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 .
Election/Restrictions
Applicant’s election without traverse of 1-17 and 19 in the reply filed on 07/21/2026 is acknowledged.
Claims 18 and 20 withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 07/21/2026.
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 1-17 and 19 are rejected under 35 USC 101 because the claimed invention is directed to an abstract idea without significantly more. The claims will be analyzed below according to MPEP 2106.
Inquiry 1: Is the claim directed to a statutory category of invention (process, machine, manufacture, or composition of matter)?
Claims 1-17 are drawn to a process.
Inquiry 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Claim 1 recites “determining with a computer, using the database and the outcome profiles, candidate glycans in the database corresponding to different extant glycans in the array.” As worded, despite requiring a computer, ‘determining’ candidate glycans may be performed in the human mind and/or with pen and paper by observing and analyzing the outputted outcome profiles alongside a database to determine candidate glycans. Other than “computer”, if the claim limitations, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components (e.g. computer), then the claim limitations fall within the “Mental Processes” grouping of abstract ideas (MPEP 2106.05(f)). Therefore, this recitation is drawn to an abstract idea. See MPEP 2106.04(a).
Claim 12 recites “calculating, for an individual address of the array, a probability that the outcome profile is observed given that a candidate glycan in the database is present at the individual address.” As worded, this reads as a mathematical calculation to ‘assign’ a candidate glycan to an outcome profile. Mathematical calculations are categorized as an abstract idea. See MPEP 2106.04(a)(2).
Claim 15 recites “determining a probability or likelihood that one or more candidate glycan in the database would generate one or more of the recognition outcomes for the extant glycan attached to one or more of the addresses of the array.” This may be reasonably construed to be a mental process that may take place in the human mind and/or with a pen and paper by observing the candidate glycans in the database, the addresses of the array, and the outputted recognition profiles. See MPEP 2106.04(a).
Claim 19 recites, in step (iii) a computer configured to “process the recognition profiles to determine a probability for each of the probes interacting with each of the candidate glycans in the database”. As worded, despite requiring a computer, ‘determining’ candidate glycans may be performed in the human mind and/or with pen and paper by observing and analyzing the outputted outcome profiles alongside a database to determine candidate glycans. Therefore, this recitation is drawn to an abstract idea. See MPEP 2106.04(a).
Claims 2-17 are dependent on claim 1 and therefore are also drawn to the abstract idea of claim 1.
Inquiry 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, claim 1 ends with the “determining” step, therefore there is no practical application of the judicial exception. Furthermore, the steps prior to the judicial exception amount to the insignificant extra-solution activity of data-gathering (MPEP 2106.05(g)) and generally applying the judicial exception through instruction (MPEP 2106.05(f)).The electronics limitations are recited at a high-level of generality (i.e., as a generic computer) such that it amounts no more than mere instructions to apply the exception using a generic computer component; wherein a general purpose computer is not a particular machine (MPEP 2106.05(b)).
Claims 2-6 and 16-17 is drawn to the steps that occur prior to the judicial exception of claim 1, so there is no practical application made within those claims. Claims 7-9 may be construed to occur after the judicial exception, but these steps amount to the insignificant extra-solution activity of data-gathering (MPEP 2106.05(g)) and generally applying the judicial exception through instruction (MPEP 2106.05(f)). Claims 10, 12, and 15 recite additional judicial exceptions as mentioned above, which does not amount to a practical application. Claims 11, 13, 14 generally apply the judicial exceptions through mere instruction (MPEP 2106.05(f)).
Regarding claim 19, the steps prior to the judicial exception in (iii) amount to the insignificant extra-solution activity of mere data gathering (MPEP 2106.05(g)), and the step after generally applying the judicial exception through instruction (MPEP 2106.05(f)). The electronics limitations are recited at a high-level of generality (i.e., as a generic computer) such that it amounts no more than mere instructions to apply the exception using a generic computer component; wherein a general purpose computer is not a particular machine (MPEP 2106.05(b)).
Inquiry 2B: Does the claim recite additional limitations that amount to significantly more than the judicial exception?
No, they do not.
