DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
As detailed on the Filing Receipt filed 2/29/2024, the instant application claims priority to as early as 12/4/2020. At this point in prosecution, all claims are accorded the earliest claimed priority date.
Information Disclosure Statement
The Information Disclosure Statements filed on 2/20/2024, 7/18/2025, 11/12/2025 and 7/21/2026 are in compliance with the provisions of 37 CFR 1.97 and have been considered in full. Signed copies of the IDS are included with this Office Action.
Claim Status
Claims 1-85 are canceled.
Claims 86-112 are pending, and under examination.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 USC §§ 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 the appropriate paragraphs of 35 USC § 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim 86 is rejected under 35 USC §§ 102(a)(1) and 102(a)(2) as being anticipated by Choo-Smith (US 2005/0283058; effectively filed 6/9/2004; on IDS filed 2/20/2024, 7/18/2025).
Claim 86 recites a method for predicting a subject's diagnostic status with respect to a disease or disorder, comprising:
exposing a biological sample of a subject to a light source;
acquiring a plurality of Raman spectra from the biological sample;
processing the plurality of Raman spectra to generate a spatial map of the plurality of Raman spectra; and
predicting the subject's diagnostic status with respect to the disease or disorder based at least in part on the spatial map of the plurality of Raman spectra.
With respect to claim 86, Choo-Smith discloses a method that combines optical coherence tomography and Raman spectroscopy to provide morphological information and biochemical specificity for detecting and characterizing incipient carious lesions found in extracted human teeth (Abstract), wherein:
delivering light energy to a tissue sample from a light source (paras. 0007-10, 0042 and 0047-48);
sweeping the source wavelength to collect Raman data from multiple excitation wavelengths (para. 0072); and
plotting the Raman spectra as a function of a point mapping array to generate an image map (paras. 0113 and 0148; Fig. 7C).
Choo-Smith notes that the size and location of a carious lesion indicated on the generated map matched very well to a photomicrograph of the sample, validating application of the selected Raman bands for caries detection (para. 0148). In this way, Choo-Smith discloses prediction of subject diagnostic status with respect to caries based on the generated map of Raman spectra.
In this way the disclosure of Choo-Smith anticipates the limitations of claim 86. Thus, the claimed invention is anticipated.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 USC § 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 USC § 102(b)(2)(C) for any potential 35 USC § 102(a)(2) prior art against the later invention.
Claims 87-97, 100-101 and 110-112 are rejected under 35 USC § 103 as being unpatentable over Choo-Smith, as applied to claim 1 above, and further in view of Butler (Nature Protocols 11(4): 664-687; published 3/10/2016).
Claim 87 recites a computer system comprising one or more processors, and memory storing one or more programs for execution by the one or more processors, the one or more programs comprising instructions for:
sampling each respective position in a plurality of positions along a reference line on a biological sample of a subject associated with a Raman signature of the subject, thereby obtaining a plurality of Raman spectra, each Raman spectrum in the plurality of Raman spectra corresponding to a different position in the plurality of positions along the reference line on the biological sample, and each position in the plurality of positions along the reference line on the biological sample representing a different period of growth of the biological sample associated with the Raman signature;
analyzing each of the plurality of Raman spectra across the reference line on the biological sample thereby obtaining a first dataset;
deriving a respective second dataset from the corresponding plurality of the Raman spectra measurements, each respective feature in the corresponding set of features being determined by a sequential variation in the Raman spectra; and
processing the features using a trained model to predict a subject's diagnostic status with respect to a disease or disorder associated with the Raman signature.
