DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This communication is in response to the Application Filed on 04/09/2024
Claims 1–18, 21 and 22 are pending in this application.
Claims 13–18 are withdrawn in this application
Drawings
The drawing(s) filed on 04/09/2024 are accepted by the Examiner.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 11/24/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claim(s) 1–3, 5–11, 21 and 22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The limitations, under their broadest reasonable interpretation, cover mental process (concept performed in a human mind, including as observation, evaluation, judgment, opinion, organizing human activity and mathematical concepts and calculations). The independent claim(s) 1 and 21 recite(s) a method and a system respectively. This judicial exception is not integrated into a practical application because the steps do not add meaningful limitations to be considered specifically applied to a particular technological problem to be solved .The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the steps of the claimed invention can be done mentally and no additional features in the claims would preclude them from being performed as such except for the generic computer elements at high level of generality (i.e., processor, memory).
According to the USPTO guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that the independent claims 1 and 21 are directed to an abstract idea as shown below:
STEP 1: Do the claims fall within one of the statutory categories? YES. Independent claims 1 and 21 are directed to a method and a system respectively.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? YES, the claims are directed toward a mental process (i.e. abstract idea).
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
Independent claims 1 and 21 comprise a mental process that can be practicably performed in the human mind (or generic computers or components configured to perform the method) and, therefore, an abstract idea.
Regarding independent claim(s) 1: the limitations recite:
receiving a set of cell component data elements in an original version, wherein each cell component data element represents a distinct cell component type of a cell (data gathering);
inferring at least one pretrained machine learning (ML)-based model on at least one first cell component data element of the set of cell component data elements in the original version, to obtain at least one second cell component data element of the set of cell component data elements in a reconstructed version (mental process including observation and evaluation, and can be done mentally in the human mind);
classifying the cell as having an anomaly based on the reconstructed version of at least one second cell component data element (mental process including observation and evaluation, and can be done mentally in the human mind).
Regarding independent claim(s) 21: the limitations recite:
receive a set of cell component data elements in an original version, wherein each cell component data element represents a distinct cell component type of a cell (data gathering);
infer at least one pretrained machine learning (ML)-based model on at least one first cell component data element of the set of cell component data elements in the original version, to obtain at least one second cell component data element of the set of cell component data elements in a reconstructed version (mental process including observation and evaluation, and can be done mentally in the human mind);
classify the cell as having an anomaly based on the reconstructed version of at least one second cell component data element (mental process including observation and evaluation, and can be done mentally in the human mind).
These limitations, as drafted, is a simple process that, under their broadest reasonable interpretation, covers performance of the limitations in the mind or by a human. The Examiner notes that under MPEP 2106.04(a)(2)(III), 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 ("‘[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).
As such, a person could mentally in claims 1 and 21 observe an image mentally and using pen paper copy the cell image to reconstruct the image for a second cell component/organelle based on the original image cell component/organelle. Based on the copy image predict if there is an anomaly. The mere nominal recitation that the various steps are being executed by a processor and memory does not take the limitations out of the mental process grouping. Thus, the claims recite a mental process.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? NO, the claims do not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to affect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
an additional element adds insignificant extra-solution activity to the judicial exception; and
an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Independent claims 1 and 21 do not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application. Independent claims 1 and 21 discloses a non-transitory memory device, which are generic computer components and/or insignificant pre/post-solution extra activity that do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea in a system.
These limitations are recited at a high level of generality (i.e. as a general action or change being taken based on the results of the acquiring step) and amounts to mere post solution actions, which is a form of insignificant extra-solution activity. Further, the claims are claimed generically and are operating in their ordinary capacity such that they do not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No, the claims do not recite additional elements that amount to significantly more than the judicial exception.
With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements:
adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
Independent claim(s) 1 and 21 do not recite any additional elements that are not well-understood, routine or conventional. The use of a generic computer elements are routine, well-understood and conventional process that is performed by computers.
