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 .
Information Disclosure Statement
The Information Disclosure Statement(s) filed on 07 October 2024, has been considered by the Examiner.
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.
Claims 1-19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-29 of U.S. Patent No. 11848094. Although the claims at issue are not identical, they are not patentably distinct from each other because both the ‘094 patent and the instant application are directed toward discriminating tissue of a specimen by using a trained SVM model on infrared spectral response data collected from the specimen and providing an indication of a classification as output.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1 and 11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite systems for discriminating tissue of a specimen. The limitations of:
Claim 1
discriminating tissue of a specimen, […]: […] comprising: [… obtaining and saving …] electromagnetic energy infrared spectral response data from the specimen in response to delivering illuminating electromagnetic energy to the specimen; and a [… organized …] model that has been [… built …] including by [… saving …] a summed response of all support vectors at each spectral training wavelength of the [… organized …] model, and configured for applying the [… organized …] model to the [… saved …] electromagnetic energy infrared spectral response data for classifying one or more locations of the specimen into a classification; and […] generating an indication for output to a user for differentiating according to available tissue classification categories.
Claim 11
[… obtaining and saving …] electromagnetic energy response data, the electromagnetic energy response data obtained from a location of the specimen in response to illuminating the specimen according to a specified electromagnetic energy illumination reduced wavelength set that is reduced with respect to a fuller wavelength set used for [… organizing a …] model using […] a training set based on the [… saved …] electromagnetic energy response data; [… obtaining …] decision equations from the […] model, using the […] model providing the decision equations based on a linear […] model representation; […] applying the obtained decision equations to the received electromagnetic energy response data corresponding to the reduced wavelength set for discriminating between at least two tissue categories of the location of the specimen; and […] generating an indication for [… outputting …] to a user for visually differentiating according to at least two tissue categories.
as drafted, is a system, which under the broadest reasonable interpretation, covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions) via the recitation of generic computer components. That is, by a human user interacting with a computing device with various ports (i.e., input port and interface port), and an output device, the claimed invention amounts to managing personal behavior or interaction between people, the Examiner notes as stated in 2106.04(a)(2), “certain activity between a person and a computer… may fall within the “certain methods of organizing human activity” grouping”. For example, but for a computing device with various ports (i.e., input port and interface port), and an output device, the claim encompasses collection of data about a specimen, to organize the collected data using a model to provide to a human user a classification based on the organization of the collected data If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a computing device with various ports (i.e., input port and interface port), and an output device which implement the abstract idea. The computing device with various ports (i.e., input port and interface port), and an output device are recited at a high-level of generality (i.e., a general-purpose computers/ computer component implementing generic computer functions; see Applicant’s specification: Figure 1, page 10) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim recites the additional elements of “receiving…”, “storing…”, “a stored trained learning model that has been trained… training a linear Support Vector Machines (SVM)”, “applying the trained learning model”, and “display to a user”. The “receiving…” steps are recited at a high-level of generality (i.e., as a general means of receiving/transmitting data) and amounts to the mere transmission and/or receipt of data, which is a form of extra-solution activity. The “storing…” is recited at a high-level of generality (i.e., as a general means of storing data) and amounts to the mere storage of data, which is a form of extra-solution activity. The “a stored trained learning model that has been trained… training a linear Support Vector Machines (SVM)” is recited at a high-level of generality (i.e., training an off-the-shelf machine learning algorithm in a generic manner) and amounts to generally linking the abstract idea to a particular technological environment. The “applying the trained learning model” is recited at a high-level of generality (i.e., using an off-the-shelf machine learning algorithm) and amounts to generally linking the abstract idea to a particular technological environment. The “display to a user” is recited at a high-level of generality (i.e., as a general displaying data in a user interface for a human user) and amounts to merely linking of the abstract idea to particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a computing device with various ports (i.e., input port and interface port), and an output device, to perform the noted steps amounts to no more than mere instructions to apply the exception using generic hardware components. Mere instructions to apply an exception using generic hardware components cannot provide an inventive concept ("significantly more").
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “receiving…”, “storing…”, “a stored trained learning model that has been trained… training a linear Support Vector Machines (SVM)”, “applying the trained learning model”, and “display to a user” were considered extra-solution activity and/or generally linking the abstract idea to particular technological environment. The “receiving…” has been re-evaluated under the "significantly more" analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in MPEP 2106.0S(d)(II)(i) "Receiving or transmitting data over a network" is well-understood, routine, and conventional. The “storing…” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in MPEP 2106.05(d)(II)(iv) “Storing and retrieving information in memory” is well-understood, routine, and conventional. The “a stored trained learning model that has been trained… training a linear Support Vector Machines (SVM)” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Coe (20190110687): see below but at least paragraphs [0003], [0019], [0128]; Ganapati (20190110687): see below but at least Figure 4, paragraph [0014]; training of a machine learning model is well-understood, routine and conventional. The “applying the trained learning model” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Coe (20190110687): see below but at least paragraphs [0116]; Ganapati (20190110687): see below but at least Figure 4, paragraph [0025]; use of a machine learning model is well-understood, routine and conventional. and “display to a user” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Coe (20190110687): see below but at least paragraphs [0017]; Ganapati (20190110687): see below but at least Figure 4, paragraph [0023]; a user interface displaying data is well-understood, routine, and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claims 2-10 and 12-19 are similarly rejected because either further define the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible.
