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 .
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 21 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The specification, while describing a model trained on calcium kinetic features capable of diagnosing Bipolar disorder, Alzheimer’s disease, or Parkinson’s disease, does not reasonably provide description for a model trained on calcium kinetic features capable of diagnosing any condition or any neurological condition or disease. It is neither disclosed in the specification nor obvious to one of ordinary skill in the art that such a trained model would be able to be generalized to diagnose other conditions. Hence the breadth of the independent claim covers a scope larger than the disclosed invention.
Claim 24 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The specification, while describing a model trained on calcium kinetic features capable of diagnosing Bipolar disorder, Alzheimer’s disease, or Parkinson’s disease, does not provide description of a model trained on calcium kinetic features capable of diagnosing schizophrenia (SCZ) or autism spectrum disorders (ASD). The only mention of these two conditions in the specification are in paragraph [0107] reciting, “BPD has historically been an intractable disease with little known regarding its pathophysiology and with few clinical breakthroughs in recent decades, just as with many other polygenic neurological disorders such as schizophrenia (SCZ) and autism spectrum disorders (ASD)”. There is no clear tie to a model trained to diagnose these particular conditions, much less trained based on neuronal calcium data comprising basal calcium level, peak calcium transience, calcium event frequency, calcium event amplitude, calcium event influx, and calcium event efflux as recited in Claim 21 to diagnose these particular conditions, nor would it be obvious to one of ordinary skill in the art.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 21-40 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “therapeutically effective amount of a therapeutic” in claim 21 is a relative term which renders the claim indefinite. The term “therapeutically effective amount” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Thereby the scope of the administering step is unclear.
Claim 21 recites the limitation "the neuronal calcium data" in lines 6-7. There is insufficient antecedent basis for this limitation in the claim. It’s unclear if this is referring to the calcium kinetic features or something else entirely.
A broad range or limitation together with a narrow range or limitation that falls within the broad range or limitation (in the same claim) may be considered indefinite if the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. See MPEP § 2173.05(c). In the present instance, claim 21 recites the broad recitation “determine a diagnosis for the patient”, and the claim also recites “the neurological condition or disease” which is the narrower statement of the range/limitation. The claim(s) are considered indefinite because there is a question or doubt as to whether the feature introduced by such narrower language is (a) merely exemplary of the remainder of the claim, and therefore not required, or (b) a required feature of the claims.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 35 rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 21 recites, “wherein the neuronal calcium data comprises (i) basal calcium level, peak calcium transience, calcium event frequency, and calcium event amplitude, and (ii) at least one of calcium event influx and calcium event efflux”. Claim 35 appears to simply state what is already required of the neuronal calcium data, reciting, “wherein the neuronal calcium data comprises basal calcium level, peak calcium transience, calcium event frequency, calcium event influx, calcium event efflux, and calcium event amplitude”. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 21-23, 25-36, and 38-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 25, 28, 31, 34, and 44-45 of U.S. Patent No. US 12223643 B2 (hereinafter referred to as Application 17616461) in view of Wall (US 20210133509 A1).
Claim 37 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 31 of U.S. Patent No. US 12223643 B2 (hereinafter referred to as Application 17616461) in view of Wall (US 20210133509 A1) and Gessert (N. Gessert et al., "Skin Lesion Classification Using CNNs With Patch-Based Attention and Diagnosis-Guided Loss Weighting," in IEEE Transactions on Biomedical Engineering, vol. 67, no. 2, pp. 495-503, May. 2019, doi: 10.1109/TBME.2019.2915839).
Regarding claim 21 of the instant application, see table below.