Thus, since independent claims 1 and 21 are: (a) directed toward an abstract idea, (b) do not recite additional elements that integrate the judicial exception into a practical application, and (c) do not recite additional elements that amount to significantly more than the judicial exception, it is clear that independent claims 1 and 21 are not eligible subject matter under 35 U.S.C 101.
Regarding claim 2, 3, 5–11 and 22: the additional limitations do not integrate the mental process into practical application or add significantly more to the mental process. The limitation(s) are mental processes including mental process including observation and evaluation, and can be done mentally in the human mind.
Regarding claim 4 and 12: the additional limitations do integrate the mental process into practical application or add significantly more to the mental process.
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 non-obviousness.
Claim(s) 1, 5–10 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Hattori et al. (US 20200151877 A1, hereafter, "Hattori") in view of Klaiman (US 20210005308 A1, hereafter, "Klaiman").
Regarding claim 1, Hattori teaches a method of cell anomaly detection (See Hattori, [Abstract], An image diagnosis assisting apparatus according to the present invention executes: processing of inputting an image of a tissue or cell) by at least one processor (See Hattori, ¶ [0008], Further, the present invention includes: a processor configured to execute various programs for performing image processing on a target image), the method comprising:
[receiving a set of cell component data elements in an original version, wherein each cell component data element represents a distinct cell component type of a cell;
inferring at least one pretrained machine learning (ML)-based model on at least one first cell component data element of the set of cell component data elements in the original version, to obtain at least one second cell component data element of the set of cell component data elements in a reconstructed version];
classifying the cell as having an anomaly based on the reconstructed version of at least one second cell component data element (See Hattori, ¶ [0095], See Hattori, ¶ [0095], The image diagnosis assisting apparatus 2 according to the second embodiment generates, from an input image, an image stained by another staining method different from a staining method for the input image. The image diagnosis assisting apparatus 2 then calculates feature amounts of tissues or cells in the input image and the generated image to determine lesion probability of the tissues or cells in the input image by using these feature amounts. Note: Examiner is interpreting the lesion as classifying the cell having an anomaly).
However, Hattori fail(s) to teach receiving a set of cell component data elements in an original version, wherein each cell component data element represents a distinct cell component type of a cell; inferring at least one pretrained machine learning (ML)-based model on at least one first cell component data element of the set of cell component data elements in the original version, to obtain at least one second cell component data element of the set of cell component data elements in a reconstructed version.
Klaiman, working in the same field of endeavor, teaches: receiving a set of cell component data elements in an original version, wherein each cell component data element represents a distinct cell component type of a cell (See Klaiman, ¶ [0034], According to embodiments, the acquired image is a digital image of the tissue sample whose pixel intensity values correlate with the amount of a first biomarker specific stain. ¶ [0034], The first biomarker is selectively contained in a particular first cell type. Note: Examiner is interpreting the cell type as the cell component data);
inferring at least one pretrained machine learning (ML)-based model (See Klaiman, ¶ [0016], According to embodiments, the MLL is a machine learning logic having been trained to (explicitly or implicitly) identify tissue regions predicted to comprise a second biomarker in an acquired image) on at least one first cell component data element of the set of cell component data elements in the original version, to obtain at least one second cell component data element of the set of cell component data elements in a reconstructed version (See Klaiman, ¶ [0017], According to embodiments, the image generated by the image transformation looks like an image generated by the same type of image acquisition system as used for acquiring the input image. For example, the output image generated from an acquired autofluorescence image may look like the original autofluorescence image and in addition comprise highlighted regions being indicative of the second biomarker. The output image generated from an acquired X-ray/bright field microscope/fluorescence image may look like the original X-ray/bright field microscope/fluorescence image and in addition comprise highlighted regions being indicative of the second biomarker. ¶ [0034], The first biomarker is selectively contained in a particular first cell type. The second biomarker is a biomarker known to be selectively contained in one out of a plurality of known sub-types of this first cell type or is a biomarker known to be selectively contained in a second cell type being different from the first cell type).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Hattori’s reference to receiving a set of cell component data elements in an original version, wherein each cell component data element represents a distinct cell component type of a cell; inferring at least one pretrained machine learning (ML)-based model on at least one first cell component data element of the set of cell component data elements in the original version, to obtain at least one second cell component data element of the set of cell component data elements in a reconstructed version based on the method of Klaiman’s reference. The suggestion/motivation would have been to diagnose of a tumor automatically (See Klaiman, ¶ [0002–0007]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Klaiman with Hattori to obtain the invention as specified in claim 1.
