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 .
Applicant's response filed 4/29/2026 has been fully considered. The following rejections
and/or objections are either reiterated or newly applied.
Status of Claims
Claims 1-29 pending.
Claims 18-29 withdrawn.
Claims 1-17 examined on the merits.
Priority
The instant application filed on 5/3/2022 claims the benefit of priority to U.S. Provisional Patent Application No. 63/184,076 filed on 5/4/2021. Thus, the effective filing date of the claims is 5/4/2021. The applicant is reminded that amendments to the claims and specification must comply with 35 U.S.C. § 120 and 37 C.F.R. § 1.121 to maintain priority to an earlier-filed application. Claim amendments may impact the effective filing date if new subject matter is introduced that lacks support in the originally filed disclosure. If an amendment adds limitations that were not adequately described in the parent application, the claim may no longer be entitled to the priority date of the earlier filing.
Claim Objections
The objection to claim 14 withdrawn in view of Applicant's claim amendments filed on 4/29/2026.
Withdrawn Rejections
35 USC § 102
Applicant asserts that "Shi neither reports recording membrane potential of one or more electrically-active cells, nor reporting a membrane potential recording to a machine learning system" and that "Shi does not disclose an autoencoder" or "representation learning", and that the cited passages evidencing such are "a prospective statement about how Shi's method could be refined in future work" (Remarks 4/29/2026 pages 1-2). Examiner notes that Applicant's own disclosure provides an example using optical means of measuring membrane potential, which Shi also utilizes (page 6 line 17 "The present invention also provides an exemplary method for assessing a cellular pathology that includes obtaining neural cells having a known pathology and causing the cells to express optical reporters of membrane electrical potential. Then, the method includes stimulating the neural cells in wells of a multi-well plate such that they exhibit action potentials. Optical signals from the optical reporters, in response to the stimulated action potential, are recorded. Action potential features are identified from the recorded optical signals."). Therefore, while perhaps Shi does not anticipate the amended "recording membrane potential of one or more electrically-active cells", Shi certainly renders the limitation obvious, as detailed below in section "Claim Rejections - 35 USC 103".
Applicant asserts that the principal components produced by the method of Shi are not "discrete features from the recording" as taught by instant claim 11 (Remarks 4/29/2026 pages 2-3). Examiner notes that the limitation of claim 11 does not specify "discrete" features in the way Applicant is presenting them in their Remarks, and that each principal component may be interpreted as a feature in itself, the group of which is subset in Shi.
Applicant also points out that several claim limitations are treated as anticipated by an "obvious design choice" rationale (specifically, claims 3, 5, and 10) (Remarks 4/29/2026 page 2). Examiner finds this argument persuasive; therefore, the rejection of claims 1-14 under 35 USC 102 are withdrawn in view of Applicant's claim amendments filed on 4/29/2026. However, these claims are still rejected under 35 USC 103, below.
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.
Claims 1-14 rejected under 35 U.S.C. 103 as being unpatentable over Shi et al. (US-20200077947).
Regarding Claim 1, Shi teaches a method for characterizing cellular activity, the method comprising recording membrane potential of activity of one or more electrically-active cells (Para.0001 "a drug screening method that utilizes functional brain physiology phenotypes and computational bioinformatics analysis" and para.0100 "the method of the present invention may rely on an autonomous robotic system capable of manipulating awake zebrafish larvae for rapid microscopic imaging of their brains at the level of cellular resolution, which may allow for rapid assessment of action potential firing across a whole zebrafish brain; as a result, a large number of whole-brain activity maps (BAMs) can be acquired for a compound library").
Shi also teaches presenting the recording to a machine learning system trained on training data comprising recordings from cells with a known pathology and cells without the pathology (Para.0004 "the step of classifying a brain activity map with the chemical compound using the functional classifiers includes a statistical analysis and/or a machine learning process" and figures 39 and 40 (para.0090-0092) show plots of seizure models having various treatments applied, versus control groups).
