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 Status
Claims 1-21 are pending and examined herein.
Claims 6-17 objected to.
Claims 1-5 and 18-21 are rejected.
It is noted that the claims received 16 June 2022 did not follow the standards for making claim amendments. However, the changes were identified and thus the claims received 16 June 2022 are examined herein.
For all future communications, each amendment document that includes a change to an existing claim, including the deletion of an existing claim, or submission of a new claim, must include a complete listing of all claims ever presented (including previously canceled and non-entered claims) in the application. After each claim number, the status identifier of the claim must be presented in a parenthetical expression, and the text of each claim under examination as well as all withdrawn claims (each with markings if any, to show current changes) must be presented. The listing will serve to replace all prior versions of the claims in the application (MPEP 714(II)(c)). The current status of all of the claims in the application, including any previously canceled or withdrawn claims, must be given. Status is indicated in a parenthetical expression following the claim number by one of the following status identifiers: (original), (currently amended), (previously presented), (canceled), (withdrawn), (new), or (not entered). The changes in any amended claim must be shown by strike-through (for deleted matter) or underlining (for added matter). See MPEP 714(II)(c) for more guidance on claim amendments.
Priority
Claims 1-5 and 18-21 are granted the claim to the benefit of priority to foreign application IN202241008032 filed 15 February 2022. Thus, the effective filling date of claims 1-5 and 18-21 is 15 February 2022.
Claim Objections
Claims 6-17 are objected to under 37 CFR 1.75(c) as being in improper form because a multiple dependent claim should refer to other claims in the alternative only. See MPEP § 608.01(n). Accordingly, the claims have not been further treated on the merits.
Claims 6-17 are improper multiple dependent claims because “any dependent claim which refers to more than one other claim ("multiple dependent claim") shall refer to such other claims in the alternative only” and “a multiple dependent claim shall not serve as a basis for any other multiple dependent claim” (MPEP 608.01(n)). If applicant wishes to keep the multiple dependent claims they must refer to the other claims in alternate form.
Claim Interpretation
112/f
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a real-time platform or TRANS-MSC (Human Mesenchymal Stem Cells) unit configured to incubate…” in claims 1-5, “a digital platform with embedded artificial intelligence (AI) and machine learning (ML) modules, augmented with robotic process framework, wherein the artificial intelligence modules are configured to predict…” in claims 1-5 and 18, and “a real-time in vitro cell based platform or TRANS-MSC unit configured to incubate…” in claim 18.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
The instant disclosure does not provide an associated structure of a real-time platform or TRANS-MSC (Human Mesenchymal Stem Cells) unit (or a real-time in vitro cell based platform or TRANS-MSC unit) that is configured to incubate.
The instant disclosure provides a structure for a digital platform with embedded artificial intelligence (AI) and machine learning (ML) modules, augmented with robotic process framework, wherein the artificial intelligence modules are configured to predict neurovirulence (or neurotoxicity) by providing the disclosure of a computer system that has an AI/ML algorithm trained on TRANS-MSC acquired phenotype micrographs and more than 250 neurotoxic genes [0034] and [0067] and shows implementing these algorithms on a computer by feeding micrographs to generate scores to predict human neurovirulent phenotypes, cellular infiltrations, adverse events, and microbiological contamination in figure 9.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
112/b
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 1-5 and 18-21 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.
Claims 1-5 recite “a real-time platform or TRANS-MSC unit configured to incubate…” which renders the metes and bounds of the claim indefinite. Claim 18 recites “a real-time in vitro cell based platform or TRANS-MSC unit configured to incubate…” which renders the metes and bounds of the claim indefinite. The indefiniteness arises because it is unclear if a real-time platform is also a unit configured to incubate or if only the TRANS-MSC is a unit that is configured to incubate. The specification does not provide a clear and precise definition of the limitation, nor would one skilled in the art recognize the metes and bounds of said limitation. Dependent claims 19-21 are further rejected by virtue of their dependency on a rejected claim without alleviating the indefiniteness. For the sake of furthering examination, a real-time platform (and real-time in vitro cell based platform) is interpretated as also being configured to incubate.