Regarding claim 5, Hattori in view of Klaiman teaches the method according to claim 1, [the method according to wherein the at least one pretrained ML-based model is pretrained so as to obtain the at least one second cell component data element in the reconstructed version, based on the at least one first cell component data element in the original version].
However, Hattori fail(s) to teach the method according to wherein the at least one pretrained ML-based model is pretrained so as to obtain the at least one second cell component data element in the reconstructed version, based on the at least one first cell component data element in the original version.
Klaiman, working in the same field of endeavor, teaches: the method according to wherein the at least one pretrained ML-based model is pretrained so as to obtain the at least one second cell component data element in the reconstructed version, based on the at least one first cell component data element in the original version (See Klaiman, ¶ [0012], The method further comprises providing a trained machine learning logic—MLL. The MLL is a machine learning logic having been trained to (explicitly or implicitly) identify tissue regions predicted to comprise a second biomarker. The method further comprises inputting the received acquired image into the MLL and automatically transforming, by the MLL, the acquired image into an output image).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Hattori’s reference the method according to wherein the at least one pretrained ML-based model is pretrained so as to obtain the at least one second cell component data element in the reconstructed version, based on the at least one first cell component data element in the original version based on the method of Klaiman’s reference. The suggestion/motivation would have been to further diagnosis of a tumor automatically (See Klaiman, ¶ [0002–0007]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Klaiman with Hattori to obtain the invention as specified in claim 5.
Regarding claim 6, Hattori teaches the method according to claim 1, The method according to wherein the at least one cell component data element is a microscopy image of the cell (See Hattori, ¶ [0036], Image data is input to the input unit 10. For example, the input unit 10 may acquire still image data or such data taken at a predetermined time interval by imaging means, such as a camera built in a microscope).
Regarding claim 7, Hattori in view of Klaiman teaches the method according to claim 1, [the method according to wherein the at least one original data element is a vector representation of a set of features extracted from a microscopy image of the cell].
However, Hattori fail(s) to teach the method according to wherein the at least one original data element is a vector representation of a set of features extracted from a microscopy image of the cell.
Klaiman, working in the same field of endeavor, teaches: the method according to wherein the at least one original data element is a vector representation of a set of features extracted from a microscopy image of the cell (See Klaiman, ¶ [0120], According to embodiments, the fully convolutional network is a convolutional networks with only layers of the form whose activation functions generate an output data vector y.sub.ij at a location (I, j) in a particular layer).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Hattori’s reference the method according to wherein the at least one original data element is a vector representation of a set of features extracted from a microscopy image of the cell based on the method of Klaiman’s reference. The suggestion/motivation would have been to further diagnosis of a tumor automatically (See Klaiman, ¶ [0002–0007]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Klaiman with Hattori to obtain the invention as specified in claim 7.
Regarding claim 8, Hattori in view of Klaiman teaches the method according to claim 1, [wherein the set of cell component data elements comprises n distinct combinations of the at least one first and at least one second cell component data elements; and
wherein the at least one ML-based model comprises n ML-based models, each corresponding to a respective combination of the n distinct combinations].
However, Hattori fail(s) to teach wherein the set of cell component data elements comprises n distinct combinations of the at least one first and at least one second cell component data elements; and wherein the at least one ML-based model comprises n ML-based models, each corresponding to a respective combination of the n distinct combinations.