With respect to claim 87, Choo-Smith discloses:
collecting Raman data from multiple excitation wavelengths (para. 0072) and sampling points along a line on the sample (paras. 0154-55; Fig. 12A), thereby acquiring spectra at different sampling locations across the surface (para. 0113;
plotting wavenumbers and intensity values of the obtained spectra (e.g., Fig. 5);
deriving secondary features based on variation in Raman spectral measurements, including depolarization ratios, anisotropy index of orientation values, and peak intensity ratios (paras. 0064, 0148, 0155, 0161-64; Figs. 7B, 12B, 15-16);
analyzing spectral data to monitor structural and morphological changes in the sample, induced by mineralization activities in caries development, predict future growth and reduction of carious lesions, and detect caries (paras. 0010, 0037-42, 0156, 0184).
Choo-Smith also describes mathematically removing background fluorescence signals by integrating a difference in the spectral data acquired at each wavelength (para. 0072), and further describes generating a derivative spectrum by subtracting two Raman spectra collected at close wavelengths (para. 0177).
Choo-Smith does not disclose embodiments wherein each position along the line represents a different period of growth of the biological sample; or processing the features using a trained model to predict a subject’s diagnostic status as claimed.
Butler discusses characterization of biological materials via Raman microspectrometry (pg. 664, Abstract), and teaches that spectral discrimination is indicative of underlying biological architecture thus allowing inference of biological information through processing of spectral data via algorithmic classification techniques (pg. 665, l. column). Butler further discusses classification of samples, via trained classifier models, based on their Raman spectra for diagnostic studies and particularly for cancer diagnosis (pg. 665, r. column; pg. 675, r. column; pp. 667-69, Table 1).
Butler teaches that Raman spectroscopy enables data to be recorded from a diverse array of sample types, and has seen application in numerous biological research fields and considerable advancement towards clinical diagnostic implementation (pg. 665, r. column).
With respect to claim 88, Choo-Smith discloses analysis of a tooth sample (paras. 0042, 0108 and 0114; Figs. 1 and 8).
With respect to claims 89-90, Choo-Smith discloses collection of time series imaging data for monitoring and comparison purposes, wherein such information relays information regarding lesion progression, arrest, reduction, demineralization and remineralization (para. 0063). In other words, is indicative of a temporal response, comprising a biological response, of the subject.
Choo-Smith also discloses co-registering data acquired at different time points, for direct comparison of measurements, to monitor demineralization and remineralization of tooth tissue over time (para. 0184). Choo-Smith states that knowledge of time series activity rate is important in forecasting future risk of defect growth and reduction (para. 0184).
With respect to claim 91, Choo-Smith discloses performing Raman spectroscopy at wavelengths in the range of 750-1000 nm, wherein the Raman detection is carried out using a sweeping of the source wavelength to collect data from multiple excitation wavelengths, e.g., differing by 1 nm (paras. 0025-26). Choo-Smith thus discloses collecting Raman spectra at each wavelength in the disclosed range, i.e., capturing a plurality of Raman spectra comprising 250 wavelengths.
With respect to claim 92, Choo-Smith discloses acquiring Raman spectra via a Raman microspectrometer (para. 0137).
With respect to claim 93, Choo-Smith discloses employment of a 50X objective and discusses observation of light scattering due to the change in refractive index as the light transitions from air to the tooth surface (para. 0117, 0137 and 0142; Fig. 11).
With respect to claim 94, Choo-Smith discloses delivering light energy to the sample from a light source (paras. 0007-10, 0042 and 0047-48), collecting Raman data from multiple excitation wavelengths (para. 0072), and using the Raman probe to sample points along a line on the sample surface (paras. 0154-55; Fig. 12A).
With respect to claim 95, Choo-Smith discloses system implementation of laser excitation to acquire Raman spectra (para. 107; Fig. 1). Choo-Smith further discloses performing Raman spectroscopy at near-infrared wavelengths in the range of 750-1000 nm, wherein the Raman detection is carried out using a sweeping of the source wavelength to collect data from multiple excitation wavelengths, e.g., differing by 1 nm (paras. 0025-26 and 0072). Choo-Smith also discusses prior application of Raman spectroscopy, utilizing a wavelength of 785 nm, to characterize advanced dental caries (para. 0100).
