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 .
Claim Status
Claims 1-31 are currently pending and under examination herein.
Claim(s) 1-31 are rejected.
Claim(s) 1-31 is objected to.
Priority
The instant application also claims benefit to U.S. provisional application No. 63153039 filed on 02/24/2021. Domestic benefit is acknowledged. As such, the effective filing date of claims 1-31 is 02/24/2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/23/2023 and 9/08/2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. A signed copy of a list of references cited from each IDS is included in this Office Action.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: 44 lacks a description within the specification. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Color photographs and color drawings are not accepted in utility applications unless a petition filed under 37 CFR 1.84(a)(2) is granted. Any such petition must be accompanied by the appropriate fee set forth in 37 CFR 1.17(h), one set of color drawings or color photographs, as appropriate, if submitted via the USPTO patent electronic filing system or three sets of color drawings or color photographs, as appropriate, if not submitted via the via USPTO patent electronic filing system, and, unless already present, an amendment to include the following language as the first paragraph of the brief description of the drawings section of the specification:
The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.
Color photographs will be accepted if the conditions for accepting color drawings and black and white photographs have been satisfied. See 37 CFR 1.84(b)(2).
Specification
The disclosure is objected to because of the following informalities: There is no description for 44 as pointed out in Figure 1C of the drawings. Appropriate correction is required.
Claim Objections
Claims 1-31 are objected to because of the following informalities: Claim 1 recites “a host bacteria” and should be corrected to “a host bacterium”. All other claims are objected to by virtue of dependency. Appropriate correction is required.
Claim Interpretation
The Examiner notes that claims 1-20 and 23-25 recite contingent limitations where the broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met (see MPEP 2111.04). Therefore, claims that recite limitations following contingent limitations are not considered in the broadest reasonable interpretation in view of the prior art. For instance, claim limitations that recite a “when” condition is contingent and not considered in view of the prior art.
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 1-31 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea and/or a natural phenomenon without significantly more.
In accordance with MPEP 2106, claims found to recite statutory subject matter (Step 1: YES) 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).
Claim 1, 21, 22 recites obtaining an estimate of a lag time: for a plurality of time points in a time window from an initial time point to an end time point, fitting one or more candidate functions over a fitting time window from the initial time point to a current time point, wherein fitting estimates a set of growth curve summary parameters comprising at least a lag time and a goodness of fit parameter; selecting a best fit function for the current time point from the one or more fitted candidate functions based on the goodness of fit parameters, wherein the one or more candidate functions each comprise a different functional form and at least one of which is a sigmoidal function, and when none of the goodness of fit estimates pass a threshold goodness of fit then classifying the time series dataset as flat and setting the lag time to the end time point; selecting from the plurality of time points, a best time point based on goodness of fit values at the plurality of time points, searching for an alternative best time point closer in time to, and greater than a minimum time point than the best time fit and updating the best time point to the alternative best time point when the goodness of fit of the alternative time point is within a threshold amount of the goodness of fit of the best time point; estimating a lag time for the best time point, wherein when the goodness of fit exceeds a threshold goodness of fit the lag time is obtained from the growth curve summary parameter otherwise classifying the time series dataset as flat and setting the lag time to the end point; and reporting at least the lag time or hold time calculated as the estimated lag time from which the lag time for a control is subtracted.
Claim 2 recites the computer implemented method as claimed in claim 1, wherein the one or more candidate functions is an ordered set of candidate functions, and fitting one or more candidate functions comprises sequentially fitting each candidate function according to an order in the ordered set and the sequential fitting is terminated and the candidate function is selected as the best fit function when the goodness of fit of the fitted candidate function exceeds a predefined threshold goodness.
Claim 3 recites the ordered set of candidate functions comprises a Gompertz function, a Logistic function and a Richards function.
Claim 4 recites the computer implemented method as claimed in claim 3, wherein when fitting of each of the Gompertz function, the Logistic function and the Richards function failed to generate a goodness of fit exceeding the predefined threshold goodness, then applying a Blackman window function to the time series data and repeating the fitting process, and when the repeated fits for each of the Gompertz function, the Logistic function and the Richards function fail to generate a goodness of fit exceeding the predefined threshold goodness, then classifying the time series dataset as flat and setting the lag time to the end time point.
Claim 5 recites the computer implemented method as claimed in claim 1, wherein prior to fitting one or more candidate functions over a fitting time window, determining a maximum height of the time series dataset, and when the maximum height is less than a threshold maximum height, then classifying the time series dataset as flat and setting the lag time to the end time point and terminating the fitting process.
Claim 6 searching for an alternative best time point comprises: determining when the best time point is less than the minimum time point and when the best time point is less than the minimum time point then the alternative time point is selected based on the closest alternative time point on or after the minimum time point with a goodness of fit within a threshold difference of the goodness of fit for the best time point, and when the best time point is greater than the minimum time point then the alternative time point is selected based on the alternative time point being greater than or equal to the minimum time point and which is the closest alternative time point to the minimum time point and with a goodness of fit within a threshold difference of the goodness of fit for the best time point.
Claim 7 recites the computer implemented method as claimed in claim 6 wherein the minimum time point is 5 hours, the goodness of fit is the coefficient of determination (R2), and the threshold difference is 0.03.
Claim 8 recites the computer implemented method as claimed in claim 6 wherein the goodness of fit is the co-efficient of determination (R2) and the predefined threshold goodness is 0.6.
Claim 9 recites the computer implemented method as claimed in claim 1, wherein the end time point is 48 hours.
Claim 10 recites the computer implemented method as claimed in claim 1. wherein the minimum time point is 5 hours.
Claim 11 recites the computer implemented method as claimed in claim 1, wherein when the time series data is classified as flat, estimating a variability measure and when the variability measure exceeds a variability threshold rejecting the time series dataset and classifying the time series dataset as abnormal.
Claim 12 recites the computer implemented method as claimed in claim 1, further comprising normalizing the time series dataset based on an associated control curve.
Claim 13 recites wherein a host-growth curve is a host only time-series and normalizing the time series dataset comprises subtracting the host only time-series dataset from the time series dataset, wherein the time series dataset and the host only time-series dataset are obtained from separate wells on a same multi-well plate.
Claim 14 recites the multi-well plate further comprises one or more media control wells and the computer implemented method further comprises performing quality assurance comprising at least identifying anomalous media control wells or anomalous host cell only wells and excluding time-series datasets associated with any identified anomalous media control wells or anomalous host cell wells.
Claim 16 recites wherein the growth curve summary parameters comprise at least a max height, a slope, a lag time, and an area under curve, and the goodness of fit comprises one or more of a coefficient of determination (R2), a parameter based upon an error term or a residual term, or a summary statistic of residuals.