Klaiman, working in the same field of endeavor, teaches: wherein the set of cell component data elements comprises n distinct combinations of the at least one first and at least one second cell component data elements (See Klaiman, ¶ [0034], According to embodiments, the acquired image is a digital image of the tissue sample whose pixel intensity values correlate with the amount of a first biomarker specific stain. ¶ [0034], The first biomarker is selectively contained in a particular first cell type. Note: Examiner is interpreting the cell type as the cell component data); and
wherein the at least one ML-based model comprises n ML-based models, each corresponding to a respective combination of the n distinct combinations (See Klaiman, ¶ [0033], According to another example, the MLL is trained to identify, based on a H&E stained acquired image, tissue regions predicted to comprise the FAP biomarker and tissue regions predicted to comprise one or more tumor specific cytokeratins; the output image highlights the regions predicted to comprise the FAP biomarker (and thus highlights stroma cells which selectively express the FAP protein) and highlights, with one or more different colors, the regions predicted to comprise the cytokines (and thus highlights tumor cells expressing said cytokeratins). Note: H&E staining reveals the cell components which are then input into the machine learning model in one combination. Examiner is interpreting n as 1 in this case).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Hattori’s reference wherein the set of cell component data elements comprises n distinct combinations of the at least one first and at least one second cell component data elements; and wherein the at least one ML-based model comprises n ML-based models, each corresponding to a respective combination of the n distinct combinations based on the method of Klaiman’s reference. The suggestion/motivation would have been to further diagnosis of a tumor automatically (See Klaiman, ¶ [0002–0007]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Klaiman with Hattori to obtain the invention as specified in claim 8.
Regarding claim 9, Hattori in view of Klaiman teaches the method according to claim 8, [wherein each ML-based model of the n ML-based models is pretrained to obtain the at least one second cell component data element in the reconstructed version, based on the at least one first cell component data element in the original version, according to the respective combination of the n distinct combinations].
However, Hattori fail(s) to teach wherein each ML-based model of the n ML-based models is pretrained to obtain the at least one second cell component data element in the reconstructed version, based on the at least one first cell component data element in the original version, according to the respective combination of the n distinct combinations.
Klaiman, working in the same field of endeavor, teaches: wherein each ML-based model of the n ML-based models is pretrained to obtain the at least one second cell component data element in the reconstructed version, based on the at least one first cell component data element in the original version, according to the respective combination of the n distinct combinations (See Klaiman, ¶ [0012], The method further comprises providing a trained machine learning logic—MLL. The MLL is a machine learning logic having been trained to (explicitly or implicitly) identify tissue regions predicted to comprise a second biomarker. The method further comprises inputting the received acquired image into the MLL and automatically transforming, by the MLL, the acquired image into an output image. ¶ [0015], For example, the presence of a particular biomarker may modify the contrast of cells or organelles, the cell membrane shape or other morphological features of a cell that may not be recognizable by the human eye and/or that may not be interpretable by the human brain, because the interrelations between various visual features and the presence of a particular biomarker may be too complex to be comprehended by a human being).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Hattori’s reference wherein each ML-based model of the n ML-based models is pretrained to obtain the at least one second cell component data element in the reconstructed version, based on the at least one first cell component data element in the original version, according to the respective combination of the n distinct combinations based on the method of Klaiman’s reference. The suggestion/motivation would have been to further diagnosis of a tumor automatically (See Klaiman, ¶ [0002–0007]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Klaiman with Hattori to obtain the invention as specified in claim 9.
Regarding claim 10, Hattori in view of Klaiman teaches The method according to claim 1, [wherein inferring the at least one pretrained machine learning (ML)-based model comprises:
inferring each ML-based model of the n ML-based models on the at least one first cell component data element of the respective combination in the original version, to obtain the at least one second cell component data element of the respective combination in the reconstructed version].
However, Hattori fail(s) to teach wherein inferring the at least one pretrained machine learning (ML)-based model comprises: inferring each ML-based model of the n ML-based models on the at least one first cell component data element of the respective combination in the original version, to obtain the at least one second cell component data element of the respective combination in the reconstructed version.