Claims 1 recites the limitations of "the vaccine/biologic aliquots collected from the produced batches of vaccine", “the degree of neuroattenuation”, and “the anomalies” in lines 4-5, line 9, and line 13 of the claim. There is insufficient antecedent basis for these limitations in the claim. The indefiniteness arises because the claim does not make clear what “the vaccine/biologic aliquots”, “the produced batches of vaccine”, “the degree of neuroattenuation”, or “the anomalies” are. This rejection could be overcome by amendment of these limitations to “vaccine/biologic aliquots”, “produced batches of vaccine”, “a degree of neuroattenuation”, and “anomalies”.
Claim 2 recites the limitations "the drug/API aliquots collected from the produced batches" and “the anomalies” in lines 3-4 and line 13 of the claim. There is insufficient antecedent basis for these limitations in the claim. The indefiniteness arises because the claim does not make clear what “the drug/API aliquots”, “the produced batches”, or “the anomalies” are. For the sake of furthering examination “the drug/API aliquots from the produced batches” is interpreted as being a mixture of a drug and a cell undergoing screening.
Claim 3 recites the limitations "the cosmetic chemical/ingredient aliquots collected from the produced batches" and “the anomalies” in lines 3-4 and in line 13 of the claim. There is insufficient antecedent basis for these limitations in the claim. The indefiniteness arises because the claim does not make clear what “the cosmetic chemical/ingredient aliquots”, “the produced batches”, “the anomalies” are. This rejection could be overcome by amendment of these limitations to “cosmetic chemical/ingredient aliquots”, “produced batches”, and “anomalies”.
Claim 4 recites the limitation "the natural product/it’s API aliquots collected from the produced batches" and “the anomalies” in lines 3-4 and in line 13 of the claim. There is insufficient antecedent basis for these limitations in the claim. The indefiniteness arises because the claim does not make clear what “the natural product/it’s API aliquots”, “the produced batches”, or “the anomalies” are. This rejection could be overcome by amendment of these limitations to “natural product/it’s API aliquots”, “produced batches”, and “anomalies”.
Claim 5 recites the limitation "the cell based drug culture supernatant collected from the produced batches" and “the anomalies” in lines 3-4 and in line 13 of the claim. There is insufficient antecedent basis for these limitations in the claim. The indefiniteness arises because the claim does not make clear what “the cell based drug culture supernatant”, “the produced batches”, or “the anomalies” are. It is further unclear if the cell in the cell based drug supernatant is the TRANS-MSC unit or is different. This rejection could be overcome by amendment of these limitations to “natural product/it’s API aliquots”, “produced batches”, and “the anomalies”.
Claim 18 recites the limitation "the test material’s aliquots collected form the batches" and “the damage” in line 4 of the claim. There is insufficient antecedent basis for these limitations in the claim. The indefiniteness arises because the claim does not make clear what “the test material’s aliquots”, “the produced batches”, or “the damage” are. Further, it is unclear what constitutes as “the test material” or if it is referring to a vaccine, drug, cosmetic, anti-venom products. Dependent claims 19-21 are further rejected by virtue of their dependency on a rejected claim without alleviating the indefiniteness. For the sake of furthering examination, the test material is interpreted as being any material that is chosen to be tested in the context of neurovirulence or neurotoxicity.
Claim 19 recites the limitation of “wherein the affected cells are categorized into cell-in-shock, infiltrated, apoptotic, necrotic, and dead” in line 1 of the claim. There is insufficient antecedent basis for this limitation in the claim. The indefiniteness arises because the claim does not make clear what “the affected cells” are. It is further unclear if “the affected cells” are the “the cells” that are used in the step of grading the cells into different categories in claim 18. For the sake of furthering examination, this limitation will be interpreted as wherein grading the cells into different categories includes affected cells that are categorized in different categories.