Claim 1 contains steps a-d in addition to the judicial exception in step e, and steps a-d amount to the insignificant extra-solution activity of mere data-gathering. See MPEP 2106.05(g). The electronics limitations are recited at a high-level of generality (i.e., as a generic computer) such that it amounts no more than mere instructions to apply the exception using a generic computer component; wherein a general purpose computer is not a particular machine (MPEP 2106.05(b)).
Claims 2-6 and 16-17 are drawn to the steps that occur prior to the judicial exception of claim 1, and they amount to the insignificant extra-solution activity of mere data-gathering. See MPEP 2106.05(g). Claims 7-9 may be construed to occur after the judicial exception, but these steps amount to the insignificant extra-solution activity of data-gathering (MPEP 2106.05(g)) and generally applying the judicial exception through instruction (MPEP 2106.05(f)); it is worth noting that the judicial exception is independent of the limitations in claims 7-9 because there is no positive recitation of any relationship or impact that the ‘determining’ step of claim 1 would have with/on the limitations of claims 7-9. Claims 10, 12, and 15 recite additional judicial exceptions as mentioned above, which does not amount to significantly more than judicial exceptions. Claims 11, 13, 14 generally apply the judicial exceptions through mere instruction (MPEP 2106.05(f)).
Regarding claim 19, the steps prior to the judicial exception in (iii) amount to the insignificant extra-solution activity of mere data gathering (MPEP 2106.05(g)), and the step after generally applying the judicial exception through instruction (MPEP 2106.05(f)). The electronics limitations are recited at a high-level of generality (i.e., as a generic computer) such that it amounts no more than mere instructions to apply the exception using a generic computer component; wherein a general purpose computer is not a particular machine (MPEP 2106.05(b)).
Furthermore, the concepts recited in claims 1-17 and 19 are well-understood, routine, and conventional in the arts: Wong et al. (US20150160217A1) describes a system and method to provide an array of glycans (modified or unmodified), contact the array with probes that recognize various kinds of glycans/carbohydrates, detect binding activity by eye or by a scanner/detector, and determine the identity of the glycans using a computer, a library of known glycans, and visual observation of the positive and negative recognition outcomes that emerge from the contacting step; Walsh et al. (US20220013197A1) describes a process and computerized apparatus for utilizing a database and positive recognition outcomes to determine the identity of glycans based on probability calculations, with the assistance of a computer and a machine learning algorithm; Seeberger et al. (US20050221337A1) and Geissner et al. (Annu. Rev. Anal. Chem., Vol. 9, June 2016, pgs. 223-247) both teach of modifying glycan arrays in order to elucidate more information about the glycans and the enzymes that may modify them in general, and teach of contacting those glycans with probes after the modification. See MPEP 2106.05(d).
Claim Rejections - 35 USC § 112
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 16-17 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.
Claim 16 recites the limitation "series of probes" in line 1. There is insufficient antecedent basis for this limitation in the claim. In the interest of compact prosecution, Examiner interprets “series of probes” to mean “plurality of different probes”, which has proper antecedent basis.
Claim 17 is rejected under 35 USC 112(b) because it is dependent on claim 16.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-7, 10-17, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wong et al. (US20150160217A1) in view of Walsh et al. (US20220013197A1).
Regarding claim 1, Wong teaches a method, comprising:
(a) providing an array (abstract and/or [0134]) (glycan arrays) of extant glycans ([0083]) (The composition of glycans on the arrays… purified glycans, naturally occurring or synthetic glycans), wherein the array comprises a plurality of addresses, wherein different extant glycans are attached to different addresses of the array ([0032]) (the substrate is addressable… an array of discrete spots);
(b) contacting the array with a plurality of different probes ([92]) (glycan solution can be applied per defined glycan probe location), the different probes recognizing different carbohydrate moieties ([104]) (nanoparticle-based probes in influenza virus subtype detection);
(c) detecting positive recognition outcomes of the plurality of different probes at individual addresses of the array, thereby producing outcome profiles for the addresses ([0144]) (binding profiles for HAs from both pandemic H1N1 (California/07/2009) (FIG. 11A) and seasonal H1N1 Brisbane/59/2007 (Br/59/07) (FIG. 11B) displayed… higher binding activities toward longer alpha-2,6-sialosides).
