DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Amendments
2. The amendment filed December 23, 2025 has been entered. Claims 1-2, 5, 10, 28, and 51 have been amended. Claims 2-3, 34,39,48,51,53-54,62-63,70-71,74,86 and 89 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to nonelected inventions, there being no allowable generic or linking claim. Claims 4, 6-9, 11-13, 16, 18-19, 21-22, 25-27, 29-33, 35-38, 40-47, 52, 55-61, 64-69, 72-73, 75-85, 87-88 and 90-91 are canceled. Claims 1, 5, 10, 12-15, 17, 20, 23-24 and 28 are under consideration in this Office Action.
Withdrawal of Claim Objections
3. The objection of claim 28 is withdrawn in view of applicants amendments and arguments.
Withdrawal of Claim Rejections
4. The rejection of claim 5 under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, is withdrawn in view of applicants amendments and arguments.
5. The rejection of claims 1, 5, 10, 12-15, 17, 20, 23-24 and 28 under 35 U.S.C. 103 as being unpatentable over Cartwright et al., in view of Puppels et al., is withdrawn in view of applicants amendments and arguments.
New Grounds of Rejection Necessitated By Applicants Amendments
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
6. Claims 1, 5, 10, 12-15, 17, 20, 23-24 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Cartwright et al., (US Pat Pub 20160146810 published May 2016; priority to July 2013) in view of Edmund et al., (WO 2018165309 published 2018-09-13; priority to 2017-03-08).
The claims are drawn to a method of detecting a microbe or microbe component, the method comprising the following steps: i) contacting a sample with an engineered microbe-targeting molecule linked to a support; ii) isolating the microbe or microbe components bound to the engineered microbe-targeting molecule; iii) contacting the microbe or microbe components with a matrix or matrix solution on a target substrate; and iv) detecting the microbe or microbe components using a mass spectrometric method; wherein the detecting of step iv outputs mass spectrometric data obtained from the sample as a sample library comprising a mass spectrum for the sample; and
wherein the detecting of step iv comprises analyzing the sample library with a control
system comprising one or more processors, the control system configured to execute machine executable code using a clustering process, wherein each cluster comprises a cluster of data points from a single molecular signal of interest in the mass spectrum.
Cartwright et al., provide methods, compositions, and kits for enhanced detection of microbes in samples and monitoring of antimicrobial activity in a subject [abstract]. Cartwright et al., disclose a method of detecting a microbe or microbe component [paragraph 0070 and 0151]. The method comprises the following steps: i) contacting a sample with an engineered microbe-targeting molecule linked to a support (sample is added to well containing FcMBL-coated beads; FcMBL is a recombinant lectin using FcMBL for capturing microbes and/or microbial matter [paragraphs 0043 and 0149]. Step ii) teach isolating the microbe or microbe components bound to the engineered microbe-targeting molecule where magnetic separation can be used to separate lectin-coated magnetic beads from the sample; the separated lectin-coated substrates, e.g. FCMBL-coated magnetic beads, are assayed for the presence or absence of microbe [paragraph 0192]. Step iii) contacting the microbe or microbe components with a matrix such as FcMBL beads were used to capture material from E. coli, S. aureus; where the beads were subjected to MALDI-TOFMS using DHB as a matrix [paragraph 0056]. And Step iv) detecting the microbe or microbe components using a mass spectrometric method to identify microbes captured by FcMBL beads using MALDI-TOFMS [paragraph 0056]. For example, the inventors have shown that detecting a reduced number of intact microbes (e.g., even by a three order of magnitude difference) is much less sensitive than detecting a significant increase in MAMPs due to lysis of microbes by an effective antibiotic within a specified time frame [para 15]. Cartwright et al., discovered that by disrupting the architectural integrity (e.g., outer layers of the cell wall) and/or fragmenting of these microbes to expose the hidden MAMPs that are not normally presented to the PRRs when the microbes are intact and live, these disrupted microbes and the exposed MAMPs can then be detected using the PRR-based assay (e.g., a lectin based assay). For example, the inventors were able to detect Klebsiella oxytoca isolates, Salmonella typhimurium isolates, Acinetobacter isolates, and Listeria monocytogenes isolates, which were otherwise undetectable as live or intact pathogens by FcMBL Sandwich ELLecSA (enzyme linked lectin sorbent assay), by using some embodiment of the assays or methods described herein. As used herein, the term “microbial detection spectrum” refers to the number of microbe species and/or groups of microbes that can be captured and/or detected by an assay [para 88].
