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 .
Continuity/reexam data
Parent data
19270223 filed 07/15/2025 is a Continuation of 18526729 , filed 12/01/2023 ,now U.S. Patent # 12374429
18526729 Claims Priority from Provisional Application 63582702 , filed 09/14/2023
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Last updated by opsgusr on 07/15/2025 18:44:23
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(*) - Request to retrieve electronic copy of foreign priority from participating receiving offices.
1. Claims presented for examination: 1-20
Information Disclosure Statement
2. The information disclosure statement (IDS) submitted on 07/24/2025, 04/08/2026 and 07/02/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
3. Claim 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-12 of U.S. Patent No. 12,374,429 B1. Although the claims at issue are not identical, they are not patentably distinct because both applications language direct to similar subject matter such receiving similar query including cell perturbations and determining similarity measure for the plurality of cell permutation and retrieving the image to the user. Claim in 429 is slightly different such
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.
4. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1 (See MPEP 2106)
Claims 1-20 are directed to a method, a system and a tangible, non-transitory computer readable medium which belongs to a statutory class.
Step 2A, Prong One:
Claims recite “identifying, based on user interaction with the perturbation query element and the machine learning embedding filter element, a plurality of cell perturbations and a machine learning embedding filter comprising at least one of: a machine learning embedding similarity threshold, a machine learning significance threshold, or a machine learning embedding concentration threshold” which is a process that, under its broadest reasonable interpretation, covers performance of the limitation by Mental Process, but for the recitation of generic computer components. Nothing in the claim element precludes the steps from practically being performed in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mental process, but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, Prong Two:
Claims recite processor and memory such as non-transitory including instruction perform the method. These are generic computer components and program which use to perform abstract ideas.
The additional elements:
“Providing, for display via a perturbation analysis graphical user interface of a client
device, a perturbation query element for selection of cell perturbations” is the computer process to allow the user to select the information presented to the user.
“Providing, for display via the perturbation analysis graphical user interface of the client
device, a machine learning embedding filter element comprising at least one of: a similarity
element for selecting machine learning embedding similarity thresholds, a significance element
for selecting machine learning embedding significance thresholds, or a concentration element
for selecting machine learning embedding concentration thresholds” is the computer process to display to filter information from the set of information.
“Responsive to the user interaction, providing, for display via the perturbation analysis
graphical user interface of the client device, a perturbation visual representation comprising
similarity measures from a plurality of machine learning embeddings for the plurality of cell
perturbations that satisfy the machine learning embedding filter” is the computer process of displaying information to the user using analysis of data.
The limitation is thus insignificant extra-solution activity. Limitations that the courts have found not to be enough to qualify as "significantly more” when recited in a claim with a judicial exception include: i. Adding the words "apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)). 2106.05(g)--Insignificant Extra-Solution Activity.
Step 2B:
As to claims 2, 11 and 17, the limitations:
“Generating a dataframe comprising machine learning embeddings corresponding to the cell perturbations” is the computer process of generating data based on input which is a generic computer operation to generate data.
“Retrieving the plurality of machine learning embeddings by performing a query of the
dataframe utilizing the plurality of cell perturbations and the machine learning embedding
filter” the generic computer process to retrieve information using the rules as to machine learning.
As to claims 3, 12 and 18, the limitations:
Claims recite ““Comparing the plurality of machine learning embeddings in a latent machine learning feature space to generate the similarity measures” which is a process that, under its broadest reasonable interpretation, covers performance of the limitation by Mental Process, but for the recitation of generic computer components. Nothing in the claim element precludes the steps from practically being performed in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mental process, but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The additional limitation:
“Providing the perturbation visual representation by providing for display via the
perturbation analysis graphical user interface of the client device, a perturbation heatmap
comprising the similarity measures” is the process to provide information to the user which is the process of retrieval information to user.
Claims 4, the limitations:
Claims recite “comparing the subset of machine learning embeddings to generate a plurality of similarity measures” which is a process that, under its broadest reasonable interpretation, covers performance of the limitation by Mental Process, but for the recitation of generic computer components. Nothing in the claim element precludes the steps from practically being performed in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mental process, but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The additional elements: “Performing a query of a dataframe comprising machine learning embeddings utilizing
the plurality of cell perturbations to extract a subset of machine learning embeddings” is the retrieval process of extracting of data.
