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 .
Status of Claims
Claims 1-18 are pending.
Claims 1-18 are rejected.
Claims 3, 5-6, 8, 10 are objected to.
Priority
Applicant’s claim for the benefit of a prior-filed application, PCT/IL2022/050638 filed 14 June 2022 and U.S. Provisional App. No. 63/202,497 filed 14 June 2021 under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Accordingly, the effective filing date of the claimed invention is 14 June 2021.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 29 Feb. 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the list of cited references was considered in full by the examiner.
Drawings
The drawings filed 14 Dec. 2023 are objected to because:
the drawings fail to comply with 37 CFR 1.84(u)(1), which states partial views intended to form one complete view, on one or several sheets, must be identified by the same number followed by a capital letter. View numbers must be preceded by the abbreviation "FIG.". Accordingly, the drawings should be labeled. FIG. 1, FIG. 2, FIG. 3, FIG. 4, FIG. 5A, and FIG. 5B.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claims 3, 5-6, 8, and 10 are objected to because of the following informalities:
Claim 3 recites “the one or more classes” in the last line. To use consistent language and increase clarity, claim 3 should be amended to recite “the one or more predefined classes”.
Claim 5 recites “provided to define belonging the sample to a given class”, which is grammatically incorrect and should recite “provided to define belonging of the sample to a given class”.
Claim 6 recites “wherein the MLM is trained to score samples for respective class…”, which is missing an article and should recite “for a respective class”.
Claim 8 recites “the rules of selection the samples between..”, which is a grammatical error and should recite “the rules of selection of samples between…”.
Claim 10 recites “; repeating operation a) for…”, “; and using the training set to obtain”, and “applying the trained machine learning model…” which should be indented similarly to the other steps in the claim (currently there is no indentation).
Claim 10 recites “generating a training set comprising, for each training sample included therein data informative…”, which is grammatically incorrect and should include a comma after “therein” to recite “generating a training set comprising, for each training sample included therein, data informative…”.
Claim 10 should include an “and” after “with bioassay data thereof” and before “at a runtime phase” to recite “with bioassay data thereof; and at a runtime phase:…”.
Appropriate correction is required.
Duplicate Claim Warning
Applicant is advised that should claim 14 be found allowable, claim 15 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m). In the instant case, both claims 14-15 are “A set of bioassays”, and the limitations “usable for classifying” and “useable for providing a score” are intended uses of the bioassays. Furthermore, a bioassay may be used for both purposes, and therefore the claims are duplicates.
Claim Interpretation - 35 USC § 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 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 is:
“A classification system for classifying… a medicinal extract in accordance with Claim 1” in claim 17 (see 112(b) below regarding “for…scoring a medicinal extract”). Accordingly, claim 17 recites “A classification system” for “obtaining results…” and “applying…”, as recited in claim 1.
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.
In cases involving a special purpose computer-implemented means-plus-function limitation, the Federal Circuit has consistently required that the structure be more than simply a general purpose computer or microprocessor and that the specification must disclose an algorithm for performing the claimed function. See, e.g., Noah Systems Inc. v. Intuit Inc., 675 F.3d 1302, 1312, 102 USPQ2d 1410, 1417 (Fed. Cir. 2012); Aristocrat, 521 F.3d at 1333, 86 USPQ2d at 1239. See MPEP 2181 II. B.
Applicant’s specification at FIG. 1 and para. [0048] discloses the hardware for the classification system comprises a graphical user interface, an I/O interface, and memory. However, Applicant’s specification does not disclose an algorithm for at least the step of “for a sample of a non-mammalian eukaryotic extract to be classified, obtaining results of a set of one or more bioassays…, thereby generating a bioassay data informative of values of attributes related to the modulating properties” clearly associated with the classification system. Applicant’s specification similarly does not disclose an algorithm for “applying to the bioassay data a trained machine learning model (MLM) to classy the sample…” associated with the classification system. Instead, Applicant’s specification discloses the classification system includes a “classification module” which performs the function already recited in claim 1, but does not disclose an algorithm for how the trained machine learning model classifies the sample.