Cartwright et al., disclose FIG. 14 shows identification of microbes captured by FcMBL beads using MALDI-TOF MS. FcMBL beads were used to capture material from late log phase cultures of E. coli, S. aureus. The beads were subjected to positive and negative voltage MALD-TOF MS using DHB as a matrix. Native beads or distilled water eluted material was analyzed and species specific peaks detected [para 56]; thus teaching the matrix solution DHB and claim 17. Analysis of elute from the FcMBL-coated beads has shown different MALDI-TOF MS profile depending on the types of microbes captured on the FcMBL-coated beads (FIG. 14) [para 162]. The analysis of the material eluted from the PRR-coated solid substrates (e.g., PRR-coated beads) can be identified to either a molecular level or a general pattern, which can be subsequently matched to a known database of profiles derived from previous isolates or patient samples. Thus teaching sample libraries of instant claim 5. The construction of a profile database and the algorithms used to match a sample to a microbe or group of microbes can rely on scores determined according to the presence or absence of known or unknown characteristics of individual microbes or microbe classes [para 163]. Thus teaching claim 10 and 28.
Cartwright et al., describe comparing the detectable signal level of MAMPs obtained from (i) to a reference level; and (iii) identifying the subject to be likely infected with at least one microbe if the detectable signal level of MAMPs is higher than the reference level; or identifying the subject to be unlikely infected with microbes if the detectable signal level is not higher than the reference level [para 232]. Thus teaching claim 15.
In one aspect, provided herein relates to a method of determining efficacy of an antimicrobial treatment regimen in a subject. The method comprise (a) assaying at least one biological sample with a pattern recognition receptor (PRR)-based assay for the presence of microbe associated molecular patterns (MAMPs), wherein the biological sample is collected from the subject who has been administered the antimicrobial treatment for no longer than a pre-determined period of time; (b) comparing the detectable signal level of MAMPs obtained from (i) to a baseline level; and (c) identifying the antimicrobial treatment to be effective if a treatment related change in the detectable signal level relative to the baseline level is present; or identifying the antimicrobial treatment to be ineffective if the treatment related change in the detectable signal level relative to the baseline level is absent [para 23]. Thus teaching claim 14. In some embodiments of various aspects described herein involving an antimicrobial treatment, the method can further comprise generating a time course profile that indicates the amount of microbes or microbial matter (e.g., MAMPs) present in the sample before and after administration of the antimicrobial treatment. In some embodiments, the time course profile can comprise at least 2 time points, including a time point before the antimicrobial treatment and a time point taken after the antimicrobial treatment. In some embodiments, the time course profile can comprise at least 3 time points, including a time point before the antimicrobial treatment and a plurality of time points taken after the antimicrobial treatment [para 33].
Cartwright et al., describe comparing the binding profile obtained from the PRR-based assay with a control sample that was assayed with a PRR-based assay without the pre-treatment; and (d) identifying the sample as containing encapsulated microbes, if MAMPs is detected by the PRR-based assay with the pre-treatment step, but not without the pre-treatment step [para 89]. Cartwright et al., also teach a kinetics profile [para 117], time course assay profiles [para 121], and PRR binding profile [para 380].
The readout can be expressed as a ratio of McFarland reads on the microparticles prior to the incubation and after the incubation, with a control (e.g., non-PRR-coated microparticles or blocked beads) of a value approaching 1. Thus teaching claim 23 and 24. The 2-sample Student's t test or Wilcoxon-Mann-Whitney U test can be used to compare continuous variables. Categorical variables can be evaluated by use of Pearson's Chi2 test, Fisher's exact test, or the Mantel-Haenzel Chi2 test, as appropriate. The association between adverse outcome and FcMBL ELLecSA level can be modeled using logistic regression analysis and be reported as an estimated odds ratio and 95% confidence interval (CI). A multivariate logistic regression model can be used to estimate the association between FcMBL levels and severe sepsis, adjusting for covariates (e.g., age, sex, presence of systemic inflammatory response syndrome [SIRS], and/or APACHE II score) [para 400].
Therefore Cartwright et al., teach a method of detecting a microbe or microbe component, the method comprising a contact step, isolation step, contacting step and detection using mass spectrometric method. However Cartwright et al., do not teach a system configured to execute machine executable code using a clustering process, wherein each cluster comprises a cluster of data points from a single molecular signal of interest.