With respect to claim 96, Choo-Smith discloses performance of Raman detection using a sweeping of the source wavelength to collect data from multiple excitation wavelengths (e.g., differing by 1 nm), thereby obtaining a plurality of Raman spectra (paras. 0025-26 and 0072). Choo-Smith discloses acquisition of Raman spectra using a Raman microspectrometer consisting of a microscope equipped with a motorized XYZ stage, and states that the sampling position was optimized by a XYZ translator (para. 0137).
One of ordinary skill in the art would understand, based on the employment of a motorized stage and the description of optimizing sampling position via a XYZ translator, that the disclosed methods of Choo-Smith acquire spectra from different sampling positions by moving the sample relative to the laser via XYZ-dimensional translation of the sample stage.
Choo-Smith also discloses acquisition of spectra from points at 140 μm steps along the x-axis, and 113 μm steps along the y-axis, to create an array map (para. 0138). As shown in Fig. 7A, the described sampling at points defined by regular steps along both the x- and y-axes is equivalent to sampling at regularly-spaced points along a diagonal line. Applying the Pythagorean theorem (1402 + 1132 = c2) indicates a line-wise step size of about 180 μm.
In this way, Choo-Smith discloses performance of Raman mapping comprising translating, wherein translating comprises moving the biological sample, with fixed step sizes given in terms of μm, from a first position to a second position of the plurality of positions along the reference line on the biological sample subsequent to acquiring a Raman spectrum of the plurality of Raman spectra. However, Choo-Smith does not disclose moving the biological sample with a step size of about 2 microns to about 5 microns.
Butler discusses characterization of biological materials via Raman microspectrometry (pg. 664, Abstract). Butler exemplifies measuring Raman spectra using a 785 nm laser with a step size of 1 μm (pg. 683, Fig. 8 caption), thereby demonstrating implementation of said step size, using prior-existing technology, in Raman mapping of biological samples. Butler describes step size as a user-specified parameter, which is selected to determine the number of spectra acquired within the mapping area (pg. 679).
Choo-Smith and Butler together demonstrate implementation of mapping step sizes between 1 μm and 180 μm, while Butler teaches that step size is a user-selected parameter that affects spectral data collection. One of ordinary skill in the art would, in the course of routine experimentation, find implementing a step size of about 2 μm to about 5 μm to be an obvious-to-try variant of the disclosed Raman spectral analysis techniques of Choo-Smith, in view of Butler, in light of these combined teachings.
With respect to claim 97, Butler characterizes exposure / integration time as a user-selected parameter that a user may wish to decrease or increase for functional reasons (pg. 666, Fig. 2; pg. 675, l. column).
With respect to claim 100, the combined teachings of Choo-Smith and Butler are considered to apply to the claim in the same manner as outlined above regarding claims 87-89.
With respect to claim 101, Butler discusses processing of spectral data via artificial neural networks, support vector machines, various unsupervised clustering and supervised classification, and partial least squares (i.e., regression) algorithms (pg. 665, l. column; pg. 666, Fig. 2 caption; pg. 675, l. column; pg. 683, Table 4).
Claim 110 is directed to a computer implemented method for predicting a subject's diagnostic status with respect to a disease or disorder, comprising: steps of substantive identity to the functional limitations of the computer system of claim 87.
With respect to claim 110, Choo-Smith discloses export of acquired data into MATLAB and performance of data manipulation (interpolation) via MATLAB (para. 0135). MATLAB is a computer programming language and statistical software. Additionally, Butler teaches that a standard computer should be sufficient for basic spectral acquisition and data analysis (pg. 676, Electronic equipment). One of ordinary skill in the art would have found computer-implementation to be obvious in light of these teachings.
The teachings of Choo-Smith, in view of Butler, are considered to read on the recited steps of the claimed method in the same manner as detailed above regarding the corresponding functional limitations of claim 87.