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 Interpretation
Applicant’s specification at para. [0003] defines the term “extract” to refer to any concentrated preparation or solution of a compound or drug derived from a non-animal eukaryotic natural source, which includes one or more plants and/or fungi (mushrooms).
Applicant’s specification at para. [0045] defines the term “modulating properties” to refer to the capability of a tested sample, e.g., extract, to inhibit or activate or increase or demonstrate a change from a threshold value indicative or characteristic of a certain bioassay or a combination of such assays.
Applicant’s specification at para. [0058] defines “bioassay data” to refer to data informative of the modulation results and/or derivatives thereof and/or metadata associated therewith. Applicant’s specification discloses a sample can be characterized by values of attributes informative of modulating properties of the sample. Accordingly, bioassay data is interpreted to refer to data (including attributes) informative of modulating properties/results (the capability of a tested sample to demonstrate a change from a threshold value indicative of a characteristic) or derivatives thereof.
Claim 7 recites “wherein generating the training set comprises providing the one or more bioassays to a plurality of training samples to yield of a feature space…”. Claim 1, from which claim 7 depends, recites “applying to the bioassay data a trained machine learning model (MLM)…, wherein the MLM is trained to classify…with the help of a training set…”. Therefore, claim 1 applies an already trained model, and does not require a step of training or generating the training set. Therefore, the limitation regarding how the training set was generated of claim 7 is interpreted to be a product by process limitation defining the process in which the trained machine learning model was previously trained on the generated training set. See MPEP 2113 I. "[E]ven though product-by-process claims are limited by and defined by the process, determination of patentability is based on the product itself. The patentability of a product does not depend on its method of production. If the product in the product-by-process claim is the same as or obvious from a product of the prior art, the claim is unpatentable even though the prior product was made by a different process." In re Thorpe, 777 F.2d 695, 698, 227 USPQ 964, 966 (Fed. Cir. 1985).
Claims 8-9 further limit the generating of the training set of claim 7, and therefore are interpreted as part of the product by process limitation of claim 7.
Claim 10 recites “upon specifying one or more bioassays relevant for the given class, a) providing for a plurality of training samples a bioassay…”. The limitation of “a) providing… a bioassay” is contingent upon specifying one or more bioassays relevant for the given class. However, because claim 10 does not require specifying one or more bioassays relevant for the given class, under the broadest reasonable interpretation of the claim, step of “a) providing…” is not required by the claim. Furthermore, because the subsequent steps of generating a training set, using the training set to obtain a machine-learning model, and applying the trained machine learning model requires bioassay data of the training samples, the step of generating a training set, using the training set, and applying the trained machine learning model are similarly not required by the claims. See MPEP 2111.04 III, which states the broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met. For example, assume a method claim requires step A if a first condition happens and step B if a second condition happens. If the claimed invention may be practiced without either the first or second condition happening, then neither step A or B is required by the broadest reasonable interpretation of the claim.
Claims 11-13 further limit the step of generating the training set, which is not required under the broadest reasonable interpretation of the claim as discussed above for claim 10.
Claims 14-15 recite “A set of bioassays usable for classifying…in accordance with Claim 1” and “A set of bioassays usable for providing a score…in accordance with Claim 11” respectively. The limitation for how the bioassays are intended to be used are interpreted as an intended use of the bioassays. See MPEP 2111.02, stating if the body of a claim fully and intrinsically sets forth all of the limitations of the claimed invention, and the preamble merely states, for example, the purpose or intended use of the invention, rather than any distinct definition of any of the claimed invention’s limitations, then the preamble is not considered a limitation and is of no significance to claim construction. Shoes by Firebug LLC v. Stride Rite Children’s Grp., LLC, 962 F.3d 1362, 2020 USPQ2d 10701 (Fed. Cir. 2020).