Application 19002503
Application 17616461
Claim 21
Claim 25
A method for treating a neurological condition or disease in a subject in need thereof, the method comprising: receiving, from a cellular imaging device, image data comprising calcium kinetic features of neuronal cultures derived from a patient
A computer-implemented method for patient screening, the method being executed by one or more processors and comprising: receiving, from a cellular imaging device, image data comprising calcium kinetic features of neuronal cultures derived from a patient
processing the image data through a machine-learning model to determine a diagnosis for the patient based on the calcium kinetic features, wherein the neuronal calcium data comprises
processing the image data through a machine-learning model to determine a diagnosis for the patient based on the calcium kinetic features, wherein the machine-learning model is trained using neuronal calcium data
(i) basal calcium level, peak calcium transience, calcium event frequency, and calcium event amplitude, and (ii) at least one of calcium event influx and calcium event efflux
and wherein the neuronal calcium data comprises (i) basal calcium level, peak calcium transience, calcium event frequency, and calcium event amplitude, and (ii) at least one of calcium event influx and calcium event efflux
providing the diagnosis to a user interface
and providing the diagnosis to a user interface
and administering a therapeutically effective amount of a therapeutic to the subject based on the diagnosis, thereby treating the neurological condition or disease
wherein the diagnosis is for bipolar disorder (BPD), Alzheimer’s disease, or Parkinson’s disease
Although the claims at issue are not identical, due to the additional explicit limitation of administering a therapeutically effective amount of a therapeutic to the subject based on the diagnosis, thereby treating the neurological condition or disease, one of ordinary skill in the art would be able to configure this based on Claim 25 of 17616461 in view of Wall (US 20210133509 A1) teaching ([0184] In another aspect, a method for administering a drug to a subject may comprise: detecting a neurological disorder of the subject with a machine learning classifier; and administering the drug to the subject in response to the detected neurological disorder). Examiner notes bipolar disorder (BPD), Alzheimer’s disease, or Parkinson’s disease in Claim 25 of 17616461 are known neurological conditions or diseases.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have modified the teachings of 17616461 to include the teachings of Wall to administer a candidate treatment/therapeutic to treat a neurological condition. Doing so would be an obvious application of the resulting diagnosis of 17616461, in order to improve the health of a patient.
Regarding Claim 22,
Application 19002503
Application 17616461
Claim 22
Claim 28
The method of claim 21, further comprising processing the image data through the machine-learning model to determine a responsiveness of a patient to the therapeutic for the neurological condition or disease.
The method of claim 25, comprising: processing the image data through the machine-learning model to determine a drug responsiveness of the patient
Although the claims at issue are not identical, there are no patentably distinct features between Claim 22 of 19002503 and Claim 28 of 17616461 in light of Wall as described above. Examiner notes the mention of the neurological condition or disease is covered by Claim 25 of 17616461 mentioning bipolar disorder (BPD), Alzheimer’s disease, or Parkinson’s disease.
Regarding Claim 23,
Application 19002503
Application 17616461
Claim 23
Claim 25
The method of claim 21, wherein the neurological condition or disease is associated with a dysregulated CRMP2 pathway.
… wherein the diagnosis is for bipolar disorder (BPD), Alzheimer’s disease, or Parkinson’s disease.
Note: only the last limitation of Claim 25 is put here for visual comparison, the rest of Claim 25 of 17616461 maps to Claim 21 of 19002503
Although they are not identical, Claim 25 of 17616461 is encompassing of Claim 23 of 19002503 because Claim 25 mentions the diagnosis is for bipolar disorder which is known to be associated with a dysregulated CRMP2 pathway as evidenced by Pickard (Pickard B. S. (2017). Genomics of Lithium Action and Response. Neurotherapeutics: the journal of the American Society for Experimental NeuroTherapeutics, 14(3), 582–587. https://doi.org/10.1007/s13311-017-0554-7) teaching ([Section: Induced Pluripotent Stem Cells Offer a Means to Explore Lithium Response in Well-Controlled Human Models, paragraph 3]: CRMP2 regulates the cellular response to semaphorin 3A (formerly known as “collapsin”), an extracellular signaling molecule that binds to neuropilin/plexin receptors. This pathway shapes dendritic spines and axonal growth cone morphology during development and in the adult—with implications for synapse formation and function… Spine morphologies were shown to be altered in vitro and in postmortem brain samples, suggesting this is a genuine bipolar disorder pathology, and one that can be ameliorated by lithium, most likely through action on the GSK3β/CRMP2/semaphorin 3A pathway). Thereby, there are no patentably distinct features between Claim 23 of 19002503 and Claim 25 of 17616461 in light of Wall as described above.