Claim 17 recites the computer implemented method as claimed in claim 12 wherein the end time point is prior to a final time point and repeating the normalization obtaining.
Claim 18 recites wherein the end time point is prior to a final time point, further comprising determining a final class confidence estimate which is an estimate that a classification of whether the phage is efficacious at the end time point matches the classification of whether the phage is efficacious at the final time point, determining a final class confidence estimate is determined based on identifying a set of similar host phage response datasets in a set of historical host phage response datasets comprising a plurality of flat host phage response datasets and a plurality of non-flat host phage response datasets and each comprising data points from a start time to the final time, wherein the final class confidence estimate is determined based on the similar host phage response datasets in which an estimate that the classification of whether the phage is efficacious at the end time point matches the classification of whether the phage is efficacious at the final time point.
Claim 19 recites the computer implemented method as claimed in claim 18 wherein the final class confidence estimate is generated using a random forest based classifier trained on the set of historical host phage response datasets.
Claim 20 recites the computer implemented method as claimed in claim 18, wherein a phage is selected based on the final class confidence estimate exceeding a stopping threshold at an end time point prior to the final time point.
The limitations of obtaining an estimate of lag time, fitting one or more candidate functions over a fitting time window from the initial time point to a current time point, wherein fitting estimates a set of growth curve summary parameters comprising at least a lag time and a goodness of fit parameter; when the time series data is classified as flat, estimating a variability measure and when the variability measure exceeds a variability threshold rejecting the time series dataset and classifying the time series dataset as abnormal; normalizing the time series dataset based on an associated control curve; a host-growth curve is a host only time-series and normalizing the time series dataset comprises subtracting the host only time-series dataset from the time series dataset, wherein the time series dataset and the host only time-series dataset are obtained from separate wells on a same multi-well plate; the end time point is prior to a final time point and repeating the normalization obtaining; determining a final class confidence estimate which is an estimate that a classification of whether the phage is efficacious at the end time point matches the classification of whether the phage is efficacious at the final time point; are verbal equivalents of mathematical calculations and therefore falls under the “mathematical concept” grouping of ideas.
The limitations of selecting a best fit function for the current time point from the one or more fitted candidate functions based on the goodness of fit parameters, wherein the one or more candidate functions each comprise a different functional form and at least one of which is a sigmoidal function, and when none of the goodness of fit estimates pass a threshold goodness of fit then classifying the time series dataset as flat and setting the lag time to the end time point; selecting from the plurality of time points, a best time point based on goodness of fit values at the plurality of time points, searching for an alternative best time point closer in time to, and greater than a minimum time point than the best time fit and updating the best time point to the alternative best time point when the goodness of fit of the alternative time point is within a threshold amount of the goodness of fit of the best time point; estimating a lag time for the best time point, wherein when the goodness of fit exceeds a threshold goodness of fit the lag time is obtained from the growth curve summary parameter otherwise classifying the time series dataset as flat and setting the lag time to the end point; fitting one or more candidate functions comprises sequentially fitting each candidate function according to an order in the ordered set and the sequential fitting is terminated and the candidate function is selected as the best fit function when the goodness of fit of the fitted candidate function exceeds a predefined threshold goodness; determining a maximum height of the time series dataset, and when the maximum height is less than a threshold maximum height, then classifying the time series dataset as flat and setting the lag time to the end time point and terminating the fitting process; determining when the best time point is less than the minimum time point and when the best time point is less than the minimum time point then the alternative time point is selected based on the closest alternative time point on or after the minimum time point with a goodness of fit within a threshold difference of the goodness of fit for the best time point, and when the best time point is greater than the minimum time point then the alternative time point is selected based on the alternative time point being greater than or equal to the minimum time point and which is the closest alternative time point to the minimum time point and with a goodness of fit within a threshold difference of the goodness of fit for the best time point; determining a final class confidence estimate which is an estimate that a classification of whether the phage is efficacious at the end time point matches the classification of whether the phage is efficacious at the final time point, determining a final class confidence estimate is determined based on identifying a set of similar host phage response datasets in a set of historical host phage response datasets comprising a plurality of flat host phage response datasets and a plurality of non-flat host phage response datasets and each comprising data points from a start time to the final time, wherein the final class confidence estimate is determined based on the similar host phage response datasets in which an estimate that the classification of whether the phage is efficacious at the end time point matches the classification of whether the phage is efficacious at the final time point; constitute a mental process and falls under the “mental process” grouping of ideas. Selecting from a plurality of time points, searching for an alternative time point with subsequent updates, estimating a lag time for the best time point based on threshold, selecting a best fit function from a set of candidate functions, fitting one or more candidate functions by sequentially fitting each function according to an order and subsequent selection; determining maximum height and classification; the process of determining an alternative time point; determining a final class confidence estimate based classification between two sets of data; reporting the estimate that the phage is efficacious based on discrimination can be practically performed in the human mind or with a pen and paper and therefore is a mental process. The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 and Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016)
The limitations reciting the minimum time point is 5 hours, the goodness of fit is the coefficient of determination (R2), and the threshold difference is 0.03; the end time point is 48 hours; the minimum time point is 5 hours; merely serve to further limit the mental process.
The limitations reciting the one or more candidate functions is an ordered set of candidate functions; the ordered set of candidate functions comprises a Gompertz function, a Logistic function and a Richards function; merely serve to further limit the mathematical concept. As such claims 1-31 recite abstract ideas.
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). This judicial exception is not integrated into a practical application because the claims do not recite additional elements that reflects an improvement to technology or applies or uses the recited judicial exception in some other meaningful way. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment. Specifically, the claims recite the following additional elements:
Claim 1 recites a computer implemented method for analyzing host phage response data, the method comprising: receiving or accessing a host phage response dataset, wherein the dataset comprises a time series dataset for a host-phage combination in which a host bacteria is grown in the presence of a phage, and each data point in the time series dataset associated comprises a measurement of a parameter indicative of the growth of the host bacteria in the presence of the phage at a specific time.
Claim 15 recites the time series dataset is obtained from a multi-well plate comprising a plurality of host-phage combinations, a set of positive control wells, a set of media control wells, a set of host cell control wells, a first set of diluted host cell wells and a second set of diluted host cell wells, and the computer implemented method is performed for each host-phage combination on the multi-well plate, and a report is generated for each host-phage combination on the multi-well plate.
Claim 17 recites receiving an updated host response dataset comprising additional data point and repeating reporting steps, wherein the reporting includes an estimate of a probability that the phage is efficacious reporting the estimate that the phage is efficacious.