Edmunds et al., teach technology relating to the detection of analytes and particularly, but not exclusively, to methods, systems, compositions, and kits for detecting analytes [abstract]. The technology provides advantages over prior technologies including, but not limited to, improved discrimination of analyte relative to background binding, improved discrimination between closely related analytes, and analysis of very dilute or low-volume specimens (e.g., with improved sensitivity and/or specificity relative to prior technologies for analysis of the same analytes) [para 7]. Analytes are isolated from a biological sample. Analytes can be obtained from any material such as cellular material (live or dead), obtained from an animal, plant, bacterium, archaeon, fungus, or any other organism, cell culture, cell colonies, sing cells or a collection of single cells [para 33 and 182]. This method permits discrimination between nonspecific probe binding and binding of the probe to the target analyte because the accumulation of position-vs-time statistics over multiple binding events of a query probe to a single target analyte yields greater confidence in the identity of the analyte than any of the following: a single binding event, a cumulative count of binding events across an observation area, or a cumulative count of probe signal across an observation area. Moreover, the method uses spatial position information and clustering based on intensity fluctuations (rather than overall signal intensity) to surpass the resolution limits of the detection apparatus to provide “super-resolution” measurements [para 11]. The clustered events represent binding events for a single analyte molecule [para 12]. A signal can be discrete in the time domain. As a mathematical abstraction, the domain of a discrete-time signal is the set of integers (or an interval thereof). Discrete signals often arise via “digital sampling” of continuous signals. The resulting stream of numbers is stored as a discrete-time digital signal [para 78].
The method comprises recording a time-dependent signal of query probe events for analytes immobilized to a surface as a function of (x, y) position on the surface; clustering events into local clusters by (x, y) position; and calculating a kinetic parameter for each event cluster to characterize the analyte [para 11]. A “signal” is a time-varying quantity associated with one or more properties of a sample that is assayed, e.g., the binding of a query probe to an analyte and/or dissociation of a query probe from an analyte. A signal can be continuous in the time domain or discrete in the time domain. As a mathematical abstraction, the domain of a continuous-time signal is the set of real numbers (or an interval thereof) and the domain of a discrete-time signal is the set of integers (or an interval thereof). Discrete signals often arise via “digital sampling” of continuous signals [para 78]. The analysis comprises use of a frequentist analysis and in the analysis comprises use of a Bayesian analysis. In some embodiments, pattern recognition systems are trained using known “training” data (e.g., using supervised learning) and in some embodiments algorithms are used to discover previously unknown patterns (e.g., unsupervised learning) [para 114]. The methods comprise grouping events into local clusters by position (e.g., x, y position) on the surface, e.g., to associate events for a single immobilized target analyte. In some embodiments, the methods comprise calculating kinetic parameters from each local cluster of events to determine whether the cluster originates from a particular analyte, e.g., from transient probe binding to a particular analyte [para 127]. Some embodiments further comprise performing clustering analysis (e.g., hierarchical clustering) on the (x, y) positions of intensity maxima and/or intensity minima to identify regions of high density of query probe binding and dissociation events. The clustering analysis produces clusters wherein each cluster contains 1 or more binding and/or dissociation event(s) that are detected within a limited region of the sensor [para 145].
The steps of the described methods are implemented in software code, e.g., a series of procedural steps instructing a computer and/or a microprocessor to produce and/or transform data as described above. In some embodiments, software instructions are encoded in a programming language such as, e.g., BASIC, C, C++, Java, MATLAB, Mathematica, Perl, Python, or R [para 151]. The embodiments provide software objects that imitate, model, or provide concrete entities, e.g., for numbers, shapes, data structures, that are manipulable. In some embodiments, software objects are operational in a computer or in a microprocessor. In some embodiments, software objects are stored on a computer readable medium [para 152].
It would have been obvious to one of ordinary skill in the art at the time of the invention to have modified the method of Cartwright et al., to provide a single molecular signal of interest in the mass spectrum which improved discrimination of analyte. One of ordinary skill in the art would have a reasonable expectation of success by incorporating the methods and databases of Edmunds et al., because the signal of query probe events comprises measuring the signal for an analyte with single-molecule sensitivity.
Furthermore, no more than routine skill would have been required to incorporate cluster analysis and cluster events to determine algorithms that recognize patterns and regularities in data, using artificial intelligence, pattern recognition, machine learning, statistical inference, neural nets and the like.
