DETAILED ACTION
Response to Arguments
Applicants’ arguments filed with respect to claims 8-14, and 21-33 have been fully considered but are moot in view of the new ground(s) of rejection. The rejections are necessitated due to claim amendments.
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 8-14, and 21-33 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. When reviewing independent claim 8, 24, and 33, and based upon consideration of all of the relevant factors with respect to the claim as a whole, 1-8, and 21-32 are held to claim an abstract idea without reciting elements that amount to significantly more than the abstract idea and is/are therefore rejected as ineligible subject matter under 35 U.S.C. 101.
The Examiner will analyze Claim 8, and similar rationale applies to independent Claims 24 and 33. The rationale, under MPEP § 2106, for this finding is explained below:
The claimed invention (1) must be directed to one of the four statutory categories, and (2) must not be wholly directed to subject matter encompassing a judicially recognized exception, as defined below. The following two step analysis is used to evaluate these criteria.
Step 1: Is the claim directed to one of the four patent-eligible subject matter categories: process, machine, manufacture, or composition of matter?
When examining the claim under 35 U.S.C. 101, the Examiner interprets that the claims is related to a process since the claim is directed to a method of performing the claim limitations.
Step 2a, Prong 1: Does the claim wholly embrace a judicially recognized exception, which includes laws of nature, physical phenomena, and abstract ideas, or is it a particular practical application of a judicial exception?
The Examiner interprets that the judicial exception applies since Claim 1 limitation of determining one or more areas of the microscopy imaging data for performing an operation are directed to an abstract.The limitations could be performed by a person by determining an area of an image to perform an operation (mental process/step).
determining probability values for each of one or more holes in a grid mesh wherein the probability values are indicative of whether the corresponding hole is in condition is also a mathematical concept.
Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions, The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012) ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same).
If/when the claim recites a judicial exception (i.e., an abstract idea enumerated in MPEP § 2106.04(a), a law of nature, or a natural phenomenon), the claim requires further analysis in Prong Two.
Step 2a, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
The additional claim limitations receiving image data and location data,and displaying data indicative of the determined area probability values for each of the one or more holes in the grid mesh is nothing more than insignificant extra solution activity.
A machine learning model and display are used to generally apply the abstract idea without limiting how it functions.
Step 2b: If a judicial exception into a practical application is not recited in the claim, the Examiner must interpret if the claim recites additional elements that amount to significantly more than the judicial exception.
The Examiner interprets that the Claims do not amount to significantly more since the Claims are generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010(2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook, 437 U.S. 584, 588-90, 198 USPQ 193, 197-98 (1978) (MPEP § 2106.05(h)).
Furthermore, the generic computer components of the processor/memory/display recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system.
Claims 9-14, 21-23, 25-32 depending on the independent claims include all the limitation of the independent claim. The Examiner finds that Claims 9-14, 21-23, 25-32 does not state significantly more since the claim only recites additional steps for analyzing image using machine learning model.
Thus, 8-14, and 21-33 recite the same abstract idea and therefore are not drawn to the eligible subject matter as they are directed to the abstract idea without significantly more.
Therefore, all claims are rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 8, 24, and 33 are rejected under 35 U.S.C. 103 as being unpatentable over Stumpe (Pub. No. US 2020/0097727) in view of Cianfrocco et al. (Pub. No. US 2023/0288354 hereinafter “Cian”).
Regarding claim 8, Stumpe teaches a method comprising: receiving/obtaining microscopy imaging data (low magnification images) and location data (motor coordinate; x-y positions) indicating sample locations relative to the microscopy imaging data [Para. 88-81]; determining, based on a machine-learning model (machine learning pattern recognizer) and the location data (respective positions), one or more areas (area of interest) of the microscopy imaging data for performing at least one operation (investigate) [Para. 81]; and causing display, on a display device (screen 107), data indicative (highlighting the region of interest 131) of the determined one or more areas (one or more regions of interest) of the microscopy imaging data (magnified pathology image) [Para. 74, and 76].
However, Stumpe doesn’t explicitly teach determining, based on a machine-learning model and the location data, probability values for each of one or more holes in a grid mesh of the microscopy imaging data, wherein the probability values are indicative of whether the corresponding hole is in condition for performing at least one operation via a charged particle microscope; and causing display, on a display device, data indicative of the determined probability values for each of the one or more holes in the grid mesh.
Ciano teaches determining, based on a machine-learning model and the location data (location provided in the meta data), probability values (quality score) for each of one or more holes in a grid mesh of the microscopy imaging data [Para. 37, 43-44, 47, and 64], wherein the probability values (quality score) are indicative of whether the corresponding hole (candidate hole) is in condition (quality) for performing at least one operation (imaging) via a charged particle microscope (electron microscope) [Para. 43, and 47 use the assess hole suitability, while paragraphs 57-58 use the assessment to select a target hole for electronic microscope image]; and causing display, on a display device, data indicative of the determined probability values (quality score) for each of the one or more holes in the grid mesh [Para. 40 provides connected display while Para. 47 implements associating numerical results with the depicted holes, Para. 48 determines results for the candidate holes, and Para 88 identifies displaying information as the machine output operation].