“Generating the similarity measures for the perturbation visual representation by filtering
the plurality of similarity measures according to the machine learning embedding cosine
similarity range” is a process producing results by applying some parameters.
As to claims 5, the limitations:
Claims recite “comparing the subset of machine learning embeddings to generate a plurality of similarity measures” which is a process that, under its broadest reasonable interpretation, covers performance of the limitation by Mental Process, but for the recitation of generic computer components. Nothing in the claim element precludes the steps from practically being performed in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mental process, but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The additional limitations:
“Performing a query of a dataframe comprising machine learning embeddings utilizing
the plurality of cell perturbations to extract a subset of machine learning embeddings” is the computer process of retrieving the data from the query.
“Generating the similarity measures for the perturbation visual representation by filtering
the plurality of similarity measures according to the machine learning embedding cosine
similarity range” is the computer process producing results by applying parameters.
As to claims 6, the limitations:
Claims recite “identifying the machine learning embedding filter comprises identifying the machine learning significance threshold and the machine learning significance threshold comprises a machine learning embedding statistical significance p-value range” which is a process that, under its broadest reasonable interpretation, covers performance of the limitation by Mental Process, but for the recitation of generic computer components. Nothing in the claim element precludes the steps from practically being performed in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mental process, but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additional limitation:
“Providing the machine learning embedding filter element comprises providing the
significance element for display via the perturbation analysis graphical user interface of the
client device” is the computer process of the providing information.
As to claim 7, the limitations:
Claims recite “aggregating a plurality of initial machine learning embeddings according to the cell perturbations to generate aggregated machine learning embeddings and generating statistical significance p-values of the aggregated machine learning Embeddings” which are processes that, under its broadest reasonable interpretation, covers performance of the limitation by Mental Process, but for the recitation of generic computer components. Nothing in the claim element precludes the steps from practically being performed in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mental process, but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The additional limitation:
“Filtering the aggregated machine learning embeddings to generate the plurality of
machine learning embeddings by comparing the statistical significance p-values of the
aggregated machine learning embeddings with the machine learning embedding statistical
significance p-value range” is process of reducing number of result by applying filtering parameters.
As to claim 8, limitations:
Claims recite “Identifying the machine learning embedding filter comprises identifying the machine learning embedding concentration threshold and the machine learning embedding
concentration threshold comprises at least one of a molecule concentration range or a soluble
factor concentration range” which is a process that, under its broadest reasonable interpretation, covers performance of the limitation by Mental Process, but for the recitation of generic computer components. Nothing in the claim element precludes the steps from practically being performed in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mental process, but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The additional element:
“Providing the machine learning embedding filter element comprises providing the
concentration element for display via the perturbation analysis graphical user interface of the
client device” is the process for retrieving information to the user.
Claim 9, limitations:
“Accessing a dataframe comprising machine learning embeddings labeled with
concentrations applied to cells to generate the machine learning embeddings” the process of retrieving data and to provide data to the user.
“Performing a query of the dataframe by comparing the concentrations applied to the
cells to generate the machine learning embeddings with at least one of the molecules
concentration range or the soluble factor concentration range” is the computer process of retrieval data.
Claims 13 and 19, the limitations:
“Identify the machine learning embedding filter by identifying the machine learning
embedding similarity threshold, wherein the machine learning embedding similarity threshold comprises a machine learning embedding cosine similarity range” is a mental process which is a process that, under its broadest reasonable interpretation, covers performance of the limitation by Mental Process, but for the recitation of generic computer components. Nothing in the claim element precludes the steps from practically being performed in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mental process, but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The additional elements:
“Provide the machine learning embedding filter element by providing the similarity
element for display via the perturbation analysis graphical user interface of the client device” is the computer process of eliminating different, retrieve and provide similar to the element.
“Generating the similarity measures for the perturbation visual representation by filtering
a plurality of similarity measures for machine learning embedding pairs according to the
machine learning embedding cosine similarity range” is a mathematical algorithm.