Claim 20 recites the limitation of “wherein the quantitative nature of the assay and the automation of the test process reduces the technical variability” in lines 1-2 of the claim. There is insufficient antecedent basis for this limitation in the claim. The indefiniteness arises because the claim does not make clear what “the assay”, “the automation of the test process”, and “the technical variability” are. Dependent claim 21 is further rejected by virtue of its dependency on a rejected claim without alleviating the indefiniteness. For sake of furthering examination, claim 20 will be interpreted as being wherein the test comprises automation.
Claim 21 recites the limitation of “the user’s library” in line 2 of the claim. There is insufficient antecedent basis for this limitation in the claim. The indefiniteness arises because the claim does not make clear what “the user’s library” is. This rejection may be overcome by amending this limitation to “a user’s library”.
Claims 1-5 and 18 recite the limitation "the test system" in line 10 of claim 1, in line 9 of claims 2, 3, 4, and 5, and in lines 6-7 in claim 18. There is insufficient antecedent basis for this limitation in the claim. The indefiniteness arises because the claim does not make clear what “the test system” is. Dependent claims 19-21 are rejected by virtue of their dependency on a rejected claim without alleviating the indefiniteness. For the sake of furthering examination, this “test system” is interpreted as a vaccine/biologic aliquot (in claim 1), a drug/API aliquot (in claim 2), a cosmetic chemical/ingredient aliquot (in claim 3), a natural product/it’s API aliquot (in claim 4), a cell based drug culture supernatant (in claim 5), and a test material’s aliquot (in claim 18).
112/b Indefiniteness Rejection in view of the 112/f claim interpretation:
Claim limitation “a real-time platform or TRANS-MSC (Human Mesenchymal Stem Cells) unit configured to incubate…” in claims 1-5 and “a real-time in vitro cell based platform or TRANS-MSC unit configured to incubate…” in claim 18 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. There is an insufficient disclosure of the corresponding structure of a real-time platform (and real-time in vitro cell based platform) or TRANS-MSC (Human Mesenchymal Stem Cells) unit for performing the entire claimed function of incubation in the disclosure. The disclosure provides the TRANS-MSC is a phenotypically responsive, genotypically reactive, functionally readable configured, characterized hiPSC based system, amenable to batch-wise large-scale production (instant disclosure [034] and [069]). The instant disclosure does not provide a structure of this TRANS-MSC unit that can perform incubation. The instant disclosure does not provide a description of the structure of the real-time platform (or real-time in vitro cell based platform) that can perform incubation. The instant disclosure provides that the TRANS-MSC unit is seeded in a 6-well plate which is incubated in an incubator (instant disclosure [075]) which shows a structure of that can perform incubation is distinct from the TRANS-MSC unit itself. Further, it is noted that the instant disclosure does not show that the incubator is a part of the real-time platform (or real time in vitro cell based platform). For the sake of furthering examination this limitation will be interpreted as any incubator system. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
112/a
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.
112/a Written Description rejection in view of the 112/f Interpretation
Claims 1-5 and 18-21 are 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.
Claims 1-5 recite “a real-time platform or TRANS-MSC (Human Mesenchymal Stem Cells) unit configured to incubate…” and claim 18 recites “a real-time in vitro cell based platform or TRANS-MSC unit configured to incubate…”. There is not an adequate written description for the structure of a real-time platform or TRANS-MSC configured to incubate. The disclosure provides the TRANS-MSC is a phenotypically responsive, genotypically reactive, functionally readable configured, characterized hiPSC based system, amenable to batch-wise large-scale production (instant disclosure [034] and [069]). The instant disclosure does not provide a structure of this TRANS-MSC unit that can perform incubation. The instant disclosure does not provide a description of the structure of the real-time platform (or real-time in vitro cell based platform) that can perform incubation. The instant disclosure provides that the TRANS-MSC unit is seeded in a 6-well plate which is incubated in an incubator (instant disclosure [075]) which shows a structure of that can perform incubation is distinct from the TRANS-MSC unit itself. Further, it is noted that the instant disclosure does not show that the incubator is a part of the real-time platform (or real time in vitro cell based platform). Therefore, there is an inadequate written description because there is insufficient disclosure for the structure for performing the function of these limitations.