Claim 111 is directed to a non-transitory computer readable storage medium and one or more computer programs embedded therein for classification, the one or more computer programs comprising instructions which, when executed by a computer system, cause the computer system to perform a method comprising: steps of substantive identity to the functional limitations of the computer system of claim 87.
With respect to claim 111, Butler advises implementation of a system with sufficient RAM access (pg. 676, Electronic equipment). RAM is a form of non-transitory computer readable storage medium.
The teachings of Choo-Smith, in view of Butler, are considered to read on the recited functional limitations of the claim in the same manner as detailed above regarding the corresponding functional limitations of claim 87.
Claim 112 is directed to method for training a model, comprising: at a computer system having one or more processors, and memory storing one or more programs for execution by the one or more processors:
for each respective training subject in a plurality of training subjects, wherein a first subset of training subjects in the plurality of training subjects have a first diagnostic status corresponding to having a first biological condition associated with a Raman signature and a second subset of training subjects in the plurality of training subjects have a second diagnostic status corresponding to not having the first biological condition associated with the Raman signature:
performing steps of substantive identity to functions (a)-(c) of claim 87; and
training an untrained or partially untrained model with the corresponding set of features of each respective second dataset of each training subject in the plurality of training subjects and the corresponding diagnostic status of each training subject in the plurality of training subjects, selected from among the first diagnostic status and the second diagnostic status, thereby obtaining a trained model that provides an indication as to whether a test subject has the first biological condition associated with the Raman signature based on values for features in a set of features acquired from a biological sample associated with the Raman signature of the test subject.
With respect to claim 112, Choo-Smith discloses generation and plotting of a Raman imaging map based on peak intensity ratios (para. 0113; Fig. 7). Choo-Smith discusses accurate spatial visualization of intensity changes, and thus of carious lesions on the sample, by the imaging map (para. 148; Fig. 7). In other words, the imaging map provides an indication as to whether the sample subject has caries based on values for derived spectral features.
However, Choo-Smith does not discuss training a model as claimed.
Butler discusses training a classifier for diagnostic analysis based on class labels of a training set of spectral data (pg. 675, r. column – pg. 676, l . column). As one of ordinary skill in the art would know, a classifier would be trained to classify subject diagnostic status, corresponding to a given biological condition, based on training data including a subset from subjects known to have the biological condition and a subset from subjects known to not have the biological condition.
The teachings of Choo-Smith, in view of Butler, are also considered to read on the corresponding steps of the claimed method (indexed under (a)(i) above) in the same manner as detailed above regarding the indicated functional limitations of claim 87.
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have implemented a trained classifier model (e.g., a neural network), as taught by Butler, to process Raman spectral data and detect caries according to the analytical framework of Choo-Smith, because Butler teaches that trained models can effectively classify samples based on processed Raman spectral data and this has previously been employed for medical diagnostics (pg. 675, r. column – pg. 676, l. column). Said practitioner would have had a reasonable expectation of success because Choo-Smith and Butler both concern analysis of Raman spectra measured from a biological sample, via stepwise microspectrometry, for diagnostic purposes.
In this way the disclosure of Choo-Smith, in view of Butler, makes obvious the limitations of claims 87-97, 100-101 and 110-112. Thus, the claimed invention is prima facie obvious.
Claims 98-99 and 103-109 are rejected under 35 USC § 103 as being unpatentable over Choo-Smith, in view of Butler, as applied to claims 87 and 100 above, and further in view of Curtin (Science Advances 4(5): eaat1293, 8 pages; published 5/30/2018; on IDS filed 7/18/2025).
With respect to claims 98-99, Choo-Smith discloses diagnosis of dental caries based on the spectral analysis (para. 0075, 0091-92 and 0161). Choo-Smith does not disclose embodiments wherein the disease or disorder comprises autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), amyotrophic lateral sclerosis (ALS), schizophrenia, irritable bowel disease (IBD), pediatric kidney disease, kidney transplant rejection, or any combination thereof.
