DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
The present application was filed as a proper National stage (371) entry of PCT application No. PCT/EP2016/076130, filed 10/28/2016, which claims benefit under 35 U.S.C. 119(e) to provisional application No. 62/247,609, filed 10/28/2015.
Claim status
Claims 1-16 and 19-40 are pending in the application. Claims 1-15 and 25-39 are withdrawn, claim 16 is amended and claims 17 and 18 are cancelled. Claims 16, 19-24 and 40 are examined below.
Claim Interpretation
Claim term “mid-infrared” has been interpreted in view of its plain meaning in the art, namely 4,000-400 cm-1 (2.5-25 mm). See, e.g., Petrich et al. (doi: 10.1081/ASR-100106156), IDS entered on 5/10/2018) on page 184.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The rejection under 35 U.S.C. 103 is maintained for reasons of record reiterated below.
Claims 16 and 40 are rejected under 35 U.S.C. 103 as being unpatentable over Baunoch et al. (US 2001/0055799 A1, see PTO-892, 04/24/2024) in view of Whelan et al. Correlative synchrotron Fourier transform infrared spectroscopy and single molecule super resolution microscopy for the detection of composition and ultrastructure alterations in single cells. ACS chemical biology. 2015 Dec 18;10(12):2874-83 and Wang et al. Application of multivariate data-analysis techniques to biomedical diagnostics based on mid-infrared spectroscopy. Analytical and bioanalytical chemistry. 2008 Jul;391(5):1641-54.
Regarding claims 16 and 40, Baunoch teaches a method of automatically reprocessing a specimen (tissue sample) for microscopic examination, involving fixation of the specimen and preparation of the embedded specimen from the fixed specimen and reprocessing the specimen if there is inadequate fixation (classify fixation state; performing remedial tissue processes if the sample is determined to be inadequately fixed; Baunoch, Abstract, lines 1-9). Baunoch further teaches that the specimen can be a cell, cell suspension, tissue section, or tissue specimen (Baunoch, page 1, paragraph [0007], lines 1-3). Baunoch further teaches that fixation of the specimen involves a fixing agent, such as formalin and an infiltrating medium, such as paraffin wax (Baunoch, page 1, paragraph [0006], lines 1-11; [0043]; Fig. 5). Baunoch further teaches that traditionally if a specimen is not properly processed, such as being inadequately fixed, there are two options: obtain another specimen or reprocess the specimen ((c1) additional fixation; (c2) rejection and obtaining a new sample; Baunoch, page 1, paragraph [0007], lines 6-13). Baunoch further teaches that after the tissue has been processed, it is infiltrated with paraffin, embedded in a paraffin block, and sliced into sections using a microtome. At that point, the operator can determine if the specimen has been processed properly (Baunoch, page 5, paragraph [0045], lines 1-4). Baunoch further teaches that the method includes the step of providing the specimen which is infiltrated with an infiltrating medium, indicating to the specimen reprocessing machine that the specimen is to be reprocessed (Baunoch, page 1, paragraph [0010]). Baunoch further teaches that after reprocessing, nuclei from specimen are prepared for DNA analysis using a fluorescent compound (labeling a fixed cellular sample; performing a labeling process on the acceptably fixed tissue sample; Baunoch, page 1, paragraph [0008], lines 6-10).
Baunoch differs from the claimed invention in that although Baunoch teaches determining if a sample is adequately fixed, Baunoch is silent as to how the operator determines the status of fixation. Baunoch fails to teach identifying a fixation signature in a mid-infrared spectroscopy spectrum derived from at least a portion of the first test sample, applying a trained classification algorithm to the identified fixation signature to automatically estimate a fixation state, wherein the fixation state is estimated as under-fixed, over-fixed, or acceptably fixed, and the algorithm is trained using a plurality of reference MIR spectra wherein the training tissue samples have been fixed with a chemical fixative and are under-fixed, over-fixed, or acceptably fixed and at least three have different fixation properties. Baunoch further fails to teach that the trained classification algorithm is one of a trained unsupervised or trained supervised classification algorithm, or a trained quantification algorithm (claim 40).