Wong describes determining candidate glycans by eye by looking at the outcome profiles and comparing them to known outcome profiles (‘fingerprint patterns’) ([42]) (serotypes can be observed by naked eyes… and be classified by glycan patterns on glass slides. FIG. 3B shows the fingerprint patterns of glycan array for each influenza serotype tested.).
Wong does not clearly describe:
(d) providing a database comprising a set of candidate glycans, the database comprising, for each candidate glycan, the probability of a positive recognition outcome for the plurality of different probes; and
(e) determining with a computer, using the database and the outcome profiles, candidate glycans in the database corresponding to different extant glycans in the array.
In the analogous art of identifying glycans in a sample, Walsh teaches:
(d) providing a database comprising a set of candidate glycans ([0184]) (assignment accuracies… use… a defined library and de-identified glycan standards), the database comprising, for each candidate glycan, the probability of a positive recognition outcome for the plurality of different probes ([0184]) (The probability of correctly identifying an unknown glycan given a distance for the unknown glycan); and
(e) determining with a computer ([45] and [46]) (the functions described may be implemented in hardware, software… Computer-readable media includes computer storage media and The system 300 may include a reference unit 302, a sample receiving unit 304, a sample point calculating unit 306 and a sample identifying unit 308), using the database and the outcome profiles, candidate glycans in the database corresponding to different extant glycans ([52] and [108]) (each reference point may be calculated from a plurality of reference measurements for… corresponding known biological compound and identifying the unknown biological sample may further include calculating a distance between the sample point and the determined nearest reference point, and calculating an accuracy score based on this distance).
Walsh teaches that the use of a computer, database, and outcome profiles to determine the identity of a glycan is advantageous because it enables the automation of glycan assignment ([8]) (The technique… allows users to compare… unknown/unidentified glycan against libraries of known/identified glycans… to identify the unknown glycan… Automated glycan assignment can then be performed).
It would have been obvious for a person having ordinary skill in the art before the effective filing date of the instant application to modify the visual determination method of Wong to instead utilize a computer and database as described by Walsh to provide: d) providing a database comprising a set of candidate glycans, the database comprising, for each candidate glycan, the probability of a positive recognition outcome for the plurality of different probes; and (e) determining with a computer, using the database and the outcome profiles, candidate glycans in the database corresponding to different extant glycans in the array. Doing so would lead to the predictable outcome of automating the determination of candidate glycans with a reasonable expectation of success (see [0083] [0032], [92], [0144], [42] of Wong and [0184], [45], [46], [52], [108], [8] of Walsh).
Regarding claim 2, modified Wong teaches the method of claim 1, wherein step (c) further comprises detecting negative recognition outcomes of the plurality of different probes at individual addresses of the array, whereby the outcome profiles for the addresses comprise the positive recognition outcomes and the negative recognition outcomes ([42] of Wong) (FIG. 3B shows the fingerprint patterns of glycan array for each influenza serotype tested). See Figure 3B of Wong, where filled circles correspond to positive recognition outcomes and hollow circles correspond to negative recognition outcomes:
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Regarding claim 3, modified Wong teaches the method of claim 2 as rejected above. Modified Wong teaches outcome profiles for the addresses comprising the positive recognition outcomes and the negative recognition outcomes ([16] and [42] of Wong) (Specifically, the assay can provide a positive or negative (y/n) determination of the presence or absence of influenza virus types and FIG. 3B shows the fingerprint patterns of glycan array for each influenza serotype tested). Modified Wong teaches a database that consists of positive recognition outcomes ([0184] of Walsh) (assignment accuracies… use… a defined library and de-identified glycan standards… The probability of correctly identifying an unknown glycan given a distance for the unknown glycan).
Modified Wong does not teach wherein the database further comprises, for each candidate glycan, the probability of a negative recognition outcome for the plurality of different probes.
Wong teaches that the determination will either result in the presence (positive recognition outcome) or absence (negative recognition outcome) of the glycans that contribute to influenza subtypes ([16] of Wong) (Specifically, the assay can provide a positive or negative (y/n) determination of the presence or absence of influenza virus types A and B). Wong teaches that certain glycans are characterized by their negative recognition outcomes in addition to their positive recognition outcomes, as evidenced by Figure 3B (‘expected result’ column), which shows that certain influenza subtypes are determined based on certain probes at certain addresses not binding to the glycan array (filled in circle = positive recognition outcome; hollow circle = negative recognition outcome):
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It would have been obvious for a person having ordinary skill in the art before the effective filing date of the instant application to try using a database that includes, for each candidate glycan, the probability of a negative recognition outcome for the plurality of different probes as suggested by Wong to provide: wherein the database further comprises, for each candidate glycan, the probability of a negative recognition outcome for the plurality of different probes. Doing so would lead to the accurate prediction of the binding behavior of certain glycans to accurately determine candidate glycans with a reasonable expectation of success ([16] and [42] of Wong; [0184] of Walsh).