Claim Rejections - 35 USC § 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.
Claim 17 is rejected under 35 U.S.C. 112(a) 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 at the time the application was filed, had possession of the claimed invention.
Claim 17 recites “A classification system for…”, which invokes 35 U.S.C. 112(f) as interpreted above. For a computer-implemented 35 U.S.C. 112(f) claim limitation, the specification must disclose an algorithm for performing the claimed specific computer function, or else the claim is indefinite under 35 U.S.C. 112(b). See Net MoneyIN, Inc. v. Verisign. Inc., 545 F.3d 1359, 1367, 88 USPQ2d 1751, 1757 (Fed. Cir. 2008). When a claim containing a computer-implemented 35 U.S.C. 112(f) claim limitation is found to be indefinite under 35 U.S.C. 112(b) for failure to disclose sufficient corresponding structure (e.g., the computer and the algorithm) in the specification that performs the entire claimed function, it will also lack written description under 35 U.S.C. 112(a). See MPEP 2181 II. As discussed above under 35 U.S.C. 112(f), Applicant’s specification does not disclose an algorithm for the classification system. Therefore, the limitation lacks written description.
For the reasons discussed above, the specification does not provide a sufficient disclosure of the limitation of “A classification system for…” recited in claim 17 to demonstrate to one of ordinary skill in the art that the inventor possessed the invention at the time the application was filed. For more information regarding the written description requirement, see MPEP §2161.01- §2163.07(b).
Claim Rejections - 35 USC § 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.
Claims 1-18 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 1, and claims dependent therefrom, are indefinite for recitation of “applying to the bioassay data…each characterized by… a class associated with the sample” in the last line of the second limitation. Claim 1 previously recites “for a sample of a non-mammalian eukaryotic extract” and “a training set comprising a plurality of training samples”. Accordingly, it is not clear if “the sample” in the last line of the second limitation is referring to the sample of a non-mammalian eukaryotic extract or if “the sample” is referring to a training sample. Clarification is requested via claim amendment. For purpose of examination, the limitation is interpreted to mean “a class associated with the respective training sample”.
Claim 2 is indefinite for recitation of “the class of the respective sample”. It is unclear which class of which sample claim 2 is referring to. Claim 1, from which claim 2 depends, recites “applying to the bioassay data a…(MLM) to classify the sample into a class” and also “a training set comprising a plurality of training samples, each characterized by….a class associated with [the respective training] sample”. As a result, it is not clear if “the class of the respective sample” is referring to the class of the sample of a non-mammalian eukaryotic extract to be classified or referring to the class of one of the respective training samples. Clarification is requested. For purpose of examination, claim 2 is interpreted to mean classifying the extract in accordance with the class of the sample of the extract.
Claim 4 is indefinite for recitation of “screening the extracts”. There is insufficient antecedent basis for “the extracts” in the claim because claim 1, from which claim 4 depends, only recites “a non-mammalian eukaryotic extract”, but does not recite multiple extracts. The claim is interpreted to mean “screening extracts to reveal…”.
Claim 6 is indefinite for recitation of “wherein the MLM is trained”. Claim 1, from which claim 6 depends recites “applying to the bioassay data a trained machine learning model (MLM)” and claim 6 also previously recites “applying to the bioassay data a trained machine learning model (MLM)”. As a result, it is not clear if “the MLM” in claim 6 is intended to refer to the trained MLM of claim 1, the trained MLM of claim 6, or if these are intended to all be the same MLM. Clarification is requested via claim amendment. For purpose of examination, “the MLM” is interpreted to refer to the MLM previously recited in claim 6. To overcome the rejection, claim 6 can be amended to recite “…applying to the bioassay data a second trained machine learning model (MLM)…, wherein the second MLM is trained…”, or another amendment that similarly distinguishes the MLM of claim 6 from the MLM of claim 1.