Claims 2, 9-10, 12 and 16 further describe use of math (i.e., an abstract idea), however math is not an additional element able to provide a practical application and or significantly more.
Claims 3-4 and 17 are directed toward the categories of classification, but does not recite any additional elements and therefore cannot provide a practical application and/or significantly more.
Claims 5-7 and 13 recites the additional elements of an Attenuated Total Reflection (ATR) probe, an imaging focal plane array (FPA), an output pixel array, an electromagnetic energy illuminator and a wavelength-selectable light source, however the various equipment is recited at a high-level of generality (i.e., off-the-shelf medical equipment implementing generic functions), and amounts to generally linking the abstract idea to a particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of an Attenuated Total Reflection (ATR) probe, an imaging focal plane array (FPA), an output pixel array, an electromagnetic energy illuminator and a wavelength-selectable light source were considered extra-solution activity and/or generally linking the abstract idea to particular technological environment. The Attenuated Total Reflection (ATR) probe has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Coe (20190110687): see below but at least paragraph [0004]; Butte (20170290515): paragraph [0037]; use of a probe to collect data is well-understood, routine and conventional. The imaging focal plane array (FPA) and output pixel array has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Coe (20190110687): see below but at least paragraph [0016], [0067]; Ganapati (20190110687): see below but at least Figure 4, paragraph [0022]; use of an array to capture and output data is well-understood, routine and conventional. The electromagnetic energy illuminator and a wavelength-selectable light source have been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Coe (20190110687): see below but at least paragraph [0014]; Ganapati (20190110687): see below but at least Figure 4, paragraph [0014]; use of a light source to illuminate a specimen is well-understood, routine and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claims 8 and 14-15 further describe organization of data for performance of the abstract idea, but does not recite any additional elements and therefore cannot provide a practical application and/or significantly more.
Claims 18 and 19 further describe the display of data, however display of data was already considered above and is incorporated herein.
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.
Claim(s) 1-19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. No. 20190110687 (hereafter “Coe”), in view of U.S. Patent App. No. 20180247153 (hereafter “Ganapati”).
Reading claim 1, Coe teaches a system for discriminating tissue of a specimen (Coe: paragraph [0013], “a system for discriminating tissue of a specimen”), the system comprising:
a computing device (Coe: Figures 1, 3, paragraph [0013], “a computing device comprising a processor and a memory in communication with the processor”), the computing device comprising:
an input port for receiving and storing electromagnetic energy infrared spectral response data from the specimen in response to delivering illuminating electromagnetic energy to the specimen (Coe: Figures 1, 3, paragraph [0003], “Infrared (IR) spectroscopy can be performed”, paragraph [0013], “obtain an IR spectrum of the specimen… wherein the computing device receives a signal representative of the detected IR spectrum from the IR detector and the computer-executable instructions cause the processor to evaluate the obtained IR spectrum using one or more metrics”, paragraph [0075], “one or more software and/or hardware components that cooperate to store, organize, sort, filter, and/or arrange data used by the computer and/or processor 321”); and
a stored trained learning model that has been trained including by storing […] support vectors at each spectral training wavelength of the trained learning model, and configured for applying the trained learning model to the stored electromagnetic energy infrared spectral response data for classifying one or more locations of the specimen into a classification (Coe: paragraph [0003], “identify tumors and distinguish tumor types. With appropriate statistical training on tissues of interest”, paragraph [0006], “a QCL is tuned to one wavelength at a time”, paragraph [0054], “metrics were combined at input to perform k-means clustering and SVM analysis which correlates the morphology of H&E with the biomolecular chemistry of IR”, paragraph [0066], “discrimination of cancerous from noncancerous tissues. The discriminating software, i.e. the metrics, algorithms, calibrant spectra, and decision equations, provide a determination of whether the tissue is abnormal or normal using a minimum of infrared (IR) wavelengths in order to be measured rapidly”, paragraph [0070], “software for discriminating tissue of a specimen”, paragraphs [0080]-[0082], “A model based on the ratio of absorbances at selected IR wavelengths employed relative weightings that were optimized for separation of tumor and nontumor tissues. The model quantifies the contributions of each metric, enabling the performance of different metrics to be quantitatively compared… The Xr1 (nontumor) and Xr2 (tumor) matrices have a column for each metric and row for each spectrum. Organization of the data in this form enables the optimization of both the relative weights (αj) and the best wavelengths”, paragraph [0119], “performed a new SVM analysis using 6 wavelengths”, paragraphs [0127]-[0128], “merge these metrics… performing SVM on such large data sets and in obtaining classifications for training… SVM training. Then the resulting decision equation is run”); and
an output device, included in or coupled to the computing device, the output device configured generating an indication for output to a user for differentiating according to available tissue classification categories (Coe: Figure 3, paragraph [0017], “the computing device displays the identified normal tissue of the specimen and/or the identified abnormal tissue of the specimen to a surgeon in an operating room, which is used to provide an assessment of the surgical margin made in the operating room by the surgeon as the abnormal tissue is removed”, paragraph [0071], “The computers may include one or more hardware components such as, for example, a processor 321, a random access memory (RAM) module 322, a read-only memory (ROM) module 323, a storage 324, a database 325, one or more input/output (I/O) devices 326, and an interface 327”, paragraph [0076], “I/O devices 326 may also include a display including a graphical user interface (GUI) for outputting information on a monitor”).
