DETAILED ACTION
Notice of 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 .
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 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.
Withdrawal of Objections and Rejections
Applicant's response, filed 04/14/2026, has been fully considered.
In view of the amendment and remarks from 04/14/2026, the objection to the drawings, the objection to the claims and the rejection of the following claims are withdrawn:
claims 9 and 18 under 35 USC § 112(a);
claims 5-6, 8-10, 14 and 17-18 under 35 U.S.C. § 112(b);
claims 2, 7, 11, 16-17 and 20 under 35 U.S.C. § 101;
claims 1-3, 6-7, 10, 12, 15-16 and 19-20 under 35 U.S.C. § 102;
claims 11 and 17 under 35 U.S.C. § 103.
The following rejections and/or objections are either maintained or newly applied for claims 1, 3-6, 8-10, 12-15, 18-19 and 21-26. They constitute the complete set applied to the instant application. Herein, "the previous Office action" refers to the Non-Final Rejection of 01/14/2026.
Status of the Claims
Claims 2, 7, 11, 16-17 and 20 are canceled.
Claims 1, 10 and 19 are objected to.
Claims 1, 3-6, 8-10, 12-15, 18-19 and 21-26 are rejected.
Priority
This US Application 17/849,653 (06/26/2022) claims no priority herein, as reflected in the filing receipt mailed on 07/08/2022. The claims to the benefit of priority are acknowledged; and the effective filing date of claims 1, 3-6, 8-10, 12-15, 18-19 and 21-26 is 06/26/2022.
Information Disclosure Statement
No Information Disclosure Statement has been filed herein.
Claim objections
Claims 1, 10 and 19 are objected to because of the following informality related to grammar/punctuation. Appropriate correction is required.
A colon is missing after "comprise" (second wherein clause). Colons should begin lists in which list elements are separated by newlines.
A colon is missing after "represents" (third wherein clause). Colons should begin lists in which list elements are separated by newlines.
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, 3-6, 8-10, 12-15, 18-19 and 21-26 are rejected under 35 USC § 101 because the claimed inventions are directed to one or more Judicial Exceptions (JEs) without significantly more. Regarding JEs, "Claims directed to nothing more than abstract ideas..., natural phenomena, and laws of nature are not eligible for patent protection" (MPEP 2106.04 §I). Abstract ideas include mathematical concepts and procedures for evaluating, analyzing or organizing information, which are a type of mental process (MPEP 2106.04(a)(2)). Any newly recited portions are necessitated by claim amendment.
101 background
MPEP 2106 organizes JE analysis into Steps 1, 2A (Prong One & Prong Two), and 2B as analyzed below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter (MPEP 2106.03)?
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
Analysis of instant claims
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
The instant claims are directed to a system (claims 1, 3-6 and 8-9), a method (claims 10, 12-15 and 18) and a CRM (claims 19 and 21-26); each of which falls within one of the categories of statutory subject matter. [Step 1: claims 1, 3-6, 8-10, 12-15, 18-19 and 21-26: Yes]
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Background
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. MPEP § 2106.04(a)(2) further explains that abstract ideas are defined as:
• mathematical concepts (mathematical formulas or equations, mathematical relationships
and mathematical calculations) (MPEP 2106.04(a)(2)(I));
• certain methods of organizing human activity (fundamental economic principles or practices, managing personal behavior or relationships or interactions between people) (MPEP 2106.04(a)(2)(II)); and/or
• mental processes (concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) (MPEP 2106.04(a)(2)(III)).
Analysis of instant claims
With respect to the instant claims, under the Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the grouping of mathematical concepts (in particular mathematical relationships and formulas) and mental processes (in particular procedures for observing, analyzing and organizing information) as well as a law of nature or a natural phenomenon are as follows.
Mathematical concepts (in particular mathematical relationships and formulas) include:
• "for each pathogenic genetic variant in the current set of pathogenic genetic variants, determining, using a machine learning model in accordance with current values of a plurality of parameters of the machine learning model, a respective importance score for the pathogenic genetic variant based on characteristics of unstructured research publications from which the pathogenic genetic variant is determined" (independent claims 1, 10 and 19);
• "wherein the plurality of parameters of the machine learning model comprise (i) a first parameter representing a clinical effect, (ii) a second parameter representing a number of validations of a research study, (iii) a third parameter representing a size of the research study, (iv) a fourth parameter representing at least one of a p-value, a z-score, or a confidence interval, (v) a fifth parameter representing a variant prevalence, and (vi) a sixth parameter representing metadata of the research study" (independent claims 1, 10 and 19);
• "wherein the respective importance score represents (i) a level of importance of the pathogenic genetic variant to the Asian population and (ii) a level of contribution of the pathogenic genetic variant to the phenotype that the pathogenic genetic variant is linked to" (independent claims 1, 10 and 19);
• " ranking, using the machine learning model, the pathogenic genetic variants in the current set according to the respective importance scores" (independent claims 1, 10 and 19);
• "determine the respective importance score for the pathogenic genetic variant using a decision tree induction technique in accordance with the current values of the plurality of parameters of the machine learning model" (claims 8 and 25).
The claims identified above read on math. The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation and determined each element performed by mathematical operation. The step directed to “executing algorithms to identify publications, extract data and determine/rank scores related to the data extracted” requires mathematical techniques as the only supported embodiments because it describes the mathematical technique of adding numbers together in words (MPEP 2106.04(a)(2) pertains). Further support for the mathematical techniques used in the claims is provided in the specification at pg. 12-13, which discloses a "logic circuitry" implemented for identification of data in publications and scoring of data found. Thus, the recited terms correspond to verbal equivalents of mathematical concepts because they constitute actions executed by a group of mathematical steps in a form of a mathematical algorithm; thus mathematical concepts (MPEP 2106.04(a)(2)). A mathematical concept need not be expressed in mathematical symbols, because "words used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). MPEP 2106.04(a)(2) pertains.
