Notice of Pre-AIA or AIA Status
The present application 19/007,630, filed on 1/2/2025 (or after March 16, 2013), is being examined under the first inventor to file provisions of the AIA (First Inventor to File).
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 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.
DETAILED ACTION
Claims 1-17 are pending in this application.
Drawings
The Drawings filed on 1/2/2025 are acceptable for examination purpose.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 3/12/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner
Priority
Acknowledgment is made of applicant’s claim for domestic priority application
U.S. Provisional Patent application serial number # 63/617,917 filed on 01/05/2024
under 35 U.S.C. 119 (e)
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 13 is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As to claim 13, it is unclear what is meant by “to identify features that have higher complexity than the identified categories and a higher degree of accuracy……..”, particularly the term “higher complexity” “higher degree”, is a relative term that makes the claim indefinite
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 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application.
Claim 1-17 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance with the 2019 Revised Patent Subject Matter Eligibility Guidance, Federal Register (84 FR 50) on January 7, 2019 hereinafter 2019 PEG
Step 1. In accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is noted that the method of claim 1,16-17, method, system and non-transitory computer readable storage medium respectively, directed to one of the eligible categories of subject matter and therefore satisfy Step 1.
Step 2A. In accordance with Step 2A prong one of the 2019 PEG, the limitations reciting the abstract idea are highlighted, and the limitations directed to additional elements are highlighted, as set forth in exemplary claim 1
“A method for particle morphology classification, the method comprising:
obtaining input imagery of a particle sample that includes powder particles that are sintered together;
generating an input dataset for clustering based on the input imagery, including (i) detecting and segmenting the powder particles in the input imagery, (ii) extracting and standardizing powder particle images, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example obtaining input imagery, generating input data set….., extracting ……..in the context of this claim encompasses the user thinning mere input[ing], generating datasets of selected segment[s] of particles imagery. tthe limitation (iii) calculating morphology metrics of the powder particles”, other than reciting “by a processor”, general-purpose computing, nothing in the claim element precludes the step from practically being performed in the mind. Consistent with the specification para 0045-0047, fig 4, one can mentally calculate metrics because, in the context of this claim limitation encompasses the user manually supplying parameter values covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “mental processes”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I).
“identifying categories in the input dataset, based on geometry or morphology- based similarities between different particles, using K-means clustering on Hu invariant moments of the powder particle images”, as drafted, is a process that, under its broadest reasonable interpretation, groupings of abstract ideas because they cover covers performance appears mere algorithm (K-means clustering on Hu invariant moments ) identifying categories of dataset(s), mere data gathering including observation, judgement and opinion.
Step 2B. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional method limitations are directed to a generic computer, at a very high level of generality and without imposing meaningful limitations on the scope of the claim. In addition para 0045-0047, fig 4, of the instant specification describe generic off-the-shelf computer-based elements for implementing the claimed invention which does not amount to significantly more than the abstract idea and is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. Further, See, e.g., Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94 (Fed. Cir. 2015) ("Just as Diehr could not save the claims in Alice, which were directed to 'implement[ing] the abstract idea of intermediated settlement on a generic computer', it cannot save O/P's claims directed to implementing the abstract idea of price optimization on a generic computer.") (citations omitted). See also, Affinity Labs of Texas LLC v. DirecTV LLC, 838 F.3d 1253, 1257-1258 (Fed. Cir. 2016) (mere recitation of a GUI does not make a claim patent-eligible); Intellectual Ventures I LLC v. Capital One Bank, 792 F.3d 1363, 1370 (Fed. Cir. 2015) ("the interactive interface limitation is a generic computer element".)
The additional elements are broadly applied to the abstract idea at a high level of generality ("similar to how the recitation of the computer in the claims in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer,") as explained in MPEP § 2106.05(f)) and they operate in a well-understood, routine, and conventional manner.
MPEP § 2106.05 (d)(II) sets forth the following:
The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g. at a high level of generality) as insignificant extra-solution activity.
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec...; TLI Communications LLC v. AV Auto. LLC...; OIP Techs., Inc., v. Amazon.com, Inc... ; buySAFE, Inc. v. Google, Inc...;
Performing repetitive calculations, Flook ... ; Bancorp Services v. Sun Life...;
Electronic recordkeeping, Alice Corp...; Ultramercial... ;
Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc...;
Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank...; and
A web browser's back and forward button functionality, Internet Patent Corp. v. Active Network, Inc...
Courts have held computer-implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking).
As to claim 2, further elaborates:
“receiving labels for the categories from a user; and
subsequently using the categories to analyze or quantify future batches of particulates based on the labels”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea..
As to claim 3, further elaborates “pre-categorizing the powder particles, based on size or other characteristics prior to clustering, in order to eliminate larger-scale differences between the powder particles”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea..