Butler teaches that Raman spectroscopy measures loss or gain of photonic energy that occurs due to interaction of provided light with discrete vibrational modes associated with the chemical bonds of polarizable molecules within the analyzed sample surface (pg. 664, l. column). Butler discusses applications of Raman spectral analysis to cancer diagnosis (pg. 665, r. column; pp. 667-69, Table 1). Butler does not specifically discuss embodiments wherein the disease or disorder comprises pediatric cancer, nor ASD, ADHD, ALS, schizophrenia, IDB, pediatric kidney disease, kidney transplant rejection, or any combination thereof.
Curtin discusses predictive modeling of subject diagnosis with ASD based on analysis of elemental concentrations, measured from the sample surface via laser ablation and mass spectroscopy, in incremental zones of teeth (pg. 1, Abstract; pg. 2, l. column and Fig. 1B caption). Curtin presents measured data in the form of temporal exposure profiles and derived recurrence plots, which Curtin analogizes to spectrograms (pg. 2, Fig 1C/E caption). Curtin also suggests application to predictive modeling of ADHD (pg. 4, r. column).
With respect to claims 103-104, Curtin teaches quantification of mean diagonal length, determinism, and entropy (pg. 2, r. column – pg. 3, l. column; Fig. 1 caption and Fig. 2).
With respect to claims 105-109, Curtin presents model achievement of 90% accuracy in classifying cases and controls, with sensitivity to ASD diagnosis ranging from 85 to 100%, specificity ranging from 90 to 100%, and AUC ranging from 0.912 to 0.945 (pg. 1, Abstract; pg. 4, Fig. 3).
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have implemented predictive modeling of ASD, as taught by Curtin, using the spectral analysis techniques of Choo-Smith, in view of Butler, because Curtin describes highly accurate predictive modeling of ASD based on spectrometric data measured from a tooth surface (pg. 1, Abstract,; pg. 2, l. column and Fig. 1 caption; pg. 4, Fig. 3). Curtin thus indicates that the spectral analyses of tooth surface composition disclosed by Choo-Smith, in view of Butler, may be applicable to modeling of ASD (in addition to detection of caries). Said practitioner would have had a reasonable expectation of success because Choo-Smith, Butler and Curtin all discuss diagnostic characterization of biological samples based on analysis of measured spectrometric data.
In this way the disclosure of Choo-Smith, in view of Butler and Curtin, makes obvious the imitations of claims 98-99 and 103-109. Thus, the claimed invention is prima facie obvious.
Claim 102 is rejected under 35 USC § 103 as being unpatentable over Choo-Smith, in view of Butler, as applied to claims 87 and 100-101 above, and further in view of Hastie (Elements of Statistical Learning: Data Mining, Inference and Prediction, 2nd ed., Springer Inc.; published 2008).
With respect to claim 102, Choo-Smith discloses classification of defects on the sample surface as sound, decalcified or cavitated based on measurements (para. 0184). Choo-Smith does not disclose employment of a gradient-boosted ensemble algorithm.
Butler teaches processing of spectral data and classification of samples via trained statistical models based on their Raman spectra (pg. 665, r. column; pg. 666, Fig. 2; pg. 675, r. column; pp. 667-69, Table 1). Butler does not teach employment of a gradient-boosted ensemble algorithm.
Hastie discusses statistical learning methods and teaches that decision trees come the closest of all well-known methods to meeting requirements for serving as an ‘off-the-shelf’ data mining procedure for a number of functional reasons (e.g., speed of construction and robustness to outliers) which have made decision trees the most popular learning method for data mining (pg. 352, paras. 2-3). Hastie also teaches that decision trees suffer from predictive inaccuracy, despite these advantages, but their accuracy can be dramatically improved by the statistical technique of boosting (pg. 352, para. 3).