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.
Claims 1-5 and 18-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
(Step 1)
Claims 1-5 fall under the statutory category of a machine and claims 18-21 fall under the statutory category of a process.
(Step 2A Prong 1)
Under the BRI, the instant claims recite judicial exceptions that are an abstract idea of the type that is in the grouping of a “mental process”, such as procedures for evaluating, analyzing or organizing information, and forming judgement or an opinion. The instant claims further recite judicial exceptions that are an abstract idea of the type that is in the grouping of a “mathematical concept”, such as mathematical relationships and mathematical equations.
Independent claim 1 recites a mental process of “predict neurovirulence, and by corollary, the degree of neuroattenuation of a vaccine along with any adventitious microbial contaminants in the test system”.
Independent claims 2, 3, 4, and 5 recite a mental process of “predict neurotoxicity of a drug/API along with any adventitious microbial contaminates in the test system”.
Independent claim 18 recites mental process of “grading the cells into different categories”, “quantifying the damaged caused”, and “generating a score…”.
The claims recite limitations of analyzing/evaluating data and making judgments of predicting neurovirulence, predicting neurotoxicity, grading cells into different categories, quantifying the damage caused and generating a score. The human mind is capable of analyzing/evaluating data and making judgements. It is noted that even though these mental processes art taken place in a computer environment they still recite mental processes because the human mind can practically perform these limitations while the computer is used merely as a tool to perform them.
(Step 2A Prong 2)
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). Integration into a practical application is evaluated by identifying whether there are any additional elements recited in the claim and evaluating those additional elements to determine whether they integrate the exception into a practical application.
The additional element in claims 1-5 of a real-time platform or TRANS-MSC unit configured to incubate aliquots and cell-based drug culture supernatant collected from the produced batches do not integrate the judicial exceptions into a practical application because this is insignificant extra solution activity because this additional element does not interact with the judicial exception.
The additional element in claims 1-5 of a digital platform with embedded artificial intelligence (AI) and machine learning (ML) modules, wherein the artificial intelligence modules does not integrate the judicial exception into a practical application because this is simply applying the judicial exception to a computer.
The additional elements in claim 18 of adding the test material collected form the produced batches into the TRANS-MSC unit seeded in a 6-well plate or 96-well plate, incubating the plate for a specified period in a CO2 incubator and the effects of the test material on the cells a, feeding a specified number of images into the digital platform
Thus, the additional elements do not integrate the judicial exceptions into a practical application and claims 1-5 and 18-21 are directed to the abstract idea.
(Step 2B)
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because:
The additional element in claims 1-5 of a real-time platform or TRANS-MSC unit configured to incubate aliquots and cell-based drug culture supernatant collected from the produced batches is conventional as shown by pages 198-199 in Talbot et al. (Methods in Molecular Biology, vol 2549. Humana, New York, NY (2021), newly cited) which shows an incubator system configured to incubate aliquots.
The additional element in claims 1-5 of a digital platform with embedded artificial intelligence (AI) and machine learning (ML) modules, wherein the artificial intelligence modules which is interpreted as a generic computer is conventional as shown by MPEP 2106.05(b) and 2106.05(d)(II).
The additional elements in claim 18 of adding the test material collected form the produced batches into the TRANS-MSC unit seeded in a 6-well plate or 96-well plate, incubating the plate for a specified period in a CO2 incubator and the effects of the test material on the cells a, feeding a specified number of images into the digital platform is conventional as shown on page 273-274 of Ryan et al. (Neurotoxicology 53 (2016): 271-281; newly cited) and pages 198-201 in Talbot et al. (Methods in Molecular Biology, vol 2549. Humana, New York, NY (2021), newly cited).
Thus, the additional elements are not sufficient to amount to significantly more than the judicial exception because they are conventional.