As to claim 4, further elaborates “wherein the pre-categorizing is performed using a size classifier that classifies the powder particles into a plurality of size categories based on particle size distribution”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea..
As to claim 5, further elaborates “wherein each size category is further categorized using a respective K-means classifier, wherein each K-means classifier categorizes the powder particles into a respective set of one or more categories”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea
As to claim 6, further elaborates “wherein the plurality of size categories includes (i) a category for small particles, which accounts for a minimal amount of a total volume of the particle sample, (ii) a category for particles within predetermined size specifications, and (iii) a category for particles that are outside of the predetermined size specifications or are defective”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea
As to claim 7, further elaborates “using multiple levels of unsupervised clustering to create groups within groups, when identifying the categories”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea
As to claim 8, further elaborates” wherein identifying the categories is performed using a multi- tiered classifier that incorporates a plurality of types of classifiers”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea
As to claim 9, further elaborates receiving a number of desired categories from a user; and applying K-means clustering on Hu invariant moments to create the number of desired categories”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea
As to claim 10, further elaborates “displaying identified categories to a user; receiving labels for the identified categories and a new set of categories after removal of redundant categories, from the user; and saving a model comprising the new set of categories and the labels for subsequent categorization of particle samples”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea
As to claim 11, further elaborates “repeating applying the K-means clustering on Hu invariant moments to categorize the particle sample based on the new set of categories”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea
As to claim 12, further elaborates “training random forest or support vector machine (SVM) classifiers on the Hu invariant moments; and using the trained random forest or SVM classifiers to further categorize the identified categories based on fundamental attributes of the identified categories”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea
As to claim 13, further elaborates: “training a convolutional neural network or a deep neural network on the input imagery based on the identified categories, to identify features that have higher complexity than the identified categories and a higher degree of accuracy than metrics-based measurements of the Hu invariant moments; and using the trained convolutional neural network or the deep neural network to identify the features for particle samples”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea
As to claim 14, further elaborates “wherein the input imagery is obtained from an optical particle measurement system, an in-line imaging system, or similar flow-based particle imaging system”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea
As to claim 15, further elaborates “wherein the K-means clustering ignores a metric of the Hu invariant moments that differentiates based on reflection, for particle morphology”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea
As to claim 16, further elaborates “A computer system for physics simulation, comprising: “one or more processors; and memory; wherein the memory stores one or more programs configured for execution by the one or more processors, and the one or more programs comprise instructions for performing the method of claim “, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea
As to claim 17, further elaborates “A non-transitory computer readable storage medium storing one or more programs configured for execution by a computer system having one or more processors and memory, the one or more programs comprising instructions for performing the method of claim 1, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea
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.
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.
Claims 1-11,14-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ruffner et al., (hereafter Ruffner), US Pub. No. 2019/0234853 published Aug, 2019 in view of Park et al., (hereafter Park), US Pub. No. 2019/0347278 published Nov, 2019
As to claim 1, Ruffner teaches a system which including “A method for particle morphology classification, the method comprising” (Ruffner: Abstract,0010, fig 1A – Ruffner teaches analysis of Morphology of particles images, classifying characteristics based on the measured data points)
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“obtaining input imagery of a particle sample that includes powder particles that are sintered together” (Ruffner:0010-0011,0025, fig 15A-B - Ruffner teaches holographic microscopy characterization, particularly obtaining images of colloidal particles measured and computed shown as grayscale images, also data from samples of silocone oil emulsion images as shown in fig 15A-B)
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“generating an input dataset for clustering based on the input imagery, including (i) detecting and segmenting the powder particles in the input imagery” (Ruffner:0036-0038, fig 10A-10B – Ruffner teaches particle scatters of the data sets generated in holograms is analyzed with Lorenz-Mie to get the size and other parameters of the particles, further holographic characterization including aggregates the data sets and create cluster aggregation)
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“(ii) extracting and standardizing powder particle images” (Ruffner: 0024 – Ruffner teaches holograms of particles flow shown in images that shows larger lower index particle’s images)