Additionally, KSR International Co. v. Teleflex Inc., 127 S. Ct. 1727, 1741 (2007), discloses combining prior art elements according to known methods to yield predictable results, thus the combination is obvious unless its application is beyond that person's skill. KSR International Co. v. Teleflex Inc., 127 S. Ct. 1727, 1741 (2007) also discloses that "The combination of familiar element according to known methods is likely to be obvious when it does no more than yield predictable results". It is well known to take a method of detection, where there is no change in the respective function of the sample, support, matrix, algorithm or spectrometric method, thus the combination would have yielded a reasonable expectation of success along with predictable results to one of ordinary skill in the art at the time of the invention. Therefore, it would have been obvious to a person of ordinary skill in the art to combine prior art elements according to known methods that is ready for improvement to yield predictable results. The claimed invention is prima facie obvious in view of the teachings of the prior art, absent any convincing evidence to the contrary.
Response to Arguments
7. Applicant's arguments filed Dec. 23, 2025 have been fully considered but they are not persuasive. Applicant’s arguments, filed December 23, 2025, with respect to the rejections of claims 1, 5, 10, 12-15, 17, 20, 23-24 and 28 under Cartwright et al., in view of Puppels et al., is have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of Edmunds et al.
Cartwright et al., teach detecting the microbe components using a mass spectrometric method; wherein the detecting of step iv outputs mass spectrometric data obtained from the sample as a sample library comprising a mass spectrum for the sample while Edmunds et al., teach the detecting of step iv comprises analyzing the sample library with a control system comprising one or more processors, the control system configured to execute machine executable code using a clustering process, wherein each cluster comprises a cluster of data points from a single molecular signal of interest in the mass spectrum. Edmunds et al., teach comparing the statistics measured as described above for each cluster of query probe binding events to statistics measured using a standard reference material (e.g., a positive control). Edmunds et al., teach comparing the statistics measured as described above for each cluster of query probe binding events to statistics measured using a negative control (e.g., a comprising no analyte, a substance closely related to the analyte, an analyte comprising a modification and/or not comprising a modification, etc.). Edmunds et al., teach comparing the statistics measured as described above for each cluster of query probe binding events to statistics measured using a standard reference material and/or a negative control is used to determine whether the cluster of query probe binding events is probable to have originated from query probe binding to a single molecule of the target analyte. Edmunds et al., teach calculating the number of clusters in the dataset that represent query probe binding to the target analyte. In some embodiments, calculating the number of clusters in the dataset that represent query probe binding to the target analyte comprises using one or more of the statistical tests described above. In some embodiments, calculating the number of clusters in the dataset that represent query probe binding to the target analyte provides a measure of the number of analytes (e.g., the apparent number of analytes) present in the region of the imaging surface that was assayed by the method. In some embodiments, calculating the number of clusters in the dataset that represent query probe binding to the target analyte provides a measure of the concentration of analyte, provides an indication that the analyte is present or absent in the sample, and/or provides an indication of the state (e.g., modified, not modified) of the analyte in the sample [para 148-149].
Pertinent Art
8. The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Eriksson et al., (Clusterwise Peak Detection and Filtering Based on Spatial Distribution to Efficiently Mine Mass Spectrometry Imaging Data. (Anal Chem. 2019 Aug 12;91(18):11888-11896) teach a sensitive peak detection method able to discover both faint and localized signals by utilizing clusterwise kernel density estimates (KDEs) of peak distributions. We show that our method can recall more ground-truth molecules, molecule fragments, and isotopes than existing methods based on binning. Furthermore, it automatically detects previously reported molecular ions of lipids, including those close in m/z, in an experimental data set.
Rao et al., (Applications of the Single-probe: Mass Spectrometry Imaging and Single Cell Analysis under Ambient Conditions. Journal of Visualized Experiments : Jove. 2016 Jun(112) teach Mass spectrometry imaging (MSI) and in-situ single cell mass spectrometry (SCMS) analysis under ambient conditions are two emerging fields with great potential for the detailed mass spectrometry (MS) analysis of biomolecules from biological samples. The single-probe, a miniaturized device with integrated sampling and ionization capabilities, is capable of performing both ambient MSI and in-situ SCMS analysis. The single-probe device can be potentially coupled with a variety of mass spectrometers for broad ranges of MSI and SCMS studies.
Pageon et al., (Molecular Biology of the Cell Vol. 27, No. 22) teach a combined cluster detection and colocalization analysis for single-molecule localization microscopy data.
Conclusion
9. No claims allowed.
10. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
11. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JA-NA A HINES whose telephone number is (571)272-0859. The examiner can normally be reached Monday thru Thursday.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Dan Kolker, can be reached on 571-272-3181. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JANA A HINES/Primary Examiner, Art Unit 1645