Mental processes, defined as concepts or steps practically performed in the human mind such as steps of observations, evaluations, judgments, analysis, opinions or organizing information include:
• "for each of the plurality of unstructured research publications, analyzing content of the unstructured research publication to determine whether the respective genetic variant is classified as a pathogenic genetic variant according to the unstructured research publication, and in response to determining that the respective genetic variant is classified as the pathogenic genetic variant" (independent claims 1, 10 and 19);
• "(ii) determining, from the content of the unstructured research publication, a phenotype that the respective genetic variant is linked to" (independent claims 1, 10 and 19);
• "(iii) determining one or more characteristics of the unstructured research publication … wherein the characteristics of the unstructured research publications from which the pathogenic genetic variant is determined comprise a number of times that each of the unstructured research publication has been cited by other publications or other data sources" (independent claims 1, 10 and 19);
• "extracting, from the text of the unstructured research publication, a conclusion with respect to the respective genetic variant by using a text mining algorithm" (claims 3, 12 and 21);
• "determining whether the conclusion classifies the respective genetic variant as the pathogenic genetic variant" (claims 3, 12 and 21);
• "determining whether the second text classifies the respective genetic variant as the pathogenic genetic variant" (claims 4, 13 and 22); and
• "extracting, from the content of the unstructured research publication, an explanation of a biological reasoning behind the pathogenic genetic variant; and for each pathogenic genetic variant in the current set of pathogenic genetic variants, assigning a respective importance score to the pathogenic genetic variant based on the characteristics of the unstructured research publications from which the pathogenic genetic variant is determined and based on the explanation of the biological reasoning behind the pathogenic genetic variant" (claims 6, 15 and 24).
The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined to each cover performance either in the mind (i.e. concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) or because the method only requires a user to manually determine action based on an added number. Under the BRI, the recited limitations are mental processes because a human mind is also sufficiently capable of determining a variant as pathogenic based on data, determining a number of times that each of the unstructured research publication has been cited, updating a database (i.e. writing a data down) as the result comes in (i.e. automatically) and determining whether a text classifies the respective genetic variant as the pathogenic. The recited "extracting a conclusion from the text of research publications" is equivalent to reading papers and evaluating gathering information from papers.
Dependent claims 3-5, 9, 12-14, 18, 21-23 and 26 recite further steps that limit the judicial exceptions in independent claims 1, 10 and 19 and, as such, also are directed to those abstract ideas. For example, claims 3-4, 12-13 and 21-22 recite further details about the content of each unstructured research publication; claims 5, 14 and 23 recite further details about the one or more characteristics of each of the unstructured research publications; claims 9, 18 and 26 recite further details about the population-specific genomic reference.
Furthermore, the instant claims recite a natural correlation by correlating a pathogenic variant naturally found in the body with its important score ranking. (see MPEP 2106.04(b).I).
[Step 2A Prong One: claims 1, 3-6, 8-10, 12-15, 18-19 and 21-26: Yes ]
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Background
MPEP 2106.04(d).I lists the following example considerations for evaluating whether a judicial exception is integrated into a practical application:
An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
Analysis of instant claims
Instant claims 1, 9-10, 18-19 and 21-26 recite additional elements that are not abstract ideas:
• "one or more computer processors; and one or more storage devices storing instructions that, when executed by the one or more computer processors" (independent claim 1);
• "computer-implemented" (independent claim 10);
• "one or more non-transitory computer storage media" (claims 19 and 21-26); and
• " invoking, using an application program interface (API), a data crawler configured to automatically identify a plurality of unstructured research publications related to human genomes of the Asian population, wherein each of the plurality of unstructured research publications refers to a respective genetic variant of a plurality of genetic variants" (independent claims 1, 10 and 19);
• "automatically updating a variant database with the ranked pathogenic genetic variants to provide a population-specific genomic reference configured to filter genetic variants during genetic screening of individuals in the Asian population" (independent claims 1, 10 and 19);
• "extracting the second text from the one or more images using optical character recognition" (claims 4, 13 and 22); and
• "transmitting the ranked pathogenic genetic variants stored in the variant database to a chip designer for constructing the chip" (claims 9, 18 and 26).
Considerations under Step 2A, Prong Two
The recited limitations in claims 1, 3-6, 8-10, 12-15, 18-19 and 21-26 are interpreted as requiring the use of a computer. Hence, the claims explicitly recite steps executed by computers and therefore can be described as computer functions or instructions to implement on a generic computer. Further steps directed to additional non-abstract elements of a computing device/computer do not describe any specific computational steps by which the "computer parts" perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions.
The judicial exceptions in the claims are considered to perform the claimed abstract idea with a computer, which is not sufficient to integrate an abstract idea into a practical application (see MPEP 2106.05(f)); since steps that can be performed mentally and merely performing the mental process in a computer environment do not negate the fact that something that can be carried out in the human mind. See MPEP 2106.04(a)(2).III.C.
Claims directed to "invoking, using an application program interface (API), a data crawler …to identify a plurality of unstructured research publications" and "extracting the second text from the one or more images using optical character recognition" read on necessary data gathering and therefore correspond to insignificant extra-solution activity.