Regarding claim 4, modified Wong teaches the method of claim 2, wherein the positive recognition outcomes comprise positive binding outcomes, and wherein the negative recognition outcomes comprise negative binding outcomes ([42] of Wong) (FIG. 3B shows the fingerprint patterns of glycan array for each influenza serotype tested). See Figure 3B, where filled circles correspond to positive recognition outcomes and hollow circles correspond to negative recognition outcomes:
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Regarding claim 5, modified Wong teaches the method of claim 1, wherein the different extant glycans are attached to glycoproteins, glycolipids, or cells at the different addresses of the array ([83] of Wong) (Many different glycoconjugates can be incorporated into the arrays… naturally occurring or synthetic glycans, glycoproteins, glycopeptides, glycolipids, bacterial and plant cell wall glycans).
Regarding claim 6, modified Wong teaches the method of claim 1, wherein the plurality of different probes comprises carbohydrate binding reagents, and wherein the positive recognition outcomes comprise positive binding outcomes ([10] and [78] of Wong) (Presenting carbohydrates in a microarray format can be an efficient way to monitor the multiple binding events of an analyte and The array is validated with a diverse set of carbohydrate binding proteins such as Influenza Hemagglutinins and anti-carbohydrate antibodies).
Regarding claim 7, modified Wong teaches the method of claim 1, further comprising contacting the array with a glycan modifying reagent ([88] of Wong) (a glycan library can be employed that has been modified to contain primary amino groups).
Regarding claim 10, modified Wong teaches the method of claim 1. Wong teaches determining candidate glycans by eye by looking at the outcome profiles and comparing them to known outcome profiles (‘fingerprint patterns’) ([42]) (serotypes can be observed by naked eyes… and be classified by glycan patterns on glass slides. FIG. 3B shows the fingerprint patterns of glycan array for each influenza serotype tested).
Modified Wong does not clearly describe wherein step (e) comprises determining with the computer, using the database and the outcome profiles, the most probable candidate glycan or most probable group of candidate glycans corresponding to different extant glycans in the array.
Walsh teaches determining with the computer, using the database and the outcome profiles, the most probable candidate glycan or most probable group of candidate glycans corresponding to different extant glycans in the array ([108] of Walsh) (identifying the unknown biological sample may further include calculating a distance between the sample point and the determined nearest reference point). Note that ‘sample point’ is an outcome profile and ‘reference point’ is a candidate glycan from a database. Walsh teaches that this approach allows for the accuracy of the determination step to be quantified ([108] of Walsh) (calculating an accuracy score… the accuracy score may include one of the following: a low…a medium… a high confidence score).
It would have been obvious for a person having ordinary skill in the art before the effective filing date of the instant application to modify the visual determination method of modified Wong to determine with the computer, using the database and the outcome profiles, the most probable candidate glycan or most probable group of candidate glycans corresponding to different extant glycans in the array as taught by Walsh to provide: wherein step (e) comprises determining with the computer, using the database and the outcome profiles, the most probable candidate glycan or most probable group of candidate glycans corresponding to different extant glycans in the array. Doing so would lead to the predictable outcome of quantifying the accuracy of the determination step with a reasonable expectation of success ([42] of Wong; [108] of Walsh).
Regarding claim 11, modified Wong teaches the method of claim 1. Modified Wong teaches determining, with a computer, the candidate glycans in the database corresponding to different extant glycans in the array (‘fingerprint patterns’) ([45], [52] of Walsh) (the functions described may be implemented in hardware, software and each reference point may be calculated from a plurality of reference measurements for… corresponding known biological compound).
Wong is silent to wherein the computer uses a machine learning algorithm to determine the candidate glycans in the database corresponding to different extant glycans in the array.