Klaiman, working in the same field of endeavor, teaches: wherein inferring the at least one pretrained machine learning (ML)-based model comprises (See Klaiman, ¶ [0012], The method further comprises providing a trained machine learning logic—MLL. The MLL is a machine learning logic having been trained to (explicitly or implicitly) identify tissue regions predicted to comprise a second biomarker):
inferring each ML-based model of the n ML-based models on the at least one first cell component data element of the respective combination in the original version, to obtain the at least one second cell component data element of the respective combination in the reconstructed version (See Klaiman, ¶ [0033], According to another example, the MLL is trained to identify, based on a H&E stained acquired image, tissue regions predicted to comprise the FAP biomarker and tissue regions predicted to comprise one or more tumor specific cytokeratins; the output image highlights the regions predicted to comprise the FAP biomarker (and thus highlights stroma cells which selectively express the FAP protein) and highlights, with one or more different colors, the regions predicted to comprise the cytokines (and thus highlights tumor cells expressing said cytokeratins)).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Hattori’s reference wherein inferring the at least one pretrained machine learning (ML)-based model comprises: inferring each ML-based model of the n ML-based models on the at least one first cell component data element of the respective combination in the original version, to obtain the at least one second cell component data element of the respective combination in the reconstructed version based on the method of Klaiman’s reference. The suggestion/motivation would have been to further diagnosis of a tumor automatically (See Klaiman, ¶ [0002–0007]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Klaiman with Hattori to obtain the invention as specified in claim 10.
Regarding claim 21, claim 21 is rejected the same as claim 1 and the arguments similar to that presented above for claim 1 are equally applicable to the claim 21, and all of the other limitations similar to claim 1 are not repeated herein, but incorporated by reference. Furthermore, Hattori teaches a system for cell anomaly detection, the system comprising: a non-transitory memory device, wherein modules of instruction code are stored, and at least one processor associated with the memory device, and configured to execute the modules of instruction code, whereupon execution of said modules of instruction code, the at least one processor is configured to (See Hattori, ¶ [0007], In order to solve the above-mentioned problems, the present invention includes: a processor configured to execute various programs for performing image processing on a target image; and a memory configured to store a result of the image processing, in which the processor executes).
Claim(s) 2 is rejected under 35 U.S.C. 103 as being unpatentable over Hattori et al. (US 20200151877 A1, hereafter, "Hattori") in view of Klaiman (US 20210005308 A1, hereafter, "Klaiman") further in view of Cox et al. (US 20060223058 A1, hereafter, "Cox").
Regarding claim 2, Hattori in view of Klaiman teaches The method according to claim 1, further comprising: [communicating the classification of anomaly to a database of cellular phenotypic information; and
obtaining, from the database, one or more recommendation data elements pertaining to a condition of the cell, said recommendation data elements selected from a list consisting of: a suggested diagnosis of an organism of the cell, a recommendation for drug treatment of the organism, a recommendation for drug dosage, to be administered to the organism, and an indication of a biochemical pathway that is associated with said treatment].
However, Hattori and Klaiman fail(s) to teach communicating the classification of anomaly to a database of cellular phenotypic information; and obtaining, from the database, one or more recommendation data elements pertaining to a condition of the cell, said recommendation data elements selected from a list consisting of: a suggested diagnosis of an organism of the cell, a recommendation for drug treatment of the organism, a recommendation for drug dosage, to be administered to the organism, and an indication of a biochemical pathway that is associated with said treatment.
Cox, working in the same field of endeavor, teaches: communicating the classification of anomaly to a database of cellular phenotypic information (See Cox, ¶ [0101], these methods include, e.g., detecting at least one in vitro cell or tissue autonomous phenotype of a cell or tissue from a patient and/or detecting a genotype correlated to the cell or tissue autonomous phenotype); and
obtaining, from the database, one or more recommendation data elements pertaining to a condition of the cell, said recommendation data elements selected from a list consisting of: a suggested diagnosis of an organism of the cell (See Cox, ¶ [0101], Accessing a database comprising correlation information regarding a correlation between the cell or tissue autonomous phenotype and/or genotype and, e.g., one or more of: disease, predisposition to the disease, prognosis of the disease, and/or treatment efficacy or response for or to the disease. Note: Examiner is interpreting the prognosis as diagnosis. Examiner only has to teach one of), a recommendation for drug treatment of the organism, a recommendation for drug dosage, to be administered to the organism, and an indication of a biochemical pathway that is associated with said treatment.