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Stumpe’s microscopy image scoring and display method to perform Cian’s complete cryo-EM process of using hole-lcoation data and a neural network to determine a numerical quality value for each grid hole, select suitable holes for electron microscope image, and display the hole associated values. This medication would improve Stumpe by enabling objective, location-indexed screening and visualization of individual acquisition sites, thereby reducing time spent imaging unsuitable holes and improving cryo-EM data collection efficiency.
Claim 24 is rejected for the same reason as claim 8 above. Furthermore, Stumpe teaches one or more processors [fig. 1 and related description]; and a memory storing instructions that, when executed by the one or more processors [fig. 1, 9 and related description].
Claim 33 is rejected for the same reason as claim 24 above. Furthermore, Stumpe teaches a charged particle microscopy device configured to perform one or more microscopy operations [fig. 1, 2 and related description].
Claims 8-11, 13, 14, 24-27, 29, 30, and 33 are rejected under 35 U.S.C. 103 as being unpatentable over Smith (Patent No. US 10,255,693) in view of Barral (Pub. No. US 2018/0046759) further in view of Cianfrocco et al. (Pub. No. US 2023/0288354 hereinafter “Cian”).
Regarding claim 8, Smith a method comprising: receiving microscopy imaging data and heat map (bounding box) [Col. 11 line 60- Col. 12 line 7, fig. 5 and related description]; determining, based on a machine-learning model and the heat map (bounding box), one or more areas of the microscopy imaging data for performing at least one operation [Col. 12 lines 8-39]; and causing display, on a display device, data indicative of the determined one or more areas of the microscopy imaging data [Col. 8 lines 32-39; fig. 1, 5, 8, and related description].
However, Smith doesn’t explicitly teach reception of location data (location information/(stage position)) indicating sample locations relative to the microscopy image data and determining an area (one or more regions of interest) of microscopy imaging data based on location data.
Barral teaches reception of location data indicating sample locations relative to the microscopy image data and determining an area of microscopy imaging data based on location data [Para. 35 “A processing apparatus may receive magnified pathology images from the digital camera either wirelessly or by wired transmission”; Para. 37 “the machine learning algorithm may be trained by, and use the location information and the magnification information about, the reference pathology images to identify the one or more regions of interest in the magnified pathology images”; Para. 38 “alerting a user of the microscope to the one or more regions of interest in the magnified pathology images” and Para. 25, 19, and 27; fig. 2 and related description].
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Smith to receive location data and determine an area of the image based on the location data, feature as taught by Barral; because the modification enables the system to improve microscope workflows by registering stage coordinates across imaging stations so that the system can reliably find again the same specimen locations without needing fiducial marks.
However, Smith in view of Barrel doesn’t explicitly teach determining, based on a machine-learning model and the location data, probability values for each of one or more holes in a grid mesh of the microscopy imaging data, wherein the probability values are indicative of whether the corresponding hole is in condition for performing at least one operation via a charged particle microscope; and causing display, on a display device, data indicative of the determined probability values for each of the one or more holes in the grid mesh.
Ciano teaches determining, based on a machine-learning model and the location data (location provided in the meta data), probability values (quality score) for each of one or more holes in a grid mesh of the microscopy imaging data [Para. 37, 43-44, 47, and 64], wherein the probability values (quality score) are indicative of whether the corresponding hole (candidate hole) is in condition (quality) for performing at least one operation (imaging) via a charged particle microscope (electron microscope) [Para. 43, and 47 use the assess hole suitability, while paragraphs 57-58 use the assessment to select a target hole for electronic microscope image]; and causing display, on a display device, data indicative of the determined probability values (quality score) for each of the one or more holes in the grid mesh [Para. 40 provides connected display while Para. 47 implements associating numerical results with the depicted holes, Para. 48 determines results for the candidate holes, and Para 88 identifies displaying information as the machine output operation].
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Smith’s machine-learning microscopy-image classification method with Barral’s location-aware identification and display of regions of interest, and further with Cian’s cryo-EM process for determining and displaying hole-specific quality values and selecting suitable grid holes for electron-microscope imaging. This combination would improve Smith by enabling objective, location-indexed screening and visualization of individual acquisition sites, thereby reducing time spent imaging unsuitable holes and increasing cryo-EM data-collection efficiency.
