., an inferred phenotype, “with a status value of {1=Yes}” (col. 15, lines 3-5 and 32-43), wherein “attribute expansion can… introduce[e] attributes of the correct resolution to maximize identities”, i.e., statistical associations, “between attribute profiles”, i.e., inferred phenotypes, “of a group of query-attribute-positive individuals” (col. 16, lines 57-61);
h) “processed genetic attributes... may be compared... SNP polymorphisms... and allele identity... can be processed by one or more of the methods herein to provide a limited comparison of the genetic content" (col. 7, lines 57-64), i.e., identifying genomic-data characteristics using at least part of the reads;
i/j) “accurate values for self-reported attributes are determined using a multipronged data collection approach… One example… is to employ a questionnaire that asks multiple different questions to acquire the same attribute” (col. 49, lines 19-20), wherein:
1. “values for an attribute can be… compared, cross-validated, deleted, filtered, adjusted, or averaged to help ensure storing accurate values for attributes… [which] can be performed during conversion and reformatting of data” (col. 49, lines 40-47), i.e., pre-processing,
3. “the identity of an individual”, i.e., personal information, “[is] linked… to their data” (pg. 51, lines 45-46);
k) “external databases… can be used to supply the data which constitutes the attributes (col. 11, lines 2-5), i.e., obtaining attributes from a data structure, wherein:
1. a later step of “ranking of the attribute combinations can… be based on whether certain attributes are present or absent in a particular attribute combination, what percentage of attributes… are modifiable, what specific modifiable attributes are present… and/or what types or categories of attributes… are present” (col. 22, lines 52-60), i.e., attribute- associated eligibility and routing criteria;
l) “preselection of individuals processed… based on particular values of attributes… Preselecting… based on possession of one or more specified attributes can serve to focus a query on the most representative population… [and] remov[e] irrelevant individuals” (col. 45, lines 42-50), wherein:
1. preselection can be performed based on particular values of attributes such as “disease status” (col. 45, line 47), i.e., phenotype, and
2. “perform[ing] statistical computations using the numerical frequencies of occurrence to obtain results (values) for strength of association between attributes and attribute combinations” (col 7, lines 4-7);
m) “query[ing] the system regarding which attributes are relevant to the specified query attribute” (col. 11, lines 37-38” thereby producing “[a] dataset… [which] contains only those attribute combinations determined to be predisposing towards the query attribute above a selected threshold of significant association for the individual” (col. 27, lines 24-29) i.e., querying attribute-associated criteria, wherein attributes can:
1. “be collectively equivalent to… phenotype” (col. 4, lines 21-23),
2. include “self-reported attributes” (col. 12, line 22), and
3. include “genetic attribute[s]” (col. 5, line 56);
n) “attributes can be rank-ordered based on… corresponding strength of association values” (col. 39, lines 3-4; Figure 20), i.e., assigned a rank based on an associated weight, wherein:
1. “ranking the attribute combinations can also based on whether certain attributes are present or absent” (col. 22, lines 52-55), i.e., additionally based on the disclosed preselection (first query) criterion,
2. “[a] corresponding strength of association value derived for a single attribute… indicate[s] that particular attribute’s contribution (or potential/predicted contribution)”, i.e., relevance, “toward predisposition to the query attribute” (col. 36, lines 15- 18), and
3. “[a] statistical computation engine… compute[s]… statistical results for strength of association (i.e., strength of association values)… statistical measures used to compute these statistical results may include… probability, absolute risk, relative risk,… odds (a.k.a. likelihood) and odds ratio” (col. 20, lines 26-28 and 31-35);
o) “Ranks (numerical rankings) assigned to attribute combinations… can likewise be subjected to inclusion, elimination, filtering, and evaluation”, i.e., selection, “based on a predetermined threshold… applied to rank, which can be specified by a user” (col. 21, lines 45-50);
p) “direct mapping of information in… databases to… attributes” (col. 10, lines 31-32), i.e., extracting associated information, wherein:
1. “datasets created by performing the above methods… can be used for… intelligent individual”, i.e., personalized, “destiny modification provided as predisposition predictions resulting from the addition or elimination of specific attribute associations” (col. 28, lines 8-9 and 14-16), and
2. “the methods… [can be] used as part of a web based health analysis and diagnostics system in which service providers utilize… attributes to provide services such as… insurance optimization (determination of recommended policies and amounts) and medication impact analysis” (col. 53, line 65 – col. 54, line 5), i.e., healthcare recommendations; and
q) “associations… are retrieved and displayed”, i.e., content is provided, “to the user” (col. 33, lines 17-18).