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Hattori’s reference to communicating the classification of anomaly to a database of cellular phenotypic information; and obtaining, from the database, one or more recommendation data elements pertaining to a condition of the cell, said recommendation data elements selected from a list consisting of: a suggested diagnosis of an organism of the cell, a recommendation for drug treatment of the organism, a recommendation for drug dosage, to be administered to the organism, and an indication of a biochemical pathway that is associated with said treatment based on the method of Cox’s reference. The suggestion/motivation would have been to accurately provide a diagnosis (See Cox, ¶ [0019]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Cox with Hattori and Klaiman to obtain the invention as specified in claim 2.
Claim(s) 3, 4, 11 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Hattori et al. (US 20200151877 A1, hereafter, "Hattori") in view of Klaiman (US 20210005308 A1, hereafter, "Klaiman") further in view of Poceviciute et al. (See NPL attached, "UNSUPERVISED ANOMALY DETECTION IN DIGITAL PATHOLOGY USING GANS", hereafter, "Poceviciute") and further in view of Amthor et al. (US 20220114725 A1, hereafter, "Amthor").
Regarding claim 3, Hattori in view of Klaiman teaches the method according to claim 1, the method according to wherein classifying the cell as having an anomaly (See Hattori, ¶ [0095], The image diagnosis assisting apparatus 2 according to the second embodiment generates, from an input image, an image stained by another staining method different from a staining method for the input image. The image diagnosis assisting apparatus 2 then calculates feature amounts of tissues or cells in the input image and the generated image to determine lesion probability of the tissues or cells in the input image by using these feature amounts) comprises:
[calculating a reconstruction error value based on the original version and the reconstructed version of the at least one second cell component data element;
classifying the cell as having an anomaly, further based on the calculated reconstruction error value].
However, Hattori and Klaiman fail(s) to teach calculating a reconstruction error value based on the original version and the reconstructed version of the at least one second cell component data element; classifying the cell as having an anomaly, further based on the calculated reconstruction error value.
Poceviciute, working in the same field of endeavor, teaches: calculating a reconstruction error value based on the original version and the reconstructed version of the at least one second cell component data element (See Poceviciute, [Pg. 1878, Fig. 1], An anomaly score is a measure of some difference between the input image x and its reconstruction
x
^
);
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Hattori’s reference to calculating a reconstruction error value based on the original version and the reconstructed version of the at least one second cell component data element; classifying the cell as having an anomaly, further based on the calculated reconstruction error value based on the method of Poceviciute’s reference. The suggestion/motivation would have been accurately identifying anomalies in cells (See Poceviciute, [Fig. 2]).
However, Hattori, Klaiman and Poceviciute fail(s) to teach classifying the cell as having an anomaly, further based on the calculated reconstruction error value.
Amthor, working in the same field of endeavor, teaches: classifying the cell as having an anomaly, further based on the calculated reconstruction error value (See Amthor, ¶ [0031], A reconstruction error can thus be used as a measure for an anomaly detection. If the reconstruction error lies above a predefined threshold value, a negative checking result is output).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Hattori’s reference to classifying the cell as having an anomaly, further based on the calculated reconstruction error value based on the method of Amthor’s reference. The suggestion/motivation would have been to give a measure to accurately determine an anomaly (See Amthor, ¶ [0031]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Amthor and Poceviciute with Hattori and Klaiman to obtain the invention as specified in claim 3.
Regarding claim 4, Hattori in view of Klaiman further in view of Poceviciute and further in view of Amthor teaches the method according to claim 1, [the method according to wherein classifying the cell as having an anomaly based on the calculated reconstruction error value further comprises classifying the cell as having an anomaly by determining that the calculated reconstruction error value is higher than a predefined reconstruction error threshold value].
However, Hattori and Klaiman fail(s) to teach the method according to wherein classifying the cell as having an anomaly based on the calculated reconstruction error value further comprises classifying the cell as having an anomaly by determining that the calculated reconstruction error value is higher than a predefined reconstruction error threshold value.