Claim 24 is rejected for the same reason as claim 8 above. Furthermore, Smith teaches one or more processors [fig. 1, 9 and related description]; and a memory storing instructions that, when executed by the one or more processors [fig. 1, 9 and related description].
Claim 33 is rejected for the same reason as claim 24 above. Furthermore, Smith teaches a charged particle (blood, minerals, fibers, sperm etc.. where all are charged particles because it’s known that they all have net electrons) microscopy device configured to perform one or more microscopy operations [Col. 23 line 4 - Col. 24 line 40].
Regarding claims 9 and 25, Smith in view of Barral teaches wherein the microscopy imaging data and the location data are received as stated above. Furthermore, Smith teaches it is received in response to a charged particle microscopy image acquisition of a microscopy device [Col. 12 lines 8-39, fig. 1, 9 and related description].
Regarding claims 10 and 26, Smith teaches wherein the machine-learning model is configured based on automatically generated/obtained training data, wherein the automatically generated training data comprises a plurality of training images generated based on modifying (different magnifications and at different focal depths) a microscopy image [Col. 5 lines 54 - Col. 6 line 18 and Col. 11 lines 6-16].
Regarding claims 11 and 27, Smith teaches wherein modifying the microscopy image comprises scaling [Col. 6 lines 10-14].
Regarding claims 13 and 29, Smith teaches wherein the machine-learning model comprises one or more of a neural network or a fully convolutional neural network [Col. 10 lines 52-56].
Regarding claims 14 and 30, Smith teaches wherein the at least one operation comprises one or more of a data acquisition operation, a data analysis operation, acquiring additional imaging data having a higher resolution that the microscopy imaging data, or analyzing the additional imaging data [Col. 12 lines 32-39 and 50-52; fig. 6 and related description].
Claims 12, 21, 28, and 31 are rejected under 35 U.S.C. 103 as being unpatentable over Smith (Patent No. US 10,255,693) in view of Barral (Pub. No. US 2018/0046759) in view of Cianfrocco et al. (Pub. No. US 2023/0288354 hereinafter “Cian”) further in view of KIM et al. (Pub. No. US 2015/0206026).
Regarding claims 12 and 28, Smith in view of Barral in view of Cian doesn’t explicitly teach the claim limitation.
However, KIM teaches wherein the automatically generated training data comprises normalized training data, and wherein the normalized training data is normalized based on determining a histogram of image intensity data of the training data, determining a normalization factor based on a percentage of the histogram, and normalizing the training data based on the normalization factor [Para. 58].
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Smith in view of Barral in view of Cian to teach the claim limitation, feature as taught by KIM; because the modification enables the system to detect a feature point from the input image based on the dominant direction, and generating a feature vector corresponding to the feature point.
Regarding claims 21 and 31, Smith in view of Barral in view of Cian doesn’t explicitly teach the claim limitation.
However, Cianfrocco teaches wherein the location data comprises coordinates of holes in a grid section of a grid mesh [Para. 37].
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Smith in view of Barral in view of Cian to teach the claim limitation, feature as taught by Cianfrocco; because the modification enables the system to provide optimized electron microscopy scanning through improved data acquisition and processing to generate the cryo-EM images.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Smith (Patent No. US 10,255,693) in view of Barral (Pub. No. US 2018/0046759) in view of Cianfrocco et al. (Pub. No. US 2023/0288354 hereinafter “Cian”) further in view of Arafati et al. (Pub. No. US 2021/0012885).
Regarding claim 22, Smith in view of Barral in view of Cian doesn’t explicitly teach the claim limitation.
However, Arafati teaches wherein the machine-learning model comprises a fully convolutional neural network converted from a convolutional neural network [Para. 26].
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Smith in view of Barral in view of Cian to teach the claim limitation, feature as taught by Arafati; because the modification enables the system to provide methods related to techniques for performing four-chamber segmentation of echocardiograms.
Claims 23 and 32 are rejected under 35 U.S.C. 103 as being unpatentable over Smith (Patent No. US 10,255,693) in view of Barral (Pub. No. US 2018/0046759) in view of Cianfrocco et al. (Pub. No. US 2023/0288354 hereinafter “Cian”) further in view of Sethi et al. (Pub. No US 2018/0232883).
Regarding claims 23 and 32, Smith in view of Barral in view of Cian doesn’t explicitly teach the claim limitation.
However, Sethi teaches wherein data indicative of the determined probability values [Para. 58, and 74].
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Smith in view of Barral in view of Cian to teach the claim limitation, feature as taught by Sethi; because the modification enables the system to aggregate information to produce disease class scores.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SOLOMON G BEZUAYEHU whose telephone number is (571)270-7452. The examiner can normally be reached on Monday-Friday 10 AM-8 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Oneal Mistry can be reached on 313-446-4912. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SOLOMON G BEZUAYEHU/
Primary Examiner, Art Unit 2666
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