Claims directed to "automatically updating a variant database with the ranked pathogenic genetic variants" read on outputting and storing the data resulting from the judicial exception which corresponds to insignificant extra-solution activity.
Claims directed to "automatically updating a variant database with the ranked pathogenic genetic variants" also read on a generic "apply it" step because the claim recites an idea of a solution or outcome without any indication of how the judicial exception impacts or influences this step. There is no evidence to indicate details of exactly how the judicial exception is being integrated by the additional elements.
Claims directed to "transmitting" read on receiving or transmitting data over a network -Symantec, 838 F.3d at 1321 - MPEP 2106.05(a) pertains; which constitutes just necessary data gathering and therefore correspond to insignificant extra-solution activity.
Hence, these are mere instructions to apply the abstract idea using a computer and insignificant extra-solution activity and therefore the claims do not integrate that abstract idea into a practical application (see MPEP 2106.04(d) § I; 2106.05(f); and 2106.05(g)).
In Step 2A, Prong One above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs).
In this Step 2A, Prong Two immediately above claim steps and/or elements were identified as part of one or more additional elements. Additional elements are further discussed in Step 2B below.
Here in Step 2A, Prong Two, no additional step or element clearly demonstrates integration of the JE(s) into a practical application.
[Step 2A Prong Two: claims 1, 3-6, 8-10, 12-15, 18-19 and 21-26: No]
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during examination that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
Claims 1, 3-6, 8-10, 12-15, 18-19 and 21-26 recite a computer or computer functions, interpreted as instructions to apply the abstract idea using a computer, where the computer does not impose meaningful limitations on the judicial exceptions; which can be performed without the use of a computer (MPEP 2106.04(d) § I; and MPEP 2106.05(f)).
The computer-related elements or the general purpose computer and the machine learning model do not rise to the level of significantly more than the judicial exception. The claims state a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, which the courts have found to not provide significantly more when recited in a claim with a judicial exception (Alice Corp., 573 U.S. at225-26, 110 USPQ2d at 1984; see MPEP 2106.05(A)).
Further, the courts have found that transmitting/outputting data is well-understood, routine, and conventional functions of a computer when claimed in a generic manner or as insignificant extra-solution activity (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), Versa ta Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, as discussed in MPEP 2106.05(d)(Il)(i)).
With respect to the instant claims, the prior art review to Li ("DRUMS: a human disease related unique gene mutation search engine." Human mutation 32(10):E2259-E2265 (2011)) discloses that methods for gene variant detection, the integration of mutation data and its phenotypic (pg. E2259 Abstract) using the web-crawler layer (pg. E2260 para. 5); wherein the web-crawler extracts and updates the database automatically (pg. E2261 Fig. 1) is routine, well-understood and conventional in the art.
When the claims are considered as a whole, they do not integrate the abstract idea into a practical application; they do not confine the use of the abstract idea to a particular technology; they do not solve a problem rooted in or arising from the use of a particular technology; they do not improve a technology by allowing the technology to perform a function that it previously was not capable of performing; and they do not provide any limitations beyond generally linking the use of the abstract idea to a broad technological environment. See MPEP 2106.05(a) and 2106.05(h).
The instant claims constitute insignificant extra solution activity, and when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(g)). Hence, these elements, when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(d)).
[Step 2B: claims 1, 3-6, 8-10, 12-15, 18-19 and 21-26: No]
Conclusion: Instant claims are directed to non-statutory subject matter
For the reasons above, the claims in this instant application, when the limitations are considered individually and as a whole, are directed to an abstract idea and lack an inventive concept not clearly anything significantly more.
Response to applicant's remarks in regard to Claim Rejection 35 U.S.C. ~ 101
The Remarks of 04/14/2026 have been fully considered but are not persuasive for the reasons below:
Applicant asserts starting in pg. 16 para. 2:
As set forth in the previous response, the claimed invention is not merely an abstract idea implemented on a generic computer. Rather_ the claims recite specific improvements to the technical field of genomic data processing and bioinformatics particularly in the domain of population-specific genetic screening, through the use of an APJ-driven data crawler and a specialized machine learning model to dynamically evaluate unstructured research publications. This approach, which avoids the time-consuming, computationally expensive, and often inaccurate processes of conventional ,whole-genome sequencing or generic genotyping, reflects a concrete improvement to computer and genomic technology … As such, the claim recites a specific sequence of steps that reduce the computational burden of sequencing while overcoming the technical inaccuracies of genetic risk prediction algorithms. As a whole this ordered combination improves the computational efficiency and accuracy of computer systems performing genetic variant analysis … The Office has not provided any evidence that it is routine or conventional for a bioinformatics system to dynamically crawl unstructured text apply bibliographic weighting based on citation counts, and recursively evaluate a six-parameter machine learning model to build a population--specific filter for genetic screening … these systems can automatically mine "hundreds of thousands" of unstructured publications … requiring less computational overhead…
It is respectfully submitted that this is not persuasive because the recited judicial elements (i.e. improvement in data processing by an algorithm) and the type of data being analyzed cannot provide an improvement. The analysis at Step 2A, Prong 2, considers the claims as a whole, i.e., the additional elements in combination with the judicial exceptions (see MPEP 2106.05(a)), although the integration or improvement provided in the claim must flow from the additional elements and not the judicial exceptions to be considered persuasive.