Whelan teaches a method of probing the biochemistry of individual cells by acquiring synchrotron-source infrared spectra of the same cells both pre- and postfixation after which the microtubule ultrastructure of these cells is imaged using the direct stochastic optical reconstruction microscopy variant of single molecule super resolution microscopy (Whelan, page 2875, 2nd paragraph, lines 1-5). Whelan further teaches that the technique is intrinsically noninvasive (Whelan, page 2875, 2nd paragraph, lines 4-5). Whelan teaches preparing cells using purposefully altered fixation protocols to trigger over-fixation and under-fixation (Whelan, page 2882, ‘Fixation’, lines 4-6) and acquiring mid-infrared spectra for cells pre-fixation and also post-fixation with 3.7% paraformaldehyde (PFA), two step glutaraldehyde, 2% glutaraldehyde and methanol (Whelan, page 2875, see Figure 1). Whelan further teaches immunostaining of tubulin of the samples (Whelan, page 2875, 2nd paragraph, line 12). Whelan further teaches differences in mid-infrared spectra of samples after suboptimal fixation with 2% PFA, 8% PFA, or 3.7% PFA after rehydration (Whelan, page 2878, see figure 3 a-c). Whelan further teaches that infrared spectroscopy can detect compositional changes caused by fixation that are not readily apparent in other images and further teaches that such fixation damage would otherwise be almost impossible to detect when imaging an unknown structure or distribution but could nonetheless drastically affect conclusions drawn from the data (Whelan, page 2876, 2nd paragraph, lines 5-10). As such, Whelan teaches identifying fixation signature in a test mid-infrared spectroscopy spectrum in samples that have been chemically fixed and have known fixation quality properties selected from under-fixed, over-fixed, or acceptably fixed.
Wang teaches that mid-infrared spectroscopy is an analysis tool for detecting biomedically relevant constituents and even diseases or disease progression that may induce changes in the chemical composition or structure of cells, tissues and bio-fluids. Wang further teaches that mid-infrared spectra of multiple constituents are usually characterized by strongly overlapping spectral features reflecting the complexity of biological samples and that consequently, mid-infrared spectra of biological samples are frequently difficult to interpret by simple data-analysis techniques. Wang further teaches that with increasing complexity of the sample matrix more sophisticated mathematical and statistical data analysis routines are required for deconvoluting spectroscopic data and for providing useful results from information-rich spectroscopic signals (Wang, page 1641, lines 4-19). Wang teaches that multivariate data analysis utilizes mathematical, statistical and computer sciences to efficiently extract useful information from data generated via chemical measurements and that the data analysis comprises multivariate classification (pattern recognition) techniques which require a training set of samples to build a robust model. Wang further teaches that this supervised pattern recognition requires a priori knowledge about the classes contained within the training samples and consequently allows for more precise classification within the class boundaries compared to unsupervised classification. Wang further teaches that a sufficiently large training set of samples is required to build a robust model (Wang, page ‘1644, ‘Multivariate data-analysis techniques’, see entire 1st and 2nd paragraph). Wang further teaches that in order to appropriately address the inherent complexity of analytical data derived from biomedical samples such as highly convoluted infrared spectra, multivariate data-analysis techniques are essential (Wang, page 1652, ‘Conclusions’, lines 12-16). Wang further teaches that that the useful information content obtained by spectroscopic techniques increases with available a priori knowledge about the samples, i.e., a detailed chemical interpretation of the molecular signatures obtained will always enhance the accuracy and reliability of classification and regression techniques merely based on pattern recognition (Wang, page 1652, [‘Conclusions’, lines 29-35).
It would have been prima facie obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have applied mid-infrared spectroscopy and a trained classification algorithm to determine the fixation state of samples when performing the method of Baunoch, namely by identifying a fixation signature in a test mid-infrared spectrum from a position/portion of the fixed test tissue of Baunoch and applying the trained algorithm as taught by Wang because of the teaching of Whelan that infrared spectroscopy can detect compositional changes caused by fixation that are not readily apparent in other images and that such fixation damage would otherwise be almost impossible to detect but could nonetheless drastically affect conclusions drawn from the data. One having ordinary skill in the art would further be motivated to have modified the method of Whelan by analyzing said mid-infrared spectroscopy data using multivariate classification techniques based on such as supervised pattern recognition (trained on training tissue samples that have a known fixation quality property selected from under-fixed, over-fixed, or acceptably fixed such as the samples of Whelan) because of the teaching of Wang that more sophisticated mathematical and statistical data analysis routines are required for deconvoluting spectroscopic data and for providing useful results from information-rich spectroscopic signals and that supervised pattern recognition allows for more precise classification within the class boundaries compared to unsupervised classification.