Hastie further teaches that boosting, in turn, reduces a number of the advantageous functional properties of decision trees (e.g., speed and robustness) thus tree boosting has been generalized, in the form of the gradient boosted model (GBM), to mitigate these drawbacks and render an accurate and effective ‘off-the-shelf’ data mining procedure (pg. 352, para. 4).
Hastie also subsequently teaches that boosting is an ensemble technique, which builds an ensemble model comprising a committee of weak classifiers (pg. 337, para. 1; pg. 605, paras. 2-4). Thus, the discussed GBM can be characterized as a gradient-boosted ensemble model.
Hastie presents generic algorithmic implementations of gradient tree boosting for regression and classification tasks, and also teaches that gradient tree boosting is implemented as the freely-available ‘gbm’ R package (pg. 360, para. 4 – pg. 361, para. 2).
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have implemented a gradient-boosted ensemble model, as taught by Hastie, to classify spectral data in combination with the analytical techniques of Choo-Smith, in view of Butler, because Hastie teaches that the GBM has numerous functional advantages (e.g., speed of training, robustness to outliers and missing data, input data flexibility, predictive accuracy) that make it a genuine ‘off-the-shelf’ data mining technique (pg. 352, paras. 2-4) that is furthermore freely available as an R package (pg. 360, para. 4 – pg. 361, para. 2). Said practitioner would have had a reasonable expectation of success because Choo-Smith, Butler and Hastie all discuss computational data analysis.
In this way the disclosure of Choo-Smith, in view of Butler and Hastie, makes obvious the imitations of claim 102. Thus, the claimed invention is prima facie obvious.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Instant claims 87-88, 93, 98, 103 and 110-111 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 68, 83, 86, 89, 92 and 97 of copending Application No. 17/616,626 (hereafter, “‘626”) in view of Choo-Smith. ‘626 shares a common assignee (Icahn School of Medicine at Mount Sinai) and joint inventors (Arora, Manish; Curtin, Paul; Austin, Christine) with the instant application.
Instant claim 87 recites a computer system comprising one or more processors, and memory storing one or more programs for execution by the one or more processors, the one or more programs comprising instructions for:
sampling each respective position in a plurality of positions along a reference line on a biological sample of a subject associated with a Raman signature of the subject, thereby obtaining a plurality of Raman spectra, each Raman spectrum in the plurality of Raman spectra corresponding to a different position in the plurality of positions along the reference line on the biological sample, and each position in the plurality of positions along the reference line on the biological sample representing a different period of growth of the biological sample associated with the Raman signature;
analyzing each of the plurality of Raman spectra across the reference line on the biological sample thereby obtaining a first dataset;
deriving a respective second dataset from the corresponding plurality of the Raman spectra measurements, each respective feature in the corresponding set of features being determined by a sequential variation in the Raman spectra; and
processing the features using a trained model to predict a subject's diagnostic status with respect to a disease or disorder associated with the Raman signature.
With respect to instant claim 87, ‘626 claims a device, for evaluating a subject for a first biological condition associated with metal metabolism, comprising one or more processors, and memory storing one or more programs for execution by the one or more processors, the one or more programs comprising instructions for:
sampling each respective position in a plurality of positions along a reference line on a biological sample of the subject, thereby obtaining a plurality of ion samples, each corresponding to a different position in the plurality of positions, and each position representing a different period of growth of the biological sample;
analyzing each ion sample with a mass spectrometer thereby obtaining a first dataset that includes a plurality of traces, each trace being a concentration of a corresponding elemental isotope over time;
deriving a second dataset from the plurality of traces that includes a set of features, each respective feature in the set of features being determined by a sequential variation of a single isotope or combination of isotopes in the traces; and
computing, by a trained classifier, an indication as to whether the subject has the first biological condition, based on the set of features (claim 86).
Although the processing device claimed by ‘626 operates upon obtained spectroscopic data, similarly to the system claimed by the instant application, ‘626 does not claim obtaining and analysis of Raman spectra.