Kenedy defines an ‘attribute’ as “a quality, trait, characteristic, relationship, property, factor or object associated with or possessed by an individual” (col. 5, lines 53-55), and describes the purpose of the disclosed methodology as “determin[ing] those combinations of attributes that promote certain behaviors and traits such as success in sports, music, school, leadership, career and relationships” (col. 2, lines 58-61). In other words, determining attribute combinations that support particular benefits for a subject.
Kenedy also discloses “industrial applications pertaining to… use of the identified attributes, combinations of attributes, and strength of association of attributes with the query attribute in making a variety of decisions related to lifestyle, lifestyle modification, diagnosis, medical treatment… [and] possibilities for destiny modification”, including embodiments wherein “the methods…are used as part of a web based health analysis and diagnostics system in which service providers utilize… attributes to provide services” (col. 53, line 53 – col. 54, line 3).
The instant specification states that “As used herein… ‘genomic routes’ are pathways to insights and/or information supporting a product, service, event or benefit for a subject” (pg. 16, para. 0054). Analysis of “attribute combinations” (as that term is defined by Kenedy) is considered equivalent to analysis of “genomic routes” (as that term is defined by the instant disclosure).
Kenedy further discloses preselection of individuals based on possession of one or more specified attribute values (e.g., particular income, occupation, disease status, zip code or marital status) to focus a subsequent query on the most representative population and remove irrelevant individuals (col. 45, lines 42-50). The disclosed preselection based on specific attribute values is viewed as equivalent to the claimed process of executing a first query in addition to, and prior to, a main query (i.e., second query).
Additionally, Kenedy discloses that “Embodiments of the present invention can be used for… neural networks and self-learning systems… decision-based training systems; complex supervised learning systems” (col. 55, lines 60, 64; col. 56, lines). In this way, Kenedy discloses implementation of their methods using trained learning models (and, when applied to the task of ranking, learning-to-rank models).
Kenedy also discusses the failure of prior methods to determine attributes that predispose individuals to most disorders, behaviors, or traits due to an inability to detect low-frequency attribute combinations, and states that determining such combinations could improve individualized diagnoses and assist in choosing effective therapeutic regimens (col. 2, lines 45-61). In particular, Kenedy discloses that prior methods of analyzing genetic predispositions have suffered from a lack of sufficient resolution, since the majority of genetic variation in the human population occurs at frequency percentages below those of typically-assessed SNPs (col. 3, lines 19-32). Kenedy discloses that complete genome sequencing presents a solution to this deficiency, by capturing lower-frequency variants (col. 3, lines 38-41).
Kenedy does not specifically disclose performing low coverage sequencing on a set of loci having variants below a frequency threshold. Neither does Kenedy disclose receiving a request for sequencing analysis; pre-processing including image transformation; evaluating read coverage; or utilizing a model that predicts relevance based on occurrences of terms.
Downs discusses “systems and methods for carrying out medical testing” (Abstract), and teaches that “a genetic test… [may] includ[e] whole genome sequencing… [a] physician can inform the system of”, i.e., submit a request for, “the specific genetic tests to be carried out… The system can conduct the genetic testing … generating a test result… [which] can be transferred to the physician” (pg. 55, para. 00278). In this way, the teachings of Downs inherently address the deficiency of Kenedy regarding receiving a request for sequencing analysis, and further teach that receiving requests allows a system to automatically perform desired embodiments of the disclosed methods. Downs does not teach performing low coverage whole genome sequencing on a set of loci having variants below a predetermined frequency threshold; pre-processing including image transformation; evaluating read coverage or utilizing a model that predicts relevance based on occurrences of terms.