Claims 14 and 20, the limitations:
Claims recite “identify the machine learning embedding filter by identifying the machine learning significance threshold, wherein the machine learning significance threshold comprises a machine learning embedding statistical significance p-value range” is a mental process which is a process that, under its broadest reasonable interpretation, covers performance of the limitation by Mental Process, but for the recitation of generic computer components. Nothing in the claim element precludes the steps from practically being performed in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mental process, but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The additional elements:
“Provide the machine learning embedding filter element by providing the significance
element for display via the perturbation analysis graphical user interface of the client device” is the process giving the data the computer to perform a task.
“Filtering aggregated machine learning embeddings to generate the plurality of machine
learning embeddings by comparing statistical significance p-values of the aggregated machine
learning embeddings with the machine learning embedding statistical significance p-value
range” is a process of reducing number result by retrieval process.
As to claim 15, the limitations:
“Identify the machine learning embedding filter by identifying the machine learning significance threshold, wherein the machine learning significance threshold comprises a machine learning embedding statistical significance p-value range” is a mental process and filtering machine learning embeddings by comparing concentrations applied to cells to
generate the machine learning embeddings with at least one of the molecule concentration
range or the soluble factor concentration range” which are processes that, under its broadest reasonable interpretation, covers performance of the limitation by Mental Process, but for the recitation of generic computer components. Nothing in the claim element precludes the steps from practically being performed in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mental process, but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The additional limitation:
“Provide the machine learning embedding filter element by providing the concentration
element for display via the perturbation analysis graphical user interface of the client device” is the process of providing information through user interface and insignificantly to amount significantly more.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
5. Claim(s) 1-2, 4-5, 10-12 and 16-17 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over NARUNIEC et al. (Pub. No US 2022/0058822 A1) in view of GAO et al. (Pub. No. US 2022/0247571 A1).
As to claim 1, NARUNIEC discloses a computer-implemented method comprising:
providing, for display via a perturbation analysis graphical user interface of a client
device, a perturbation query element for selection of cell perturbations (transform module 220 applies one or more perturbations to an original image 281…) (paragraph 0045)
providing, for display via the perturbation analysis graphical user interface of the client
device, a machine learning embedding filter element comprising at least one of: a similarity
element for selecting machine learning embedding similarity thresholds, a significance element
for selecting machine learning embedding significance thresholds, or a concentration element
for selecting machine learning embedding concentration thresholds (the threshold condition is a predetermined value or range for the mean squared error associated with the loss function… stability of landmark…) (paragraph 0069);
identifying, based on user interaction with the perturbation query element and the
machine learning embedding filter element (transform module 220 performs one or more perturbation on the original image 281 to obtain a set perturbed image) (paragraph 0042), a plurality of cell perturbations and a machine learning embedding filter comprising at least one of: a machine learning embedding similarity threshold, a machine learning significance threshold, or a machine learning embedding concentration threshold (image stabilization loss module 240 repeats the training process for multiple iteration until a threshold condition is archived) (paragraph 0043).
NARUNIEC doses not explicitly disclose responsive to the user interaction, providing, for display via the perturbation analysis graphical user interface of the client device, a perturbation visual representation comprising similarity measures from a plurality of machine learning embeddings for the plurality of cell perturbations that satisfy the machine learning embedding filter.
GAO discloses responsive to the user interaction, providing, for display via the perturbation analysis graphical user interface of the client device, a perturbation visual representation comprising similarity measures from a plurality of machine learning embeddings for the plurality of cell perturbations that satisfy the machine learning embedding filter (based on the related machine learning algorithm, PCA dimension reduction is perform on the image data, the data is clustered with a cosine similarity in vector space, and a threshold is set to filter an image…) (paragraph 0139).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date to include responsive to the user interaction, providing, for display via the perturbation analysis graphical user interface of the client device, a perturbation visual representation comprising similarity measures from a plurality of machine learning embeddings for the plurality of cell perturbations that satisfy the machine learning embedding filter as disclosed by GAO in order to provide similar data.