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 is rejected under 35 U.S.C. 103 as being unpatentable over Costa (Biologicals 53 (2018): 19-29; newly cited) in view of Andriasyan et al. (BioRxiv (2019): 798074; newly cited).
Claim 1 is directed to a real-time platform or TRANS-MSC (Human Mesenchymal Stem Cells) unit configured to incubate the vaccine/biologic aliquots collected from the produced batches of vaccine
Costa et al. shows the incubation of live vaccine virus strain samples in cells where the live vaccine virus strain samples were produced in batches as virus stock production (Costa et al. page 21 left col.-right col.) and implicitly shows a system for incubating vaccine/biologic aliquots. Costa et al. shows infecting neural cell cultures with these live vaccine virus strain samples (Costa et al. page 20). Further, Costa et al. shows that neurovirulence is virus-induced neuronal homeostasis perturbation (Costa et al. page 19 right col.).
Costa et al. does not show a digital platform with embedded artificial intelligence (AI) and machine learning (ML) modules, augmented with robotic process automation framework, wherein the artificial intelligence modules are configured to predict neurovirulence, and by corollary, the degree of neuroattenuation of a vaccine along with any adventitious microbial contaminants in the test system, wherein the digital platform is trained with various human virus and bacteria- induced neurovirulent and neurotoxic cellular morphology patterns configured to develop a bandwidth for detecting the anomalies in real-time assaying.
When combined with Costa et al, Andriasyan et al. shows a digital platform with artificial intelligence modules that predict neurovirulence by determining infected neuron cells with live vaccine virus strains based on nuclei morphology. Andriasyan et al. shows performing live cell fluorescence imaging in high-throughput mode to collect image data to be processed by an artificial intelligence module (Andriasyan et al. page 4 left col.). Andriasyan et al. shows an artificial intelligence module for labeling virus infection based on nuclear phenotypes (Andriasyan et al. page 2 right col.- left col. and page 3 figure 1A). Andriasyan et al. shows this artificial intelligence module implements a pipeline scoring the morphology of infected cell nuclei and found that infected cell-nucleic are distinct form uninfected nuclei (Andriasyan et al. page 2 left col.). Andriasyan et al. shows this AI module was trained with morphology patterns from images labeled infected and uninfected (Andriasyan et al. page 2 right col.). It is noted that the limitation of “and, by corollary, the degree of neuroattenuation of a vaccine along with an adventitious microbial contaminates in the test system” is interpretated as a deduction which follows the predicted neurovirulence and the AI system is not configured to explicitly predict this.
An invention would have been obvious to one or ordinary skill in the art if some motivation in the prior art would have led that person to combine reference teachings to arrive at the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filling date to have to combine the incubator system which incubates live vaccine virus strains with neuron cells of Costa et al. with the digital platform embedded with artificial intelligence modules configured to predict infected cells by the change of morphology in the nucleus of Andriasyan et al. because this would allow for a system which predicts virus-induced neuronal homeostasis perturbation from image data (i.e. neurovirulence (Costa et al. page 19 right col.)) by an artificial intelligence module which intakes cell images to score cells as infected or not infected based on nuclear morphology (Andriasyan et al. page 2 right col.). One would have a reasonable expectation of success because Costa et al. shows a system that incubates live viral vaccines before imaging the cultures to analyze neurovirulence while Andriasyan et al. shows a digital platform for analyzing images of virus infected cells to predict infected cells based on the changes in nuclei morphology when infected with a virus (i.e. viral induced homeostasis perturbation).
Claims 2-5 are rejected under 35 U.S.C. 103 as being unpatentable over Ryan et al. (Neurotoxicology 53 (2016): 271-281; newly cited) in view of Jimenez-Carretero et al. (PLOS Computational Biology 14(11): e1006238; newly cited) in view of Fisch et al. (Cellular Microbiology 23.7 (2021): e13349; newly cited).