One of ordinary skill in the art would have a reasonable expectation of success because Baunoch teaches that after the samples are fixed and sliced in sections the operator can determine the fixation state but is silent on the method of determining the fixation state and because of the teaching of Whelan that mid-infrared spectroscopy is intrinsically noninvasive. As such one of ordinary skill in the art would not expect the method of Whelan to interfere with the method of Baunoch. One of ordinary skill of the art would further have a reasonable expectation of success in analyzing the data of Whelan using a trained classification algorithm as taught by Wang because Wang teaches analysis of mid-infrared spectroscopy data which is the type of data collected in the method of Whelan.
Claims 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Baunoch in view of Whelan and Wang as applied to claim 16 above, and further in view of Zeng et al. Structure characterization of protein fractions from lotus (Nelumbo nucifera) seed. Journal of molecular structure. 2011 Aug 24;1001(1-3):139-44 and Mazur et al. Evaluating different fixation protocols for spectral cytopathology, part 1. Analytical chemistry. 2012 Feb 7;84(3):1259-66 (see PTO-892, 04/24/2024).
Regarding claim 19, Baunoch and the cited art above teach a method substantially as claimed.
Baunoch does not teach a difference in the fixation signature is a change in amplitude and/or peak position between 1615 cm-1 and 1640 cm-1 in a second derivative spectrum.
Zeng teaches that the Amide I region has the most intense absorption band in proteins and is very sensitive to changes arising from different types of secondary structure. Zeng further teaches that there are characteristic IR-absorption bands for each type of secondary structure such as β-sheet and α-helices, β-turns and random coils. Zeng further teaches that the amide I band provides information on secondary protein structure for example β-sheet at 1615-1640 and 1689-1698cm-1.(Zeng, page 142, ‘3.2. Secondary conformation analyses’, lines 1-21). Zeng further teaches calculating the second derivative compositions in order to analyze the number of peaks in the amide I region and determining component peaks, locations and percentage areas by curve fitting analysis, which reflect relative populations of various conformational states assigned to the amide I band (Zeng, page 142, 2nd column, 3rd paragraph, lines 1-12).
Mazur teaches Infrared spectroscopy used to interrogate biochemical components of cellular samples and multivariate statistical methods such as principal component analysis to analyze and diagnose spectra. Mazur further teaches studying the effects of fixation protocols and further teaches that fixative and duration of sample storage contribute to minor spectral changes (Mazur, page 1259, see Abstract). Mazur further teaches that the largest differences between two fixatives, comprising formalin, can be seen in the amide I shoulder at 1635 cm-1 (Mazur, page 1264, 3rd paragraph, lines 2-3 and page 1262, Figure 4, arrow). Mazur further teaches that in general, second-derivative spectra are more sensitive toward the detection of small spectral changes (Mazur, page 1262, lines 7-9).
It would have been prima facie obvious to one having ordinary skill in the art before the effective filing date of the claimed invention when performing the method as taught by Baunoch and the cited prior art, that the algorithm be trained based on changes in the amide I band and specifically between 1615 cm-1 and 1640 cm-1, reflecting the β-sheet, because of the teaching of Zeng that the amide I band provides information on the secondary structure of protein and the teaching of Mazur that the largest effect of fixation can be seen at 1635 cm-1 , which reflects a change in the β-sheet conformation (as taught by Zeng). It would have further been obvious to assess the second derivative spectrum, because of the teaching of Mazur that in general these spectra are more sensitive toward the detection of small spectral changes and because the changes measured due to different fixatives are small.