As to claim 2, NARUNIEC discloses the computer-implemented method of claim 1, further comprising:
generating a dataframe (one or more frames) (paragraph 0034) comprising machine learning embeddings corresponding to the cell perturbations (randoms perturbation) (paragraph 0033); and
retrieving the plurality of machine learning embeddings by performing a query of the
dataframe utilizing the plurality of cell perturbations and the machine learning embedding
filter (… the landmark outputs relative to the landmarks in the original image, and can represent a measure of the stability of the returned landmarks…) (paragraph 0065).
As to claim 4, NARUNIEC discloses the computer-implemented method of claim 1 excepting for wherein: providing the machine learning embedding filter element comprises providing the similarity element for display via the perturbation analysis graphical user interface of the client device, and identifying the machine learning embedding filter comprises identifying the machine learning embedding similarity threshold and the machine learning embedding similarity threshold comprises a machine learning embedding cosine similarity range. However, GAO discloses wherein: providing the machine learning embedding filter element comprises providing the similarity element for display via the perturbation analysis graphical user interface of the client device, and identifying the machine learning embedding filter comprises identifying the machine learning embedding similarity threshold and the machine learning embedding similarity threshold comprises a machine learning embedding cosine similarity range ((based on the related machine learning algorithm, PCA dimension reduction is perform on the image data, the data is clustered with a cosine similarity in vector space, and a threshold is set to filter an image…) (paragraph 0139).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to include providing the machine learning embedding filter element comprises providing the similarity element for display via the perturbation analysis graphical user interface of the client device, and identifying the machine learning embedding filter comprises identifying the machine learning embedding similarity threshold and the machine learning embedding similarity threshold comprises a machine learning embedding cosine similarity range
As to claim 5, NARUNIEC discloses the computer-implemented method of claim 4 excepting for comprising providing the perturbation visual representation for display via the perturbation analysis graphical user interface of the client device by: performing a query of a dataframe comprising machine learning embeddings utilizing the plurality of cell perturbations to extract a subset of machine learning embeddings; comparing the subset of machine learning embeddings to generate a plurality of similarity measures; and generating the similarity measures for the perturbation visual representation by filtering the plurality of similarity measures according to the machine learning embedding cosine similarity range (based on the related machine learning algorithm, PCA dimension reduction is perform on the image data, the data is clustered with a cosine similarity in vector space, and a threshold is set to filter an image…) (paragraph 0139).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to include responsive to the user interaction, providing, for display via the perturbation analysis graphical user interface of the client device, a perturbation visual representation comprising similarity measures from a plurality of machine learning embeddings for the plurality of cell perturbations that satisfy the machine learning embedding filter by GAO to provide data.
Claim 10 is rejected under the same reason as to claim 1, NARUNIEC discloses a system comprising: at least one processor; and at least one non-transitory computer-readable storage medium (none-volatile storage for application…) (paragraph 0026) storing instructions (a software program) (paragraph 0027) that, when executed by the at least one processor (processor(s)) (paragraph 0027) to perform.
Claim 11 is rejected under the same reason as to claim 2.
As to claim 13, NARUNIEC discloses the system of claim 10, further comprising instructions (software program) (paragraph 0026) that, when executed by the at least one processor (processor(s)) paragraph 0027), cause the system to: provide the machine learning embedding filter element by providing the similarity element for display via the perturbation analysis graphical user interface of the client device; identify the machine learning embedding filter by identifying the machine learning embedding similarity threshold, wherein the machine learning embedding similarity threshold comprises a machine learning embedding cosine similarity range; and generating the similarity measures for he perturbation visual representation by filtering a plurality of similarity measures for machine learning embedding pairs according to the machine learning embedding cosine similarity range (each comparison includes a binary value (e.g., o correspond to a test model metric greater than or equal to the observed model metric; 1 corresponds to a test model metric less than the observed model metric), wherein the aggregated of comparison (e.g., average) represent a statistical of comparison (e.g., average) represents a statistical measure (e.g., p-value, probability, etc.)…) (paragraph 0114).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to include provide the machine learning embedding filter element by providing the similarity element for display via the perturbation analysis graphical user interface of the client device; identify the machine learning embedding filter by identifying the machine learning embedding similarity threshold, wherein the machine learning embedding similarity threshold comprises a machine learning embedding cosine similarity range; and generating the similarity measures for he perturbation visual representation by filtering a plurality of similarity measures for machine learning embedding pairs according to the machine learning embedding cosine similarity range as to GAO in order to provide analysis to data.