Choo-Smith discloses: collecting Raman data from multiple excitation wavelengths (para. 0072) and sampling points along a line on the sample (paras. 0154-55; Fig. 12A), thereby acquiring spectra at different sampling locations across the surface (para. 0113); plotting wavenumbers and intensity values of the obtained spectra (e.g., Fig. 5); deriving peak intensity ratios based on variation in Raman spectral measurements (paras. 0064, 0113, 0118, 0141, 0147-48, 0155, 0161-64; Figs. 7B-C, 12B); and determining whether a tooth region exhibits spectral properties indicative of carious enamel or sound enamel, based on Raman peak ratios, thereby detecting caries (paras. 0036, 0042-43, 0046 and 0154-56).
Choo-Smith discusses the correspondence of spectral peak positions with chemical composition of the tooth surface, and the assignment of measured Raman bands (peaks) to particular chemical species (paras. 0132 and 0145-46). Choo-Smith also presents findings that an observed spatial gradient in intensity of a Raman band assigned to carbonate corresponded to an expected spatial gradient in carbonate content of the sample (para. 0153). Choo-Smith thus demonstrates that Raman peak intensities accurately represent concentrations of corresponding chemical species in the sample.
With respect to instant claim 88, ‘626 claims embodiments wherein the biological sample is selected from the group consisting of a hair shaft, a tooth, and a nail (claim 92).
With respect to instant claim 98, ‘626 claims embodiments wherein the biological condition is selected from the group consisting of ASD, ADHD, ALS, schizophrenia, IBD, pediatric kidney transplant rejection, and pediatric cancer (claim 89).
With respect to instant claim 103, ‘626 claims a method comprising steps of substantial similarity to the functions of the claimed device, wherein the set of features is selected from the group consisting of a mean diagonal length, a determinism, a recurrence time, an entropy, a trapping time, and a laminarity (claims 68 and 83).
With respect to instant claim 110, ‘626 claims a method comprising steps of substantial similarity to the functions of the claimed device (claim 68).
With respect to instant claim 111, ‘626 claims a non-transitory computer readable storage medium and one or more computer programs embedded therein for classification, the one or more computer programs comprising instructions which, when executed by a computer system, cause the computer system to perform a method comprising: steps of substantial similarity to the functions of the claimed device (claim 97).
An invention would have been obvious to one of ordinary skill in the art if simple substitution of one known element for another, to yield predictable results, would have led one of ordinary skill in the art to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have modified the analytical techniques of ‘626 to substitute measuring and analysis of Raman spectral data for the disclosed measuring and analysis of mass spectroscopic data, because Choo-Smith indicates that Raman peak intensities likewise correspond to the chemical composition of a tooth surface and thus have diagnostic utility in the same manner as the mass spectroscopic data (paras. 0036, 0042-43, 0046, 0132, 0145-46 and 0154-56). Said practitioner would have had a reasonable expectation of success because ‘626 and Choo-Smith both concern analysis of data indicative of chemical concentrations measured from a tooth sample, via linear stepwise spectrometry, for diagnostic purposes.
In this way, instant claims 87-88, 93, 98, 103 and 110-111 are not patentably distinct from claims of ‘626, in view of Choo-Smith. This is a provisional nonstatutory double patenting rejection.
Instant claims 86-91, 96-99, 100-102 and 111 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 23-27, 32-35, 37-38 and 46 of copending Application No. 18/872,175 (hereafter, “‘175”). ‘175 shares a common assignee (Icahn School of Medicine at Mount Sinai) and joint inventors (Arora, Manish; Curtin, Paul; Austin, Christine) with the instant application.
Instant claim 86 recites a method for predicting a subject's diagnostic status with respect to a disease or disorder, comprising:
exposing a biological sample of a subject to a light source;
acquiring a plurality of Raman spectra from the biological sample;
processing the plurality of Raman spectra to generate a spatial map of the plurality of Raman spectra; and
predicting the subject's diagnostic status with respect to the disease or disorder based at least in part on the spatial map of the plurality of Raman spectra.