Regarding Claim 25,
Application 19002503
Application 17616461
Claim 25
Claim 25
The method of claim 21, wherein the neurological condition or disease is bipolar disorder (BPD).
… wherein the diagnosis is for bipolar disorder (BPD), Alzheimer’s disease, or Parkinson’s disease.
Note: only the last limitation of Claim 25 is put here for visual comparison, the rest of Claim 25 of 17616461 maps to Claim 21 of 19002503
There are no patentably distinct features between Claim 25 of 19002503 and Claim 25 of 17616461 in light of Wall as described above.
Regarding Claim 26, the claim recites, “the method of claim 22, wherein the therapeutic comprises a mood stabilizer, an antipsychotic, an antidepressant, an antidepressant-antipsychotic, or an anti-anxiety medication, or any combination thereof”.
Although an identical equivalent is not found in 17616461, these therapeutics are well known in the art as taught by Wall teaching a mood stabilizer in at least ([0529] The platforms, systems, devices, methods, and media described anywhere herein may be used to administer a drug to treat bipolar disorder, such as topiramate, lamotrigine, oxcarbazepine, haloperidol, risperidone, quetiapine, olanzapine, aripiprazole, or fluoxetine). See the combination rationale as explained above regarding Claim 21 of 19002503.
Regarding Claim 27, the claim recites, “the method of claim 26, wherein the mood stabilizer is selected from the group consisting of lithium, valproic acid, divalproex sodium, carbamazepine, and lamotrigine; wherein the antipsychotic is selected from the group consisting of olanzapine,risperidone, quetiapine, aripiprazole, ziprasidone, lurasidone, and asenapine; wherein the antidepressant is selected from the group consisting of citalopram, escitalopram,fluoxetine, fluvoxamine, paroxetine, sertraline, vortioxetine, and vilazodone; wherein the antidepressant-antipsychotic is selected from the group consisting of olanzapine/fluoxetine, amitriptyline/perphenazine, aripiprazole/sertraline,flupentixol/melitracen, and tranylcypromine/trifluoperazine; and wherein the anti-anxiety medication is selected from the group consisting of benzodiazepines, beta-blockers, buspirone, selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNR~s), and tricyclic antidepressants“.
Although an identical equivalent is not found in 17616461, these therapeutics are well known in the art as taught by Wall teaching lamotrigine in at least ([0529] The platforms, systems, devices, methods, and media described anywhere herein may be used to administer a drug to treat bipolar disorder, such as topiramate, lamotrigine, oxcarbazepine, haloperidol, risperidone, quetiapine, olanzapine, aripiprazole, or fluoxetine). See the combination rationale as explained above regarding Claim 21 of 19002503.
Regarding Claim 28,
Application 19002503
Application 17616461
Claim 28
Claim 25
The method of claim 21, wherein the neurological condition or disease is Alzheimer's disease or Parkinson's disease
… wherein the diagnosis is for bipolar disorder (BPD), Alzheimer’s disease, or Parkinson’s disease.
Note: only the last limitation of Claim 25 is put here for visual comparison, the rest of Claim 25 of 17616461 maps to Claim 21 of 19002503
There are no patentably distinct features between Claim 28 of 19002503 and Claim 25 of 17616461 in light of Wall as described above.
Regarding Claim 29, the claim recites, “the method of claim 28, wherein the therapeutic is selected from the group consisting of a cholinesterase inhibitor, a dopamine agonist, a MAO B inhibitor, a catechol O-methyltransferase (COMT) inhibitor, and an anticholinergic”.
Although an identical equivalent is not found in 17616461, these therapeutics are well known in the art as taught by Wall teaching a dopamine agonist ([0558] The platforms, systems, devices, methods, and media described anywhere herein may be used to administer a drug to treat Parkinson's disease, such as rivastigmine, selegiline, rasagiline, bromocriptine, amantadine, cabergoline, or benztropine). See the combination rationale as explained above regarding Claim 21 of 19002503.