Walsh teaches of using a machine learning algorithm to determine the candidate glycans in the database corresponding to different extant glycans in the array ([43] and [66] of Walsh) (methods will be described in the following detailed description and illustrated in the accompanying drawings by various blocks, components, circuits, processes, algorithms, etc. and the reference measurements in the experimental library may be used as training data to form a glycan training point which may then be converted into machine learning input for optimizing machine learning parameters of a machine learning algorithm). Walsh teaches that machine learning algorithms are advantageous in this pursuit because the accuracy of the determinations improve over time ([62]) (Based on the comparison, the machine learning parameters may be adjusted…until the machine learning parameters are optimized… until an average difference between the predicted measurements and the experimentally obtained measurements is below a predetermined threshold).
It would have been obvious for a person having ordinary skill in the art before the effective filing date of the instant application to modify the determination method of Wong to provide a machine learning algorithm to determine the candidate glycans in the database corresponding to different extant glycans in the array as suggested by Walsh to provide: wherein the computer uses a machine learning algorithm to determine the candidate glycans in the database corresponding to different extant glycans in the array. Doing so would lead to the predictable outcome of improving the accuracy of the determinations over time with a reasonable expectation of success.
Regarding claim 12, modified Wong teaches the method of claim 1 as rejected above. Modified Wong teaches determining, with a computer, the candidate glycans in the database corresponding to different extant glycans in the array (‘fingerprint patterns’) ([45], [52] of Walsh) (the functions described may be implemented in hardware, software and each reference point may be calculated from a plurality of reference measurements for… corresponding known biological compound). Modified Wong teaches that the array comprises individual addresses ([0032] of Wong) (the substrate is addressable… an array of discrete spots).
Modified Wong does not teach wherein the determining of step (e) comprises calculating, for an individual address of the array, a probability that the outcome profile is observed given that a candidate glycan in the database is present at the individual address.
Walsh teaches calculating, for an individual address of the array, a probability that the outcome profile is observed given that a candidate glycan in the database is present at the individual address ([108] of Walsh) (identifying the unknown biological sample may further include calculating a distance between the sample point and the determined nearest reference point, and calculating an accuracy score based on this distance). Note that ‘sample point’ is an outcome profile and ‘reference point’ is a candidate glycan from a database. Walsh teaches that this approach allows for the accuracy of the determination step to be quantified ([108] of Walsh) (calculating an accuracy score… the accuracy score may include one of the following: a low…a medium… a high confidence score).
It would have been obvious for a person having ordinary skill in the art before the effective filing date of the instant application to modify the determining step of Modified Wong to a probability that the outcome profile is observed given that a candidate glycan in the database is present at the individual address as taught by Walsh to provide: wherein the determining of step (e) comprises calculating, for an individual address of the array, a probability that the outcome profile is observed given that a candidate glycan in the database is present at the individual address. Doing so would lead to the predictable outcome of quantifying the accuracy of the determination step with a reasonable expectation of success ([0032], [45], [52] of Wong, [108] of Walsh).
Regarding claim 13, modified Wong teaches the method of claim 12 as rejected above. Modified Wong teaches determining, with a computer, the candidate glycans in the database corresponding to different extant glycans in the array (‘fingerprint patterns’) ([45], [52] of Walsh) (the functions described may be implemented in hardware, software and each reference point may be calculated from a plurality of reference measurements for… corresponding known biological compound). Modified Wong teaches that the array comprises individual addresses ([0032] of Wong) (the substrate is addressable… an array of discrete spots).
Modified Wong does not clearly describe wherein the calculating, for the individual address of the array, is performed for a plurality of candidate glycans in the database.
Walsh teaches wherein the calculating, for the individual address of the array, is performed for a plurality of candidate glycans in the database ([108] of Walsh) (identifying the unknown biological sample may further include calculating a distance between the sample point and the determined nearest reference point, and calculating an accuracy score based on this distance). Note that “nearest reference point” confirms the presence of other candidate glycans and therefore the probability of a plurality of candidate glycans would be calculated. Walsh teaches that this approach allows for the accuracy of the determination step to be quantified ([108] of Walsh) (calculating an accuracy score… the accuracy score may include one of the following: a low…a medium… a high confidence score).