Regarding the argued "steps that reduce the computational burden of sequencing", "automatically mining" large amount of data" and "reducing computational overhead", these are steps directed to additional non-abstract elements of a computing device/computer, however these do not describe any specific computational steps by which the "computer parts" perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer are used to implement these functions; which provides evidence that the claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Despite the argued improved performance, the judicial exceptions in the claims are considered to perform the claimed abstract idea with a generic computer, which is not sufficient to integrate an abstract idea into a practical application (see MPEP 2106.05(f)). There are no other additional elements that provide evidence of integration of the identified judicial elements into a practical application at Step 2A Prong 2.
Regarding the argument refereeing to a lack of evidence to support a "bioinformatics system to dynamically crawl unstructured text apply bibliographic weighting based on citation counts, and recursively evaluate a six-parameter machine learning model to build a population--specific filter for genetic screening," the Examiner would like to highlight that the argued elements are performed by the identified judicial exceptions (i.e. algorithms). Under Step 2B, conventionality is only evaluated for the additional elements that are part of the broadest reasonable interpretation of the claim. In this instant case, the conventionality of the identified additional elements in this instant application were described as routine and conventional as in Claim Rejections above.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter 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 pre-AIA 35 U.S.C. 103(a) 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.
A. Claims 1, 3, 5-6, 8, 10, 12, 14-15, 19, 21 and 23-25 are rejected under 35 U.S.C. 103(a) as being unpatentable over Dong ("iCAGES: integrated CAncer GEnome Score for comprehensively prioritizing driver genes in personal cancer genomes." Genome medicine 8(1):135 (2016)) in view of Horn "Automated extraction of mutation data from the literature: application of MuteXt to G protein-coupled receptors and nuclear hormone receptors." Bioinformatics 20(4):557-568 (2004)) in view of Mercier ("Senticite: An approach for publication sentiment analysis." arXiv preprint arXiv: 1910.03498 (2019)) in view of Monteiro ("Advanced Text Mining for Annotation of Genomic Variants. MS thesis. Universidade do Minho – Portugal (2018)) in view of in view of Böschen ("Evaluation of JATS decoder as an automated text extraction tool for statistical results in scientific reports" Scientific Reports 11(1):19525 (2021)), as cited on the 01/14/2026 Form PTO-892. Any newly recited portions are necessitated by claim amendment.
Claim 1 recites a system comprising: one or more computer processors; and one or more storage devices storing instructions that, when executed by the one or more computer processors, cause the one or more computer processors to perform operations for determining pathogenic genetic variants specific to an Asian population, the system operations comprising steps. Claim 10 recites a computer-implemented method comprising said steps. Claim 19 recites one or more non-transitory computer storage media storing instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising said steps.
The prior art to Dong discloses an integrated cancer genome score (pg. 1 Title) performed by a statistical framework that infers driver variants by integrating contributions from genetic variants to identify cancer driver genes (pg. 1 para. 1); wherein a program package is written in Perl and user interface (i.e. application program interface) written in Ruby on Rails, JavaScript, and HTML5 (i.e. reading on media and memory) (pg. 8 col. 1 para. 3).
The steps performed by the system of claim 1, a method of claim 10, and a non-transitory computer-readable medium of claim 19 comprise:
invoking, using an application program interface (API), a data crawler configured to automatically identify a plurality of unstructured research publications related to human genomes of the Asian population,
• Dong teaches as a program package written in Perl and user interface (i.e. application program interface) written in Ruby on Rails, JavaScript, and HTML5 (pg. 8 col. 1 para. 3), wherein the testing dataset included a The Cancer Genome Atlas data portal web crawler (i.e. knowledge extraction engine/ data crawler) to obtain single nucleotide variants and indels (pg. 4 col. 1 para. 2); wherein testing data comprised admixed Americans, Europeans, Asians, and African populations (i.e. human genomes of the particular population) (pg. 3 col. 2 para. 1).
• Dong does not teach "a data crawler configured to automatically identify a plurality of unstructured research publications." However, Horn teaches a computational method (pg. 557 Abstract) for automated extraction of mutation data (pg. 557 Title); wherein text information is extracted from journal articles (pg. 560 Table 1).
wherein each of the plurality of unstructured research publications refers to a respective genetic variant of a plurality of genetic variants
for each of the plurality of unstructured research publications, analyzing content of the unstructured research publication to determine whether the respective genetic variant is classified as a pathogenic genetic variant according to the unstructured research publication, and
• Dong teaches as modeling the patterns of putative cancer drivers observed in The Cancer Genome Atlas data with a logistic regression model and outputs a prioritized list of genes ranked by their cancer driving potential (i.e. determine whether the respective genetic variant is classified as a pathogenic genetic variant) (pg. 10 col. 1 para. 1).
• Dong does not teach "wherein each of the plurality of unstructured research publications refers to a respective genetic variant of a plurality of genetic variants" and "analyzing content of the unstructured research publication" to evaluate data. However, Horn teaches a computational method (pg. 557 Abstract) for automated extraction of mutation data (pg. 557 Title); wherein text information is extracted from journal articles (pg. 560 Table 1); wherein each of the plurality of unstructured research publications refers to a respective genetic variant of a plurality of genetic variants as shown in Fig. 5 (pg. 566); wherein the algorithm can analyze 1000 articles in less than 10hrs with specificity >86% (i.e. analyzing content of the unstructured research publication) (pg. 567 col. 1 para. 3).
in response to determining that the respective genetic variant is classified as the pathogenic genetic variant
(i) adding the respective genetic variant to a current set of pathogenic genetic variants,
(ii) determining, from the content of the unstructured research publication, a phenotype that the respective genetic variant is linked to, and
(iii) determining one or more characteristics of the unstructured research publication;
• Dong teaches a systematic prediction of cancer genetic drivers with a first layer identifying coding mutations, non-coding mutations, and structural variations, and a second layer links these mutation features to genes using a statistical model with prior biological knowledge on cancer driver genes for specific subtypes of cancer (i.e. (ii) determining a phenotype that the respective genetic variant is linked to) (pg. 2 col. 2 para. 2); wherein included radial support vector machine learning models learned the patterns of cancer drivers from a set of confident and experimentally confirmed driver and passenger missense mutations to broadly prioritize cancer genetic drivers for both recurrent and rare mutations (pg. 18 col. 1 para. 1); wherein the algorithm output can be accessed via output files (i.e. (i) adding the respective genetic variant to a current set of pathogenic genetic variants) (pg. 9 Table 2).