The ordinary artisan would have a reasonable expectation of success, because Mazur as well as the combination of the prior art above teaches the measurement of the effect of fixation on formalin treated tissue by mid-infrared spectroscopy and also because this position is known to reflect a change based on fixation and Zeng also applies data analysis to spectral data from infrared spectrometry within the same wavelengths as Mazur.
Regarding claim 20, Baunoch and the cited art above teach a method substantially as claimed.
Baunoch and the cited art above fails to teach a difference in the fixation signature is a change in amplitude and/or peak position between 1615 cm-1 and 1640 cm-1 in a principal component plot.
Mazur teaches infrared spectroscopy used to interrogate biochemical components of cellular samples and multivariate statistical methods such as principal component analysis to analyze and diagnose spectra. Mazur further teaches studying the effects of fixation protocols and further teaches that fixative and duration of sample storage contribute to minor spectral changes (Mazur, page 1259, see Abstract). Mazur further teaches that the largest differences between two fixatives, comprising formalin, can be seen in the amide I shoulder at 1635 cm-1 (Mazur, page 1264, 3rd paragraph, lines 2-3 and page 1262, Figure 4, arrow). Mazur further teaches that principal component plots depict individual, rather than averaged cell spectra to allow an assessment of the variability of spectra and the magnitude of spectral changes in each of the experiments (Mazur, page 1261, 2nd column, lines 8-11).
It would have been prima facie obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have interrogated changes in amplitude at 1635 cm-1 (between 1615 cm-1 and 1640 cm-1), because of the teaching of Mazur that that is where the largest effect of fixation can be seen, as discussed previously above. It would have further been obvious to use principal component analysis, because of the teaching of Mazur that the individual plots depict individual, rather than averaged cell spectra to allow an assessment of the variability of spectra and the magnitude of spectral changes.
The ordinary artisan would have a reasonable expectation of success, because Mazur as well as the combination of the prior art above teaches the measurement of the effect of fixation on formalin treated tissue by mid-infrared spectroscopy.
Claims 21, 23, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Baunoch in view of Whelan and Wang as applied to claim 16 above, and further in view of Bird et al., US20010055799A1 (see PTO-892, 04/24/2024).
Regarding claim 21, Baunoch and the prior art above teaches a method substantially as claimed.
Baunoch teaches that the data is acquired using an imaging infrared spectrometer.
Baunoch and the cited art above fails to teach that the test spectrum is obtained by quantum cascade laser based microscopy.
Bird teaches analyzing biological specimens by infrared spectral imaging to provide a medical diagnosis (Bird, see Abstract). Bird further teaches that the sample sections were cut from formalin fixed paraffin embedded cell blocks (Bird, page 51, paragraph [0194], line 1). Bird further teaches that the infrared data acquisition may be carried out with tunable laser-based imaging instruments, such as Fourier transform infrared imaging microspectrometers or quantum cascade devices (Bird, page 25, paragraph [0108], lines 1-3).
It would have been prima facie obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have used a quantum cascade laser to acquire the data as a simple substitution of one art-recognized data acquisition method over another, both known for acquiring mid-infrared spectroscopy data in formalin fixed, paraffin-embedded samples.
The ordinarily skilled artisan would have been motivated to do so, because both methods perform the same function in substantially the same way and produce substantially the same result of detecting mid-infrared spectra in formalin fixed, paraffin embedded samples.
The ordinary artisan would have a reasonable expectation of success, because both Baunoch in view of Whelan and Wang, and Bird acquire the same type of data from the same type of sample, namely infrared spectra from formalin fixed, paraffin embedded tissue.
Regarding claims 23 and 24, Baunoch and the prior art above teaches a method substantially as claimed.
Baunoch teaches that tissue sections are formalin fixed and paraffin-embedded (Baunoch, page 1, paragraph [00063-11], lines 5-6).
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Baunoch in view of Whelan and Wang, and Bird as applied to claim 21 above, and further in view of Kröger et al. Quantum cascade laser–based hyperspectral imaging of biological tissue. Journal of biomedical optics. 2014 Nov 1;19(11):6 pages (see PTO-892, 04/24/2024).
Regarding claim 22, Baunoch and the cited art above teach a method substantially as claimed.