Claim 18 recites the computer implemented method as claimed in claim 1, wherein the end time point is prior to a final time point, reporting the estimate that the phage is efficacious further comprises reporting the estimate that the phage is efficacious at the end time point and final class confidence estimate.
Claim 21 recites a non-transitory, computer program product comprising computer executable instructions for analyzing host phage response data, the instructions executable by a computer for: receiving a host phage response dataset, wherein the dataset comprises a time series dataset for a host-phage combination in which a host bacteria is grown in the presence of a phage, and each data point in the time series dataset comprises a measurement of a parameter indicative of the growth of the host bacteria in the presence of the phage at a specific time and reporting at least the lag time or a hold time calculated as the estimated lag time from which the lag time for a control is subtracted.
Claim 22 recites a computing apparatus comprising: at least one memory, and at least one processor wherein the memory comprises instructions to configure the at least one processor for: receiving a host phage response dataset, wherein the dataset comprises a time series dataset for a host-phage combination in which a host bacteria is grown in the presence of a phage, and each data point in the time series dataset comprises a measurement of a parameter indicative of the growth of the host bacteria in the presence of the phage at a specific time and reporting at least the lag time or a hold time calculated as the estimated lag time from which the lag time for a control is subtracted.
Claim 23, 26, and 29 recites wherein the host bacteria is grown as planktonic cells.
Claim 24, 27, and 30 recites wherein the host bacteria is grown as a biofilm.
Claim 25, 28, and 31 recites wherein phage-phage synergy, antibiotic-phage synergy, or antibiotic-phage-phage synergy is measured.
The limitations of receiving a host phage response dataset, wherein the dataset comprises a time series dataset for a host-phage combination in which a host bacteria is grown in the presence of a phage, and each data point in the time series dataset comprises a measurement of a parameter indicative of the growth of the host bacteria in the presence of the phage at a specific time; reporting at least the lag time or a hold time calculated as the estimated lag time from which the lag time for a control is subtracted; receiving an updated host response dataset comprising additional data point and repeating reporting steps, wherein the reporting includes an estimate of a probability that the phage is efficacious; receiving an updated host response dataset comprising additional data point and repeating reporting steps; constitute insignificant extra solution activity as they are mere data gathering or data outputting steps. Of note, the courts have ruled in Electric Power Group, LLC V. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) that the collection, analysis, and display of data are considered insignificant extra-solution activity and does not integrate the judicial exception into a practical application (see MPEP 2106.05(g)).
The limitations reciting the time series dataset is obtained from a multi-well plate comprising a plurality of host-phage combinations, a set of positive control wells, a set of media control wells, a set of host cell control wells, a first set of diluted host cell wells and a second set of diluted host cell wells; the reporting includes an estimate of a probability that the phage is efficacious reporting the estimate that the phage is efficacious further comprises reporting the estimate that the phage is efficacious at the end time point and final class confidence estimate, wherein determining a final class confidence estimate is determined based on identifying a set of similar host phage response datasets in a set of historical host phage response datasets comprising a plurality of flat host phage response datasets and a plurality of non-flat host phage response datasets and each comprising data points from a start time to the final time, wherein the final class confidence estimate is determined based on the similar host phage response datasets in which an estimate that the classification of whether the phage is efficacious at the end time point matches the classification of whether the phage is efficacious at the final time point; host bacteria is grown as planktonic cells; the host bacteria is grown as a biofilm; and phage-phage synergy, antibiotic-phage synergy, or antibiotic-phage-phage synergy is measured merely serve to further limit the insignificant data gathering step and does not integrate into a practical application.
Furthermore, there are no limitations that indicate that the claimed computer, processor, input device or computer-readable medium require anything other than generic computing systems. 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. As such, claims 1-31 do not integrate the abstract ideas into a practical application.
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 amount to mere instructions to implement the abstract idea in a generic field-of-use and/or technological environment. The instant claims recite the following additional elements:
Claim 1 recites a computer implemented method for analyzing host phage response data, the method comprising: receiving or accessing a host phage response dataset, wherein the dataset comprises a time series dataset for a host-phage combination in which a host bacteria is grown in the presence of a phage, and each data point in the time series dataset associated comprises a measurement of a parameter indicative of the growth of the host bacteria in the presence of the phage at a specific time; obtaining an estimate of a lag time.
Claim 15 recites the time series dataset is obtained from a multi-well plate comprising a plurality of host-phage combinations, a set of positive control wells, a set of media control wells, a set of host cell control wells, a first set of diluted host cell wells and a second set of diluted host cell wells, and the computer implemented method is performed for each host-phage combination on the multi-well plate, and a report is generated for each host-phage combination on the multi-well plate.
Claim 17 recites receiving an updated host response dataset comprising additional data point and repeating reporting steps, wherein the reporting includes an estimate of a probability that the phage is efficacious reporting the estimate that the phage is efficacious.
Claim 18 recites the computer implemented method as claimed in claim 1, wherein the end time point is prior to a final time point, reporting the estimate that the phage is efficacious further comprises reporting the estimate that the phage is efficacious at the end time point and final class confidence estimate.
Claim 21 recites a non-transitory, computer program product comprising computer executable instructions for analyzing host phage response data, the instructions executable by a computer.
Claim 22 recites a computing apparatus comprising: at least one memory, and at least one processor wherein the memory comprises instructions to configure the at least one processor.
Claim 23, 26, and 29 recites wherein the host bacteria is grown as planktonic cells.
Claim 24, 27, and 30 recites wherein the host bacteria is grown as a biofilm.
Claim 25, 28, and 31 recites wherein phage-phage synergy, antibiotic-phage synergy, or antibiotic-phage-phage synergy is measured.