It would have been obvious for a person having ordinary skill in the art before the effective filing date of the instant application to modify the determination method of Modified Wong to calculate the probability of a glycan being a candidate glycan for a plurality of candidate glycans as taught by Walsh to provide: wherein the calculating, for the individual address of the array, is performed for a plurality of candidate glycans in the database. Doing so would lead to the predictable outcome of quantifying the accuracy of each determination/calculation with a reasonable expectation of success ([0032] of Wong, [108] of Walsh).
Regarding claim 14, modified Wong teaches the method of claim 13 as rejected above. Modified Wong teaches that the array comprises individual addresses ([0032] of Wong) (the substrate is addressable… an array of discrete spots).
Modified Wong does not clearly teach wherein the calculating is performed for a plurality of individual addresses in the array.
Walsh teaches wherein the calculating is performed for a plurality of individual addresses in the array ([108] of Walsh) (identifying the unknown biological sample may further include calculating a distance between the sample point and the determined nearest reference point, and calculating an accuracy score based on this distance). Walsh teaches that this approach allows for the accuracy of the determination step to be quantified ([108] of Walsh) (calculating an accuracy score… the accuracy score may include one of the following: a low…a medium… a high confidence score).
It would have been obvious for a person having ordinary skill in the art before the effective filing date of the instant application to modify the calculation of a probability of a glycan being a candidate glycan as taught by Modified Wong by performing the calculation at a plurality of addresses in the array as taught by Walsh to provide: wherein the calculating, for the individual address of the array, is performed for a plurality of candidate glycans in the database. Doing so would lead to the predictable outcome of quantifying the accuracy of each determination/calculation with a reasonable expectation of success ([0032] of Wong, [108] of Walsh).
Regarding claim 15, modified Wong teaches the method of claim 1 as rejected above. Modified Wong teaches that the array comprises individual addresses ([0032] of Wong) (the substrate is addressable… an array of discrete spots).
Modified Wong does not clearly describe wherein step (e) comprises determining a probability or likelihood that one or more candidate glycan in the database would generate one or more of the recognition outcomes for the extant glycan attached to one or more of the addresses of the array.
Walsh teaches determining a probability or likelihood that one or more candidate glycan in the database would generate one or more of the recognition outcomes for the extant glycan attached to one or more of the addresses of the array ([62] of Walsh) (the machine learning algorithm to obtain predicted measurements for the known biological compounds. The predicted measurements may then be compared against the experimentally obtained reference measurements of the known biological compounds). Walsh teaches that machine learning algorithms are advantageous in this pursuit because the accuracy of the determinations improve over time ([62] of Walsh) (Based on the comparison, the machine learning parameters may be adjusted…until the machine learning parameters are optimized… until an average difference between the predicted measurements and the experimentally obtained measurements is below a predetermined threshold).
It would have been obvious for a person having ordinary skill in the art before the effective filing date of the instant application to modify the determination method of modified Wong to provide determining a probability or likelihood that one or more candidate glycan in the database would generate one or more of the recognition outcomes for the extant glycan attached to one or more of the addresses of the array as taught by Walsh. Doing so would lead to the predictable outcome of improving the accuracy of the determinations over time with a reasonable expectation of success.
Regarding claim 16, modified Wong teaches the method of claim 1, wherein individual probes in the series of probes are each promiscuous for a subset of different glycans in the array ([32] of Wong) (a plurality of glycan capture probes, each of which can recognize a different target influenza serotype, are attached to the substrate in an array of discrete spots). Note that a “serotype” would be a subset of different glycans.