• Dong does not teach "from the content of the unstructured research publication" and "(iii) determining one or more characteristics of the unstructured research publication." However, Horn teaches a computational method (pg. 557 Abstract) for automated extraction of mutation data (pg. 557 Title); wherein text information is extracted from journal articles (i.e. from the content of the unstructured research publication) (pg. 560 Table 1); wherein the algorithm looks for protein names in the abstract of each article using the name dictionary and the list of synonyms (i.e. (iii) determining one or more characteristics of the unstructured research publication) (pg. 559 col. 2 para. 4).
for each pathogenic genetic variant in the current set of pathogenic genetic variants, determining, using a machine learning model in accordance with current values of a plurality of parameters of the machine learning model, a respective importance score for the pathogenic genetic variant based on characteristics of unstructured research publications from which the pathogenic genetic variant is determined.
• Dong teaches that parameters of the support vector machine model were tuned to enhance its modeling performance of the patterns of cancer driver mutations (i.e. using a machine learning model in accordance with current values of a plurality of parameters of the machine learning model) (pg. 6 col. 1 para. 1) while integrating multiple scoring methods are applied to enhance the accuracy of classifying Mendelian disease variants (i.e. assign a respective importance score to each pathogenic genetic variant) which included radial support vector machine learning models that learned the patterns of cancer drivers from a set of confident and experimentally confirmed driver and passenger missense mutations to broadly prioritize cancer genetic drivers for both recurrent and rare mutations (i.e. based on characteristics of research publications from which the pathogenic genetic variant is determined) (pg. 18 col. 1 para. 1); wherein the algorithm outputs a prioritized list of genes ranked by their cancer driving potential, or iCAGES gene scores (pg. 10 col. 1 para. 1).
• Dong does not teach "characteristics of unstructured research publications." However, Horn teaches a computational method (pg. 557 Abstract) for automated extraction of mutation data (pg. 557 Title); wherein text information is extracted from journal articles (i.e. characteristics of unstructured research publications) (pg. 560 Table 1); wherein the algorithm looks for protein names in the abstract of each article using the name dictionary and the list of synonyms (i.e. characteristics of unstructured research publications) (pg. 559 col. 2 para. 4).
wherein the characteristics of the unstructured research publications from which the pathogenic genetic variant is determined comprise a number of times that each of the unstructured research publication has been cited by other publications or other data sources
• Dong does not teach the recitation above. However, Mercier teaches a method for analyzing scientific documents wherein the algorithm Senticite classifies the references/citations in a scientific publication into positive, neutral and negative classes (i.e. number of times that each of the unstructured research publication has been cited by other publications or other data sources) (pg. 3 col. 2 para. 1)
wherein the plurality of parameters of the machine learning model comprise
(i) a first parameter representing a clinical effect,
(ii) a second parameter representing a number of validations of a research study,
(iii) a third parameter representing a size of the research study,
(iv) a fourth parameter representing at least one of a p-value, a z-score, or a confidence interval,
(v) a fifth parameter representing a variant prevalence,
and (vi) a sixth parameter representing metadata of the research study
• Dong does not teach "(i) a first parameter representing a clinical effect, (ii) a second parameter representing a number of validations of a research study and (iii) a third parameter representing a size of the research study." However, Monteiro teaches Text-Mining Machine Learning-based algorithm that aimed at providing the user with a likelihood of whether such variants are more probable to be ‘Benign’ or ‘Pathogenic’ in a given disease context (pg. 6 para. 1); wherein clinical unstructured text pre-processing of clinical texts (i.e. reading on (i) a first parameter representing a clinical effect) applied steps to reduce the size of datasets collected (i.e. reading on (iii) a third parameter representing a size of the research study) (pg. 51 para. 1); wherein a machine learning function – random Forest (pg. 117 para. 3) – using k=10 for the cross-validation resampling technique (pg. 118 para. 2) to prepare and divide the dataset into the training and test sets using a repeated k fold cross validation (i.e. (ii) a second parameter representing a number of validations of a research study) (pg. 118 para. 1).
• Dong does not teach "(iv) a fourth parameter representing at least one of a p-value, a z-score, or a confidence interval." However, Böschen teaches the automated text extraction tool JATSdecoder that extracts all reported statistical results from text (pg. 1 para. 1) and allow an estimation of the sample size which a study is based on (pg. 11 para. 9); wherein a statistical test results mostly consist of a varying set of results (test statistic, degree/s of freedom, an effect measure, p value, confidence interval and/or a Bayes Factor) (i.e. (iv) a fourth parameter representing at least one of a p-value, a z-score, or a confidence interval) (pg. 2 para. 7).
• Dong teaches the radial support vector machine model being selected to model the patterns of cancer driver mutations, with its parameters further tuned to enhance its performance (i.e. (v) a fifth parameter representing a variant prevalence) (pg. 6 col. 1 para. 1).
