DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Instant application does claim the benefit of priority to application number 63/254,420 filed on October 11, 2021 and given this priority date.
Information Disclosure Statement
The IDS filed on 1/26/2023 was considered by the Examiner.
Drawings
The Drawings filed on 7 October 2022 are accepted.
Specification (Abstract)
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided. (FP 6.16) (MPEP § 608.01(b))
Patent abstracts are expected to be in a narrative format, not structured as a series of enumerated inputs and steps. Enumeration of steps using “(a)”, “(i)”, “(ii)”, “(iii)”, “(b)”, “(c)” structure turns the abstract into a claim-like paragraph rather than a concise summary. Applicants are advised to avoid this type of formatting for the abstract.
Specification
The specification and amended specification filed on 7 October 2022 and on 18 June 2024 respectively are accepted.
Status of Claims
Claim 1-20 are currently pending and are under examination herein.
Claim 1-20 are rejected.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION. —The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 10, 13, 16, 19, and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 19 and 20 recite “most compatible” in line 3, step f; and (c)(iv), respectively. The metes and bounds of said limitation are unclear. The specification is silent regarding a clear and precise definition of the phrase “most compatible”. One skilled in the art would not recognize the metes and bounds of said limitation.
The claim 10 is rejected under 35 U.S.C. 112(b) as being indefinite or lack of antecedent basis for failing to particularly point out and distinctly claim the subject matter in the parent claim which the applicant regards as the invention because it refers to an element “Pseudo protein” not defined in claim 4 (the parent claim). (MPEP 2173.05(e))
A dependent claim must further limit the scope of the claim it refers to. Introducing a new component “pseudo protein” without a prior mention of a pseudo protein makes the scope of the invention unclear.
Regarding claim 13, the phrases “lacks a subset of the full-length amino acid sequences” is indefinite. It fails to delineate the metes and bounds of the invention. It is unclear if phrase refers to removing a portion of the linear sequence or removing specific types of amino acids. Without defining “subset,” a person of ordinary skill in the art cannot determine the scope of the invention. (MPEP § 2173.02 and MPEP § 2173)
Regarding claim 16, the term “non-binary values” is broad that it fails to set the boundaries of the invention and renders indefinite. It does not clearly define the metes and bounds of the claim, as non-binary could encompass a vast range of representations (e.g., integers, continuous float values, probabilities, descriptive text, or complex vectors). The specification does not narrow this to a specific type of non-binary value, so, a person having ordinary skill in the art (PHOSITA) would be unable to determine the scope of the "binding profile." (MPEP § 2173.02)
Claim Rejections - 35 USC § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A, Prong 1
In accordance with MPEP § 2106, the instant claims 1-20, 21, are drawn to a process (method), claims 22 are drawn to a system, and therefore are found to recite statutory subject matter (Step 1: YES). The instant claims are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). The instant claims recite the following limitations that equate to an abstract idea:
Claim 1 recites
Providing a database comprising information characterizing or identifying a plurality of candidate proteins. (Mental process-falling under the grouping of collecting information)
Determining a probability for each of the affinity reagents binding to each of the candidate protein…according to the binding model. (Mental process)
Providing a binding model for each of the different affinity reagents. (Mental process- step (providing) represents an evaluation, analysis, or mental judgment)
Identifying a subset of entities in the plurality of entities in the 2D map. (Mental process)
Computing probabilities of binding profiles comprising positive binding outcomes and negative binding outcomes. (Mathematical concept))
Identifying the extant proteins as selected candidate proteins. (Mental process)
Claim 2 recites a probability of a non-specific binding event occurring for one or more of the different affinity reagents (law of nature or natural phenomenon)
Claim 4 recites determining probability of positive binding events occurring between each candidate protein (Mental process or mathematical concept)
Claim 5 recites the probability of the positive binding event is normalized with respect to the lengths of the candidate proteins. (Mathematical concept)
Claim 6 recites the positive binding event is normalized using a binomial approximation (mathematical concept)
Claim 7 recites determining probability of a negative binding event occurring between each candidate protein. (Mental process)
Claim 8 recites the probability of the negative binding event is normalized with respect to the lengths of the candidate proteins. (Mathematical concept)
Claim 9 recites the probability of the negative binding event is normalized using a binomial approximation. (Mathematical concept)
Claim 10 recites determining probability of a negative binding event occurring between each pseudo protein in a plurality pseudo protein and each of the affinity reagents. (Mathematical concept or mental process)