Claim 16 is rejected under the same reason as to claim 1, discloses a non-transitory computer-readable storage medium (storage 114 includes non-volatile storage…) (paragraph 0026) storing instructions (programs) (paragraph 0027) that, when executed by at least one processor (processor(s)) (paragraph 0027), cause a computing device to.
Claim 17 is rejected under the same reason as to claim 2.
Claim 19 is rejected under the same reason as to claim 13.
6. Claim(s) 6-7, 14 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over NARUNIEC et al. (Pub. No US 2022/0058822 A1) in view of GAO et al. (Pub. No. US 2022/0247571 A1) and further in view of Alvarez et al. (Pub. No. US 2025/0006296 A1).
As to claim 6, NARUNIEC discloses the computer-implemented method of claim 1 excepting for wherein: providing the machine learning embedding filter element comprises providing the significance element for display via the perturbation analysis graphical user interface of the client device, and identifying the machine learning embedding filter comprises identifying the machine learning significance threshold and the machine learning significance threshold comprises a machine learning embedding statistical significance p-value range.
However, Alverez discloses providing the machine learning embedding filter element comprises providing the significance element for display via the perturbation analysis graphical user interface of the client device, and identifying the machine learning embedding filter comprises identifying the machine learning significance threshold and the machine learning significance threshold comprises a machine learning embedding statistical significance p-value range (each comparison includes a binary value (e.g., o correspond to a test model metric greater than or equal to the observed model metric; 1 corresponds to a test model metric less than the observed model metric), wherein the aggregated of comparison (e.g., average) represent a statistical of comparison (e.g., average) represents a statistical measure (e.g., p-value, probability, etc.)…) (paragraph 0114).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to include providing the machine learning embedding filter element comprises providing the significance element for display via the perturbation analysis graphical user interface of the client device, and identifying the machine learning embedding filter comprises identifying the machine learning significance threshold and the machine learning significance threshold comprises a machine learning embedding statistical significance p-value range as to GAO in order to provide analysis to data.
As to claim 7, NARUNIEC discloses the computer-implemented method of claim 6 excepting for further comprising providing the perturbation visual representation for display via the perturbation analysis graphical user interface of the client device by: aggregating a plurality of initial machine learning embeddings according to the cell perturbations to generate aggregated machine learning embeddings; generating statistical significance p-values of the aggregated machine learning embeddings; and filtering the aggregated machine learning embeddings to generate the plurality of machine learning embeddings by comparing the statistical significance p-values of the aggregated machine learning embeddings with the machine learning embedding statistical significance p-value range.
However, Alvarez discloses providing the perturbation visual representation for display via the perturbation analysis graphical user interface of the client device by: aggregating a plurality of initial machine learning embeddings according to the cell perturbations to generate aggregated machine learning embeddings; generating statistical significance p-values of the aggregated machine learning embeddings; and filtering the aggregated machine learning embeddings to generate the plurality of machine learning embeddings by comparing the statistical significance p-values of the aggregated machine learning embeddings with the machine learning embedding statistical significance p-value range (each comparison includes a binary value (e.g., o correspond to a test model metric greater than or equal to the observed model metric; 1 corresponds to a test model metric less than the observed model metric), wherein the aggregated of comparison (e.g., average) represent a statistical of comparison (e.g., average) represents a statistical measure (e.g., p-value, probability, etc.)…) (paragraph 0114).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to include providing the perturbation visual representation for display via the perturbation analysis graphical user interface of the client device by: aggregating a plurality of initial machine learning embeddings according to the cell perturbations to generate aggregated machine learning embeddings; generating statistical significance p-values of the aggregated machine learning embeddings; and filtering the aggregated machine learning embeddings to generate the plurality of machine learning embeddings by comparing the statistical significance p-values of the aggregated machine learning embeddings with the machine learning embedding statistical significance p-value range as to GAO in order to provide analysis to data.
