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 .
Election/Restrictions
Applicant’s election of Group I, claims 1-17 drawn to a method of characterizing cellular activity, in the reply filed on 12/19/2025 is acknowledged. Because applicant did not distinctly and specifically point out the supposed errors in the restriction requirement, the election has been treated as an election without traverse (MPEP § 818.01(a)). Claims 18-29 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 12/19/2025.
Status of Claims
Claims 1-29 are pending.
Claims 18-29 are withdrawn.
Claims 1-17 are 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.
Information Disclosure Statement
The information disclosure statements (IDS) filed on 10/31/2024 and 7/25/2025 have been entered and considered. A signed copy of the corresponding 1449 forms have been included with this Office action.
Claim Objections
Claim 14 objected to because of the following informalities:
Claim 14 line 4, "samples that have not be exposed" should read "samples that have not been exposed".
Appropriate correction is required.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-14 rejected under 35 U.S.C. 102(a)(2) as being anticipated by Shi et al. (US-20200077947).
Regarding Claim 1, Shi teaches a method for characterizing cellular activity, the method comprising making a recording 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").
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).
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 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.
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.
Conclusion
No claims are allowed.
Inquiries
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/R.A.P./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686