Claim 2 is directed to a computer-implemented system for test predicting induced human neurotoxicity in a drug comprising: a real-time platform or TRANS-MSC unit configured to incubate the drug/API aliquots collected from the produced batches
Ryan et al. incubating cells exposed to compounds for 72 h at 37 Celsius and 5% CO2 which implicitly shows the use of an incubator system with temperature control and CO2 percent control (Ryan et al. page 273 right col.). Ryan et al. further shows the use of an imaging system ImageXpress Micro XLS system to capture image and a digital platform Meta Xpress 5 software for image processing to assess morphological features of cultured neurons to identify compound-induced cytotoxicity in neuron cultures (Ryan et al. page 274 left col.). It would have been obvious one of ordinary skill in the art that this incubator system can incubate a cell with any chemical compound such as drugs.
Ryan et al. does not show utilizing drug based cultures or a digital platform with embedded artificial intelligence (Al) and machine learning (ML) modules, augmented with robotic process automation framework, wherein the artificial intelligence modules are configured to predict neurotoxicity of a drug/API along with any adventitious microbial contaminants in the test system, wherein the digital platform is trained with various human virus, mycoplasma like fungi and bacteria-induced neurovirulent and neurotoxic cellular morphology patterns configured to develop a bandwidth for detecting the anomalies in real- time assaying.
When combined with Ryan et al., Jimenez-Carretero et al. shows a digital platform embedded with an artificial intelligence module that processes neuron image data to predict drug induced neurotoxicity in a drug induced cell culture. Jimenez-Carretero et al. shows an artificial intelligence model as a convolutional neural network which classifies cells as healthy or toxicity-affected based on cell morphology (Jimenez-Carretero et al. page 7-8).
Ryan et al. in view of Jimenez-Carretero et al. does not show an artificial intelligence module that predicts adventitious microbial contaminates in a test system.
When combined with Ryan et al. in view of Jimenez-Carretero et al., Fisch et al. shows an artificial intelligence module that predicts adventitious microbial contaminates in a the neuronal cell culture. Fisch et al. shows a digital platform HRMAn 2.0 which is an ensemble of machine learning and artificial intelligence algorithms (Fisch et al. page 2 right col.). Fisch et al. shows an infection analysis module which intakes cell-morphology features and pathogen morphology features to predict values such as pathogen load, percent of infected cells, and infection levels/distribution (Fisch et al. page 3 figure 1). This implicitly shows an artificial intelligence module which can predict microbial contamination in a host cell because this analysis can predict pathogen load, percent of infected cells, and infection levels which all indicate a microbe being present in a cell culture. Fisch et al. shows HRMAn 2.0 utilizes cell morphology features and pathogen morphology features of infection analysis host-pathogen interaction and implicitly shows the models being trained on microbial pathogen cellular morphology patterns (Fisch et al. page 3 figure 1).
An invention would have been obvious to one or ordinary skill in the art if some motivation in the prior art would have led that person to modify reference teachings to arrive at the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified chemical compound used in the cell culture and the image analysis software for predicting cytotoxicity in neuron toxicity screening utilizing an incubation system and digital platform of Ryan et al. with the CNN model for predicting drug-induced toxicity based on image analysis of Jimenez-Carretero et al. because this would allow for screening neural cells with drugs to predict cytotoxicity (Jimenez-Carretero et al. page 3 para. 3) and the use of an AI module with CNN models that provide a robust, sensitive, and cost-effective tool for in vitro screening of drug-induced toxicity (Jimenez-Carretero et al. abstract). It would have been further obvious to one of ordinary skill in the art before the effective filling date of the invention to have combined the system for neuron toxicity screening with an incubator system and a digital platform with an AI module for predicting drug induced toxicity embedded with a CNN to predict toxicity with the AI modules for predicting values of pathogen load, percent of infected cells, and infection levels/distribution Fisch et al. because this allows for an additional AI module that detects pathogens presence based on cell morphologies obtained from image data (Fisch et al. page 3 figure 1). One would have a reasonable expectation of success because Ryan et al. shows an incubator system to process neural cell samples with chemical compounds before measuring image data while Jimenez-Carretero et al. and Fisch et al. both utilize cell image data in a digital platform embedded with AI modules to analyze cell image data.