Claim 7, and claims dependent therefrom, are indefinite for recitation of “among the plurality of training samples”. Claim 1, from which claim 7 depends, recites “a training set comprising a plurality of training samples” and claim 7 also recites “a plurality of training samples”. Therefore, it is not clear if “the plurality of training samples” is referring to the training samples of claim 1 or claim 7, or if these are all intended to be the same plurality of training samples. Clarification is requested via claim amendment. Claim 8 also recites “the training samples
Given claim 7 later selects, among the plurality of training samples, training samples to be included in the training set, for purpose of examination, claim 7 is interpreted to mean that the one or more bioassays are provided to a plurality of candidate training samples, and training samples to be included in the training set are selected among the plurality of candidate training samples.
Claim 7, and claims dependent therefrom, are indefinite for recitation of “…providing the one or more bioassays to a plurality of training samples to yield a feature space representing the obtained bioassay data”. Claim 1, from which claim 7 depends, recites “for a sample…to be classified, obtaining results of a set of one or more bioassays” and later recites “a training set comprising a plurality of training samples, each characterized by a respective bioassay data..”. Therefore it is not if the obtained bioassay data is referring to the obtained results of the one or more bioassays of claim 1, the respective bioassay data for each training sample of claim 1, or if “the obtained bioassay data” is intended to be a separate set of bioassay data. For purpose of examination, claim 7 is interpreted to mean “representing obtained bioassay data for the plurality of candidate training samples”, such that the bioassay data is a set of bioassay data for the candidate training samples.
Claim 8 is indefinite for recitation of “the minimal and/or the maximal numbers of selected training samples in the training set”, “the rules of selection the samples between the clusters” and “the rules of selection within the clusters”. There is insufficient antecedent basis for this limitations in the claim because claim 7, from which claim 8 depends, does not recite minimal and/or maximum numbers of selecting training samples, rules of selection between the clusters, or rules of selection within the clusters.
Claim 9 is indefinite for recitation of “the relevance of bioassays and/or attributes…”. There is insufficient antecedent basis for this limitation in the claim because claim 7 does not recite a relevance of bioassays and/or attributes, and therefore it is not clear what relevance is being referenced. For purpose of examination, claim 9 is interpreted to mean “ranking a relevance of…”.
Claim 9 is indefinite for recitation of “wherein the predefined rules consider ranking the relevance…”.The phrase "consider" renders the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. See MPEP § 2173.05(d). For example, it is not clear if the predefined rules are actually required to comprise ranking, or if predefined rules may only consider ranking, but ranking does not have to be used as a predefined rule. Clarification is requested via claim amendment. Claim 9 is interpreted to mean, applying the predefined rules comprises ranking a relevance as claimed.
Claim 9 is indefinite for recitation of “the respective classes”. It is not clear what classes “the respective classes are referring to. For example, claim 1 from which claim 9 ultimately depends recites “trained to classify samples into the one or more predefined classes”. However, claim 1 does not require multiple classes. Furthermore, claim 7 from which claim 9 depends recites training samples (interpreted to be candidate training samples above), but does not recite any particular classes for the training samples. As a result, it is not clear what classes are being referenced. Clarification is requested via claim amendment.
Claim 10, and claims dependent therefrom, are indefinite for recitation of “A computer-based method of providing a medical extract……the method comprising: …a) providing for a plurality of training samples a bioassay…to obtain bioassay results…; providing the one or more bioassays for a new sample, thereby generating bioassay data…”. The metes and bounds of what is intended to be encompassed by providing a sample a bioassay to obtain bioassay results is not clear, particularly given the method is claimed as being “computer-based”. Given claim 10 recites the method is “computer-based”, this suggest the ”providing” of bioassays for a sample may only require assigning particular a particular bioassay to a sample and/or obtaining already generated bioassay results from provided bioassays (given this is within the scope of what a computer can perform). Alternatively, if Applicant intends to require the claim requires a step of carrying out a bioassay on a sample, it is unclear in what way this is intended to be “computer-based” and/or in what way “computer-based” is intending to limit the steps of the claim, given a generic computer cannot perform an assay and Applicant’s specification does not appear to provide support for automated machines/robots for carrying out assays. For purpose of examination, given claim 10 recites the method is “computer-based”, the limitation is interpreted to mean either (1) a computer obtains bioassay results of a bioassay applied to a sample (training samples or the new sample in each limitation respectively), consistent with what a computer can perform and Applicant’s specification, or (2) that the computer does not perform the step and the step requires performing a bioassay on a sample. If Applicant intends to require performing the assays, then the preamble should be amended to remove “computer-based” or to specify only certain limitations are performed by a computer.