Poceviciute, working in the same field of endeavor, teaches: the method according to wherein classifying the cell as having an anomaly based on the calculated reconstruction error value (See Poceviciute, [Pg. 1878, Fig. 1], An anomaly score is a measure of some difference between the input image x and its reconstruction
x
^
);
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Hattori’s reference the method according to wherein classifying the cell as having an anomaly based on the calculated reconstruction error value based on the method of Poceviciute’s reference. The suggestion/motivation would have been accurately identifying anomalies in cells (See Poceviciute, [Fig. 2]).
However, Hattori, Klaiman and Poceviciute fail(s) to teach further comprises classifying the cell as having an anomaly by determining that the calculated reconstruction error value is higher than a predefined reconstruction error threshold value.
Amthor, working in the same field of endeavor, teaches: further comprises classifying the cell as having an anomaly by determining that the calculated reconstruction error value is higher than a predefined reconstruction error threshold value (See Amthor, ¶ [0031], A reconstruction error can thus be used as a measure for an anomaly detection. If the reconstruction error lies above a predefined threshold value, a negative checking result is output).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Hattori’s reference further comprises classifying the cell as having an anomaly by determining that the calculated reconstruction error value is higher than a predefined reconstruction error threshold value based on the method of Amthor’s reference. The suggestion/motivation would have been to give a measure to accurately determine an anomaly (See Amthor, ¶ [0031]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Amthor and Poceviciute with Hattori and Klaiman to obtain the invention as specified in claim 4.
Regarding claim 11, Hattori in view of Klaiman teaches the method according to claim 8, wherein classifying the cell as having an anomaly (See Hattori, ¶ [0095], The image diagnosis assisting apparatus 2 according to the second embodiment generates, from an input image, an image stained by another staining method different from a staining method for the input image. The image diagnosis assisting apparatus 2 then calculates feature amounts of tissues or cells in the input image and the generated image to determine lesion probability of the tissues or cells in the input image by using these feature amounts) comprises: for each combination of the n distinct combinations (See Klaiman, ¶ [0033], According to another example, the MLL is trained to identify, based on a H&E stained acquired image, tissue regions predicted to comprise the FAP biomarker and tissue regions predicted to comprise one or more tumor specific cytokeratins; the output image highlights the regions predicted to comprise the FAP biomarker (and thus highlights stroma cells which selectively express the FAP protein) and highlights, with one or more different colors, the regions predicted to comprise the cytokines (and thus highlights tumor cells expressing said cytokeratins). Note: H&E staining reveals the cell components which are then input into the machine learning model in one combination. Examiner is interpreting n as 1 in this case), [calculating a reconstruction error value based on the original version and the reconstructed version of at least one respective second cell component data element; classifying the cell as having an anomaly, further based on the calculated reconstruction error values].
However, Hattori and Klaiman fail(s) to teach calculating a reconstruction error value based on the original version and the reconstructed version of at least one respective second cell component data element; classifying the cell as having an anomaly, further based on the calculated reconstruction error values.
Poceviciute, working in the same field of endeavor, teaches: calculating a reconstruction error value based on the original version and the reconstructed version of at least one respective second cell component data element (See Poceviciute, [Pg. 1878, Fig. 1], An anomaly score is a measure of some difference between the input image x and its reconstruction
x
^
);
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Hattori’s reference calculating a reconstruction error value based on the original version and the reconstructed version of at least one respective second cell component data element based on the method of Poceviciute’s reference. The suggestion/motivation would have been accurately identifying anomalies in cells (See Poceviciute, [Fig. 2]).
However, Hattori, Klaiman and Poceviciute fail(s) to teach classifying the cell as having an anomaly, further based on the calculated reconstruction error values.