As aforementioned, there are no limitations that indicate that the claimed computer, processor, input device or computer-readable medium require anything other than generic computing systems. 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 limitations reciting receiving or accessing a host phage response dataset, wherein the dataset comprises a time series dataset for a host-phage combination in which a host bacteria is grown in the presence of a phage, and each data point in the time series dataset associated comprises a measurement of a parameter indicative of the growth of the host bacteria in the presence of the phage at a specific time; obtaining an estimate of a lag time; the time series dataset is obtained from a multi-well plate comprising a plurality of host-phage combinations, a set of positive control wells, a set of media control wells, a set of host cell control wells, a first set of diluted host cell wells and a second set of diluted host cell wells, and the computer implemented method is performed for each host-phage combination on the multi-well plate, and a report is generated for each host-phage combination on the multi-well plate; receiving an updated host response dataset comprising additional data point and repeating reporting steps, wherein the reporting includes an estimate of a probability that the phage is efficacious reporting the estimate that the phage is efficacious amount; wherein the end time point is prior to a final time point, reporting the estimate that the phage is efficacious further comprises reporting the estimate that the phage is efficacious at the end time point and final class confidence estimate; a non-transitory, computer program product comprising computer executable instructions for analyzing host phage response data, the instructions executable by a computer; a computing apparatus comprising: at least one memory, and at least one processor wherein the memory comprises instructions to configure the at least one processor; the host bacteria is grown as planktonic cells; the host bacteria is grown as a biofilm; and phage-phage synergy, antibiotic-phage synergy, or antibiotic-phage-phage synergy is measured; amount to receiving or obtaining specific types of data via computer systems, non-transitory mediums, or computer program products which are well-understood, routine and conventional activities. Specifically, the courts have identified steps of receiving data over a network or storing and retrieving information in memory as conventional computer functions in Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC V. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., V. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); 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); and Versata Dev. Group, Inc. V. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
There are no additional elements that 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 1-31 are not patent eligible.
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.
The present rejection(s) reference specific passages from cited prior art. However,
Applicant is advised that the rejections are based on the entirety of each cited prior art. That is,
each cited prior art reference “must be considered in its entirety”. (See MPEP 2141.02(VI))
Therefore, Applicant is advised to review all portions of the cited prior art if traversing a
rejection based on the cited prior art.
Claim(s) 1-2, 5-6, 9-22, 24, 27, and 30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Amgarten et al. (“Three novel Pseudomonas phages isolated from composting provide insights into the evolution and diversity of tailed phages”, BMC Genomics: (2017) 18:346) as filed in the IDS on 8/23/2023 in view of Buchanan et al. (“When is simple good enough: a comparison of the Gompertz, Baranyi, and three-phase linear models for fitting bacterial growth curves”, Food Microbiology, 1997, 14, 313-326) as filed in the IDS on 8/23/2023.
Regarding claim 1, Amgarten teaches:
A computer implemented method (see “Acknowledgements”; computational analyses and computational tool support is explicitly mentioned; page 16) for analyzing host phage response data, the method comprising:
receiving or accessing a host phage response dataset, wherein the dataset comprises a time series dataset for a host-phage combination in which a host bacteria is grown in the presence of a phage, and each data point in the time series dataset associated comprises a measurement of a parameter indicative of the growth of the host bacteria in the presence of the phage at a specific time (time-series samples of a composting unit were obtained and data sets were generated wherein presence of phages/host were verified per sample as well as their relative abundance; see page 10 last paragraph to page 11).
Amgarten does not teach obtaining an estimate of a lag time comprising: for a plurality of time points in a time window from an initial time point to an end time point, fitting one or more candidate functions over a fitting time window from the initial time point to a current time point, wherein fitting estimates a set of growth curve summary parameters comprising at least a lag time and a goodness of fit parameter; selecting a best fit function for the current time point from the one or more fitted candidate functions based on the goodness of fit parameters, wherein the one or more candidate functions each comprise a different functional form and at least one of which is a sigmoidal function, and when none of the goodness of fit estimates pass a threshold goodness of fit then classifying the time series dataset as flat and setting the lag time to the end time point; selecting from the plurality of time points, a best time point based on goodness of fit values at the plurality of time points; searching for an alternative best time point closer in time to, and greater than, a minimum time point than the best time fit and updating the best time point to the alternative best time point when the goodness of fit of the alternative time point is within a threshold amount of the goodness of fit of the best time point; estimating a lag time for the best time point, wherein when the goodness of fit exceeds a threshold goodness of fit the lag time is obtained from the growth curve summary parameter otherwise classifying the time series dataset as flat and setting the lag time to the end time point; and reporting at least the lag time or a hold time calculated as the estimated lag time from which the lag time for a control is subtracted.
Buchanan teaches:
obtaining an estimate of a lag time comprising: for a plurality of time points in a time window from an initial time point to an end time point, fitting one or more candidate functions over a fitting time window from the initial time point to a current time point, wherein fitting estimates a set of growth curve summary parameters comprising at least a lag time and a goodness of fit parameter (see “Fitting experimental data using three-phase linear, Gompertz, and Baranyi models”; where the goodness of fit parameter is determined via the root mean square values in addition to assessing the impact of growth rates and lag durations (e.g. tlag, t max, etc.) on page 320);
selecting a best fit function for the current time point from the one or more fitted candidate functions based on the goodness of fit parameters, wherein the one or more candidate functions each comprise a different functional form and at least one of which is a sigmoidal function (see page 320; where Table 2 is mentioned to have been used for a comparison of RMS values (goodness of fit parameter) between the different models including those based on a sigmoidal function like the Gompertz or Baranyi models), and when none of the goodness of fit estimates pass a threshold goodness of fit then classifying the time series dataset as flat and setting the lag time to the end time point (see “Claim Interpretation” above).
selecting from the plurality of time points, a best time point based on goodness of fit values at the plurality of time points (Table 2 is obtained from a plurality of time points and selecting a best time point based on goodness of fit is implied from the table analysis); Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Buchanan’s curve-fitting software into Amgarten’s host phage analysis pipeline in order to better define the physiological basis underpinning growth models to develop a conceptual framework that accounts for both individual cells and bacterial growth. This would have been accomplished with reasonable expectation of success as both are directed to the same problem of growth analysis in the same field of endeavor.
Buchanan does not explicitly teach searching for an alternative best time point closer in time to, and greater than, a minimum time point than the best time fit and updating the best time point to the alternative best time point when the goodness of fit of the alternative time point is within a threshold amount of the goodness of fit of the best time point; estimating a lag time for the best time point, wherein when the goodness of fit exceeds a threshold goodness of fit the lag time is obtained from the growth curve summary parameter otherwise classifying the time series dataset as flat and setting the lag time to the end time point (see “Claim Interpretation” for contingent claim limitations); and reporting at least the lag time or a hold time calculated as the estimated lag time from which the lag time for a control is subtracted. However, Buchanan does disclose Table 2 which discloses the root mean square value (goodness of fit), lag phase duration, and other parameters that would allow for one of ordinary skill in the art to search for an alternative best time point closer in time to, and greater than, a minimum time point than the best time fit and update the best time point to the alternative best time point when the goodness of fit of the alternative time point is within a threshold amount of the goodness of fit of the best time point; estimate a lag time for the best time point, wherein when the goodness of fit exceeds a threshold goodness of fit the lag time is obtained from the growth curve summary parameter otherwise classifying the time series dataset as flat and setting the lag time to the end time point and report at least the lag time or a hold time calculated as the estimated lag time from which the lag time for a control is subtracted (see Table 2 on page 324 with values). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to update the best time point, estimate and report the best lag time based on the best time point (Table 2 provides a finite number of predictable solutions for reporting the best lag time at the best time point because the table is limited to those particular values and the table is designed as a summary of growth kinetics which would include reporting the post-update best lag time; see page 320 paragraphs 3-4). There would be a reasonable expectation of success as both are directed to the same problem of growth analysis in the same field of endeavor.