Claim 11 recites about amino acid sequences in the plurality of pseudo proteins have full-lengths that are identical to the full-lengths for amino acid sequences in the plurality of candidate proteins. (Mathematical concept or mental process)
Claim 14 recites pseudo proteins are generated by sampling of amino acid sequences in the plurality of candidate proteins using a Markov chain. (Mathematical calculation)
Claim 15 recites determining the probability that the extant protein identified in step (f) is the selected candidate protein. (Mental process)
Claim 16 recites the positive binding outcomes and negative binding outcomes are represented by non-binary values in the binding profile. (Mathematical concept)
Claim 17 recites computing a probability matrix comprising the probabilities of a positive binding outcome for each of the affinity reagents……. (Mathematical concept)
Claim 18 recites computing a probability matrix comprising the probabilities of a negative binding outcome for each of the affinity …. (Mathematical concept)
Claim 19 recites all of the active steps that are same as claim 1 and the following:
Processing the plurality of binding profiles in the detection system to determine a probability for each of the affinity reagents binding to each of the candidate proteins in the database according to the binding model (Mathematical concept)
Claim 20 recites a database comprising information characterizing or identifying a plurality of candidate proteins. (Mental process)
Claim 12 and 13 provide information with further limitation without active steps.
As such, claims 1-20 recite an abstract idea (Step 2A, Prong 1: YES).
Step 2A, Prong 2
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). Specifically, the claims recite the following additional elements:
Claim 1 recites
Acquiring binding data from step (a), wherein the binding data comprises a plurality of binding profiles.
A measure of binding between an extant protein of step (a) and a different affinity reagent of the plurality of different affinity reagents.
Contacting a plurality of different affinity reagents with a plurality of extant proteins in a sample.
Claim 3 recites the non-specific binding event comprises binding of the one or more of the different affinity reagents to a solid support attached to the extant protein.
Claim 19 recites
Acquiring signals from a plurality of binding reactions carried out in a detection system, wherein….in a sample.”
Outputting from the detection system an identification of selected candidate proteins, …. the plurality of binding outcomes for the extant proteins.
Claim 20 recites
A detector configured to acquire signals from a plurality of binding reactions occurring between a plurality of different affinity reagents and a plurality of extant proteins.
Output an identification of selected candidate proteins, the selected candidate proteins being candidate … extant proteins.
A computer processor configured to: (i) communicate with the database, (ii) process the signals to produce a …. extant protein.
The limitations about a computer implemented detection system to acquire and process serves as being merely an insignificant, routine, or conventional post-solution activity and used an input for the judicial exception. The gathering, organizing data and analysis of all steps (Claim 1-20) merely serve as calculation of mathematical calculations, mental process or human organizing activity and does not add any significant practical application. Acquiring, measuring, and processing signals in a detection system, output an identification using display and a processor communicating with the database using a computing system are common and conventional practice and serve no practical application. While the limitations of contacting a plurality of different affinity reagents and binding of the one or more of the different affinity reagents to a solid support is generally considered physical processes. These steps are commonly used in sequencing technologies and pipeline; however, these limitations alone make them ineligible for practical application. Therefore, it lacks specific and substantial utility.
Therefore, these limitations are mere data gathering or analyzing activities and displaying the results using a conventional computational detection system. As set forth in MPEP 2106.05(g), mere data gathering and analyzing activity has been identified by the courts as insignificant extra-solution activity that does not provide a practical application.
There are no limitations that indicate that the computing implemented detection method require anything other than a generic computing system. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
The above recited additional elements do not provide a practical application of the recited judicial exception. As such, claims 1-20 are directed to an abstract idea (Step 2A, Prong 2: NO).
Step 2B
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to mere instructions to apply the recited exception in a generic computing detection environment or well-understood, routine and conventional activity. Also, remaining additional elements are routine and conventional in bioinformatic pipelines and merely serve extra solution activity.
As discussed above, there are no additional limitations to indicate that the claimed processor requires anything other than generic computer components (a detection system) in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
Furthermore, the additional elements recited in the claims amount to well-understood, routine and conventional activity.
As such, the combination of additional elements recited in the claims is well-understood, routine and conventional. The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-20 are not patent eligible.