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And” (iii) calculating morphology metrics of the powder particles” (Ruffner: 0038-0039, fig 10 - multiple particles generated, analyzed particularly to get size, shape and index as data set determining morphology of the particle samples and calculated as protein aggregates as morphology metrics); and
“identifying categories in the input dataset, based on geometry or morphology- based similarities between different particles” (Ruffner: fig 3-4, 0013-0014,0062 – Ruffner teaches input data sets in multi-dimensional size and distinguish particles of different categories including different shape of the distribution size(s))
“using clustering on Hu invariant moments of the powder particle images” (Ruffner: Abstract, 0021, 0023, 0053 - Hu’s moments are used in combination with holographic microcopy for identification of materials in a sample, particularly Hu moments detailed in fig 11A, further Hu moments are invariant with respect to rotation, scale and translation and Hu moments provies distinction of different geometrical shapes as detailed in 0053). It is however, noted that Ruffner does not disclose “using K-means clustering”, although Ruffner supports cluster aggregation, various shapes of particles aggregates and analyzed using for example Lorenz-Mie analysis (Ruffner: 0037). On the other hand, Park disclosed “using K-means clustering” (Park: Abstract, fig 1,0037,0051 - Park teaches K-means clustering on histogram based on a number of clusters, also K-means clustering module calculates a distance between cluster and distributed individual points
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It would have been obvious to a person of ordinary skill in the art at the time of filing the claimed invention performing K-means clustering, particularly generating plurality of data buckets determines number of times the selected data buckets satisfying the threshold of Park et al., into holographic images characterization using Hu moments of Ruffner et al., because that would have allowed users of Ruffner to process complex large datasets provides the scalability, while partitioning data into clusters based on similarity makes it easy to implement in improving quality and reliability of the system (Park: 0012-0013)
As to claim 2, the combination of Ruffner, Park disclosed
“receiving labels for the categories from a user” (Ruffner: 0053); and
“subsequently using the categories to analyze or quantify future batches of particulates based on the labels” (Ruffner: 0053-0054)
As to claim 3 , the combination of Ruffner, Park disclosed
“pre-categorizing the powder particles, based on size or other characteristics prior to clustering, in order to eliminate larger-scale differences between the powder particles” (Ruffner: 0023-0024,0033).
As to claim 4, the combination of Ruffner, Park disclosed “wherein the pre-categorizing is performed using a size classifier that classifies the powder particles into a plurality of size categories based on particle size distribution” (Ruffner: 0023-0024,0033,0035)
As to claim 5, the combination of Ruffner, Park disclosed “wherein each size category is further categorized using a respective, classifier categorizes the powder particles into a respective set of one or more categories” (Ruffner: 0033-0036).
On the other hand, Park disclosed “K-means classifier” (Abstract, fig 1, 0015,0051-0052)
As to claim 6, the combination of Ruffner, Park disclosed “wherein the plurality of size categories includes (i) a category for small particles, which accounts for a minimal amount of a total volume of the particle sample (Ruffner: 0024-0026,0044), (ii) a category for particles within predetermined size specifications” (Ruffner: 0023-0024), and (iii) a category for particles that are outside of the predetermined size specifications or are defective” (Ruffner:0010-0011, 0033-0034)
As to claim 7, the combination of Ruffner, Park disclosed “using multiple levels of unsupervised clustering to create groups within groups, when identifying the categories” (Ruffner: 0037-0038,0042).
As to claim 8, the combination of Ruffner, Park disclosed “wherein identifying the categories is performed using a multi- tiered classifier that incorporates a plurality of types of classifiers” (Ruffner:0061-0062)
As to claim 9, the combination of Ruffner, Park disclosed ”receiving a number of desired categories from a user; Hu invariant moments to create the number of desired categories” (Ruffner: 0056-0057,0072). On the other hand, park disclosed “and applying K-means clustering” (Park: Abstract, fig 1)
As to claim 10, the combination of Ruffner, Park disclosed “displaying identified categories to a user” (Ruffner: 0071-0072);
“receiving labels for the identified categories and a new set of categories after removal of redundant categories, from the user” (Ruffner: 0023-0024,0033,0035); and
“saving a model comprising the new set of categories and the labels for subsequent categorization of particle samples” (Ruffner: 0020,0027).
As to claim 11, the combination of Ruffner, Park disclosed “ Hu invariant moments to categorize the particle sample based on the new set of categories” (Ruffner: 0021,0023,0025). On the other hand, Park disclosed” repeating applying the K-means clustering”.
As to claim 14, the combination of Ruffner, Park disclosed “wherein the input imagery is obtained from an optical particle measurement system, an in-line imaging system, or similar flow-based particle imaging system” (Ruffner: 0007, 0063,0072).
As to claim 15, the combination of Ruffner, Park disclosed “the Hu invariant moments that differentiates based on reflection, for particle morphology” (Ruffner: 0023, 0025, 0053). On the other hand, Park disclosed “wherein the K-means clustering ignores a metric” (Park: 0051-0052).
As to claim 16, the combination of Ruffner, Park disclosed “A computer system for physics simulation, comprising: one or more processors (Ruffner: fig 19); and memory Ruffner: fig 19, element 160); wherein the memory stores one or more programs configured for execution by the one or more processors, and the one or more programs comprise instructions for performing the method of claim 1 (Ruffner: fig 19,0080-0081).