The Examiner also notes that the classification of a time data series as flat (meaning demonstrating no growth as recited by the applicant’s specification in paragraph [0009]) is an inherent property when there is no best fit function selection. Accordingly, a flat time data series necessarily involves selecting a best fit function for the current time point from the one or more fitted candidate functions based on the goodness of fit parameters wherein the goodness of fit estimates are not considered in the selection process due to being less than a threshold. It is elementary that the mere recitation of a newly discovered function or property, inherently possessed by things in the prior art, does not cause a claim drawn to distinguish of the prior art. Under the principles of inherency, if a prior art device, in its normal and usual operation, would necessarily perform the method claimed, then the method claimed will be considered to be anticipated by the prior art device. Additionally, where the Patent Office has reason to believe that a functional limitation asserted to be critical for establishing novelty in the claimed subject matter may, in fact, be an inherent characteristic of the prior art, it possesses the authority to require the applicant to prove that the subject matter shown to be in the prior art does not possess the characteristic relied on (see MPEP § 2112).
Regarding claim 2, Buchanan teaches:
The computer implemented method as claimed in claim 1, wherein the one or more candidate functions is an ordered set of candidate functions, and fitting one or more candidate functions comprises sequentially fitting each candidate function according to an order in the ordered set and the sequential fitting is terminated and the candidate function is selected as the best fit function when the goodness of fit of the fitted candidate function exceeds a predefined threshold goodness (Table 2 discloses Gompertz and Baranyi functions to be fitted on experimental data in the included set of 3 models and the models are compared to one another to assess the best fit; see page 320-321 in 5th paragraph). The Examiner notes that although the Applicant states that they sequentially fit each candidate function in an ordered set, for similar reasons as above, there is a finite number of solutions within the available table and therefore, it would be obvious to try to fit each function until the best fit function is selected (Table 2 provides a finite number of predictable solutions for reporting the best lag time at the best time point because the table is limited to those particular values and the table is designed as a summary of growth).
Regarding claim 5, Buchanan teaches:
The computer implemented method as claimed in claim 1, wherein prior to fitting one or more candidate functions over a fitting time window, determining a maximum height of the time series dataset (see Fig. 2 for different heights on a growth curve prior to fitting any of the models on page 315), and when the maximum height is less than a threshold maximum height, then classifying the time series dataset as flat and setting the lag time to the end time point and terminating the fitting process (contingent claim; see rejection on claim 1 and “Claim Interpretation” above).
Regarding claim 6, Buchanan teaches:
The computer implemented method as claimed in claim 1. wherein searching for an alternative best time point comprises:
determining when the best time point is less than the minimum time point (see “Fitting experimental data using three-phase linear, Gompertz, and Baranyi models”; where the goodness of fit parameter is determined via the root mean square values in addition to assessing the impact of growth rates and lag durations (e.g. tlag, t max, etc.) on page 320; see also Table 2 on page 324 with values where determination of the best time point can be made with the available parameters);
and when the best time point is less than the minimum time point then the alternative time point is selected based on the closest alternative time point on or after the minimum time point with a goodness of fit within a threshold difference of the goodness of fit for the best time point (contingent claim; see rejection on claim 1 and “Claim Interpretation” above), and
when the best time point is greater than the minimum time point then the alternative time point is selected based on the alternative time point being greater than or equal to the minimum time point and which is the closest alternative time point to the minimum time point and with a goodness of fit within a threshold difference of the goodness of fit for the best time point (contingent claim; see rejection on claim 1 and “Claim Interpretation” above).
Regarding claim 9, Amgarten teaches:
The computer implemented method as claimed in claim 1, wherein the end time point is 48 hours (biofilms degradation was performed in intervals of 24 or 48 hours and therefore data was collected during that duration as recited in “Pseudomonas aeruginosa biofilm degradation” on page 10).
Regarding claim 10, Buchanan teaches:
The computer implemented method as claimed in claim 1. wherein the minimum time point is 5 hours (see Figure 3 with a minimum timepoint of 5 hours being used as the standard; see also Table 2 on page 324 with values where determination of the best time point can be made with the available parameters);
Regarding claim 11, Amgarten as modified teaches:
The computer implemented method as claimed in claim 1, wherein when the time series data is classified as flat, estimating a variability measure and when the variability measure exceeds a variability threshold rejecting the time series dataset and classifying the time series dataset as abnormal. The Examiner notes that the claim limitation is contingent and therefore, not required to occur (see prior art rejection on claim 1 and “Claim Interpretation” above).
Regarding claim 12, Amgarten teaches:
The computer implemented method as claimed in claim 1, further comprising normalizing the time series dataset based on an associated control curve (see Fig. 7 for the normalized time series and Fig. 6 shows the control step and discussed as the physiological basis for the model).
Regarding claim 13,
Amgarten does not explicitly teach the computer implemented method as claimed in claim 12, wherein a host-growth curve is a host only time-series and normalizing the time series dataset comprises subtracting the host only time-series dataset from the time series dataset, wherein the time series dataset and the host only time-series dataset are obtained from separate wells on a same multi-well plate. However, Amgarten does teach a normalization step wherein raw read counts for phages and divided by the total number of reads in each sample and normalized by the genome size of the organism (given in percentage) (see Fig. 7 on page 13 for normalized host-growth curve). The mere recitation of an alternative normalization step does not support the patentability of subject matter encompassed by the prior art unless there is evidence to indicate that the alternative normalization step is critical and therefore amounts to routine optimization (see MPEP 2144.05). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to normalize the host-growth curve based on the available datasets in order to obtain a relationship between the growth of a phage and host as seen in Fig. 7 of Amgarten’s disclosure. This optimization would have been accomplished with reasonable expectation of success as Amgarten already provides a normalization step in their disclosure to establish a relationship between phage and host.