Claim Rejections - 35 USC § 102
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. (FP 7.06)
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: (FP 7.07.aia)
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention. (FP 7.08.aia)
Claims 1-9 and 15, 19 and 20 are rejected under 35 U.S.C. 102 as being anticipated by Patel et al. (U.S Patent No US 2020/0082914).
Regarding claim 1, Patel et al. teaches a method and system for unknown protein using affinity reagent binding measurement. In his invention, discloses
Uses of plurality of affinity reagents probing unknown protein (Protein binding assay) (Abstract, paragraph [0019], [0023], [0108], [0164], Fig1) reads the limitation of a) contacting a plurality of different affinity reagents with a plurality of extant proteins in a sample.
Collects binding measurements (binding/non-binding) of affinity reagents to unknown protein (Paragraph [0093], [ 0157], [0165], [0182], [0236] and Fig 3(shows signal vector corresponding to reagent/probe interactions) which reads the limitation of b) collecting or acquiring binding data.
Comparing at least a portion of the information of binding measurements against a database comprising a plurality of protein sequence which reads to the limitation of “providing a database comprising information characterizing or identifying a plurality of candidate proteins.”
A computer-implemented method for identifying candidate proteins within a sample of unknown proteins, the method comprising: (a) receiving, by said computer, information of binding measurements of each of a plurality of affinity reagent probes to said unknown proteins in said sample, each affinity reagent probe configured to selectively bind to one or more candidate proteins among a plurality of candidate proteins; (b) comparing, by said computer, at least a portion of said information of binding measurements against a database comprising a plurality of protein sequences, each protein sequence corresponding to a candidate protein among said plurality of candidate proteins which reads to the limitation of “providing a binding model for each of the different affinity reagents.”
Compare against a database of candidate protein sequences. Discloses use of a reference proteome database for candidate matching (Paragraph [0096], [0084], [0085], [0027-0032], [0067] and Fig 1) maps the limitation of e) determining a probability for each of the affinity reagents binding to each of the candidate proteins in the database according to the binding model.
Computational probabilities of affinity reagents binding to each candidate protein (Paragraph [0091, 0096, 0098]) maps the limitation of e) computing probabilities for the positive binding outcomes and for the negative binding outcomes.
Discloses contacting multiple affinity reagents to protein in a sample (Fig 2 shows multi probe interaction scheme) which maps the limitation of a) contacting of affinity reagent to extant protein.
Discloses binding profile for each candidate protein that is associated with a set of signals across probe(reagent). In this disclosure where he shows negative binding effect. (Paragraph [0061], Abstract, and Fig 3). The set of probe-specific measurements disclosed constitute a binding profile comprising a plurality of binding outcome as claimed maps to the limitation of e) binding profiles comprising positive binding outcomes and negative binding outcomes.
Uses product of probabilities across multiple probes (Paragraph [0105, 0106]) reads to limitation of determining a probability for each of the affinity reagents binding to each of the candidate proteins in the database according to the binding model.
Describe probe specific binding characteristic (binding profile), including specifically epitope interaction likelihood. (Fig 4 illustrates probe target relationship) maps to the limitation of providing a binding model for each of the different affinity reagents.
Discloses computing likelihoods/probabilities of candidate proteins given observed probe signals according to binding model. Equations shows probabilities modeling of probe-protein interactions. (Paragraph [0099, 0100, 0162, 091], Eqn. (1)-(3)). The probabilities set forth in equation in 1-3 computes likelihood of candidate proteins based on probe measurements, corresponding to the claimed probabilities determination maps to e) determining a probability for each of the affinity reagents binding to ………. weighted more heavily relative to the negative binding outcomes.
Discloses both type of binding (signals present) and non-binding (absence of signals), a phenomenon of explicit use of negative binding. (Paragraph [0022, 0044, 0070, 0071, 0173, 0174]). Discloses likelihood function that incorporates both observed binding signals and absence of binding, contributing to overall probability (Paragraph [0022, 0023, 0170-0173]). The above teaching emphasizes that both binding signals and the absence of signals are critical and should contribute to overall probability maps the claim's condition of weighting positive outcomes more heavily relative to negative outcomes.
The reference inherently assigns greater evidence of weight to positive binding outcome [Paragraph 0164, 0166]. And selecting the candidate protein with the highest computed probabilities corresponds to identifying the protein most compatible with observed binding outcomes. (Paragraph 0100, 0105, and Fig 5) maps to f) positive binding outcomes are weighted more heavily relative to the negative binding outcomes and having a probability for binding each of the affinity reagents that is most compatible with the plurality of binding outcomes.