Regarding Claim 30, the claim recites, “the method of claim 21, wherein the therapeutic is selected from the group consisting of carbidopa-levodopa, amantadine, tacrine, rivastigmine, galantamine, donepezil and memantine”.
Although an identical equivalent is not found in 17616461, these therapeutics are well known in the art as taught by Wall teaching amantadine ([0558] The platforms, systems, devices, methods, and media described anywhere herein may be used to administer a drug to treat Parkinson's disease, such as rivastigmine, selegiline, rasagiline, bromocriptine, amantadine, cabergoline, or benztropine). See the combination rationale as explained above regarding Claim 21 of 19002503.
Regarding Claim 31, the claim recites, “the method of claim 30, wherein the therapeutic is selected from the group consisting of carbidopa-levodopa, and amantadine”.
Although an identical equivalent is not found in 17616461, these therapeutics are well known in the art as taught by Wall teaching amantadine ([0558] The platforms, systems, devices, methods, and media described anywhere herein may be used to administer a drug to treat Parkinson's disease, such as rivastigmine, selegiline, rasagiline, bromocriptine, amantadine, cabergoline, or benztropine). See the combination rationale as explained above regarding Claim 21 of 19002503.
Regarding Claim 32, the claim recites, “The method of Claim 21, wherein the machine-learning model comprises at least one of a linear regression algorithm, a naïve bayes algorithm, a random forest algorithm, a one versus all algorithm, a support vector classifier module algorithm, or a k-nearest neighbor algorithm”.
Although an identical equivalent is not found in 17616461, Claim 31 of 17616461 establishes that the machine learning model is a type of classifier. Further, Wall teaches a machine learning model for predicting disorders can be at least one of SVM, linear regression, or Naïve bayes. ([0516]: the machine learning algorithm utilizes a predictive model such as a neural network, a decision tree, a support vector machine, or other applicable model. In some embodiments, the machine learning algorithm is selected from the group consisting of a supervised, semi-supervised and unsupervised learning, such as, for example, a support vector machine (SVM), a Naïve Bayes classification, a random forest, an artificial neural network, a decision tree, a K-means, learning vector quantization (LVQ), self-organizing map (SOM), graphical model, regression algorithm (e.g., linear, logistic, multivariate, association rule learning, deep learning, dimensionality reduction and ensemble selection algorithms).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have modified the teachings of the 17616461 and Wall combination, to further include the teachings of Wall by substituting the generic mention of a machine learning model capable of determining a diagnosis for a patient taught by 17616461 by a particular machine learning model, such as Random Forest, or a linear classifier, capable of predicting a medical disorder as taught by Wall. Doing so would provide the predictable result of a machine learning model capable of determining a diagnosis.
Regarding Claim 33,
Application 19002503
Application 17616461
Claim 33
Claim 31
The method of claim 21, wherein the machine-learning model comprises a gradient boosting classifier to prevent over fitting and not to over bias the machine- learning model.
The method of claim 25, wherein the machine-learning model comprises a gradient boosting classifier to prevent over fitting and not to over bias the machine-learning model.
There are no patentably distinct features between Claim 33 of 19002503 and Claim 31 of 17616461 in light of Wall as described above.
Regarding Claim 34,
Application 19002503
Application 17616461
Claim 34
Claim 34
The method of claim 21, wherein the neuronal calcium data is acquired from in vitro neural cultures.
The method of claim 25, wherein the neuronal calcium data is acquired from in vitro neural cultures.
There are no patentably distinct features between Claim 34 of 19002503 and Claim 34 of 17616461 in light of Wall as described above.
Regarding Claim 35,
Application 19002503
Application 17616461
Claim 35
Claim 25
The method of claim 21, wherein the neuronal calcium data comprises basal calcium level, peak calcium transience, calcium event frequency, calcium event influx, calcium event efflux, and calcium event amplitude
…and wherein the neuronal calcium data comprises (i) basal calcium level, peak calcium transience, calcium event frequency, and calcium event amplitude, and (ii) at least one of calcium event influx and calcium event efflux…
Note: only the relevant section of Claim 25 is mapped here for visual purposes. The rest of Claim 25 of 17616461 maps to Claim 21 of 19002503 as shown above.