Claim 10, and claims dependent therefrom, are indefinite for recitation of “generating a training set comprising, for each training sample included therein, data informative…”. There is insufficient antecedent basis for any training samples included in the training set. As a result, it is not clear if “each training sample included herein” is referring to each training sample in the plurality of training samples recited in the first limitation of claim 10, if each training sample in the training set may include only some training samples in the plurality of training samples, or if each training sample in the training set is referring to completely different training samples. Clarification is requested via claim amendment. For purpose of examination, claim 10 is interpreted to mean “generating a training set comprising, for training samples from the plurality of training samples included therein,…”, such that at least two training samples from the plurality of training samples are included in the training set.
Claim 10, and claims dependent therefrom, are indefinite for recitation of “applying the trained machine learning model to its bioassay data…”. Claim 10 previously recites “bioassay data for the training samples”, “for each training sample included therein data informative of its bioassay data”, and “providing the one or more bioassays for a new sample, thereby generating bioassay data…”. Therefore, it is not clear which bioassay data “its bioassay data” is referring to. For purpose of examination, claim 10 is interpreted to mean “applying the trained machine learning model to the bioassay data of the new sample to provide…”.
Claim 12 is indefinite for recitation of “the minimal and/or the maximal numbers of selected training samples in the training set”, “the rules of selection the samples between the clusters” and “the rules of selection within the clusters”. There is insufficient antecedent basis for this limitations in the claim because claims 10-11, from which claim 12 depends, do not recite minimal and/or maximum numbers of selecting training samples, rules of selection between the clusters, or rules of selection within the clusters.
Claim 13 is indefinite for recitation of “the relevance of bioassays and/or attributes…”. There is insufficient antecedent basis for this limitation in the claim because claim 11 does not recite a relevance of bioassays and/or attributes, and therefore it is not clear what relevance is being referenced. For purpose of examination, claim 13 is interpreted to mean “ranking a relevance of…”.
Claim 13 is indefinite for recitation of “wherein the predefined rules consider ranking the relevance…”.The phrase "consider" renders the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. See MPEP § 2173.05(d). For example, it is not clear if the predefined rules are actually required to comprise ranking, or if predefined rules may only consider ranking, but ranking does not have to be used as a predefined rule. Clarification is requested via claim amendment. Claim 13 is interpreted to mean, applying the predefined rules comprises ranking a relevance as claimed.
Claim 13 is indefinite for recitation of “the respective classes”. It is not clear what classes “the respective classes are referring to. For example, claim 10 from which claim 13 ultimately depends recites “a given class”. However, claim 10 does not require multiple classes. Furthermore, claim 11 from which claim 13 depends recites training samples, but does not recite any particular classes for the training samples. As a result, it is not clear what classes are being referenced. Clarification is requested via claim amendment.
Claim 16 is indefinite for recitation of “One or more computing devices…the one or more computing devices configured, via computer-executable instructions, to perform operations for operating…in accordance with Claim 1”. It is unclear if “to perform operations for operating…in accordance with Claim 1” only requires the computer is configured to perform operations with the intended use of operating in accordance with claim 1 (but the performed operations do not have to be operations of claim 1), or if the limitation intends to require the computing devices are configured to carry out the method of claim 1. Clarification is requested. For purpose of examination, claim 16 is interpreted to mean “the one or more computing devices configured, via computer-executable instructions, to perform the method of claim 1”.