Pasaniuc discusses “Genome wide association studies (GWAS)”, and teaches use of “low-coverage sequencing” (pg. 1, Abstract) in such studies. Pasaniuc further teaches removal of “data at all SNPs covered at more than 4x” (pg. 7, ¶ 1), and that imputation accuracy is a function of read coverage (pg. 2, ¶ 4 – pg. 3, ¶ 1; pg. 10, Fig. 1). In this way, the teachings Pasaniuc address the deficiencies of Kenedy regarding performing low-coverage whole genome sequencing; and evaluating read coverage.
Additionally, Pasaniuc teaches that “extremely low-coverage sequencing (0.1x – 0.5x) captures almost as much of the common (>5%) and low-frequency (1-5%) variation across the genome as SNP arrays” (pp. 1-2, Abstract), and “association statistics obtained using ultra low-coverage sequencing data attain similar P-values… as genotyping arrays… [with] reductions in sample preparation and sequencing costs” (pg. 2, ¶ 1).
Pasaniuc does not particularly teach sequencing loci having variants below a predetermined frequency threshold; pre-processing including image transformation; or utilizing a model that predicts relevance based on occurrences of terms.
Matsuguchi discusses an integrated method of analyzing subject biologic data and medical history data to query a database of therapies and determine a subset for which the subject qualifies (Abstract). Matsuguchi teaches that biologic data may be generated by whole genome sequencing of a subject sample (pg. 14, para. 0108), and can be analyzed to identify one or more genomic aberrations that appear at a frequency of less than about 5% (pg. 4, para. 0015), i.e., variants with frequency below a predetermined threshold. Matsuguchi further teaches that medical history data can pertain to nutrition, habits, or exercise regimen of a subject (pg. 32, para. 0198), i.e., self-reported data.
Additionally, Matsuguchi teaches that collected medical records can be converted to an electronic or digital file format for efficient processing (pg. 29, para. 0180), can include digital images (pg. 32, para. 0198), and can be processed algorithmically to extract keywords or organize and label text sections (pg. 2, para. 0007; pp. 29-30, para. 0182). In this way, the teachings of Matsuguchi address the deficiency of Kenedy regarding pre-processing of self-reported data including image transformation. Matsuguchi does not teach utilizing a model that predicts relevance based on occurrences of terms as claimed.
Lashkari discusses “information retrieval” (pg. 385, Abstract) and teaches that “The Standard Boolean model, one of the earliest and simplest retrieval methods,… match[es] documents to a user ‘query’ or information request by finding documents that are ‘relevant’ in terms of matching the words in the query” (pg. 386, r. column). Lashkari further teaches that “a vector is used to represent each item or document in a collection. Each component of the vector reflects a particular concept… associated with the given document. The value assigned to that component reflects the importance of the term in representing… the document” (pg. 387, l. column).
Additionally, Lashkari discusses “Probabilistic models [which] attempt to estimate the probability that the user will find a particular document relevant. Retrieved documents are ranked by their odds of relevance -- the ratio of the probability that the document is relevant to the probability that the document is not relevant to the query” (pg. 388, l. column) and teaches “a simple way to calculate the weight of terms… calculate the frequency of them” (pg. 389, l. column). In this way, Lashkari is considered to teach utilizing a model that predicts relevance based on occurrences of terms as claimed.
With respect to claim 2, Kenedy discloses use of their method “for individual destiny modification”, wherein the method “is used to identify and report attributes that the individual may modify to increase or decrease their chances of having a particular attribute or outcome” (col. 34, lines 18-21; Figs. 25-26 and 29). Kenedy further discloses embodiments wherein “the methods… are used as part of a web based health analysis and diagnostics system in which service providers utilize pangenetic information (attributes) in conjunction with physical, situational, and behavioral”, e.g., self-reported, “attributes to provide services such as longevity analysis, insurance optimization (determination of recommended policies and amounts) and medication impact analysis” (col. 53, line 65 – col. 54, line 5), and “Embodiments… used for… disease and health management and assessment; genetic assessment and counseling… marketing and advertising” (col. 55, line 60; col. 56, lines 9-10, 36 and 42-43).