Amthor, working in the same field of endeavor, teaches: classifying the cell as having an anomaly, further based on the calculated reconstruction error values (See Amthor, ¶ [0031], A reconstruction error can thus be used as a measure for an anomaly detection. If the reconstruction error lies above a predefined threshold value, a negative checking result is output).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Hattori’s reference classifying the cell as having an anomaly, further based on the calculated reconstruction error values based on the method of Amthor’s reference. The suggestion/motivation would have been to give a measure to accurately determine an anomaly (See Amthor, ¶ [0031]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Amthor and Poceviciute with Hattori and Klaiman to obtain the invention as specified in claim 11.
Regarding claim 22, claim 22 is rejected the same as claim 1 and the arguments similar to that presented above for claim 1 are equally applicable to the claim 22, and all of the other limitations similar to claim 1 are not repeated herein, but incorporated by reference.
Claim(s) 12 is rejected under 35 U.S.C. 103 as being unpatentable over Hattori et al. (US 20200151877 A1, hereafter, "Hattori") in view of Klaiman (US 20210005308 A1, hereafter, "Klaiman") further in view of Amthor et al. (US 20220114725 A1, hereafter, "Amthor").
Regarding claim 12, Hattori in view of Klaiman teaches the method according to claim 1, [wherein classifying the cell as having an anomaly based on the calculated reconstruction error values further comprises classifying the cell as having an anomaly by determining that at least one of the calculated reconstruction error values is higher than a respective predefined reconstruction error threshold value].
However, Hattori and Klaiman fail(s) to teach wherein classifying the cell as having an anomaly based on the calculated reconstruction error values further comprises classifying the cell as having an anomaly by determining that at least one of the calculated reconstruction error values is higher than a respective predefined reconstruction error threshold value.
Amthor, working in the same field of endeavor, teaches: wherein classifying the cell as having an anomaly based on the calculated reconstruction error values further comprises classifying the cell as having an anomaly by determining that at least one of the calculated reconstruction error values is higher than a respective predefined reconstruction error threshold value (See Amthor, ¶ [0031], A reconstruction error can thus be used as a measure for an anomaly detection. If the reconstruction error lies above a predefined threshold value, a negative checking result is output).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Hattori’s reference wherein classifying the cell as having an anomaly based on the calculated reconstruction error values further comprises classifying the cell as having an anomaly by determining that at least one of the calculated reconstruction error values is higher than a respective predefined reconstruction error threshold value based on the method of Amthor’s reference. The suggestion/motivation would have been to give a measure to accurately determine an anomaly (See Amthor, ¶ [0031]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Amthor with Hattori and Klaiman to obtain the invention as specified in claim 12.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gregson et al. (US 20200372638 A1) teaches systems and methods are provided for screening a set of histopathology tissue samples representing a region of interest for abnormalities. A pattern recognition classifier is trained on a first set of images, each representing a tissue sample that is substantially free of abnormalities, and a second set of images, each representing one of the set of histopathology tissue samples representing the region of interest. At least one performance metric from the pattern recognition classifier is generated. A given performance metric represents one of an accuracy of the classifier in discriminating between images representing tissue that is substantially free of abnormalities and images of histopathology tissue samples representing the region of interest and a training rate of the pattern recognition classifier. A likelihood of abnormalities in the region of interest is determined from the at least one performance metric from the pattern recognition classifier.
Koller et al. (US 20210366577 A1) teaches embodiments of the disclosure include implementing a ML-enabled cellular disease model for validating an intervention, identifying patient populations that are likely responders to an intervention, and developing a therapeutic structure-activity relationship screen. To generate a cellular disease model, data is combined from human genetic cohorts, from the literature, and from general-purpose cellular or tissue-level genomic data to unravel the set of factors (e.g., genetic, environmental, cellular factors) that give rise to a particular disease. In vitro cells are engineered using the set of factors to generate training data for training machine learning models that are useful for implementing cellular disease models.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DION J SATCHER whose telephone number is (703)756-5849. The examiner can normally be reached Monday - Thursday 5:30 am - 2:30 pm, Friday 5:30 am - 9:30 am PST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Henok Shiferaw can be reached at (571) 272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DION J SATCHER/Patent Examiner, Art Unit 2676
/Henok Shiferaw/Supervisory Patent Examiner, Art Unit 2676
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