As to claim 17, the combination of Ruffner, Park disclosed “A non-transitory computer readable storage medium storing one or more programs configured for execution by a computer system having one or more processors and memory, the one or more programs comprising instructions for performing the method of claim 1” (Ruffner: fig 19, 0080-0081)
Claims 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ruffner et al., (hereafter Ruffner), US Pub. No. 2019/0234853 published Aug, 2019 Park et al., (hereafter Park), US Pub. No. 2019/0347278 published Nov, 2019 in view of Godrich et al., (hereafter Godrich), US Pub. No. 2023/0196583 published Jun, 2023
As to claim 12, the combination of Ruffner, Park disclosed “classifiers on the Hu invariant moments” (Ruffner: Ruffner: 0023, 0025, 0053). It is however, noted that both Ruffner, Park do not teach “training random forest or support vector machine (SVM) classifiers; and using the trained random forest or SVM classifiers to further categorize the identified categories based on fundamental attributes of the identified categories”. On the other hand, Godrich disclosed “training random forest or support vector machine (SVM) classifiers; and using the trained random forest or SVM classifiers to further categorize the identified categories based on fundamental attributes of the identified categories”(Godrich: fig 8-9,0046,0049,0051 – Godrich teaches training images used to train one or more machine learning systems for identifying unknown and/or abnormal morphologies, particularly training cluster generates the data sets i.e, one or more trained machine learning system for example clustering algorithms capable of extracting vectors, SVM corresponds to Godrich’s SVM para 0048,0056 it should be noted that Godrich teaches classifier module element 113 configured to output a trained . classifier (0048). The prior art of Godrich teaches classifier module also supports various known and/or unknown category to the cluster module because clustering algorithm may include mixture model, K-means, agglomerative clustering and like (0052)
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It would have been obvious to a person of ordinary skill in the art at the time of filing the claimed invention processing of electronic images identifying morphologies using trained machine learning model of Godrich et al., into holographic images characterization using Hu moments of Ruffner et al., performing K-means clustering, particularly generating plurality of data buckets determines number of times the selected data buckets satisfying the threshold of Park et al., because that would have allowed users of Ruffner, Park discover new and/or unknown patterns of morphology training data sets particularly associated unknown morphology cluster predicting abnormal pattern of particle images to gain greater insight into the identified regions (Godrich: 0007-0008,0047), thereby improves the quality and reliability of image analysis
As to claim 13, the combination of Ruffner, Park, Godrich disclosed “Hu invariant moments” (Ruffner: Abstract, 0021, 0023, 0053). On the other hand, Godrich disclosed “training a convolutional neural network or a deep neural network on the input imagery based on the identified categories,(Godrich: 0062-0063, convolutional neural network corresponds to Gorrich’s CNN as detailed in 0063) to identify features that have higher complexity than the identified categories and a higher degree of accuracy than metrics-based measurements (Godrich: 0051-0052,0081-0082);and using the trained convolutional neural network or the deep neural network to identify the features for particle samples” (Godrich: 0046,0048-0049).
Conclusion
The prior art made of record
a. US Pub. No. 2019/0234853
b. US Pub. No. 2019/0347278
c. US Oub. No. 2023/0196583
Examiner's Note: Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
SEE MPEP 2141.02 [R-5] VI. PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, INCLUDING DISCLOSURES THAT TEACH AWAY FROM THE CLAIMS: A prior art reference must be considered in its entirety, i.e., as a whole, including portions that would lead away from the claimed invention. W.L. Gore & Associates, Inc. v. Garlock, Inc., 721 F.2d 1540, 220 USPQ 303 (Fed. Cir. 1983), cert. denied, 469 U.S. 851 (1984) In re Fulton, 391 F.3d 1195, 1201,73 USPQ2d 1141, 1146 (Fed. Cir. 2004). >See also MPEP §2123.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
The prior art made of record, listed on form PTO-892, and not relied upon, if any, is considered pertinent to applicant's disclosure
Authorization for Internet Communications
The examiner encourages Applicant to submit an authorization to communicate with the examiner via the Internet by making the following statement (from MPEP 502.03):
“Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.”
Please note that the above statement can only be submitted via Central Fax (not Examiner's Fax), Regular postal mail, or EFS Web using PTO/SB/439.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Srirama Channavajjala whose telephone number is 571-272-4108. The examiner can normally be reached on Monday-Friday from 8:00 AM to 5:30 PM Eastern Time.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Gorney, Boris, can be reached on (571) 270- 5626. The fax phone numbers for the organization where the application or proceeding is assigned is 571-273-8300 Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free)
/Srirama Channavajjala/Primary Examiner, Art Unit 2154