Bird does not teach that the test spectrum is obtained in 30 minutes or less.
Kröger teaches quantum cascade laser-based hyperspectral imaging of biological tissue (Kröger, see title). Kröger further teaches rapid image acquisition of an unstained tissue section within 5 min with diffraction limited spatial resolution and further teaches that this reduces acquisition time by more than one or three orders of magnitude compared to standard Fourier transform infrared imaging or mapping (Kröger, see Abstract). Kröger further teaches that data acquisition using Fourier transform infrared mapping can take up to several days, which limits the practical use of the technique (Kröger, page 1, 2nd paragraph, lines 5-10). Kröger further teaches that the sample is perfusion fixed in 4% paraformaldehyde and paraffin-embedded (Kröger, page 2, ‘2.1 Sample Preparation”, lines 1-3).
It would have been prima facie obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Baunoch and the cited art above with the method of Kröger in order to obtain a test spectrum in 5 minutes, because of the teaching of Kröger that long acquisition times limit the practical application of infrared spectroscopy.
The ordinary artisan would have a reasonable expectation of success, because the sample examined was a formaldehyde fixed paraffin embedded specimen, as where the specimen of the prior art.
Response to Arguments
Applicant's arguments filed 07/30/2025 have been fully considered but they are not persuasive.
Applicant argues, starting on page 7, that Baunoch does not disclose or suggest a classification algorithm, let alone a trained classification algorithm as required by the claimed invention, but rather that Baunoch discloses a workflow for reprocessing a sample and provides no guidance to the skilled artisan on how to identify the improper processing of a specimen. Applicant argues that Baunoch merely describes that an operator decides whether a sample should be reprocessed and that Baunoch provides no guidance on how to train a classification algorithm or how to use any trained classification algorithm to automatically estimate a fixation state of a fixed test tissue sample.
This argument is not persuasive.
As applicant argues on pages 7 and 8, citing the non-final office action, Baunoch is not relied on “to teach identifying a fixation signature in a mid-infrared spectroscopy spectrum” nor “applying a classification or quantification algorithm to the fixation signature” nor a “trained classification algorithm, which is trained using one or more reference MIR spectra”. Applicant argues that therefore Baunoch fails to teach the elements recited in steps (a) and (b) of the claimed method and therefore cannot offer any guidance to the skilled artisan on training a classification algorithm or using a trained classification algorithm for estimating a fixation state of a fixed test sample.
In response to arguments, see new rejection under 35 U.S.C. 103 the combination of Baunoch (teaching a method of labeling a fixed test sample and performing one or more remedial tissue processes if the fixation state is unacceptable), Whelan, teaching a method of distinguishing between over-fixed, under-fixed, and adequately fixed samples using mid-infrared spectroscopy, and Wang, teaching a trained classification algorithm to analyze such data, addresses the limitations of claim 16.
Applicant further argues on page 8 that none of the secondary references provide the requisite level of guidance for the skilled artisan to arrive at the claimed invention with any reasonable expectation of success. Applicant argues that Mazur describes fixing normal tissues with a variety of different fixatives which results in only small spectral changes whereas there are large variations between diseased tissue and normal tissue. Applicant further argues that Mazur is directed to understanding the effects of fixation-induced changes using different fixatives on samples and/or different times in the context of using spectral cytopathology for disease diagnosis and according to Mazur, spectral cytopathology is an approach for disease diagnosis that utilizes infrared spectroscopy to interrogate biochemical components to analyze and diagnose spectra.
Applicant further argues that prior to Mazur the effects of fixation and storage of cells were largely unknown and as such, prior to Mazur the skilled artisan would not have appreciated whether different fixation protocols could have affected disease diagnosis because Mazur concludes that sample preparation using formalin fixation, alcohol fixation, rapid desiccation, or cells left for prolonged times in fixative solutions produce minor spectral changes that are negligible in comparison to changes induced by disease.