Regarding claim 17, modified Wong teaches the method of claim 16, wherein the individual probes each comprise a carbohydrate binding reagent that recognizes two or more different carbohydrate epitopes present in the different extant glycans in the array ([21] of Wong) (providing at least one type of nanoparticle probe comprising detector moieties, wherein the detector moieties on each type of probe has a configuration that can bind to the target analyte). From this, it is clear that a ‘probe’ as described by Wong has a plurality of detector moieties which may bind to a plurality carbohydrate epitopes, as evidenced by annotated Figure 3B below, which shows that a carbohydrate binding probe contacted at a certain address in an array may bind to a plurality of carbohydrate epitopes (arrows are pointing to the probe that recognizes a plurality of carbohydrate epitopes):
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Regarding claim 19, Wong teaches:
(a) a detector configured to acquire signals from a plurality of interactions occurring between a plurality of different probes and a plurality of different extant glycans in a sample ([123] and [125] of Wong) (glycans with different structures can target hemagglutinin specific to H5N1, H3N1, and H1N1 serotypes for influenza virus detection and Results, such as Influenza serotypes, can be observed by… barcode scanner or laser pen on glass slides and classify by fingerprint patterns on glycan array); (Note that “glycans” are extant glycans and “hemagglutinin specific to H5N1, H3N1, and H1N1 serotypes for influenza virus” is a plurality of different probes)
Wong is silent to:
(b) a database comprising information characterizing or identifying a plurality of candidate glycans;
(c) a computer processor configured to:
(i) communicate with the database,
(ii) process the signals to produce a plurality of outcome profiles, wherein each of the outcome profiles comprises a plurality of recognition outcomes for interaction of an extant glycan of (a) to the plurality of different probes, wherein individual recognition outcomes of the plurality of recognition outcomes comprise a measure of interaction between an extant glycan of (a) and a different probe of the plurality of different probes,
(iii) process the recognition profiles to determine a probability for each of the probes interacting with each of the candidate glycans in the database according to an interaction model for each of the probes; and
(iv) output an identification of selected candidate glycans, the selected candidate glycans being candidate glycans in the database having a probability for interaction with each of the probes that is most compatible with the plurality of recognition outcomes for the extant glycans.
In the analogous art of detecting and characterizing glycans, Walsh teaches:
(b) a database comprising information characterizing or identifying a plurality of candidate glycans ([0184]) (assignment accuracies… use… a defined library and de-identified glycan standards);
(c) a computer processor configured to:
(i) communicate with the database ([47]) (the system 300 (e.g. the sample identifying unit 308) may be configured to… comparing the sample point against the plurality of stored reference points),
(ii) process the signals to produce a plurality of outcome profiles, wherein each of the outcome profiles comprises a plurality of recognition outcomes for interaction of an extant glycan of (a) to the plurality of different probes, wherein individual recognition outcomes of the plurality of recognition outcomes comprise a measure of interaction between an extant glycan of (a) and a different probe of the plurality of different probes ([47]) (may be configured to calculate a sample point in the two-dimensional plot from the more than two sample measurements for the unknown biological sample), (Note that the calculated sample points from the sample measurements is being interpreted as a plurality of outcome profiles)
(iii) process the recognition profiles to determine a probability for each of the probes interacting with each of the candidate glycans in the database according to an interaction model for each of the probes ([47]) (may be configured to identify the unknown biological sample by comparing the sample point against the plurality of stored reference points in the two-dimensional plot); and
(iv) output an identification of selected candidate glycans, the selected candidate glycans being candidate glycans in the database having a probability for interaction with each of the probes that is most compatible with the plurality of recognition outcomes for the extant glycans ([47] and [0184]) (may be configured to identify the unknown biological sample and The probability of correctly identifying an unknown glycan given a distance for the unknown glycan).
See also figure 3 of Walsh:
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Walsh teaches that the use of a computer, database, and outcome profiles to determine the identity of a glycan is advantageous because it enables the automation of glycan assignment ([8]) (The technique… allows users to compare… unknown/unidentified glycan against libraries of known/identified glycans… to identify the unknown glycan… Automated glycan assignment can then be performed).
It would have been obvious for a person having ordinary skill in the art before the effective filing date of the instant application to modify the visual determination method of Wong to instead utilize a computer and database as described by Walsh to provide: (b) a database comprising information characterizing or identifying a plurality of candidate glycans; (c) a computer processor configured to: (i) communicate with the database, (ii) process the signals to produce a plurality of outcome profiles, wherein each of the outcome profiles comprises a plurality of recognition outcomes for interaction of an extant glycan of (a) to the plurality of different probes, wherein individual recognition outcomes of the plurality of recognition outcomes comprise a measure of interaction between an extant glycan of (a) and a different probe of the plurality of different probes, (iii) process the recognition profiles to determine a probability for each of the probes interacting with each of the candidate glycans in the database according to an interaction model for each of the probes; and (iv) output an identification of selected candidate glycans, the selected candidate glycans being candidate glycans in the database having a probability for interaction with each of the probes that is most compatible with the plurality of recognition outcomes for the extant glycans because doing so would lead to the predictable outcome of automating the determination of candidate glycans with a reasonable expectation of success (see [123], [125], [0078] of Wong and [47], [8] of Walsh).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wong et al. (US20150160217A1) in view of Walsh et al. (US20220013197A1) as applied to claim 7 above, further in view of Seeberger et al. (US20050221337A1).