Regarding claim2, Patel et al. discloses the calculation of a non-specific binding rate (Paragraph 0103). Patel et al. describes statistical frameworks that compute binding likelihoods while adjusting for background signals, noise, and non-specific binding (Paragraphs 0099, 0100, 0162, 0910, Equations 1-3).
Regarding claim 3, Patel et al. teaches multiplex protein-affinity reagent binding assays in formats utilizing immobilized proteins on solid supports (e.g., Paragraph 0043). The reference discloses that the observed binding signals encompass both specific and non-specific interactions with the support surface, which are subsequently accounted for using probabilistic modeling, teaching the non-specific binding event comprises the binding of one or more different affinity reagents to a solid support attached to the extant protein.
Regarding claim 4, Patel et al. discloses computational frameworks for protein identification that evaluate binding likelihoods between candidate proteins and multiple affinity reagents (Paragraphs 0099, 0100, 0162). Patel et al. calculates a protein-reagent binding probability score value corresponding to observed binding events (and the absence of binding events) using established statistical models (Paragraphs 0099, 0100, 0162, 0910, and Equations 1-3). Furthermore, Patel discloses evaluating these binding likelihoods against candidate protein databases (Paragraphs 0027-0032, 0067, 0084, 0085, 0096; Abstract; Figures 1 and 3). Patel therefore discloses each and every limitation of claim 4.
Regarding claim 5, Patel et al. discloses the normalization of positive binding event probabilities by taking into account the lengths of the candidate proteins (see, e.g., paragraphs [0234] and [0238]). Therefore, this claim is anticipated under 35 U.S.C. 102.
Regarding claim 6, Patel et al., discloses normalizing positive binding events utilizing binomial approximations, including Poisson binomials or estimated Poisson binomials (Paragraphs 0088 and 0182).
Furthermore, methods for normalizing protein-probe binding data were well-known and conventional in the art. Therefore, it would have been obvious to a person of ordinary skill in the art (POSITA) at the time of the effective filing date to apply these standard binomial approximation techniques to normalize the data.
Regarding claim 7 , Patel et al. discloses methods and systems for characterizing and quantifying binding interactions between candidate proteins and affinity reagents, which inherently encompasses the evaluation of non-binding or "negative binding events." Specifically, Patel details the screening, profiling, and discrimination of candidate molecules based on their binding affinities, including instances where affinity reagents fail to bind or exhibit negative binding events (Paragraphs 0022 and 0171, and Table 2).
Because the step of “determining a probability” of such an event is an analytical step or mathematical evaluation of the underlying interaction data, it reads directly onto Patel’s disclosure of monitoring, calculating, and evaluating binding/non-binding pairs. Therefore, Patel et al. discloses each and every limitation of claim 7.
Regarding claim 8, Patel et al. teaches the normalization of binding event probabilities (paragraphs [0234] and [0238]) with respect to the lengths of candidate proteins, rendering the specific limitation of claim 8 devoid of patentable novelty.
Regarding claim 9, Patel et al. discloses normalizing positive binding events utilizing binomial approximations, including Poisson binomials and estimated Poisson binomials (paragraphs [0088] and [0182]).
While Patel et al. primarily exemplifies the normalization of positive binding events, the distinction between positive and negative binding events represents functionally equivalent binary states. Because Patel et al. fully teaches the application of binomial approximations and Poisson binomials for normalizing protein-probe binding data, utilizing the exact same mathematical framework for a negative binding event is merely the application of a known technique to an analogous use.
Regarding claim 15, Patel et al. discloses determining a confidence level (probability) that an identified candidate protein is correct. (Abstract, Paragraph [0003], [0004] and Fig 1).
Regarding claim 19, Patel et al. discloses methods and systems for the identification and quantification of proteins/biological samples. This process includes receiving binding measurement information of affinity reagent probes and utilizing computer-implemented databases to evaluate and identify proteins.
Patel et al. describes performing these steps through its iterative identification, data comparison, and confidence level evaluation algorithms. Therefore, Patel et al. discloses each and every limitation of claim 19.
Regarding claim 20, Patel et al. inherently disclose a system comprising processors, databases, and memory designed to execute the protein identification and binding analysis. Therefore, Patel et al. discloses each and every limitation of claim 20.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. § 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 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 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 10–13 is rejected under 35 U.S.C. 103 as unpatentable over Patel et al. (US 20200082914A1) as applied to claims 1-9, 15, 19, and 20 above, in view of Liu et al. (Nature communication, Scientific Reports | 5:15479) and Pseudo amino acid composition - Wikipedia.