• Dong does not teach "(vi) a sixth parameter representing metadata of the research study." However, Mercier teaches a method for analyzing scientific documents wherein the algorithm Senticite classifies the references/citations in a scientific publication into positive, neutral and negative classes (i.e. (vi) a sixth parameter representing metadata of the research study) (pg. 3 col. 2 para. 1).
wherein the respective importance score represents
(i) a level of importance of the pathogenic genetic variant to the Asian population and
(ii) a level of contribution of the pathogenic genetic variant to the phenotype that the pathogenic genetic variant is linked to,
ranking, using the machine learning model, the pathogenic genetic variants in the current set according to the respective importance scores
• Dong teaches a ranked list of candidate genes is outputted based on their association with specific subtypes of cancer (i.e. reading on ranking, using the machine learning model, the pathogenic genetic variants in the current set according to the respective importance scores) (pg. 12 col. 1 para. 1); wherein testing data comprised admixed Americans, Europeans, Asians, and African populations (i.e. reading on (i) a level of importance of the pathogenic genetic variant to the Asian population) (pg. 3 col. 2 para. 1); wherein the algorithm outputs a prioritized list of genes ranked by their cancer driving potential, or iCAGES gene scores (i.e. (ii) a level of contribution of the pathogenic genetic variant to the phenotype that the pathogenic genetic variant is linked to) (pg. 10 col. 1 para. 1).
automatically updating a variant database with the ranked pathogenic genetic variants to provide a population-specific genomic reference configured to filter genetic variants during genetic screening of individuals in the Asian population
• Dong teaches as outputting ranked list of candidate genes based on their association with specific subtypes of cancer (pg. 12 col. 1 para. 1); wherein testing data comprised admixed Americans, Europeans, Asians, and African populations (pg. 3 col. 2 para. 1); wherein the algorithm outputs a prioritized list of genes ranked by their cancer driving potential, or iCAGES gene scores(pg. 10 col. 1 para. 1); wherein the user needs to specify which sample he/she needs to analyze so that iCAGES can perform a single patient analysis based on the particular patient of interest (i.e. reading on automatically updating a database since a computer algorithm outputs a list upon a request from the proposed web interface of iCAGES) (pg. 8 col. 1 para. 4). The recited "to provide a population-specific genomic reference configured to filter genetic variants during genetic screening of individuals in the Asian population" is interpreted as intended use and, therefore, not a requirement.
Claims 3, 12 and 21 recite
wherein the content of each unstructured research publication includes text, and wherein for each unstructured research publication, analyzing the content of the unstructured research publication to determine whether the respective genetic variant is classified as the pathogenic genetic variant comprises: extracting, from the text of the unstructured research publication, a conclusion with respect to the respective genetic variant by using a text mining algorithm; and determining whether the conclusion classifies the respective genetic variant as the pathogenic genetic variant
• Dong teaches Phenolyzer, a database-mining tool, to utilize valuable prior biological knowledge generated from numerous research studies on cancer and integrate databases on gene–gene and gene–phenotype interaction networks to the model (pg. 19 col. 1 para. 2); and to output a ranked list of candidate genes based on their association with specific subtypes of cancer (i.e. conclusion that classifies the respective genetic variant) (pg. 12 col. 1 para. 1wherein the testing dataset included a The Cancer Genome Atlas data portal web crawler (i.e. knowledge extraction engine/ data crawler) to obtain single nucleotide variants and indels (pg. 4 col. 1 para. 2); wherein testing data comprised admixed Americans, Europeans, Asians, and African populations (i.e. human genomes of the particular population) (pg. 3 col. 2 para. 1).
• Dong does not teach "content of unstructured research publications." However, Horn teaches a computational method (pg. 557 Abstract) for automated extraction of mutation data (pg. 557 Title); wherein text information is extracted from journal articles (i.e. text from unstructured research publications) (pg. 560 Table 1); wherein the algorithm looks for protein names in the abstract of each article using the name dictionary and the list of synonyms (i.e. content of unstructured research publications) (pg. 559 col. 2 para. 4).
Claims 5, 14 and 23 recite
wherein the one or more characteristics of each of the unstructured research publications further include one or more of:
(a) a size of a research study associated with the unstructured research publication;
(b) a p-value that represents quality of test results derived by the research study; or
(c) a confidence interval of research findings described in the unstructured research publication
• Dong does not teach the recitation above. However, Böschen teaches the automated text extraction tool JATSdecoder that extracts all reported statistical results from text (pg. 1 para. 1) and allow an estimation of the sample size which a study is based on (pg. 11 para. 9); wherein a statistical test results mostly consist of a varying set of results (test statistic, degree/s of freedom, an effect measure, p value, confidence interval and/or a Bayes Factor) (pg. 2 para. 7).
• Dong does not teach " one or more characteristics of each of the unstructured research publications." However, Horn teaches a computational method (pg. 557 Abstract) for automated extraction of mutation data (pg. 557 Title); wherein text information is extracted from journal articles (i.e. one or more characteristics of each of the unstructured research publications) (pg. 560 Table 1); wherein the algorithm looks for protein names in the abstract of each article using the name dictionary and the list of synonyms (i.e. one or more characteristics of each of the unstructured research publications) (pg. 559 col. 2 para. 4).