Regarding claim 8, modified Wong teaches the method of claim 7 as rejected above. Modified Wong teaches of using a glycan modifying reagent that changes the chemical makeup of the glycans to contain primary amino groups ([88] of Wong) (a glycan library can be employed that has been modified to contain primary amino groups… After attachment of all the desired glycans, slides can further be incubated with ethanolamine buffer to deactivate remaining NHS functional groups on the solid support).
Modified Wong is silent to wherein the glycan modifying reagent comprises an N-glycosidase or endoglycosidase, hydrazine, ammonia, ammonium carbonate, N-bromosuccinimide, sodium hypochlorite, sodium hydroxide, or sodium borohydride.
In the analogous art of microarrays and microspheres comprising glycans, Seeberger teaches the use of endoglycosidase as a glycan modifying reagent ([0078] of Seeberger) (A third class of enzymes suitable for carbohydrate modification is endoglycosidases). Seeberger teaches that this changes the chemical makeup of the glycan substrate, causing it to exhibit different binding characteristics ([76] of Seeberger) (These enzymes often have different substrate specificity or carry out a different chemical reaction).
It would have been obvious to a person having ordinary skill in the art to modify the glycan modification reagent of Wong to be an endoglycosidase as described by Seeberger because doing so would lead to the predictable outcome of modifying the glycans to contain primary amino groups or other structural moieties with a reasonable expectation of success (see [88] of Wong; [76], [78], [88] of Seeberger).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wong et al. (US20150160217A1) in view of Walsh et al. (US20220013197A1) as applied to claim 7 above, further in view of Geissner et al. (Annu. Rev. Anal. Chem., Vol. 9, June 2016, pgs. 223-247).
Regarding claim 9, modified Wong teaches the method of claim 7, further comprising. Modified Wong teaches contacting the glycan array with a glycan modifying reagent that changes the chemical makeup of the glycans to contain primary amino groups ([88] of Wong) (a glycan library can be employed that has been modified to contain primary amino groups... After attachment of all the desired glycans, slides can further be incubated with ethanolamine buffer to deactivate remaining NHS functional groups on the solid support).
Wong does not clearly describe after contacting the array with the glycan modifying reagent:
(f) contacting the array with a second plurality of different probes, the different probes recognizing different carbohydrate moieties; and
(g) detecting positive recognition outcomes of the second plurality of different probes at individual addresses of the array, thereby producing second outcome profiles for the addresses.
In the analogous art of glycan arrays, Giessner teaches of contacting glycans with glycan modifying reagents (glycosyltransferases) and then contacting the array with a second plurality of different probes (lectins) (pg. 230 third paragraph) (Glycan modifications can be detected for glycosyltransferases after incubation on slides printed with chemically defined oligosaccharides using fluorescent lectins specific for the transferred sugar (70)). Giessner teaches that this approach enables the substrate scope of glycan-targeting enzymes to be studied. (pg. 230 third paragraph) (With this detection assay, several β(1→2) core-xylosylated N-glycans were examined as targets for galactosyl-, N-acetylgalactosaminyl-, and fucosyltransferases).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to combine the method of Modified Wong with the method of contacting the array with a second plurality of different probes to detect positive recognition outcomes as taught by Giessner to provide: (f) contacting the array with a second plurality of different probes, the different probes recognizing different carbohydrate moieties; and (g) detecting positive recognition outcomes of the second plurality of different probes at individual addresses of the array, thereby producing second outcome profiles for the addresses because doing so would lead to the predictable outcome of studying the substrate scope of glycan-targeting enzymes (see [88] of Wong; pg. 230 third paragraph of Giessner).
Conclusion
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/H.D.C./Examiner, Art Unit 1758
/HENRY H NGUYEN/Primary Examiner, Art Unit 1758
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