Patel et al. are applied to claims 1-9, 15, 19, and 20..
Regarding claim 10, Patel et al. discloses a system for identifying unknown proteins in a sample, teaching:
Multi-affinity reagent binding measurements and database evaluations of candidate proteins. (Paragraph [0096], [0084], [0085], [0027-0032], [0067] and Fig 1) (Abstract, paragraph [0019], [0023], [0108], [0164], Fig1)
Binding profiles incorporating both positive and negative binding outcomes (paragraphs [0088] and [0182]) suggesting limitations of claim 10 “computing of the probabilities for the negative binding outcomes …. affinity reagents”
Probe-specific binding models and probabilistic determinations using maximum likelihood. (Paragraph [0099, 0100, 0162, 091], Eqn. (1)-(3))
However, Patel et al. does not disclose generating pseudo-proteins from full-length candidate proteins to test them for binding affinity.
Liu et al. and Pseudo amino acid composition - Wikipedia teaches the generation of pseudo-proteins using amino acid composition (pseudo-amino acid composition, or PseAAC) and profile-based protein representations, (Abstract; pg.2, middle). Liu demonstrates that these computational pseudo-proteins can effectively predict protein binding interactions, (Abstract, Table 2; Figure 3., teaching the limitation of “each pseudo protein in a plurality pseudo protein and each of the affinity reagents.”.
Under KSR guidelines, a person having ordinary skill in the art (PHOSITA) evaluating Patel et al. large-scale proteomic data for binding probabilities would be motivated to consult Liu et al. Because Patel et al. seeks to identify unknown proteins based on binding probability profiles, a POSITA would find it obvious to apply Liu et al.’s pseudo-protein computational models (such as PseSAC) to Patel et al.’s system.
Substituting and generating a vast array of pseudo-proteins via Liu et al.’s established algorithms allows for a broader, comprehensive mapping of positive and negative binding outcomes to improve a known diagnostic method (Patel et al.) by enhancing the identification accuracy of unknown, higher-affinity binding proteins.
It would be an expectation of success of using all these cited arts together because they are all in the same technology (Proteomics), and addressing the similar problem (single-molecule sensitivity for comprehensive proteome analysis).
Regarding claim 11, Liu et al. and Pseudo amino acid composition - Wikipedia teaches that pseudo proteins created using full-lengths identical to the full-lengths for amino acid sequences in the candidate protein (pg. 7, Figure 6 and Wikipedia-Pseudo amino acid composition).
Regarding claim 12, Liu et al. and Pseudo amino acid composition - Wikipedia teaches that pseudo proteins created using fragments of the full-lengths for amino acid sequences in the candidate protein (pg. 7, Figure 6 and Wikipedia-Pseudo amino acid composition) suggesting the limitation of “the plurality of pseudo proteins lacks any full-length amino acid sequences.”
Regarding claim 13, Liu et al. and Pseudo amino acid composition - Wikipedia teaches that pseudo proteins created using fragments (pseudo proteins lacks a subset of the full- length amino acid sequences of candidate protein) (pg. 7, Figure 6 and Wikipedia-Pseudo amino acid composition) suggesting the limitation of “the plurality of pseudo proteins lacks any subsets full-length amino acid sequences.”
Claims 14 and 16 are rejected under 35 U.S.C. 103 as unpatentable over Patel et al. in view of Liu et al. and Pseudo amino acid composition - Wikipedia, as applied to claims 1-9, 15, 19, 20 and 10-13 above, and further in view of Repecka et al.(Expanding functional protein sequence space using generative adversarial networks)
Patel et al. and Liu et al. are applied to claims 1-9, 15, 19, 20 and 10-13.
Patel et al. and Liu et al. does not disclose generating pseudo protein by sampling of amino acid sequences in the plurality of candidate proteins using a Markov chain, generative adversarial network or length-based binning.
Repecka et al. discloses ProGAN / ProteinGAN a generative adversarial network to generate viable protein sequences details using a GAN to generate novel, functional-like protein sequences that resemble training data (candidate proteins) suggesting limitation of “pseudo proteins are generated by sampling of amino acid sequences in the plurality of candidate proteins using a Markov chain, generative adversarial network or length-based binning”.