As to claim 14, NARUNIEC discloses the system of claim 10 excepting for comprising instructions that, when executed by the at least one processor, cause the system to: provide the machine learning embedding filter element by providing the significance element for display via the perturbation analysis graphical user interface of the client device; identify the machine learning embedding filter by identifying the machine learning significance threshold, wherein the machine learning significance threshold comprises a machine learning embedding statistical significance p-value range; and filtering aggregated machine learning embeddings to generate the plurality of machine learning embeddings by comparing statistical significance p-values of the aggregated machine learning embeddings with the machine learning embedding statistical significance p-value range.
However, Alverez discloses provide the machine learning embedding filter element by providing the significance element for display via the perturbation analysis graphical user interface of the client device; identify the machine learning embedding filter by identifying the machine learning significance threshold, wherein the machine learning significance threshold comprises a machine learning embedding statistical significance p-value range; and filtering aggregated machine learning embeddings to generate the plurality of machine learning embeddings by comparing statistical significance p-values of the aggregated machine learning embeddings with the machine learning embedding statistical significance p-value range (each comparison includes a binary value (e.g., o correspond to a test model metric greater than or equal to the observed model metric; 1 corresponds to a test model metric less than the observed model metric), wherein the aggregated of comparison (e.g., average) represent a statistical of comparison (e.g., average) represents a statistical measure (e.g., p-value, probability, etc.)…) (paragraph 0114).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to include provide the machine learning embedding filter element by providing the significance element for display via the perturbation analysis graphical user interface of the client device; identify the machine learning embedding filter by identifying the machine learning significance threshold, wherein the machine learning significance threshold comprises a machine learning embedding statistical significance p-value range; and filtering aggregated machine learning embeddings to generate the plurality of machine learning embeddings by comparing statistical significance p-values of the aggregated machine learning embeddings with the machine learning embedding statistical significance p-value range as to GAO in order to provide analysis to data.
Claim 20 is rejected under the same reason as to claim 14.
7. Claim(s) 8-9 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over NARUNIEC et al. (Pub. No US 2022/0058822 A1) in view of GAO et al. (Pub. No. US 2022/0247571 A1) and further in view of Elliot et al. (Pub. No. US 2017/0304359 A1).
As to claim 8, NARUNIEC discloses the computer-implemented method of claim 1 excepting for wherein: providing the machine learning embedding filter element comprises providing the concentration element for display via the perturbation analysis graphical user interface of the client device, and identifying the machine learning embedding filter comprises identifying the machine learning embedding concentration threshold and the machine learning embedding concentration threshold comprises at least one of a molecule concentration range or a soluble factor concentration range.
However, Eliot discloses providing the machine learning embedding filter element comprises providing the concentration element for display via the perturbation analysis graphical user interface of the client device, and identifying the machine learning embedding filter comprises identifying the machine learning embedding concentration threshold and the machine learning embedding concentration threshold comprises at least one of a molecule concentration range or a soluble factor concentration range (the concentration of molecular iodine demonstrated to induce apoptosis and suppress proliferation are similar for breast cancer cell and fibrocystic cells, which indicates…) (paragraph 0137).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to include providing the machine learning embedding filter element comprises providing the concentration element for display via the perturbation analysis graphical user interface of the client device, and identifying the machine learning embedding filter comprises identifying the machine learning embedding concentration threshold and the machine learning embedding concentration threshold comprises at least one of a molecule concentration range or a soluble factor concentration range as disclosed by Eliot to provide information to user.
As to claim 9, NARUNIEC discloses the computer-implemented method of claim 8 excepting for providing the perturbation visual representation for display via the perturbation analysis graphical user interface of the client device by: accessing a dataframe comprising machine learning embeddings labeled with concentrations applied to cells to generate the machine learning embeddings; and performing a query of the dataframe by comparing the concentrations applied to the cells to generate the machine learning embeddings with at least one of the molecule concentration range or the soluble factor concentration range.