With respect to claim 3, as noted above, Kenedy discloses querying an attribute set using a first query (col. 11, lines 37-38; col. 27, lines 24-29), and ranking and/or prioritizing of attributes (col. 28, lines 23-29; col. 39, lines 3-4; Figure 20). Kenedy exemplifies use of their methods with various types of machine learning (i.e., trained) systems and models, including “neural networks and self-learning systems… classifier-based systems… [and] complex supervised learning systems” (col. 55, lines 64 – col. 56, line 2). One of ordinary skill in the art would understand this to teach use of such systems to perform data processing within the methods disclosed, i.e., ranking and/or prioritizing attributes.
With respect to claim 4, Kenedy discloses using STS marker-tagged DNA sequence data (col. 5, lines 33-34; col. 7, lines 34-36) to predict associated attribute profiles, i.e., inferred phenotypes (col. 4, lines 21-23; col. 16, lines 57-61). STS marker sequences are present at unique genomic loci, as would be understood by one of ordinary skill in the art, and thus Kenedy discloses analysis of multiple genomic loci to determine inferences of phenotype.
Pasaniuc teaches “using all data… off-target and on-target” (pg. 3) and discloses that “higher off-target coverage… leads to… A similar λGC {genomic control) value… for imputed data as compared to… typed data… [and] similar association statistics and effect sizes as compared to SNP arrays” (pg. 3), thus validating their conclusion: “genome-wide SNP genotypes can be inferred… using off-target data” (pg. 2). Pasaniuc thereby teaches that use of both off-target and on-target data has utility in imputing genotypes (and thereby making inferences of phenotype) using low-coverage sequencing data.
With respect to claim 5, Kenedy discusses limited coverage of total genetic information as a deficiency of prior methods, and states that “Genetic markers such as single nucleotide polymorphisms (SNPs) do not provide a complete picture of a gene’s nucleotide sequence or the total genetic variability of the individual… Other markers such as STS, gene locus markers and chromosome loci markers also provide very low resolution and incomplete coverage of the genome” (col. 3, line 19-24 and 35-37). Kenedy further teaches that analysis of complete genome, i.e., high coverage whole genome, sequencing data can remedy this deficiency (col. 3, lines 38-41).
Kenedy exemplifies various genetic attribute types that can be considered by their method, including any genome, chromosome locus, gene locus, SNP and STS (col. 5, lines 56-64). In this way, Kenedy teaches combined consideration of multiple genomic loci within on-target, off-target and high-coverage whole genome sequencing data as claimed.
With respect to claim 6, Kenedy discloses use of their method wherein the “[final] dataset can be presented in… a report which contains only those attribute combinations determined to be predisposing toward the query attribute above a selected threshold of significant association for the individual” (col. 27, lines 25-29), i.e., attributes amounting to phenotype will not be reported if they are not significantly associated with the query.
With respect to claim 7, Kenedy discloses use of their method wherein a “[final] dataset can be presented in… a report which contains only those attribute combinations determined to be predisposing toward the query attribute above a selected threshold of significant association for the individual” (col. 27, lines 25-29), i.e., attributes amounting to phenotype will be reported if they are significantly associated with the query.
Claims 8-14 recite substantively similar limitations to those of claims 1-7. The cited art is considered to apply to the substantively similar limitations of claims 8-14 in the same manner as detailed regarding those of claims 1-7. Claim 8 further recites unique limitations directed to computer hardware (“A system comprising: one or more processors; and memory… encoded with a set of instructions configured to perform” the claimed method, lines 1-4).
With respect to the unique limitations of claim 8 and dependents therefrom, Kenedy discloses “computer-based systems” (col. 17, line 23) for implementation of their methods.
Claims 15-20 recite substantively similar limitations to those of claims 1-6. The cited art is considered to apply to the substantively similar limitations of claims 8-14 in the same manner as detailed regarding those of claims 1-6. Claim 15 further recites unique limitations directed to a storage medium (“A non-transitory computer readable storage medium storing instructions that… cause the computing system to perform” the claimed method, lines 1-3).