Under BRI, prior art that describes any of the methods describes to generate pseudo protein such as “Markov chain, generative adversarial network or length-based binning” would suffice to reject the claim.
Under KSR guidelines, a person having ordinary skill in the art (PHOSITA) would be motivated to combine these prior art references because applying known machine learning techniques to existing biological data yields a predictable, expected result. Specifically, a PHOSITA would utilize Patel et al.’s large-scale proteomic binding probabilities to utilize Liu et al.’s pseudo-protein generated by PseSAC, and would be motivated by Repecka et al. to apply Generative Adversarial Networks (GANs) to this framework for generating more robust candidate pseudo-proteins for binding profile.
Substituting and generating a vast array of pseudo-proteins via Repecka et al.’s established algorithms allow for a broader, comprehensive mapping of positive and negative binding outcomes to improve a known diagnostic method (Patel et al.) by enhancing the identification accuracy of unknown, higher-affinity binding proteins.
It would be an expectation of success of using all these cited arts together because they are all in the same technology (Proteomics), and addressing the similar problem (single-molecule sensitivity for comprehensive proteome analysis).
Claim 16. The claim recites that positive and negative binding outcomes are represented by non-binary values in the binding profile. However, at the effective filing date, it would have been obvious to a person having ordinary skill in the art (PHOSITA) to use such non-binary values for the following reasons:
In the field of protein-protein binding assays, it is a well-established convention to measure and represent the intensity or affinity of molecular binding using non-binary, continuous variables (e.g., gradient concentrations, values, or fluorescence intensities).
A PHOSITA would readily recognize that binary (i.e., yes/no or 1/0) values are insufficient to capture the nuanced gradients of binding affinity typically observed in biological assays. The use of non-binary values merely represents a routine optimization and a conventional application of established laboratory techniques to the claimed method.
Claims 17 and 18 are rejected under 35 U.S.C. 103 as unpatentable over Patel et al. as applied to claim 1-9, 15, 19, and 20 above., in view of Gonzalez et al. (Prediction of contact maps for protein–protein interactions,” Bioinformatics, 2013).
Patel et al. is applied to claim 1-9, 15, 19, and 20.
In addition to teaching by Patel et al. for claim 1-9, 15,19 and 20 rejections, Patel et al. also discloses a method for identifying unknown proteins in a sample using multi-affinity reagent binding measurements. The reference teaches:
Multi-affinity reagent binding measurements and binding profiles comprising both positive and negative binding outcomes. (Paragraph [0096], [0084], [0085], [0027-0032], [0067] and Fig 1) (paragraphs [0088] and [0182])
A database of candidate proteins and probe-specific binding models. (Paragraph [0096], [0084], [0085], [0027-0032], [0067] and Fig 1)
Probabilistic determination using both binding and non-binding evidence (e.g., iteratively generating a probability for candidate proteins based on binding measurements evaluated against a database of protein sequences).
Identification of the target protein based on maximum likelihood (Paragraph [0099, 0100, 0162, 091], Eqn. (1)-(3))
Patel et al. does not explicitly disclose generating a formal probability matrix of protein-affinity reagent binding (encompassing both positive and negative outcomes).
Gonzalez et al. teaches the prediction of a contact matrix for protein-protein interactions, which inherently represents a probability matrix for protein-protein binding interactions both positive and negative (Abstract).
A person having ordinary skill in the art (POSITA) evaluating Patel’s analysis of large-scale proteomic data—which evaluates binding probabilities to identify unknown proteins—would be motivated to consult Gonzalez. Gonzalez provides a known, standard technique for modeling interactions using probability matrices. Applying Gonzalez’s probability matrix generation to Patel’s probabilistic pipeline would have been obvious to a POSITA to streamline the analysis and enhance the accuracy of identifying extant proteins.
A POSITA would have had a reasonable expectation of success in combining these teachings. Patel already utilizes probabilistic determinations and maximum likelihood functions to calculate the likelihood of candidate proteins. Incorporating Gonzalez's probability matrix techniques would use known computational methodologies in a straightforward manner to yield predictable improvements in characterizing binding outcomes.
Conclusion
No claims are allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ARSHAD KHAN whose telephone number is (571)272-9812. The examiner can normally be reached Mon-Fri-7:30-5:00 PM.
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, Larry Riggs can be reached at 5712703062. 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.
/AK/Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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