Coe may not explicitly teach (underlined below for clarity):
a stored trained learning model that has been trained including by storing a summed response of all support vectors at each spectral training wavelength of the trained learning model, and configured for applying the trained learning model to the stored electromagnetic energy infrared spectral response data for classifying one or more locations of the specimen into a classification;
Ganapati teaches a stored trained learning model that has been trained including by storing a summed response of all support vectors at each spectral training wavelength of the trained learning model, and configured for applying the trained learning model to the stored electromagnetic energy infrared spectral response data for classifying one or more locations of the specimen into a classification (Ganapati: Figure 4, paragraphs [0014]-[0017], “image data captured by the camera is provided to a trained machine learning model… The machine learning model may be trained prior to use using multispectral imaging and samples of classes of tissue to determine a spectrum of illumination of the programmable light source that optimally distinguishes between the classes of tissue… electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated”, paragraphs [0032]-[0035], “MLM training engine 270 may perform a machine learning process to locally train one or more machine learning models for performing multi-class tissue type classification or further refine an already trained MLM. In embodiments, each machine learning model utilized by MLM analysis engine 265 enables differentiation between a set of two or more types of tissue… supply training data to the MLM training engine 270 in the form of measured reflectance of one or more tissue samples in response to a plurality of different discrete illumination wavelengths… Hyperspectral imaging yields reflectance for a plurality of discrete wavelengths for every pixel of image data captured by the hyperspectral imager… machine learning model 610 can be iteratively trained by MLM analysis engine 265 using any of the standard machine learning techniques from the spectral reflectivity inputs of each pixel (e.g., red, green, and blue pixels), and the desired output being (e.g., a vector of probabilities having a one for the known tissue type and a zero for all other tissue types)”, paragraph [0051], “the machine learning model is iteratively trained using a plurality of different wavelengths of illumination of each tissue sample and a plurality of different samples of the same and different type”. Training data responses may be combined at each iteration for each wavelength and in combination with Coe teaches what is required under the broadest reasonable interpretation);
One of ordinary skill in the art before the effective filing date would have found it obvious to include summing data as taught by Ganapati within the SVM trained for each spectral wavelength as taught by Coe with the motivation of providing “high contrast between the cancerous tissue and the healthy tissue so that the cancerous tissue can be removed while making no/minimal damage to surrounding healthy tissue” (Ganapati: paragraph [0003]).
Regarding claim 2, Coe and Ganapati teach the limitations of claim 1, and further teach wherein the stored trained learning model has been trained including by storing decision equations represented as (1) a β spectrum that includes the summed response of all support vectors at each spectral training wavelength of the trained learning model, (2) an average or other central tendency of training spectra, (3) a standard deviation or other spread of the training spectra, and (4) a bias or offset constant (Coe: paragraph [0003], “identify tumors and distinguish tumor types. With appropriate statistical training on tissues of interest”, paragraph [0006], “a QCL is tuned to one wavelength at a time”, paragraph [0029], “the average spectra”, paragraphs [0034]-[0036], “an average IR spectrum… β-sheet 1304 extracted from a library of protein IR spectra are used as calibrants”, paragraphs [0045]-[0048], “decision equation values… uses full IR spectrum with 451 wavelengths yielding an unoccupied hard SVM margin”, paragraph [0066], “The discriminating software, i.e. the metrics, algorithms, calibrant spectra, and decision equations, provide a determination of whether the tissue is abnormal or normal using a minimum of infrared (IR) wavelengths in order to be measured rapidly”, paragraphs [0082]-[0084], “In spite of variations in the absorption offsets, the nontumor and tumor groups have similar average offsets… comparing the average of the spreads of the nontumor (σ1) and tumor (σ2) metric values relative to the distance between the centroids… The centroids are the averages down the columns”, paragraph [0101], “a library of full range IR spectra”, paragraph [0117], “comparing the decision equation histograms using 6 wavelengths (bottom) to the full spectrum of 451 wavelengths (top)”; Ganapati: paragraph [0039], “full spectral information can be used to train the machine learning model”, as well, as at least, paragraphs [0014]-[0017], [0032]-[0035], [0051]).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding claim 3, Coe and Ganapati teach the limitations of claim 1, and further teach wherein the computing device is configured for applying the trained learning model to the stored electromagnetic energy infrared spectral response data for classifying one or more locations of the specimen into a classification including at least one of at least three available categories including (1) a tumor category; (2) a non-tumor category; and (3) a third category that is different from a tumor category and different from a non-tumor category (Coe: Figure 32, paragraph [0003], “identify tumors and distinguish tumor types”, paragraph [0008], “determining normal tissue of the specimen from abnormal tissue of the specimen comprises determining non-cancerous regions of the specimen from cancerous regions of the specimen”, paragraph [0014], “detecting chemical and molecular signatures of tissue specific lesions to include, but not limited to, cancer, preneoplasia, intracellular accumulations (e.g. steatosis), inflammation, and wound healing”, paragraph [0027], “locations of the nontumor and tumor regions”, paragraph [0055], “FIGS. 32A-32C illustrate nonlinear SVM decision equation histograms for the lymphocyte rich, nontumor, and tumor groups of colorectal cancer metastatic to the liver (FIG. 32A). FIG. 32B illustrate these results can also be presented as images showing that there is more information in the decision equation than just classification into the group”, paragraph [0127], “K-means cluster analysis was run with merged H&E an IR metrics and clusters were assigned to three groups: tumor, nontumor, and lymphocyte rich regions”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding claim 4, Coe and Ganapati teach the limitations of claim 3, and further teach wherein the third category is a histology category that includes at least one of histology subcategory including at least one of: a blood-dominated tissue histology subcategory; a non-blood-dominated tissue histology subcategory; a basal tissue histology subcategory; a squamous tissue histology subcategory; a lymphocyte-rich tissue histology subcategory; a non-lymphocyte-rich tissue histology subcategory; a keratinous tissue histology subcategory; or a non-keratinous tissue histology subcategory (Coe: Figure 32, paragraph [0055], “FIGS. 32A-32C illustrate nonlinear SVM decision equation histograms for the lymphocyte rich, nontumor, and tumor groups of colorectal cancer metastatic to the liver (FIG. 32A). FIG. 32B illustrate these results can also be presented as images showing that there is more information in the decision equation than just classification into the group”, paragraph [0127], “K-means cluster analysis was run with merged H&E an IR metrics and clusters were assigned to three groups: tumor, nontumor, and lymphocyte rich regions”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding claim 5, Coe and Ganapati teach the limitations of claim 1, and further teach including or coupled to an Attenuated Total Reflection (ATR) probe for receiving the electromagnetic energy response data from the specimen (Coe: Figures 1, 5, paragraph [0008], “performing infrared (IR) spectroscopy on a specimen using a probe such as an attenuated total reflection (ATR) probe”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding claim 6, Coe and Ganapati teach the limitations of claim 1, and further teach including or coupled to an imaging focal plane array (FPA) including pixels corresponding to the electromagnetic energy response data from different locations of the specimen configured for receiving and storing the electromagnetic energy response data from the specimen, including receiving electromagnetic energy response data from different locations of the specimen (Coe: paragraph [0016], “the IR detector of the system comprises a thermal microbolometer array detector as available”, paragraph [0027], “in different locations of the nontumor and tumor regions”, paragraph [0091], “All of these metrics are extracted from matrix representations of the data, i.e. matrices are created in which each row contains an IR spectrum (either different spectral measurements with an IR probe or for each pixel in an IR imaging data set)”, paragraph [0128], “patients yield a 2 mm×2 mm tissue slice each, there will be 302 pixels by 320 pixel (at 6.25 μm per pixel) yielding 102,400 IR spectra”; Ganapati: paragraph [0022], “a pixel array of the image sensor 118”);
wherein the classifying includes, using the computing device, classifying individual pixels using the trained learning model and the stored electromagnetic energy response data to categorize an individual pixel into one of at least three categories (Coe: Figure 32, paragraph [0003], “identify tumors and distinguish tumor types”, paragraph [0008], “determining normal tissue of the specimen from abnormal tissue of the specimen comprises determining non-cancerous regions of the specimen from cancerous regions of the specimen”, paragraph [0014], “detecting chemical and molecular signatures of tissue specific lesions to include, but not limited to, cancer, preneoplasia, intracellular accumulations (e.g. steatosis), inflammation, and wound healing”, paragraph [0027], “locations of the nontumor and tumor regions”, paragraph [0055], “FIGS. 32A-32C illustrate nonlinear SVM decision equation histograms for the lymphocyte rich, nontumor, and tumor groups of colorectal cancer metastatic to the liver (FIG. 32A). FIG. 32B illustrate these results can also be presented as images showing that there is more information in the decision equation than just classification into the group”, paragraph [0108], “identifies each image pixel with one of 25 clusters”, paragraph [0127], “K-means cluster analysis was run with merged H&E an IR metrics and clusters were assigned to three groups: tumor, nontumor, and lymphocyte rich regions”); and
providing, via the computing device and an output pixel array, a color imaging representation of the classified individual pixels, using different colors of individual pixels in the output pixel array to represent different ones of the categories, and using an intensity indication of the individual pixels in the output pixel array to represent classification strength information from the trained learning model (Coe: Figure 3, 18, paragraph [0017], “the computing device displays the identified normal tissue of the specimen and/or the identified abnormal tissue of the specimen to a surgeon in an operating room, which is used to provide an assessment of the surgical margin made in the operating room by the surgeon as the abnormal tissue is removed”, paragraph [0025], “with nontumor portion in dark red at bottom right and tumor portion of lighter color at top left”, paragraph [0076], “I/O devices 326 may also include a display including a graphical user interface (GUI) for outputting information on a monitor”, paragraph [0112], “the results shown in FIG. 18 are obtained, which are color coded. A tumor is shown in FIG. 18A. FIG. 18B shows tumor groups were colored with hot colors and nontumor groups were colored with cool colors”; Ganapati: paragraph [0026], “The color overlay image can include a different color for each type of tissue rendered in real time over the captured image data for a surgeon or other medical professional. That is, for each classifiable tissue type, a different color is assigned, and a probability mapping of each tissue type to pixels of a captured image can govern the opacity of the color over the image to, in embodiments, distinguish between different tissue types and provide an indication of the likely accuracy of the determined classification. Thus, different color overlays are created for each tissue type at each pixel of captured image data to differentiate regions of tissue type, increase contrast between tissue types, warn a medical professional using endoscope of the location of specific types of tissue, etc.”