With respect to the unique limitations of claim 15 and dependents therefrom, Kenedy discloses that “methods… of the present invention can be embodied on a computer-readable media (medium)… and program storage devices readable by a machine” (col. 53, lines 38-42). Kenedy further discloses “storage units” such as “electronic, magnetic, electromagnetic, optical, opto-magnetic and electro-optical storage” (col. 23, lines 15-27) for implementation of their methods.
With respect to claim 21, as noted above, Kenedy discloses that “[a] statistical computation engine… compute[s]… statistical results for strength of association (i.e., strength of association values)” (col. 20, lines 26-28), wherein “[a] corresponding strength of association value derived for a single attribute… indicate[s] that particular attribute’s contribution (or potential/predicted contribution) toward predisposition to the query attribute” (col. 36, lines 15-18) and “attributes can be rank-ordered based on… corresponding strength of association values” (col. 39, lines 3-4; Figure 20). In this way, Kenedy discloses execution of a query comprising ranking based on strengths of association with the query. This is equivalent to execution of a query performed in part by using a ranking model.
Pasaniuc teaches “imputation of untyped variants”, i.e., nucleotides for which there were no observations in the set of reads, “using… reference panels” (pg. 2, ¶ 4), and exemplifies use of reference panels comprising “polymorphic sites identified in the European samples of the 1000 Genomes Project” (pg. 3, ¶ 2), i.e., reference loci from population data of a second set of subjects, for imputation of “sequencing data from… individuals of European ancestry… from the International HIV Controllers Study (IHCS)… Swedish Schizophrenia Study (SCZ)… and Autism NIHM Controls Study (AUT)” (pg. 3, ¶ 2).
The imputation of missing values within of low-coverage sequencing data using reference loci, as taught by Pasaniuc, entails alignment of at least one read in part with the reference loci. Furthermore, “alignment” of reads is mentioned explicitly by Pasaniuc at pg. 4, ¶ 2.
Pasaniuc further teaches that “We observe high accuracy at ultra-low coverage when reference panels are used… for both common (>5% minor allele frequency), as well as low-frequency variants (1 to 5% minor allele frequency)” (pg. 2, ¶ 4 – pg. 3, ¶ 1).
With respect to claim 22, Lashkari discusses “information retrieval” (pg. 385, Abstract) and teaches that “The Standard Boolean model, one of the earliest and simplest retrieval methods,… match[es] documents to a user ‘query’ or information request by finding documents that are ‘relevant’ in terms of matching the words in the query” (pg. 386, r. column).
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have implemented receipt of requests for sequencing analysis, as taught by Downs, to enhance the sequencing analysis framework taught by Kenedy, because Downs teaches that receiving requests enables automated performance of sequencing analysis by a system (pg. 55, para. 00278). Said practitioner would have had a reasonable expectation of success because Kenedy and Downs both discuss performance of association analysis using whole genome sequencing data of a subject.
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have used low-coverage whole genome sequencing and evaluation of read coverage, as taught by Pasaniuc, to enhance the sequencing analysis framework taught by Kenedy, because Pasaniuc teaches advantages of low-coverage whole genome sequencing and imputation over other methods for the purpose of association analysis (pg. 2, ¶ 1), and teaches utility to evaluation of read coverage to assess confidence in imputed genotypes (pg. 2, ¶ 4 – pg. 3, ¶ 1; pg. 10, Fig. 1).
Additionally, said practitioner would have analyzed both off-target and on-target reads, as taught by Pasaniuc, because Pasaniuc teaches that analysis of both off-target and on-target data allows a user to impute missing values within low-coverage sequencing data and thus remedy a common analytical deficiency of cost-efficient low-coverage techniques (pg. 2, ¶ 1-2 and pg. 3, ¶ 2-3).
Additionally, said practitioner would have imputed missing values using population data, as taught by Pasaniuc, because Pasaniuc teaches that imputation using population reference panels provides missing values in a highly accurate manner (pg. 2, ¶ 4 – pg. 3, ¶ 1). Said practitioner would have had a reasonable expectation of success because Kenedy and Pasaniuc both discuss performance of association analysis using whole genome sequencing data of a subject.