There are no patentably distinct features between Claim 35 of 19002503 and Claim 25 of 17616461 in light of Wall as described above.
Regarding Claim 36,
Application 19002503
Application 17616461
Claim 36
Claim 25
The method of claim 21, wherein the machine-learning model is trained using neuronal calcium data.
…wherein the machine-learning model is trained using neuronal calcium data…
Note: only the relevant section of Claim 25 is mapped here for visual purposes. The rest of Claim 25 of 17616461 maps to Claim 21 of 19002503 as shown above.
There are no patentably distinct features between Claim 36 of 19002503 and Claim 25 of 17616461 in light of Wall as described above.
Regarding Claim 37, the claim recites, “The method of claim 36, wherein down sampling is employed during training of the machine-learning model”.
Although an identical equivalent is not found in 17616461, Claim 31 of 17616461 establishes that the machine learning model is a type of classifier. Additionally, Wall establishes that for medical image diagnosis prediction, convolutional networks may be used for feature extraction ([0352] The training module may comprise feature selection. One or more feature selection algorithms (such as support vector machine, convolutional neural nets) followed by a classifier ([0346] The training module 110 can utilize a machine learning algorithm or other algorithm to construct and train an assessment model to be used in the assessment procedure… given feature value may have a different predictive utility for classifying each of the plurality of developmental disorders to be evaluated in the assessment procedure). Further, down sampling is a technique well known in the art for CNNs used in a medical classification scheme as taught by Gessert ([abstract]: This work addresses two key problems of skin lesion classification. The first problem is the effective use of high-resolution images with pretrained standard architectures for image classification, [introduction, paragraph 4]: Typically, images are down sampled to a lower input resolution for CNNs, as memory and computational resources are limited).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have modified the teachings of 17616461 and Wall combination to include the teachings of Gessert by substituting Wall’s teachings of an image input to a CNN for feature extraction and classification of a medical disorder for Gessrt’s explicit mention of a down sampled image input to a CNN for medical image classification. Doing so would provide the predictable result of a machine learning model to extract features and classify medical image data and improve the speed of the machine learning model.
Regarding Claim 38,
Application 19002503
Application 17616461
Claim 38
Claim 44
The method of claim 21, wherein the image data comprises intracellular calcium level traces.
The method of claim 25, wherein the image data comprises intracellular calcium level traces.
There are no patentably distinct features between Claim 38 of 19002503 and Claim 44 of 17616461 in light of Wall as described above.
Regarding Claim 39,
Application 19002503
Application 17616461
Claim 39
Claim 45
The method of claim 21, wherein the neuronal cultures comprise Collapsin Response Mediator Protein-2 (CRMIP2)-knock out (KO), CRMIP2-knock in (KI), and WT E16.5 primary hippocampal neurons.
The method of claim 25, wherein the neuronal cultures comprise Collapsin Response Mediator Protein-2 (CRMP2)-knock out (KO), CRMP2-knock in (KI), and WT E16.5 primary hippocampal neurons.
There are no patentably distinct features between Claim 39 of 19002503 and Claim 45 of 17616461 in light of Wall as described above.
Regarding Claim 40,
Application 19002503
Application 17616461
Claim 40
Claim 46
The method of claim 21, wherein the neuronal cultures are derived from blood samples from the patient.
The method of claim 25, wherein the neuronal cultures are derived from blood samples from the patient.
There are no patentably distinct features between Claim 40 of 19002503 and Claim 46 of 17616461 in light of Wall as described above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JANICE VAZ whose telephone number is (703)756-4685. The examiner can normally be reached Monday-Friday 9:00-5:00pm.
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/JANICE E. VAZ/Examiner, Art Unit 2667
/MICHAEL ROBERT CAMMARATA/Primary Examiner, Art Unit 2667
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