Claim 3 is directed to a real-time platform or TRANS-MSC unit configured to incubate the cosmetic chemical/ingredient aliquots collected from the produced batches
Ryan et al. incubating cells exposed to compounds for 72 h at 37 Celsius and 5% CO2 which implicitly shows the use of an incubator system with temperature control and CO2 percent control (Ryan et al. page 273 right col.). Ryan et al. further shows the use of an imaging system ImageXpress Micro XLS system to capture image and a digital platform Meta Xpress 5 software for image processing to assess morphological features of cultured neurons to identify compound-induced cytotoxicity in neuron cultures (Ryan et al. page 274 left col.). It would have been obvious one of ordinary skill in the art that this incubator system can incubate a cell with any chemical compound such as those that have been found in cosmetics.
Ryan et al. does not show a digital platform with embedded artificial intelligence (Al) and machine learning (ML) modules, augmented with robotic process automation framework, wherein the artificial intelligence modules are configured to predict neurotoxicity of a drug/API along with any adventitious microbial contaminants in the test system, wherein the digital platform is trained with various human virus, mycoplasma like fungi and bacteria-induced neurovirulent and neurotoxic cellular morphology patterns configured to develop a bandwidth for detecting the anomalies in real- time assaying.
When combined with Ryan et al., Jimenez-Carretero et al. shows a digital platform embedded with an artificial intelligence module that processes neuron image data to predict drug induced neurotoxicity in a cell culture. Jimenez-Carretero et al. shows an artificial intelligence model as a convolutional neural network which classifies cells as healthy or toxicity-affected based on cell morphology (Jimenez-Carretero et al. page 7-8).
Ryan et al. in view of Jimenez-Carretero et al. does not show an artificial intelligence module that predicts adventitious microbial contaminates in a test system.
When combined with Ryan et al. in view of Jimenez-Carretero et al., Fisch et al. shows an artificial intelligence module that predicts adventitious microbial contaminates in a the neuronal cell culture. Fisch et al. shows a digital platform HRMAn 2.0 which is an ensemble of machine learning and artificial intelligence algorithms (Fisch et al. page 2 right col.). Fisch et al. shows an infection analysis module which intakes cell-morphology features and pathogen morphology features to predict values such as pathogen load, percent of infected cells, and infection levels/distribution (Fisch et al. page 3 figure 1). This implicitly shows an artificial intelligence module which can predict microbial contamination in a host cell because this analysis can predict pathogen load, percent of infected cells, and infection levels which all indicate a microbe being present in a cell culture. Fisch et al. shows HRMAn 2.0 utilizes cell morphology features and pathogen morphology features of infection analysis host-pathogen interaction and implicitly shows the models being trained on microbial pathogen cellular morphology patterns (Fisch et al. page 3 figure 1).
An invention would have been obvious to one or ordinary skill in the art if some motivation in the prior art would have led that person to modify reference teachings to arrive at the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the image analysis software for predicting cytotoxicity in neuron toxicity screening utilizing an incubation system and digital platform of Ryan et al. with the CNN model for predicting drug-induced toxicity based on image analysis of Jimenez-Carretero et al. because the CNN models provide robust, sensitive, and cost effective tools for in vitro screening of drug-induced toxicity (Jimenez-Carretero et al. abstract). It would have been further obvious to one of ordinary skill in the art before the effective filling date of the invention to have combined the system for neuron toxicity screening with an incubator system and a digital platform with an AI module for predicting drug induced toxicity embedded with a CNN to predict toxicity with the AI modules for predicting values of pathogen load, percent of infected cells, and infection levels/distribution Fisch et al. because this allows for an additional AI module that detects pathogens presence based on cell morphologies obtained from image data (Fisch et al. page 3 figure 1). One would have a reasonable expectation of success because Ryan et al. shows a incubator system to process neural cell samples with chemical compounds before measuring image data while Jimenez-Carretero et al. and Fisch et al. both utilize cell image data in a digital platform embedded with AI modules to analyze cell image data.