Shi also teaches reporting, by the machine learning system, a phenotype of the electrically-active cells (Para.0133 "these results support that BAMs provide a spatially and functionally encoded phenotype directly associated with therapeutic potential of different (or different types of) CNS drugs").
It is recognized that the citations and evidence provided above are derived from potentially different embodiments of a single reference. Nevertheless, it 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 to employ combinations and sub-combinations of these complementary embodiments, because Shi et al. explicitly motivates doing so at least in paragraph [0187] (para.0187 "It will be appreciated by persons skilled in the art that numerous variations and/or modifications may be made to the invention as shown in the specific embodiments without departing from the spirit or scope of the invention as broadly described. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive") and otherwise motivating experimentation and optimization. Additionally, doing so merely combines prior art elements according to known methods to yield predictable results.
Regarding Claim 2, Shi teaches the recording comprises one or more action potentials exhibited by the electrically-active cells (Para.0100 "the method of the present invention may rely on an autonomous robotic system capable of manipulating awake zebrafish larvae for rapid microscopic imaging of their brains at the level of cellular resolution, which may allow for rapid assessment of action potential firing across a whole zebrafish brain").
Regarding Claims 3 and 5, Shi teaches the machine learning system reports the phenotype of the electrically-active cells as having, or not having, the pathology; and the machine learning system reports the phenotype of the electrically active cells as reverting from having the pathology to not having the pathology with exposure to test compound (Para.0121 "The difference in spike-counts between the post- and pre-treatment periods was calculated and projected (by summing up along the Z-axis) to a two-dimensional surface to construct a BAM that reflects changes in brain activity and physiology of an individual larva in response to treatment", coupled with the machine learning model the specific outcomes to be predicted such as having or not having the pathology before and/or after a treatment is obvious because all of the necessary data is present for training a model to do such predictions).
Regarding Claim 4, Shi teaches exposing the electrically-active cells to a test compound (Para.0118 "to generate the functional BAM of a larva in response to a 15-minute period of chemical perfusion, or “treatment”, each larva was imaged over a 10-minute period before and after treatment").
Regarding Claim 6, Shi teaches the recording is a digital movie made by imaging the electrically-active cells through a microscope with a CMOS images sensor (Para.0106 "the imaging module 102 may process image raw data of the image frames 108 obtained by an imager 114 capturing the living species 104 so as to generate images 108 of the brain of the living species. For example, the imaging module 102 may further process the image frames by combining multiple image frames of similar objects being captured or to extract important information from the image raw data in multiple images so as to generate an output image that may be more suitable for further analysis" and para.0107 "For example, the imaging process may be performed on a fully automated inverted fluorescent microscope (Olympus IX81) equipped with a cooled sCMOS camera (Neo, ANDOR) with a 10× (NA, 0.4) objective").
Regarding Claim 7, Shi teaches the machine learning system is resident in a computer system comprising a processor coupled to memory, and the recording is saved in the memory (Para.0136 "The processing module 112 and/or the transformation module 106 may be implemented using a computer or a computer server. The computer may comprise suitable components necessary to receive, store and execute appropriate computer instructions, such that when these computer instructions are executed by the processing unit in the computer device").
Regarding Claim 8, Shi teaches the recording captures action potentials, and the method further comprises measuring, and storing, a plurality of features from the action potentials (Para.0100 "the method of the present invention may rely on an autonomous robotic system capable of manipulating awake zebrafish larvae for rapid microscopic imaging of their brains at the level of cellular resolution, which may allow for rapid assessment of action potential firing across a whole zebrafish brain" and Para.0136 (from claim 7)).