Regarding claim 14,
Amgarten as modified teaches the computer implemented method as claimed in claim 13 wherein the multi-well plate further comprises one or more media control wells (biofilms were formed on 8-well chambers stainless slides as disclosed in “Study of bacteriophages effects on biofilm formation” on page 16 and the quality assurance step to identify anomalies is seen via the biofilm degradation assays with control versus variant discrimination). However, Amgarten does not teach and the computer implemented method further comprises performing quality assurance comprising at least identifying anomalous media control wells or anomalous host cell only wells and excluding time-series datasets associated with any identified anomalous media control wells or anomalous host cell wells. Campbell teaches a method of removing plates that have a poor Z value which is an indication of quality (see “Critical Parameters” on page 11; wherein one or two bad control wells can reduce the Z value and evaluate whether those should be discarded or investigated). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Campbell’s quality assurance method into Amgarten as modified’s growth analysis pipeline in order to help identify plates that encountered suboptimal conditions which will improve the quality of the data (see “Critical Parameters” on page 11). This incorporation would have been accomplished with reasonable expectation of success as Amgarten as modified already includes 8-well chamber stainless slides in his existing pipeline.
Regarding claim 15,
Amgarten as modified teaches the claimed invention substantially as claimed above.
Amgarten as modified does not explicitly teach the computer implemented method as claimed in claim 1, wherein the time series dataset is obtained from a multi-well plate comprising a plurality of host-phage combinations, a set of positive control wells, a set of media control wells, a set of host cell control wells, a first set of diluted host cell wells and a second set of diluted host cell wells, and the computer implemented method is performed for each host-phage combination on the multi-well plate, and a report is generated for each host-phage combination on the multi-well plate. However, Campbell teaches multiple schematics of 384-well plate layouts containing wells including negative controls, positive controls, and a variety of different rows to accommodate different types of wells (see Figs 2-4 on pages 16-18). Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Campbell’s various large-well layouts into Amgarten as modified’s growth analysis pipeline in order to increase the sample size to improve the statistical significance as recited by Campbell (see page 4). This incorporation could have been accomplished with reasonable expectation of success as both inventions are directed to assessment of bacterial growth.
Regarding claim 16, Buchanan teaches:
The computer implemented method as claimed in claim 1, wherein the growth curve summary parameters comprise at least a max height, a slope, a lag time, and an area under curve, and the goodness of fit comprises one or more of a coefficient of determination (R2), a parameter based upon an error term or a residual term, or a summary statistic of residuals (see listed parameters on Table 2 containing root mean squared as the measure of goodness of fit and lag time; see Fig. 3 for max height, area under curve, and a slope).
Regarding claim 21, Amgarten as modified teaches:
A non-transitory, computer program product comprising computer executable instructions for analyzing host phage response data, the instructions executable by a computer (see “Acknowledgements”; computational analyses and computational tool support is explicitly mentioned; page 16). Accordingly, computational analyses and computational tool support necessarily involve non-transitory, computer program product comprising computer executable instructions. It is elementary that the mere recitation of a newly discovered function or property, inherently possessed by things in the prior art, does not cause a claim drawn to distinguish of the prior art. Under the principles of inherency, if a prior art device, in its normal and usual operation, would necessarily perform the method claimed, then the method claimed will be considered to be anticipated by the prior art device. Additionally, where the Patent Office has reason to believe that a functional limitation asserted to be critical for establishing novelty in the claimed subject matter may, in fact, be an inherent characteristic of the prior art, it possesses the authority to require the applicant to prove that the subject matter shown to be in the prior art does not possess the characteristic relied on (see MPEP § 2112).
Regarding the following limitations reciting: receiving a host phage response dataset, wherein the dataset comprises a time series dataset for a host-phage combination in which a host bacteria is grown in the presence of a phage, and each data point in the time series dataset comprises a measurement of a parameter indicative of the growth of the host bacteria in the presence of the phage at a specific time; normalizing the time series dataset based on an associated control curve; obtaining an estimate of a lag time comprising: for a plurality of time points in a time window from an initial time point to an end time point, fitting one or more candidate functions over a fitting time window from the initial time point to a current time point, wherein fitting estimates a set of growth curve summary parameters comprising at least a lag time and a goodness of fit parameter; selecting a best fit function for the current time point from the one or more fitted candidate functions based on the goodness of fit parameters, wherein the one or more candidate functions each comprise a different functional form and at least one of which is a sigmoidal function, and when none of the goodness of fit estimates pass a threshold goodness of fit then classifying the time series dataset as flat and setting the lag time to the end time point; selecting from the plurality of time points, a best time point based on goodness of fit values at the plurality of time points; searching for an alternative best time point closer in time to, and greater than, a minimum time point than the best time fit and updating the best time point to the alternative best time point when the goodness of fit of the alternative time point is within a threshold amount of the goodness of fit of the best time point; and estimating a lag time for the best time point, wherein when the goodness of fit exceeds a threshold goodness of fit the lag time is obtained from the growth curve summary parameter otherwise classifying the time series dataset as flat and setting the lag time to the end time point; and reporting at least the lag time or a hold time calculated as the estimated lag time from which the lag time for a control is subtracted (see rejection on claim 1).
Regarding claim 22, Amgarten as modified teaches:
A computing apparatus comprising: at least one memory, and at least one processor wherein the memory comprises instructions to configure the at least one processor (see “Acknowledgements”; computational analyses and computational tool support is explicitly mentioned; page 16). Accordingly, computational analyses and computational tool support necessarily involve a computing apparatus with memory and a processor. It is elementary that the mere recitation of a newly discovered function or property, inherently possessed by things in the prior art, does not cause a claim drawn to distinguish of the prior art. Under the principles of inherency, if a prior art device, in its normal and usual
operation, would necessarily perform the method claimed, then the method claimed will be
considered to be anticipated by the prior art device. Additionally, where the Patent Office has
reason to believe that a functional limitation asserted to be critical for establishing novelty in the
claimed subject matter may, in fact, be an inherent characteristic of the prior art, it possesses
the authority to require the applicant to prove that the subject matter shown to be in the prior art
does not possess the characteristic relied on (see MPEP § 2112).