Claims 6, 15 and 24 recite
wherein the operations further comprise
for each of the plurality of unstructured research publications, in response to determining that the respective genetic variant is classified as the pathogenic genetic variant: extracting, from the content of the unstructured research publication, an explanation of a biological reasoning behind the pathogenic genetic variant; and for each pathogenic genetic variant in the current set of pathogenic genetic variants, assigning a respective importance score to the pathogenic genetic variant based on the characteristics of the unstructured research publications from which the pathogenic genetic variant is determined and based on the explanation of the biological reasoning behind the pathogenic genetic variant
• Dong teaches Phenolyzer, database-mining tool, applied to quantify prior knowledge generated from decades of cancer genetic and its associated genes (i.e. determine characteristics of research publications from which the pathogenic genetic variant is determined) (pg. 12 col. 1 para. 1) and integrate databases on gene–gene and gene–phenotype interaction networks to the model (i.e. extracting, from the content of the research publication) (pg. 19 col. 1 para. 2); and to output a ranked list of candidate genes based on their association with specific subtypes of cancer (i.e. explanation of the biological reasoning behind the pathogenic genetic variant) (pg. 12 col. 1 para. 1).
• Dong does not teach "content of unstructured research publications." However, Horn teaches a computational method (pg. 557 Abstract) for automated extraction of mutation data (pg. 557 Title); wherein text information is extracted from journal articles (i.e. text from unstructured research publications) (pg. 560 Table 1); wherein the algorithm looks for protein names in the abstract of each article using the name dictionary and the list of synonyms (i.e. content of unstructured research publications) (pg. 559 col. 2 para. 4).
Claims 8 and 25 recite
wherein for each pathogenic genetic variant in the current set of pathogenic genetic variants, the machine learning model is configured to determine the respective importance score for the pathogenic genetic variant using a decision tree induction technique in accordance with the current values of the plurality of parameters of the machine learning model
• Dong does not teach the recitation above. However, Monteiro teaches Text-Mining Machine Learning-based tool that aimed at providing the user with a likelihood of whether such variants are more probable to be ‘Benign’ or ‘Pathogenic’ in a given disease context (pg. 6 para. 1); wherein a tree structure was built by the Decision Tree model for the Frequency Matrix with Disease Score (i.e. machine learning model is configured to determine the respective importance score for the pathogenic genetic variant using a decision tree induction technique) (i.e. (Fig. 15 pg. 107); wherein document-term matrices were used to build and evaluate the machine learning model for genomic variants classification (i.e. in accordance with the current values of the plurality of parameters of the machine learning model) (pg. 95 para. 2);
Rationale for combining (MPEP §2142-2143)
Regarding claims 1, 3, 5-6, 8, 10, 12, 14-15, 19, 21 and 23-25, 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 combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Dong in view of Hong, Mercier, Monteiro and Böschen because all references disclose methods for applying text-mining tools for detection of information from research in the web. The motivation would have been to:
• incorporate extracted genetic mutation data validated via filters and stored in a database (pg. 558 col. 2 para. 2 Horn);
incorporate a state-of-the-art method for the evaluation of scientific publications (pg. 1 para. 1 Mercier);
• understand the relation between a variant present in an individual and a certain disease/phenotype (pg. 2 para. 3 Monteiro); and
• with a little additive text extraction effort, to detect all investigated variables or effects within a research (pg. 11 para. 9 Böschen).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the method for applying text-mining tools for detection of information from research in the web of Dong to the methods by Horn, Mercier, Monteiro and Böschen because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for applying text-mining tools for detection of information from research in the web.
B. Claims 4, 13 and 22 are rejected under 35 U.S.C. 103(a) as being unpatentable over Dong, Horn, Mercier, Monteiro and Böschen as applied to claims 1, 10 and 19 above further in view of Baltoumas ("OnTheFly2. 0: a text-mining web application for automated biomedical entity recognition, document annotation, network and functional enrichment analysis." NAR Genomics and Bioinformatics 3(4) (2021)), as cited on the 01/14/2026 Form PTO-892. Any newly recited portions are necessitated by claim amendment.
Claims 4, 13 and 22 recite
wherein the content of each unstructured research publication includes one or more images, the one or more images including second text, and wherein for each unstructured research publication, analyzing the content of the unstructured research publication to determine whether the respective genetic variant is classified as the pathogenic genetic variant comprises: extracting the second text from the one or more images using optical character recognition; and determining whether the second text classifies the respective genetic variant as the pathogenic genetic variant.
• Neither Dong, Horn, Mercier, Monteiro or Böschen teach the recitation above. However, Baltoumas teaches a web application for extracting biomedical entities from individual files such as plain texts, office documents, PDF files or images (pg. 1 col. 1 para. 1) using the extract tagging service to perform named entity recognition for genes/proteins, chemical compounds, organisms, tissues, environments, diseases, phenotypes and gene ontology terms (pg. 1 col. 1 para. 1); wherein the application can utilize optical character recognition to produce PDF files with parseable text (pg. 3 col. 1 para. 1).
Rationale for combining (MPEP §2142-2143)
Regarding claims 4, 13 and 22, 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 combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Dong, Horn, Mercier, Monteiro and Böschen in view of Baltoumas because all references disclose methods for applying text-mining tools for detection of information from research in the web. The motivation would have been to detect genes/proteins, genetic variants, and diseases mentioned in documents (pg. 2, col. 1, para. 2 Baltoumas).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the method for applying text-mining tools for detection of information from research in the web of Dong, Horn, Mercier, Monteiro and Böschen to the methods by Baltoumas because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for applying text-mining tools for detection of information from research in the web.