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have implemented analysis of variants below a predetermined frequency threshold, as taught by Matsuguchi, to enhance the sequencing analysis framework taught by Kenedy, because Kenedy discloses that most predisposing human genetic variation occurs at low-frequency (col. 3, lines 19-32 and 38-41) while Matsuguchi teaches that low-frequency genetic variation data can be analyzed together with self-reported data to determine personalized therapies (Abstract; pg. 4, para. 0015; pg. 32, para. 0198).
Additionally, said practitioner would have implemented pre-processing including image transformation, as taught by Matsuguchi, because Matsuguchi teaches that such pre-processing enables efficient processing of various medical records including self-reported data (pp. 29-30, paras. 0180 and 0182; pg. 32, para. 0198). Said practitioner would have had a reasonable expectation of success because Kenedy and Matsuguchi both discuss analysis of subject data, including whole genome sequencing data and self-reported data, to determine individualized results.
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have implemented a model that predicts relevance based on occurrence of terms, as taught by Lashkari, to enhance the sequencing analysis framework taught by Kenedy, because Lashkari teaches that prediction of relevance based on occurrence of terms is an effective method of retrieving associated information based on a query (pg. 388, l. column; pg. 389, l. column).
Additionally, said practitioner would have implemented a Boolean model, as taught by Lashkari, because Lashkari teaches that using a Boolean model is a simple, routine method of query-based information retrieval (pg. 386, r. column). Said practitioner would have had a reasonable expectation of success because Kenedy and Lashkari both discuss query-based methods of searching sets of information.
In this way the disclosure of Kenedy, in view of Downs, Pasaniuc, Matsuguchi and Lashkari, makes obvious the limitations of claims 1-22. Thus, the invention is prima facie obvious.
Response to Arguments - Claim Rejections Under 35 USC § 103
In the Remarks filed 6/7/2025, Applicant traverses the rejections under 35 USC § 103 and highlights particular points of alleged distinction between the instant claim limitations and the teachings of the cited prior art.
Applicant highlights direction of Kenedy to processes of querying subject-associated data to obtain a plurality of attributes of interest, including subject traits/characteristics, for diagnosis of a particular disease, and alleges distinction of the associated teachings of Kenedy from the claimed process of querying eligibility of a set of genomic routes out of plurality of genomic routes for the subject based on genomic data and self-reported data (pg. 14, para. 1).
Regarding the recited term “genomic routes”, the instant specification states: “As used herein… ‘genomic routes’ are pathways to insights and/or information supporting a product, service, event or benefit for a subject” (pg. 16, para. 0054). Kenedy discloses embodiments wherein “the methods… are used as part of a web based health analysis and diagnostics system in which service providers utilize pangenetic information (attributes) in conjunction with physical, situational, and behavioral attributes to provide services such as longevity analysis, insurance optimization (determination of recommended policies and amounts) and medication impact analysis” (col. 53, line 65 – col. 54, line 5).
The disclosure in Kenedy of service implementations utilizing pangenetic attributes in conjunction with physical, situational, and behavioral attributes to, e.g., determine recommended insurance policies is viewed as reading on the claimed process of querying eligibility of a set of genomic routes out of plurality of genomic routes for the subject based on genomic data and self-reported data.
Applicant highlights disclosure in Kenedy of processes of ranking combinations of obtained attributes based on constitutive percentages of member attributes, and alleges distinction of the associated teachings of Kenedy from the claimed process of weighting (i.e., ranking) each genomic route from the set of genomic routes based on a first query and a second query, i.e., self-reported characteristics and genomic-data characteristics (pg. 14, para. 1).
Kenedy discloses preselection of individuals based on possession of one or more specified attribute values (e.g., particular income, occupation, disease status, zip code or marital status) to focus a subsequent query on the most representative population and remove irrelevant individuals (col. 45, lines 42-50). The disclosed preselection based on specific attribute values is viewed as equivalent to the claimed process of executing a first query in addition to, and prior to, a main query (i.e., second query).
Thus, the argument of distinction from the teachings of Kenedy is found unpersuasive and the rejections are maintained.
Conclusion
At this point in prosecution, no claims are allowed.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/T.C.S./Examiner, Art Unit 1685
/JESSE P FRUMKIN/Primary Examiner, Art Unit 1685 October 17, 2025
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