Claim 4 is directed to a real-time platform or TRANS-MSC unit configured to incubate the natural product/it's API aliquots collected from the produced batches
Ryan et al. incubating cells exposed to compounds for 72 h at 37 Celsius and 5% CO2 which implicitly shows the use of an incubator system with temperature control and CO2 percent control (Ryan et al. page 273 right col.). Ryan et al. further shows the use of an imaging system ImageXpress Micro XLS system to capture image and a digital platform Meta Xpress 5 software for image processing to assess morphological features of cultured neurons to identify compound-induced cytotoxicity in neuron cultures (Ryan et al. page 274 left col.). It would have been obvious one of ordinary skill in the art that this incubator system can incubate a cell with any chemical compound such as natural products.
Ryan et al. does not show a digital platform with embedded artificial intelligence (Al) and machine learning (ML) modules, augmented with robotic process automation framework, wherein the artificial intelligence modules are configured to predict neurotoxicity of a drug/API along with any adventitious microbial contaminants in the test system, wherein the digital platform is trained with various human virus, mycoplasma like fungi and bacteria-induced neurovirulent and neurotoxic cellular morphology patterns configured to develop a bandwidth for detecting the anomalies in real- time assaying.
When combined with Ryan et al., Jimenez-Carretero et al. shows a digital platform embedded with an artificial intelligence module that processes neuron image data to predict drug induced neurotoxicity in a cell culture. Jimenez-Carretero et al. shows an artificial intelligence model as a convolutional neural network which classifies cells as healthy or toxicity-affected based on cell morphology (Jimenez-Carretero et al. page 7-8).
Ryan et al. in view of Jimenez-Carretero et al. does not show an artificial intelligence module that predicts adventitious microbial contaminates in a test system.
When combined with Ryan et al. in view of Jimenez-Carretero et al., Fisch et al. shows an artificial intelligence module that predicts adventitious microbial contaminates in a the neuronal cell culture. Fisch et al. shows a digital platform HRMAn 2.0 which is an ensemble of machine learning and artificial intelligence algorithms (Fisch et al. page 2 right col.). Fisch et al. shows an infection analysis module which intakes cell-morphology features and pathogen morphology features to predict values such as pathogen load, percent of infected cells, and infection levels/distribution (Fisch et al. page 3 figure 1). This implicitly shows an artificial intelligence module which can predict microbial contamination in a host cell because this analysis can predict pathogen load, percent of infected cells, and infection levels which all indicate a microbe being present in a cell culture. Fisch et al. shows HRMAn 2.0 utilizes cell morphology features and pathogen morphology features of infection analysis host-pathogen interaction and implicitly shows the models being trained on microbial pathogen cellular morphology patterns (Fisch et al. page 3 figure 1).
An invention would have been obvious to one or ordinary skill in the art if some motivation in the prior art would have led that person to modify reference teachings to arrive at the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the image analysis software for predicting cytotoxicity in neuron toxicity screening utilizing an incubation system and digital platform of Ryan et al. with the CNN model for predicting drug-induced toxicity based on image analysis of Jimenez-Carretero et al. because the CNN models provide robust, sensitive, and cost effective tools for in vitro screening of drug-induced toxicity (Jimenez-Carretero et al. abstract). It would have been further obvious to one of ordinary skill in the art before the effective filling date of the invention to have combined the system for neuron toxicity screening with an incubator system and a digital platform with an AI module for predicting drug induced toxicity embedded with a CNN to predict toxicity with the AI modules for predicting values of pathogen load, percent of infected cells, and infection levels/distribution Fisch et al. because this allows for an additional AI module that detects pathogens presence based on cell morphologies obtained from image data (Fisch et al. page 3 figure 1). One would have a reasonable expectation of success because Ryan et al. shows an incubator system t