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding claim 7, Coe and Ganapati teach the limitations of claim 1, and further teach comprising an electromagnetic energy illuminator configured for being controlled by the computing device for selectively controlling a wavelength of illumination light for delivery to the specimen (Coe: Figure 1, paragraph [0013], “a reduced set of IR wavelengths provided by the IR source”, paragraph [0067], “As shown in FIG. 1, the system 100 comprises an IR source 102. In one aspect, the IR source 102 comprises a quantum cascade IR laser (QCL) that is a tunable mid-infrared laser with a reduced set of selected IR wavelengths that have been optimized for detecting the chemical and molecular signatures of tissue specific lesions to include, but not limited to, cancer, preneoplasia, intracellular accumulations (e.g. steatosis), inflammation, and wound healing… The IR source 102 may also be controlled by the computing device 112”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding claim 8, Coe and Ganapati teach the limitations of claim 1, and further teach wherein the learning model is trained using a fuller wavelength set, relative to a reduced wavelength set for delivering the illuminating electromagnetic energy to the specimen (Coe: Figures 22-25, paragraph [0003], “statistical training on tissues of interest”, paragraphs [0045]-[0048], “FIGS. 22A and 22B illustrate histograms of decision equation values for two cases of colorectal cancer metastatic to the liver. FIG. 22A uses full IR spectrum with 451 wavelengths yielding an unoccupied hard SVM margin, i.e. the gap in the middle… FIGS. 24A-24E show decision equation histograms for the full spectra range”, paragraph [0101], “a library of full range IR spectra of colorectal cancer metastatic to the liver”, paragraph [0123], “performed an rbf SVM with the full spectrum… Next, we reduced the wavelengths”, paragraphs [0127], “merge these metrics… performing SVM on such large data sets and in obtaining classifications for training… SVM training. Then the resulting decision equation is run”; Ganapati: paragraph [0039], “full spectral information can be used to train the machine learning model when using a hyperspectral imager, as discussed in greater detail above, the full spectral information is not utilized by MLM analysis engine 265 when using the trained machine learning model to classify different tissue types in image data. Rather, that information is encoded into the one or more programmable light source(s) 112 using the machine learning model as trained”), and
wherein delivering the illuminating electromagnetic energy to the specimen for the classifying and providing the tissue output classification indication includes using a reduced wavelength set relative to the fuller wavelength set (Coe: Figure 1, paragraph [0008], “wherein the IR spectroscopy is performed using a reduced set of IR wavelengths; obtaining an IR spectrum of the specimen from the probe; and evaluating the obtained IR spectrum using one or more metrics, wherein the one or more metrics determine normal tissue of the specimen from abnormal tissue of the specimen”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding claim 9, Coe and Ganapati teach the limitations of claim 8, and further teach wherein the trained model includes a trained model representation that includes at least: (1) a J3 spectrum including the fuller wavelength set; (2) a central tendency indicator (μ) of the training set; (3) a spread indicator (σ) of the training set; and (4) a scaling factor (Coe: paragraph [0003], “identify tumors and distinguish tumor types. With appropriate statistical training on tissues of interest”, paragraph [0006], “a QCL is tuned to one wavelength at a time”, paragraph [0029], “the average spectra”, paragraphs [0034]-[0036], “an average IR spectrum… β-sheet 1304 extracted from a library of protein IR spectra are used as calibrants”, paragraphs [0045]-[0048], “decision equation values… uses full IR spectrum with 451 wavelengths yielding an unoccupied hard SVM margin”, paragraph [0066], “The discriminating software, i.e. the metrics, algorithms, calibrant spectra, and decision equations, provide a determination of whether the tissue is abnormal or normal using a minimum of infrared (IR) wavelengths in order to be measured rapidly”, paragraphs [0082]-[0084], “In spite of variations in the absorption offsets, the nontumor and tumor groups have similar average offsets… comparing the average of the spreads of the nontumor (σ1) and tumor (σ2) metric values relative to the distance between the centroids… The centroids are the averages down the columns”, paragraph [0101], “a library of full range IR spectra”, paragraphs0 [0116]-[0117], “ƒj is a multiplicative scaling factor… comparing the decision equation histograms using 6 wavelengths (bottom) to the full spectrum of 451 wavelengths (top)”; Ganapati: paragraph [0039], “full spectral information can be used to train the machine learning model”, as well, as at least, paragraphs [0014]-[0017], [0032]-[0035], [0051]).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding claim 10, Coe and Ganapati teach the limitations of claim 9, and further teach wherein the trained model representation is represented according to:
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wherein dk represents one of k decision equations or other classifier criteria, b is an offset constant, βj is referred to as a "beta spectrum", Trainj represents an average of a training set, and σTrainj represents a standard deviation or other spread indicator of the training set (Coe: paragraphs [0081]-[0083], “where αj values are relative weights for each metric (to be determined by fitting) and Ixcm−1 is the measured absorbance at a particular value x”, paragraph [0114]-[0117], “The linear SVM decision equation gives the perpendicular distance and direction of a test metric in scaled metric space from the optimized hyperplane:
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where i is an index over the support vectors, j is an index over the metrics, b is the bias constant, αi are the weights, Si,j′ are the scaled support vectors, ƒj is a multiplicative scaling factor, oj is an offset, and Tj is the metric data to be tested”. Also see, at least paragraphs [0121]-[0123]).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding claim 11, Coe teaches a system for discriminating tissue of a specimen (Coe: paragraph [0013], “a system for discriminating tissue of a specimen”), the system comprising:
a computing device (Coe: Figures 1, 3, paragraph [0013], “a computing device comprising a processor and a memory in communication with the processor”), the computing device comprising:
an input port for receiving and storing electromagnetic energy response data, the electromagnetic energy response data obtained from a location of the specimen in response to illuminating the specimen according to a specified electromagnetic energy illumination reduced wavelength set that is reduced with respect to a fuller wavelength set [… and …] training a linear Support Vector Machines (SVM) model using a computing device and a training set based on the stored electromagnetic energy response data (Coe: Figure 1, 3, 22-25, paragraph [0003], “statistical training on tissues of interest”, paragraph [0008], “wherein the IR spectroscopy is performed using a reduced set of IR wavelengths; obtaining an IR spectrum of the specimen from the probe; and evaluating the obtained IR spectrum using one or more metrics, wherein the one or more metrics determine normal tissue of the specimen from abnormal tissue of the specimen”, paragraph [0013], “obtain an IR spectrum of the specimen… wherein the computing device receives a signal representative of the detected IR spectrum from the IR detector and the computer-executable instructions cause the processor to evaluate the obtained IR spectrum using one or more metrics”, paragraphs [0045]-[0048], “FIGS. 22A and 22B illustrate histograms of decision equation values for two cases of colorectal cancer metastatic to the liver. FIG. 22A uses full IR spectrum with 451 wavelengths yielding an unoccupied hard SVM margin, i.e. the gap in the middle… FIGS. 24A-24E show decision equation histograms for the full spectra range”, paragraph [0101], “a library of full range IR spectra of colorectal cancer metastatic to the liver”, paragraphs [0127]-[0128], “merge these metrics… performing SVM on such large data sets and in obtaining classifications for training… SVM training. Then the resulting decision equation is run”. Also see, paragraph [0071]);
an interface port for obtaining decision equations from the trained linear SVM model, using the computing device, with the trained linear SVM model providing the decision equations based on a linear SVM model representation (Coe: Figures 1, 3, paragraphs [0072]-[0073], “The computer program instructions may be loaded into RAM 322 for execution by processor 321… load instructions into RAM 322 for execution by processor 321”, paragraphs [0127]-[0128], “SVM decision equations were obtained for each group… performing SVM on such large data sets and in obtaining classifications for training… SVM training. Then the resulting decision equation is run”);
wherein the computing device is configured for applying the obtained decision equations to the received electromagnetic energy response data corresponding to the reduced wavelength set for discriminating between at least two tissue categories of the location of the specimen (Coe: paragraph [0003], “identify tumors and distinguish tumor types. With appropriate statistical training on tissues of interest”, paragraph [0054], “perform k-means clustering and SVM analysis which correlates the morphology of H&E with the biomolecular chemistry of IR”, paragraph [0066], “discrimination of cancerous from noncancerous tissues. The discriminating software, i.e. the metrics, algorithms, calibrant spectra, and decision equations, provide a determination of whether the tissue is abnormal or normal using a minimum of infrared (IR) wavelengths in order to be measured rapidly”, paragraph [0070], “software for discriminating tissue of a specimen”, paragraphs [0080]-[0082], “A model based on the ratio of absorbances at selected IR wavelengths employed relative weightings that were optimized for separation of tumor and nontumor tissues. The model quantifies the contributions of each metric, enabling the performance of different metrics to be quantitatively compared”, paragraph [0119], “performed a new SVM analysis using 6 wavelengths”, paragraphs [0127], “performing SVM on such large data sets and in obtaining classifications for training… SVM training. Then the resulting decision equation is run”); and
using the computing device, generating an indication for display to a user for visually differentiating according to at least two tissue categories (Coe: Figure 3, paragraph [0017], “the computing device displays the identified normal tissue of the specimen and/or the identified abnormal tissue of the specimen to a surgeon in an operating room, which is used to provide an assessment of the surgical margin made in the operating room by the surgeon as the abnormal tissue is removed”, paragraph [0071], “The computers may include one or more hardware components such as, for example, a processor 321, a random access memory (RAM) module 322, a read-only memory (ROM) module 323, a storage 324, a database 325, one or more input/output (I/O) devices 326, and an interface 327”, paragraph [0076], “I/O devices 326 may also include a display including a graphical user interface (GUI) for outputting information on a monitor”).