As discussed previously in detail above, Applicant is referred to the new grounds of rejection, Whelan teaches that infrared spectroscopy can detect compositional changes caused by fixation that are not readily apparent in other images and further teaches that such fixation damage would otherwise be almost impossible to detect when imaging an unknown structure or distribution but could nonetheless drastically affect conclusions drawn from the data. Whelan further teaches that it had previously been argues that fixation induced effects are not significant enough to confound disease-state changes or other differences being examined but while these differences are indeed relatively small, they limit the sensitivity of such experiments (Whelan, page 2881, 3rd paragraph, lines 17-28).
Applicant further argues that Mazur does not provide any guidance on training a classification algorithm or using a trained classification algorithm to estimate a fixation state of a fixed test sample. Applicant argues that the principal component algorithm is not a classification algorithm because it reveals only slight spectral changes between normal samples fixed using three different fixation methods and also in normal samples over time. Applicant argues that Mazur uses principal component analysis to show that the degree of change induced by how the tissue was fixed is minimal compared to diseased tissue and therefore can be ignored.
In response, Applicant is referred to the new grounds of rejection set forth in detail above. In the pending grounds of rejection, Mazur is not relied on to teach a classification algorithm. Rather, Wang teaches using a trained classification algorithm on data derived from mid-infrared spectroscopy results in enhanced accuracy and reliability of classification and regression techniques merely based on pattern recognition.
Applicant further argues that Lasch is directed to detecting bovine spongiform encephalopathy (BSE) in bovine serum samples and discloses training a classification algorithm using infrared spectra in BSE-positive samples, healthy control samples, and samples from animals suffering from bacterial or viral infections, i.e. Lasch trains on diseased versus healthy tissue. Applicant further argues that Mazur teaches that there are only slight spectral differences between normal tissue samples fixed with different types of fixatives, but that there are large spectral variations between normal tissue and diseased tissue. Applicant argues that there would not have been any reasonable expectation of success that Lasch’s trained algorithm, designed to distinguish the large spectral shifts between diseased serum and healthy serum, could ascertain the slight spectral differences Mazur describes between normal tissue samples fixed with different fixatives such that it could be used to estimate the fixation state of a fixed test sample, especially since Mazur describes slight differences in spectra with different types of fixatives and not based on a state of fixation of any fixed tissue sample.
In response to these arguments, Lasch is no longer relied on in the new grounds of rejection and therefore the argument is moot.
Applicant further argues on page 10 that neither Mazur nor Lasch disclose or suggest that the slight spectral changes between samples fixed with different fixatives could be used to estimate a fixation state of a fixed test sample with any reasonable expectation for success because Mazur indicates that any spectral changes between different fixatives are slight and that normal cells regardless of fixation procedures exhibit very homogeneous spectra and as such there would not have been any reasonable expectation for success in training a classification algorithm using very homogenous spectra to be able to distinguish between different fixation states and ultimately provide an estimate a fixation state of a fixed test specimen. Further, applicant argues, neither Mazur nor Lasch disclose training any algorithm based on differentially fixed training samples (and Lasch does not teach a fixed sample at all). Applicant argues that as such Mazur and Lasch do not provide the rationale to modify Baunoch and certainly not with any reasonable expectation of success.
Neither Mazur nor Lasch are relied on to teach differences in fixation state in the new grounds of rejection which rely on Whelan to teach distinguishing different fixation states such as under-, over- or adequate fixation. Therefore, the argument is moot.
Applicant further argues that Lasch discloses a training algorithm with bovine serum samples, but that the samples were not fixed and therefore cannot meet the claim recitation that one or more fixed training samples have been fixed with a chemical fixative. Applicant further argues that the experimental procedures of Lasch do not describe any step involving a chemical fixative and therefore Lasch cannot teach that at least two of the training tissue samples have different known fixation quality properties.
Lasch is no longer relied on in the new grounds of rejection and therefore the argument is moot.
For all the reasons above, the arguments are not persuasive.
Communication
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEFANIE J KIRWIN whose telephone number is (571)272-6574. The examiner can normally be reached Monday - Friday 7.30 - 4 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bao-Thuy Nguyen can be reached at (571) 272-0824. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/STEFANIE J. KIRWIN/Examiner, Art Unit 1677
/ELLEN J MARCSISIN/Primary Examiner, Art Unit 1677
Prosecution Insights