With respect to claim 86, ‘175 claims a method for predicting a subject's diagnostic status with respect to a disease or disorder, comprising:
exposing a biological sample of a subject to a light source;
acquiring a plurality of Raman spectra from the biological sample;
processing the plurality of Raman spectra to generate a spatial map of the plurality of Raman spectra; and
predicting the subject's diagnostic status with respect to the disease or disorder based at least in part on the spatial map of the plurality of Raman spectra (claim 1).
With respect to claim 87, ‘175 claims a device comprising one or more processors, and memory storing one or more programs for execution by the one or more processors, the one or more programs comprising instructions for functions of substantial similarity to the steps of the claimed method (claim 23).
With respect to claim 88, ‘175 claims embodiments wherein the biological sample comprises a tooth sample, a hair sample, a nail sample, or any combination thereof (claim 24).
With respect to claim 89, ‘175 claims embodiments wherein the instructions further comprise detecting or monitoring changes in the Raman spectra across the plurality of positions indicative of a temporal response of the subject (claim 25).
With respect to claim 90, ‘175 claims embodiments wherein the temporal response comprises a biological response, a physiological response, an anatomical response, a treatment response, a stress related response, or a combination thereof response (claim 26).
With respect to claim 91, ‘175 claims embodiments wherein the plurality of Raman spectra comprises from about 200 to about 3700 wave numbers (claim 27).
With respect to claim 96, ‘175 claims embodiments wherein the instructions further comprise translating, wherein translating comprises moving the biological sample with a step size of about 2 microns to about 5 microns from a first position to a second position of the plurality of positions subsequent to acquiring a Raman spectrum of the plurality of Raman spectra (claim 32).
With respect to claim 97, ‘175 claims embodiments wherein the translating is performed using an integration time of about 0.2 seconds to about 0.3 seconds (claim 33).
With respect to claim 98, ‘175 claims embodiments wherein the disease or disorder comprises autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), amyotrophic lateral sclerosis (ALS), schizophrenia, irritable bowel disease (IBD), pediatric kidney disease, kidney transplant rejection, pediatric cancer, or any combination thereof (claim 34).
With respect to claim 99, ‘175 claims embodiments wherein the disease or disorder comprises autism spectrum disorder (claim 35).
With respect to claims 100-101, ‘175 claims embodiments wherein the trained model is selected from the group consisting of: a neural network algorithm, a support vector machine algorithm, a decision tree algorithm, an unsupervised clustering algorithm, a supervised clustering algorithm, a regression algorithm, a gradient-boosting algorithm, and any combination thereof (claim 37).
With respect to claim 102, ‘175 claims embodiments wherein the trained model comprises a gradient-boosted ensemble model (claim 38).
With respect to instant claim 111, ‘175 claims a non-transitory computer readable storage medium and one or more computer programs embedded therein for classification, the one or more computer programs comprising instructions which, when executed by a computer system, cause the computer system to perform a method comprising: functions of substantial similarity to the functions of the claimed device (claim 46).
In this way, instant claims 86-91, 96-99, 100-102 and 111 are not patentably distinct from claims of ‘175. This is a provisional nonstatutory double patenting rejection.
Conclusion
At this point in prosecution, no claims are allowed.
The following prior art, made of record and not relied upon, is considered pertinent to applicant’s disclosure:
Ham (US 5,553,616; filed 11/30/1993) discloses computerized analysis of Raman scattering characteristics of a biological sample via a trained artificial neural network discriminator (Abstract);
Matousek (WO 2007/113570; effectively filed 5/4/2006) discloses detection of tissue calcifications based on measurement of tissue composition via Raman spectroscopy (Abstract).
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/T.C.S./Examiner, Art Unit 1685
/JESSE P FRUMKIN/Primary Examiner, Art Unit 1685 July 27, 2026
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