Coe may not explicitly teach (underlined below for clarity):
an input port for receiving and storing electromagnetic energy response data, the electromagnetic energy response data obtained from a location of the specimen in response to illuminating the specimen according to a specified electromagnetic energy illumination reduced wavelength set that is reduced with respect to a fuller wavelength set used for training a linear Support Vector Machines (SVM) model using a computing device and a training set based on the stored electromagnetic energy response data;
Ganapati teaches an input port for receiving and storing electromagnetic energy response data, the electromagnetic energy response data obtained from a location of the specimen in response to illuminating the specimen according to a specified electromagnetic energy illumination reduced wavelength set that is reduced with respect to a fuller wavelength set used for training a linear Support Vector Machines (SVM) model using a computing device and a training set based on the stored electromagnetic energy response data (Ganapati: Figure 4, paragraphs [0014]-[0017], “image data captured by the camera is provided to a trained machine learning model… The machine learning model may be trained prior to use using multispectral imaging and samples of classes of tissue to determine a spectrum of illumination of the programmable light source that optimally distinguishes between the classes of tissue”, paragraphs [0032]-[0035], “MLM training engine 270 may perform a machine learning process to locally train one or more machine learning models for performing multi-class tissue type classification or further refine an already trained MLM. In embodiments, each machine learning model utilized by MLM analysis engine 265 enables differentiation between a set of two or more types of tissue”, paragraph [0039], “full spectral information can be used to train the machine learning model when using a hyperspectral imager, as discussed in greater detail above, the full spectral information is not utilized by MLM analysis engine 265 when using the trained machine learning model to classify different tissue types in image data. Rather, that information is encoded into the one or more programmable light source(s) 112 using the machine learning model as trained”, paragraph [0051], “the machine learning model is iteratively trained using a plurality of different wavelengths of illumination of each tissue sample and a plurality of different samples of the same and different type”);
One of ordinary skill in the art before the effective filing date would have found it obvious to include training using a full wavelength set as taught by Ganapati within the reduced wavelength set and SVM trained for discrimination as taught by Coe with the motivation of providing “high contrast between the cancerous tissue and the healthy tissue so that the cancerous tissue can be removed while making no/minimal damage to surrounding healthy tissue” (Ganapati: paragraph [0003]).
Regarding claim 12, Coe and Ganapati teach the limitations of claim 11, and further teach wherein the trained linear SVM model representation includes at least: (1) a stored SVM j3 spectrum including the fuller wavelength set used for training the linear SVM model representation; (2) a central tendency indicator(μ) of the training set; (3) a spread indicator (cr) of the training set; and (4) a scaling factor (Coe: paragraph [0003], “identify tumors and distinguish tumor types. With appropriate statistical training on tissues of interest”, paragraph [0006], “a QCL is tuned to one wavelength at a time”, paragraph [0029], “the average spectra”, paragraphs [0034]-[0036], “an average IR spectrum… β-sheet 1304 extracted from a library of protein IR spectra are used as calibrants”, paragraphs [0045]-[0048], “decision equation values… uses full IR spectrum with 451 wavelengths yielding an unoccupied hard SVM margin”, paragraph [0066], “The discriminating software, i.e. the metrics, algorithms, calibrant spectra, and decision equations, provide a determination of whether the tissue is abnormal or normal using a minimum of infrared (IR) wavelengths in order to be measured rapidly”, paragraphs [0082]-[0084], “In spite of variations in the absorption offsets, the nontumor and tumor groups have similar average offsets… comparing the average of the spreads of the nontumor (σ1) and tumor (σ2) metric values relative to the distance between the centroids… The centroids are the averages down the columns”, paragraph [0101], “a library of full range IR spectra”, paragraphs0 [0116]-[0117], “ƒj is a multiplicative scaling factor… comparing the decision equation histograms using 6 wavelengths (bottom) to the full spectrum of 451 wavelengths (top)”; Ganapati: paragraph [0039], “full spectral information can be used to train the machine learning model”, as well, as at least, paragraphs [0014]-[0017], [0032]-[0035], [0051]).
The motivation to combine is the same as in claim 11, incorporated herein.
Regarding claim 13, Coe and Ganapati teach the limitations of claim 11, and further teach wherein the electromagnetic energy response data is obtained from the location of the specimen in response to illuminating the specimen according to a specified electromagnetic energy illumination reduced wavelength set using a wavelength-selectable light source, wherein the reduced wavelength set is specified to correspond to wavelengths falling within an output wavelength range of the wavelength-selectable light source (Coe: Figure 1, paragraph [0008], “wherein the IR spectroscopy is performed using a reduced set of IR wavelengths; obtaining an IR spectrum of the specimen from the probe; and evaluating the obtained IR spectrum using one or more metrics, wherein the one or more metrics determine normal tissue of the specimen from abnormal tissue of the specimen”, paragraph [0047]-[0048], “QCL ranges defined… FTIR range. The black vertical dotted lines 2504 define the range of the ATR probe on our FTIR spectrometer”, paragraph [0081], “a smaller spectral range”).
The motivation to combine is the same as in claim 11, incorporated herein.
Regarding claim 14, Coe and Ganapati teach the limitations of claim 11, and further teach wherein the trained linear SVM model is trained using a training set that includes, pre-processing of spectral data of the fuller wavelength set using a second derivative of the spectral data across wavenumbers of the fuller wavelength set before determining the linear SVM model representation to help inhibit an effect of water vapor or other gas phase interference (Coe: paragraph [0075], “software and/or hardware components that cooperate to store, organize, sort, filter, and/or arrange data used by the computer and/or processor 321”, paragraph [0097], “normalize all IR spectra before calculating these scores”; Ganapati: paragraph [0039], “preprocessing provides a surgeon or other medical professional utilizing endoscope 210 and image processing device 250 the benefit of hyperspectral information”. The Examiner notes that “to help inhibit an effect of water vapor or other gas phase interference” is an intended use of the preprocessing that is not required to occur. This feature has been fully considered by the Examiner; however, the limitation does not provide patentable distinction over the cited prior art because it is an intended use or result of the preprocessing).