Regarding Claim 9, Shi teaches measuring features from the recording and presenting the features to the machine learning system (Para.0004 "the step of classifying a brain activity map with the chemical compound using the functional classifiers includes a statistical analysis and/or a machine learning process" and figures 39 and 40 (para.0090-0092) show plots of seizure models having various treatments applied, versus control groups)
Shi also teaches the features comprise one or more of spike rate, spike height, spike width, depth of afterhyperpolarization, onset timing, timing of cessation of firing, inter-spike interval, adaptation over a constant stimulation, a first derivative of spike waveform, and a second derivative of spike waveform (Para.0036 "the transformation module is arrange to construct the brain activity maps based on counting neural spikes representing changes of brain activity as detected on the images obtained" and para.0056 "FIG. 9 is a plot showing the change of action potential firing rate in selected neuronal cells from a zebrafish brain").
Regarding Claim 10, which recites operating the machine learning system under control of a budget wrapper that limits a number of features that are presented to the machine learning system, wrapping existing code in a UI that presents some number of features (limited or otherwise) is a predictable variation that does not show a technical improvement, therefore is considered an obvious design choice.
Regarding Claim 11, Shi teaches extracting greater than 100 features from the recording and further wherein a budget wrapper presents fewer than about 20 of the features to the machine learning system (Para.0140 describes a clustering method using a limited number of dimensions; "The processing module 112 is further arranged to generate the functional classifiers based on the plurality of characteristic features obtained by a supervised clustering processing or an unsupervised clustering processing. Referring to FIG. 19, unsupervised classification may be applied to detect, in blinded fashion, the intrinsic coherence among similar T-score BAMs, subsequently defined as functional clusters of the tested drugs. Specifically, consensus clustering 21 may be applied to the library of Pheno-Prints (in 20 dimensions) corresponding to the 179 clinical drugs").
Regarding Claim 12, Shi teaches the machine learning system comprises a neural network (Para.0183 "the clustering analysis can be refined by taking advantage of more advanced machine learning methods such as recursive cortical network or deep learning algorithms").
Regarding Claim 13, Shi teaches the neural network is an autoencoder neural network that operates by representation learning (Para.0183 "relevant structure-specific information in the BAMS may be spatially encoded for analysis").
Regarding Claim 14, Shi teaches the autoencoder has been trained using manually selected training data comprising the recordings from cells with the known pathology and the cells without the pathology in samples that have been exposed to known compounds with known efficacy and control samples that have not be exposed to the known compounds (Para.0004 "the step of classifying a brain activity map with the chemical compound using the functional classifiers includes a statistical analysis and/or a machine learning process" and figures 39 and 40 (para.0090-0092) show plots of seizure models having various treatments applied, versus control groups).
Claims 15-17 rejected under 35 U.S.C. 103 as being unpatentable over Shi et al. (US-20200077947) as applied to claims 1-14 above, and further in view of Saravanan et al. (Saravanan et al. Neuron Behav Data Anal Theory. 2020; Epub 2020 Jul 21. PMID: 33644783; PMCID: PMC7906290).
Shi et al. is applied to claims 1-14 above.
Regarding claims 15-17, Shi does not explicitly teach that the machine learning system is trained using a hierarchical bootstrapping algorithm that recursively samples and re-samples from an arbitrary number of levels with replacement.
However, Saravanan teaches a hierarchical bootstrapping method for recursively sampling data with replacement (Page 22 figure 1 legend "In the “Hierarchical Bootstrap” method, we create new datasets Nbootstrap times by resampling with replacement first at the level of subjects followed by neurons within a subject").
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Shi as taught by Saravanan in order to keep the type-I error rate within expected bounds and retain statistical power ("The hierarchical bootstrap, when applied sequentially over the levels of the hierarchical structure, keeps the Type-I error rate within the intended bound and retains more statistical power than summarizing methods"). One skilled in the art would have a reasonable expectation of success because both methods are concerned with analyzing neuron-based data and using models that benefit from a bootstrapping approach.
Response to Arguments under 35 USC § 103
Applicant’s arguments filed 4/29/2026 are fully considered but they are not persuasive.