Claim 17 is indefinite for recitation of “A classification system for classifying and/or scoring a medicinal extract in accordance with claim 1”. The metes and bounds of the claim are not clear because claim 1 does not recite a method of scoring a medicinal extract. Therefore, it is unclear what is required by the system by “A classification for…scoring…in accordance with claim 1”. For purpose of examination, claim 17 is interpreted to mean “A classification system for classifying a medicinal extract in accordance with Claim 1”.
Claim 17 is indefinite for recitation of ““A classification system for classifying…a medicinal extract in accordance with claim 1”, which 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 for the reasons discussed above under the 35 U.S.C. 112(f) claim interpretation. 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.
For purpose of applying prior art, the hardware for the classification system is interpreted to be a generic computer, and the computer is configured to perform the steps recited in claim 1.
Claim Rejections - 35 USC § 112(d)
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.
Claims 14-15 are rejected under 35 U.S.C. 112(d) 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 14 recites “A set of bioassays usable for classifying…in accordance with Claim 1”. Therefore, claim 14 recites a product that depends on claim 1, but does not require the limitations of claim 1 given “usable for…” is merely an intended use of the set of bioassays. See MPEP 608.1(n) III, which states, when examining a dependent claim, the examiner should determine whether the claim complies with 35 U.S.C. 112(d), which requires that dependent claims contain a reference to a previous claim in the same application, specify a further limitation of the subject matter claimed, and include all the limitations of the previous claim. Therefore, claim 14 is not a proper dependent claim of claim 1 because it does not include all the limitations of claim 1.
Claim 15 recites “A set of bioassays usable for providing a score…in accordance with Claim 11”, which recites a product that depends from claim 1, but does not require the limitations of claim 11 given “usable for…” is merely an intended use of the set of bioassays. Therefore, claim 15 is not a proper dependent claim of claim 11 because it does not include all the limitations of claim 11.
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.
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 1-13 and 16-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
The Supreme Court has established a two-step framework for this analysis, wherein a claim does not satisfy § 101 if (1) it is “directed to” a patent-ineligible concept, i.e., a law of nature, natural phenomenon, or abstract idea, and (2), if so, the particular elements of the claim, considered “both individually and as an ordered combination,” do not add enough to “transform the nature of the claim into a patent-eligible application.” Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016) (quoting Alice, 134 S. Ct. at 2355). Applicant is also directed to MPEP 2106.
Step 1: The instantly claimed invention (claims 1, 10, and 16-18 being representative) is directed to a method and system. Therefore, the instantly claimed invention falls into one of the four statutory categories. [Step 1: YES]
Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in in Prong Two if the recited judicial exception is integrated into a practical application of that exception.
Step 2A, Prong 1: Under the MPEP § 2106.04, the Step 2A (Prong 1) analysis requires determining whether a claim recites an abstract idea, law of nature, or natural phenomenon.
Claims 1 and 16-18 recite the following steps which fall under the mathematical concepts and/or mental processes groupings of abstract ideas:
for a sample of a non-mammalian eukaryotic extract to be classified, obtaining results of a set of one or more bioassays informative of modulating properties of the sample, thereby generating a bioassay data informative of values of attributes related to the modulating properties (mental process); and
applying to the bioassay data a trained machine learning (MLM) to classify the sample into a class of one or more predefined classes, wherein the MLM is trained to classify samples into the one or more predefined classes in accordance with bioassay data thereof, the training provided with the help of a training set comprising a plurality of training samples, each characterized by a respective bioassay data and a class associated with the sample (mental process and mathematical concept).