The motivation to combine is the same as in claim 11, incorporated herein.
Regarding claim 15, Coe and Ganapati teach the limitations of claim 14, and further teach wherein the second derivative of the spectral data across wavenumbers of the fuller wavelength set includes, skipping one or more steps of wavenumbers for performing the second derivative (Coe: paragraph [0052], “successively doubling the skipped wavelengths”, paragraphs [0082]-[0083], “The “r” in these names stands for “reduced” since the metric matrices have only three columns, while the raw data in the X1 and X2 matrices have 451 columns for each wavenumber step in the IR spectrum… The wavenumber positions in the metric definitions were also varied”).
The motivation to combine is the same as in claim 11, incorporated herein.
Regarding claim 16, Coe and Ganapati teach the limitations of claim 11, and further teach wherein the decision equations include more than two decision equations corresponding to respective tissue classification categories (Coe: Figure 32, paragraph [0055], “FIGS. 32A-32C illustrate nonlinear SVM decision equation histograms for the lymphocyte rich, nontumor, and tumor groups of colorectal cancer metastatic to the liver (FIG. 32A). FIG. 32B illustrate these results can also be presented as images showing that there is more information in the decision equation than just classification into the group”, paragraph [0127], “K-means cluster analysis was run with merged H&E an IR metrics and clusters were assigned to three groups: tumor, nontumor, and lymphocyte rich regions”).
The motivation to combine is the same as in claim 11, incorporated herein.
Regarding claim 17, Coe and Ganapati teach the limitations of claim 16, and further teach wherein the at least two categories includes a histology category that includes at least two mutually-exclusive histology subcategories (Coe: Figure 32, paragraph [0055], “FIGS. 32A-32C illustrate nonlinear SVM decision equation histograms for the lymphocyte rich, nontumor, and tumor groups of colorectal cancer metastatic to the liver (FIG. 32A). FIG. 32B illustrate these results can also be presented as images showing that there is more information in the decision equation than just classification into the group”, paragraph [0127], “K-means cluster analysis was run with merged H&E an IR metrics and clusters were assigned to three groups: tumor, nontumor, and lymphocyte rich regions”).
The motivation to combine is the same as in claim 11, incorporated herein.
Regarding claim 18, Coe and Ganapati teach the limitations of claim 1, and further teach wherein the electromagnetic energy response data is obtained from various locations within an area of the specimen, and wherein the computing device is configured for generating an image of the area of the specimen for display to a user, the image visually differentiating displayed locations in the area of the specimen according to the at least two categories using different colors or shading of displayed locations in the area of the specimen (Coe: Figure 3, 18, paragraph [0017], “the computing device displays the identified normal tissue of the specimen and/or the identified abnormal tissue of the specimen to a surgeon in an operating room, which is used to provide an assessment of the surgical margin made in the operating room by the surgeon as the abnormal tissue is removed”, paragraph [0025], “with nontumor portion in dark red at bottom right and tumor portion of lighter color at top left”, paragraph [0076], “I/O devices 326 may also include a display including a graphical user interface (GUI) for outputting information on a monitor”, paragraph [0112], “the results shown in FIG. 18 are obtained, which are color coded. A tumor is shown in FIG. 18A. FIG. 18B shows tumor groups were colored with hot colors and nontumor groups were colored with cool colors”; Ganapati: paragraph [0026], “The color overlay image can include a different color for each type of tissue rendered in real time over the captured image data for a surgeon or other medical professional. That is, for each classifiable tissue type, a different color is assigned… Thus, different color overlays are created for each tissue type at each pixel of captured image data to differentiate regions of tissue type, increase contrast between tissue types, warn a medical professional using endoscope of the location of specific types of tissue, etc.”).
The motivation to combine is the same as in claim 11, incorporated herein.
Regarding claim 19, Coe and Ganapati teach the limitations of claim 1, and further teach wherein the visually differentiating including using pixels representing the displayed locations in an area with corresponding pixel intensities based on a strength indication provided by the computing device using the decision equations (Ganapati: paragraph [0026], “The color overlay image can include a different color for each type of tissue rendered in real time over the captured image data for a surgeon or other medical professional. That is, for each classifiable tissue type, a different color is assigned, and a probability mapping of each tissue type to pixels of a captured image can govern the opacity of the color over the image to, in embodiments, distinguish between different tissue types and provide an indication of the likely accuracy of the determined classification. Thus, different color overlays are created for each tissue type at each pixel of captured image data to differentiate regions of tissue type, increase contrast between tissue types, warn a medical professional using endoscope of the location of specific types of tissue, etc.”).
The motivation to combine is the same as in claim 11, incorporated herein.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Pub. No. 20170290515 (hereafter “Butte”) teaches tissue classification using sample and real-time fluorescence data.
U.S. Patent Pub. No. 20230039380 (hereafter “Perston”) teaches a raman spectrometer for classifying tissue of a specimen.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Andrew E Lee whose telephone number is (571)272-8323. The examiner can normally be reached M-Th 9-5:00 PM.
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/A.E.L./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684