Regarding the following limitations reciting: receiving a host phage response dataset, wherein the dataset comprises a time series dataset for a host-phage combination in which a host bacteria is grown in the presence of a phage, and each data point in the time series dataset comprises a measurement of a parameter indicative of the growth of the host bacteria in the presence of the phage at a specific time; normalizing the time series dataset based on an associated control curve; obtaining an estimate of a lag time comprising: for a plurality of time points in a time window from an initial time point to an end time point, fitting one or more candidate functions over a fitting time window from the initial time point to a current time point, wherein fitting estimates a set of growth curve summary parameters comprising at least a lag time and a goodness of fit parameter; selecting a best fit function for the current time point from the one or more fitted candidate functions based on the goodness of fit parameters, wherein the one or more candidate functions each comprise a different functional form and at least one of which is a sigmoidal function, and when none of the goodness of fit estimates pass a threshold goodness of fit then classifying the time series dataset as flat and setting the lag time to the end time point; selecting from the plurality of time points, a best time point based on goodness of fit values at the plurality of time points; searching for an alternative best time point closer in time to, and greater than, a minimum time point than the best time fit and updating the best time point to the alternative best time point when the goodness of fit of the alternative time point is within a threshold amount of the goodness of fit of the best time point; and estimating a lag time for the best time point, wherein when the goodness of fit exceeds a threshold goodness of fit the lag time is obtained from the growth curve summary parameter otherwise classifying the time series dataset as flat and setting the lag time to the end time point; and reporting at least the lag time or a hold time calculated as the estimated lag time from which the lag time for a control is subtracted (see rejection on claim 1).
Regarding claim 24, Amgarten teaches:
The computer implemented method of claim 1, wherein the host bacteria is grown as a biofilm (biofilms were formed on 8-well chambers stainless slides as disclosed in “Study of bacteriophages effects on biofilm formation” on page 16).
Regarding claim 27, Amgarten teaches:
The non-transitory, computer program product of claim 21, wherein the host bacteria is grown as a biofilm (biofilms were formed on 8-well chambers stainless slides as disclosed in “Study of bacteriophages effects on biofilm formation” on page 16).
Regarding claim 30, Amgarten teaches:
The computing apparatus of claim 22, wherein the host bacteria is grown as a biofilm (biofilms were formed on 8-well chambers stainless slides as disclosed in “Study of bacteriophages effects on biofilm formation” on page 16).
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Amgarten et al. as filed in the IDS on 8/23/2023 in view of Buchanan et al. as filed in the IDS on 8/23/2023 further in view of Niu (Niu YD, Liu H, Du H, Meng R, Sayed Mahmoud E, Wang G, McAllister TA and Stanford K (2021) Efficacy of Individual Bacteriophages Does Not Predict Efficacy of Bacteriophage Cocktails for Control of Escherichia coli O157. Front. Microbiol. 12:616712. doi: 10.3389/fmicb.2021.616712) as evidenced by Rotella.
Regarding claim 17,
Amgarten as modified does not explicitly teach the computer implemented method as claimed in claim 12 wherein the end time point is prior to a final time point and the method further comprising receiving an updated host response dataset comprising additional data points and repeating the normalization obtaining and reporting steps (see Fig. 7 for the normalized time series; see Table 1 on page 322-323), wherein the reporting includes an estimate of a probability that the phage is efficacious. However, Niu teaches a method for calculating an odds ratio to quantify the likelihood of complete inhibition of bacterial growth in the presence of bacteriophages (see “Bacteriophages, Bacteria, and Media”; see also Table 1 on page 6-7). The Examiner notes that although an odds ratio is not a probability, the probability can be derived from an odds ratio to inform the user of the efficacy of a phage as evidenced by Rotella (see attached document).
In addition, the courts have held In re Harza, 274 F.2d 669, 124 USPQ 378 (CCPA 1960), that mere duplication of parts has no patentable significance unless a new and unexpected result is produced.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Amgarten et al. as filed in the IDS on 8/23/2023, as applied in claim 2, in view of Buchanan et al. as filed in the IDS on 8/23/2023, further in view of Tournoud et al. (US 20170349932 A1) as filed in the IDS on 8/23/2023.
Regarding claim 3, Buchanan teaches:
The computer implemented method as claimed in claim 2. wherein the ordered set of candidate functions comprises a Gompertz function (see “Introduction”; page 313; page 320 also discusses comparisons of different models including a Gompertz function). Buchanan is silent as to the integration of a Logistic function and a Richards function. Tournoud teaches applications of functions that can be used for growth kinetics including lag, growth, and stationary phases (see table listed in [0118] where Logistic and Richards functions are temporal models that can be used to monitor the growth kinetics of microorganisms). Therefore, it would have been obvious to one of ordinary skill in the art to incorporate Tournod’s various temporal models into Amgaren as modified’s host phage analysis pipeline in order to apply a known technique to the overarching pipeline as viable alternatives to one another and identification of the parameters of growth for a bacterial population is known in the art as recited by Tournod (see [0118] and [0121]; Gompertz is also included as a viable temporal model in addition to the others). This could be accomplished with reasonable expectation of success as both inventions are directed to the same problem of trying to model the growth of a microorganism population.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Amgarten et al. as filed in the IDS on 8/23/2023, as applied in claim 2, in view of Buchanan et al. as filed in the IDS on 8/23/2023, further in view of Tournoud et al. (US 20170349932 A1) as filed in the IDS on 8/23/2023.
Regarding claim 4,
Amgarten as modified does not teach the computer implemented method as claimed in claim 3, wherein when fitting of each of the Gompertz function, the Logistic function and the Richards function failed to generate a goodness of fit exceeding the predefined threshold goodness, then applying a Blackman window function to the time series data and repeating the fitting process, and when the repeated fits for each of the Gompertz function, the Logistic function and the Richards function fail to generate a goodness of fit exceeding the predefined threshold goodness, then classifying the time series dataset as flat and setting the lag time to the end time point. Galardini teaches a method of extracting PM curve parameters through one of three sigmoid functions (Logistic, Gompertz, and Richards) and inputting the parameters through a Blackman window (see page 4 paragraph 2; flat time data series is an inherent property of having no selection). Therefore, it would have been obvious before the effective filing date of the claimed invention to incorporate Galardini’s blackman window method into Amgarten as modified’s pipeline in order to avoid incorrect fittings or even failures due to spurious peaks in the curves as recited by Galardini (see page 4 paragraph 2; see also Figure 3). There would have been a reasonable expectation of success of combining Amgarten as modified with Galdarini as both inventions are directed to the same problem in the same field of endeavor. The Examiner notes in the interest of compact prosecution, the rejection on claim 4 using Galardini has been made, however, the claim limitation is contingent and therefore, not required to occur (see rejection on claim 1 and “Claim Interpretation” above).
Claim(s) 7-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Amgarten et al. as filed in the IDS on 8/23/2023, as applied in claim 2, in view of Buchanan et al. as filed in the IDS on 8/23/2023, further in view of Tournoud et al. (US 20170349932 A1) as filed in the IDS on 8/23/2023.