However, Eliot discloses providing the perturbation visual representation for display via the perturbation analysis graphical user interface of the client device by: accessing a dataframe comprising machine learning embeddings labeled with concentrations applied to cells to generate the machine learning embeddings; and performing a query of the dataframe by comparing the concentrations applied to the cells to generate the machine learning embeddings with at least one of the molecule concentration range or the soluble factor concentration range (the concentration of molecular iodine demonstrated to induce apoptosis and suppress proliferation are similar for breast cancer cell and fibrocystic cells, which indicates…) (paragraph 0137).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to include providing the perturbation visual representation for display via the perturbation analysis graphical user interface of the client device by: accessing a dataframe comprising machine learning embeddings labeled with concentrations applied to cells to generate the machine learning embeddings; and performing a query of the dataframe by comparing the concentrations applied to the cells to generate the machine learning embeddings with at least one of the molecule concentration range or the soluble factor concentration range as disclosed by Eliot to provide information to user.
As to claim 15, NARUNIEC discloses a system of claim 10 excepting for provide the machine learning embedding filter element by providing the concentration element for display via the perturbation analysis graphical user interface of the client device; identify the machine learning embedding filter by identifying the machine learning embedding concentration threshold and the machine learning embedding concentration threshold comprises at least one of a molecule concentration range or a soluble factor concentration range; and filtering machine learning embeddings by comparing concentrations applied to cells to generate the machine learning embeddings with at least one of the molecule concentration range or the soluble factor concentration range.
However, Eliot discloses provide the machine learning embedding filter element by providing the concentration element for display via the perturbation analysis graphical user interface of the client device; identify the machine learning embedding filter by identifying the machine learning embedding concentration threshold and the machine learning embedding concentration threshold comprises at least one of a molecule concentration range or a soluble factor concentration range; and filtering machine learning embeddings by comparing concentrations applied to cells to generate the machine learning embeddings with at least one of the molecule concentration range or the soluble factor concentration range (the concentration of molecular iodine demonstrated to induce apoptosis and suppress proliferation are similar for breast cancer cell and fibrocystic cells, which indicates…) (paragraph 0137).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to include as disclosed by Eliot to provide information to provide the machine learning embedding filter element by providing the concentration element for display via the perturbation analysis graphical user interface of the client device; identify the machine learning embedding filter by identifying the machine learning embedding concentration threshold and the machine learning embedding concentration threshold comprises at least one of a molecule concentration range or a soluble factor concentration range; and filtering machine learning embeddings by comparing concentrations applied to cells to generate the machine learning embeddings with at least one of the molecule concentration range or the soluble factor concentration range user.
8. Claim(s) 3, 12 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over NARUNIEC et al. (Pub. No US 2022/0058822 A1) in view of GAO et al. (Pub. No. US 2022/0247571 A1) and further in view of Garraway et al. (Pub. No. US 2020/0390786 A1).
As to claim 3, NARUNIEC discloses the computer-implemented method of claim 1 excepting comparing the plurality of machine learning embeddings in a latent machine learning feature space to generate the similarity measures; and providing the perturbation visual representation by providing for display via the perturbation analysis graphical user interface of the client device, a perturbation heatmap comprising the similarity measures. However, Garraway discloses comparing the plurality of machine learning embeddings in a latent machine learning feature space to generate the similarity measures; and providing the perturbation visual representation by providing for display via the perturbation analysis graphical user interface of the client device, a perturbation heatmap comprising the similarity measures (FIG. 3d is a heatmap showing the overlap as a measure of module similarity…) (paragraph 0029).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to include comparing the plurality of machine learning embeddings in a latent machine learning feature space to generate the similarity measures; and providing the perturbation visual representation by providing for display via the perturbation analysis graphical user interface of the client device, a perturbation heatmap comprising the similarity measures. However, Garraway discloses comparing the plurality of machine learning embeddings in a latent machine learning feature space to generate the similarity measures; and providing the perturbation visual representation by providing for display via the perturbation analysis graphical user interface of the client device, a perturbation heatmap comprising the similarity measures as disclosed Garraway in order to provide messurement.
Claim 12 is rejected under the same reason as to claim 3.
Claim 18 is rejected under the same reason as to claim 3.
Conclusion
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BAOQUOC N. TO
Examiner
Art Unit 2154
/BAOQUOC N TO/Primary Examiner, Art Unit 2154
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