Applicant asserts that "Shi does not describe training a machine learning system on bootstrapped samples", among other steps, and instead applies bootstrapping "to evaluate clustering robustness, not to train a machine learning system" (Remarks 4/29/2026 pages 3-4). Applicant also asserts that the purpose of Saravanan's method is statistical hypothesis testing, that it is missing several key phrases related to machine learning models, and that it provides no suggestion for augmenting training data and the Office Action uses impermissible hindsight reconstruction to supply the motivation to combine Shi and Saravanan (Remarks 4/29/2026 pages 4-5). Examiner notes that Shi does suggest using bootstrapping in para.0142, and again that the motivation for variation or modifications to the invention are suggested in Shi as well in para.0187. Therefore, as noted above, it would have been obvious to one of ordinary skill in the art to adapt the hierarchical bootstrapping method of Saravanan (though not explicitly used by Saravanan for sampling model training data) for sampling of training data by Shi, which does not rely on hindsight reasoning. Regarding augmenting data, Saravanan in fact teaches augmenting the datasets by resampling them using the hierarchical bootstrap method (Page 22 figure 1 legend "In the “Hierarchical Bootstrap” method, we create new datasets Nbootstrap times by resampling with replacement first at the level of subjects followed by neurons within a subject"), as claim 17 is not explicit in what kind of augmentation is intended in the limitation.
Therefore, the rejection of claims 1-17 under 35 USC 103 is maintained.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-17 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-18 of US-20220357313 in view of Shi et al. (US-20200077947). Although the claims at issue are not identical, they are not patentably distinct from each other because both involve identifying feature of electrically-excitable cells in the presence of a compound (recording activity) and predicting efficacy of said compound against a disease (a phenotype output).
While US-20220357313 does not explicitly teach using a trained machine learning system for prediction using the recorded data, it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Shi as described above for claim 1 of the instant application, in order to perform a statistical analysis on the identified features for classifying/predicting efficacy of a drug on a phenotype (Para.0004 "the step of classifying a brain activity map with the chemical compound using the functional classifiers includes a statistical analysis and/or a machine learning process" and figures 39 and 40 (para.0090-0092) show plots of seizure models having various treatments applied, versus control groups). One skilled in the art would have a reasonable expectation of success because both approaches are capturing and analyzing cell activity data for prediction.
Claims 1-17 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-19 of US-12449414 in view of Shi et al. (US-20200077947). Although the claims at issue are not identical, they are not patentably distinct from each other because both involve stimulating a cell sample and detecting optical signals (recording activity) and predicting a level of activity of the cells in response to the stimulus (a phenotype output).
While US-12449414 does not explicitly teach using a trained machine learning system for prediction using the recorded data, it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Shi as described above for claim 1 of the instant application, in order to perform a statistical analysis on the identified features for classifying/predicting efficacy of a drug on a phenotype (Para.0004 "the step of classifying a brain activity map with the chemical compound using the functional classifiers includes a statistical analysis and/or a machine learning process" and figures 39 and 40 (para.0090-0092) show plots of seizure models having various treatments applied, versus control groups). One skilled in the art would have a reasonable expectation of success because both approaches are capturing and analyzing cell activity data for prediction.
Response to Double Patenting
Applicant’s arguments filed 4/29/2026 are fully considered but they are not persuasive.
Applicant requests that both nonstatutory double patenting rejections be held in abeyance. Examiner notes that the Nonstatutory Double Patenting rejections will stand until Applicant argues the merits, amends or cancels the appropriate claims, or files a Terminal Disclaimer.
Therefore, the rejection of claims 1-20 on the ground of nonstatutory double patenting is maintained.
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Michelucci et al. "Estimating neural network’s performance with bootstrap: A tutorial." Machine Learning and Knowledge Extraction 3.2 (2021): 357-373
Bootstrapping methods for reducing overfitting
Argha et al. "Artificial intelligence based blood pressure estimation from auscultatory and oscillometric waveforms: a methodological review." IEEE reviews in biomedical engineering 15 (2020): 152-168
Cites several publications which have used data augmentation in order to overcome a lack of training data
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the TH REE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this finaI action.
Inquiries
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/R.A.P./Examiner, Art Unit 1686
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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