Regarding claim 7,
Although Buchanan does not explicitly teach the computer implemented method as claimed in claim 6 wherein the minimum time point is 5 hours, the goodness of fit is the coefficient of determination (R2), and the threshold difference is 0.03, he does teach a goodness of fit parameter (root mean square error) and provides two tables with information to allow one of ordinary skill in the art to derive a minimum time point and a threshold difference depending on the values selected (see Tables 1-2 on pages 322-324; see also explicit disclosure of a minimum timepoint in Figure 3 being 5 hours). Yao teaches various common linear regression evaluation metrics and their subsequent implementation (see “Root Mean Squared Error” and “R-squared” in attached document). Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to substitute the RMSE parameter as the goodness of fit parameter to the R-squared parameter in order to provide a more intuitive measure of how well the predictions fit the observations. This substitution could have been accomplished with reasonable expectation of success as these regression evaluation metrics are known in the art as recited by Yao (see first line in document explicitly recited as “common linear evaluation metrics”).
Regarding claim 8, Buchanan teaches:
The computer implemented method as claimed in claim 6 wherein the goodness of fit is the co-efficient of determination (R2) and the predefined threshold goodness is 0.6 (see rejection on claim 7). The Examiner notes that although the claim limitation recites “predefined threshold goodness is 0.6”, the predefined threshold can be determined from the available information recited in Tables 1-2 to obtain a desired result.
Claim(s) 25, 28, and 31 is/are rejected under 35 U.S.C. 103 as being unpatentable over Amgarten et al. as filed in the IDS on 8/23/2023 in view of Buchanan et al. as filed in the IDS on 8/23/2023 as evidenced by Hernández-Jiménez. (Biofilm vs. planktonic bacterial mode of growth: Which do human macrophages prefer?, Biochemical and Biophysical Research Communications, Volume 441, Issue 4, 2013, Pages 947-952).
Regarding claim 23, Amgarten teaches:
The computer implemented method of claim 1, wherein the host bacteria is grown as planktonic cells (biofilms were formed on 8-well chambers stainless slides as disclosed in “Study of bacteriophages effects on biofilm formation” on page 16). The Examiner notes that use of plankton cells versus biofilm for macrophage growth is already known in the art as evidenced by Hernández-Jiménez (see “Abstract” on page 947 for discussion of naturally growing bacteria compared to what is ubiquitous in scientific research).
Regarding claim 26, Amgarten teaches:
The non-transitory, computer program product of claim 21, wherein the host bacteria is grown as planktonic cells (biofilms were formed on 8-well chambers stainless slides as disclosed in “Study of bacteriophages effects on biofilm formation” on page 16). The Examiner notes that use of plankton cells versus biofilm for macrophage growth is already known in the art as evidenced by Hernández-Jiménez (see “Abstract” on page 947 for discussion of naturally growing bacteria compared to what is ubiquitous in scientific research).
Regarding claim 29, Amgarten teaches:
The computing apparatus of claim 22, wherein the host bacteria is grown as planktonic cells (biofilms were formed on 8-well chambers stainless slides as disclosed in “Study of bacteriophages effects on biofilm formation” on page 16). The Examiner notes that use of plankton cells versus biofilm for macrophage growth is already known in the art as evidenced by Hernández-Jiménez (see “Abstract” on page 947 for discussion of naturally growing bacteria compared to what is ubiquitous in scientific research).
Claim(s) 25, 28, and 31 is/are rejected under 35 U.S.C. 103 as being unpatentable over Amgarten et al. as filed in the IDS on 8/23/2023 in view of Buchanan et al. as filed in the IDS on 8/23/2023, further in view of Liu (Phage-Antibiotic Synergy Is Driven by a Unique Combination of Antibacterial Mechanism of Action and Stoichiometry. mBio. 2020 Aug 4;11(4):e01462-20).
Regarding claim 25,
Amgarten as modified does not teach the computer implemented method of claim 1, wherein phage-phage synergy, antibiotic-phage synergy, or antibiotic-phage-phage synergy is measured. However, Liu teaches an optically based real-time microtiter plate readout with a matrix-like heat map of treatment potencies to measure phage and antibiotic synergy (PAS). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Liu’s measuring method into Amgarten as modified’s computer implemented method for analyzing host phage response data in order to gain account for the antagonism between phage and antibiotics that may decrease efficacy (see “Abstract” on page 1). The incorporation could be accomplished with reasonable expectation of success as both inventions operate in the same field of endeavor.
Regarding claim 28, Liu teaches:
The non-transitory, computer program product of claim 21, wherein phage-phage synergy, antibiotic-phage synergy, or antibiotic-phage-phage synergy is measured (see rejection on claim 25).
Regarding claim 31, Liu teaches:
The computing apparatus of claim 22, wherein phage-phage synergy, antibiotic-phage synergy, or antibiotic-phage-phage synergy is measured (see rejection on claim 25).
Claims 18-20 are free from prior art.
Regarding claim 18,
Although Amgarten does have a visual representation of the efficacy of different assays (see Fig. 6 on where the efficacy of different phages can be seen in the biofilm degradation assay), he does not explicitly teach the computer implemented method as claimed in claim 1, wherein the end time point is prior to a final time point, further comprising determining a final class confidence estimate which is an estimate that a classification of whether the phage is efficacious at the end time point matches the classification of whether the phage is efficacious at the final time point and reporting the estimate that the phage is efficacious further comprises reporting the estimate that the phage is efficacious at the end time point and final class confidence estimate, wherein determining a final class confidence estimate is determined based on identifying a set of similar host phage response datasets in a set of historical host phage response datasets comprising a plurality of flat host phage response datasets and a plurality of non-flat host phage response datasets and each comprising data points from a start time to the final time, wherein the final class confidence estimate is determined based on the similar host phage response datasets in which an estimate that the classification of whether the phage is efficacious at the end time point matches the classification of whether the phage is efficacious at the final time point. Therefore, claim 18 is free from prior art.
Regarding claim 19,
Although Amgarten does have a visual representation of the efficacy of different assays (see Fig. 6 on where the efficacy of different phages can be seen in the biofilm degradation assay), he does not teach the computer implemented method as claimed in claim 18 wherein the final class confidence estimate is generated using a random forest-based classifier trained on the set of historical host phage response datasets. By virtue of dependency on claim 18, claim 19 is free from prior art.
Regarding claim 20,
Although Amgarten does have a visual representation of the efficacy of different assays (see Fig. 6 on where the efficacy of different phages can be seen in the biofilm degradation assay), he does not teach the computer implemented method as claimed in claim 18, wherein a phage is selected based on the final class confidence estimate exceeding a stopping threshold at an end time point prior to the final time point. As aforementioned, by virtue of dependency on claim 19, claim 20 is free from prior art.
Conclusion
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/P.N./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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