C. Claims 9, 18 and 26 are rejected under 35 U.S.C. 103(a) as being unpatentable over Dong, Horn, Mercier, Monteiro and Böschen as applied to claims 1, 10 and 19 above further in view of Castaldo ("Molecular diagnostics: between chips and customized medicine." Clinical Chemistry & Laboratory Medicine 48(7) (2010)), as cited on the 01/14/2026 Form PTO-892. Any newly recited portions are necessitated by claim amendment.
Claims 9, 18 and 26 recite
wherein the population-specific genomic reference is a chip configured to decode human genomes of individuals in the Asian population and wherein the instructions further comprise transmitting the ranked pathogenic genetic variants stored in the variant database to a chip designer for constructing the chip
• Neither Dong, Horn, Mercier, Monteiro or Böschen teach the recitation above. However, Castaldo teaches scanning a high number of single nucleotide polymorphisms in large groups of patients and controls using chips can reveal novel susceptibility loci related to complex diseases (i.e. reading on population-specific genomic reference is a chip) (pg. 974 col. 1 para. 2); wherein chip platforms designed to analyze (i.e. reading on information transmitted for the construction of a designed chip) thousands of SNPs related to human diseases are already available in clinical molecular biology laboratories for routine diagnosis or to assess susceptibility to a number of complex diseases (i.e. reading on accessing importance/ranking of pathogenic variants) (pg. 974 col. 2 para. 1 and Table 1).
Rationale for combining (MPEP §2142-2143)
Regarding claims 1, 3, 5-6, 8, 10, 12, 14-15, 19, 21 and 23-25, 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 combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Dong, Horn, Mercier, Monteiro and Böschen in view of Castaldo because all references disclose methods for applying text-mining tools for detection of information from research in the web. The motivation would have been to apply genotyping chip platforms to study nucleic acids in fine detail (pg. 973 col. 2 para. 3) revealing a large number of genetic loci potentially associated with complex diseases (pg. 976 col. 2 para. 4 Castaldo).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the method for applying text-mining tools for detection of information from research in the web of Dong, Horn, Mercier, Monteiro and Böschen to the methods by Castaldo because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for applying text-mining tools for detection of information from research in the web.
Response to applicant's remarks in regard to Claim Rejection 35 U.S.C. ~ 103
The Remarks of 04/14/2026 have been fully considered but are not persuasive for the reasons below:
Applicant asserts starting in pg. 21 para. 2:
First, Dong fails to teach or suggest the technical requirement of an API- driven crawler configured to identify and analyze the content of "unstructured research publications"… Second, Dong lacks any disclosure of bibliometric-based weighting logic, specifically the use of a "number of times that … publication has been cited" … Third, Dong does not teach or suggest "automatically updating a variant database … Fourth, nowhere does Dong teach or suggest "for each pathogenic genetic variant in the current set of pathogenic genetic variant, determining using a machine learning model in accordance with current values of a plurality of parameters of the machine learning model …
It is respectfully submitted that this argument is persuasive because Dong teaches searches through source databases, which are inherently structured and therefore not equivalent to a data crawler to identify unstructured research publications. To address the amended limitations related to the content from unstructured research publications the prior art to Horn has been included to address the amended limitation.
Regarding the argued "disclosure of bibliometric-based weighting logic, specifically the use of a "number of times that … publication has been cited," said claim element is taught by Mercier as a method for analyzing scientific documents wherein the algorithm Senticite classifies the references/citations in a scientific publication into positive, neutral and negative classes (i.e. reading on the number of times that each of the unstructured research publication has been cited by other publications or other data sources) (pg. 3 col. 2 para. 1).
Regarding the argued "automatically updating a variant database …," said claim element is taught by Dong as the algorithm outputs a prioritized list of genes ranked by their cancer driving potential, or iCAGES gene scores(pg. 10 col. 1 para. 1); wherein the user needs to specify which sample he/she needs to analyze so that iCAGES can perform a single patient analysis based on the particular patient of interest (i.e. reading on automatically updating a database since a computer algorithm outputs a list upon a request from the proposed web interface of iCAGES) (pg. 8 col. 1 para. 4). The recited "to provide a population-specific genomic reference configured to filter genetic variants during genetic screening of individuals in the Asian population" is interpreted as intended use and, therefore, not a requirement.
Regarding the argued "determining using a machine learning model in accordance with current values of a plurality of parameters of the machine learning model," said claim element is taught by Dong as parameters of the support vector machine model being tuned to enhance its modeling performance of the patterns of cancer driver mutations (i.e. using a machine learning model in accordance with current values of a plurality of parameters of the machine learning model) (pg. 6 col. 1 para. 1) while integrating multiple scoring methods are applied to enhance the accuracy of classifying Mendelian disease variants (i.e. assign a respective importance score to each pathogenic genetic variant) which included radial support vector machine learning models that learned the patterns of cancer drivers from a set of confident and experimentally confirmed driver and passenger missense mutations to broadly prioritize cancer genetic drivers for both recurrent and rare mutations (i.e. based on characteristics of research publications from which the pathogenic genetic variant is determined) (pg. 18 col. 1 para. 1).
The prima facie case of obviousness has been established. MPEP 2141.III for "RATIONALES TO SUPPORT REJECTIONS UNDER 35 U.S.C. 103"; wherein "(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention." The rejection as explained above does establish a prima facie case of obviousness under the applicable legal standards.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FRANCINI A FONSECA LOPEZ whose telephone number is (571)270-0899. The examiner can normally be reached Monday - Friday 8AM - 5PM ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Olivia Wise can be reached at (571) 272-2249. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/F.F.L./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
Prosecution Insights