Claim 10 recites the following steps which fall under the mathematical concepts and/or mental processes groupings of abstract ideas:
at a setup phase: upon specifying one or more bioassays relevant for the given class (mental process);
generating a training set comprising, for each training sample included therein data informative of its bioassay data and a respective sample's score (mental process);
using the training set to obtain a machine-learning model trained to score samples (mathematical concept); and
at a runtime phase…applying the trained machine learning model to its bioassay data to provide a score of the new sample with regard to the given class (mental process and mathematical concept);
The identified claim limitations falls into one of the groups of abstract ideas of mathematical concepts and/or mental processes for the following reasons. Regarding claims 1 and 16-18, obtaining results of bioassays to generate bioassay data informative of values of attributes related to the modulating properties can be practically performed in the mind by collecting and analyzing information from bioassays to organize the information into a bioassay data set with values of attributes. It is noted this step is claimed as being performed by a computer in claims 16-18, and therefore encompasses simply analyzing assay information, and not carrying out a bioassay. Applying a trained MLM to the bioassay data to make a classification can be practically performed in the mind by inputting values into an already trained linear regression model, and performing weighted addition to calculate a probability used to classify a sample. This limitation further recites a mathematical concept because it amounts to a textual equivalent to performing mathematical calculations (e.g. weighted addition). See MPEP 2106.04(a)(2) I. C., which states, for example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation. Other than reciting the limitations are carried out by a processor in claims 16-18, nothing in the claims precludes the identified limitations from being practically performed in the mind.
Regarding claim 10, specifying bioassays relevant for a class can be practically performed in the mind by determining a bioassay to be performed that provides information that can be used to make a classification. Generating a training set as claimed can be practically performed in the mind by determining a subset of training samples to include in the training set and organizing information relating to bioassay data and scores for each training sample to form the training set. Using the training set to obtain a machine-learning model trained to score samples recites a mathematical concept because, in light of Applicant’s specification, the limitation amounts to a textual equivalent to performing mathematical calculations. For example, training the machine learning model to score samples encompasses applying a linear regression algorithm to iteratively calculate scores using bioassay data (similar to the discussion of applying the trained machine learning model above), calculate a difference between a known score and the predicted score, and adjust model parameters. Last, applying the trained machine learning model to bioassay data to provide a score can be practically performed in the mind and also recites a mathematical concept because the limitation encompasses inputting numerical values into a linear regression model, and performing weighted addition to calculate a score.
Dependent claims 2-9 and 11-13 further recite an abstract idea and/or further limit the abstract idea identified above. Dependent claim 2 further recites the mental process of classifying the extract based on the class of the sample. Dependent claim 3 further recites the mental process for standardization of medical extracts in accordance with the one or more classes (e.g. determining quantities of medical extracts across the classes with a same amount of active ingredient). Dependent claim 4 further recites the mental process of screening the extracts to reveal extracts belonging to a given class, which can be performed by applying a model as in claim 1 to classify multiple extracts. Dependent claim 5 further recites the mental process of predefining a set of bioassays and respective attributes to the given class. Dependent claim 6 further recites the mental process and mathematical concept of applying the bioassay data to a trained MLM to obtain a score indictive of efficacy of the sample for the class it has been classified, and then further limits the trained MLM which is part of the abstract idea. Dependent claim 7 further limits the abstract idea of applying the trained machine learning model by specifying how the model was previously trained (see claim interpretation), and therefore is part of the abstract idea of claim 1 above. Dependent claims 8 and 12 further recite the mental process and mathematical concept of clustering the samples in the feature space, and then further limits the mental process of applying the predefined rules to be based on certain rules. Dependent claims 9 and 13 further limits the application of the predefined rules to comprise the mental process of ranking a relevance of bioassays or attributes .Dependent claim 11 further limits the generation of the training set to comprise generating a feature space representing bioassay data, and applying rules to the feature space to select training samples. Therefore, claims 1-13 and 16-18 recite an abstract idea. [Step 2A, Prong 1: YES]
Step 2A: Prong 2: Under the MPEP § 2106.04, the Step 2A, Prong 2 analysis requires identifying whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluating those additional elements to determine whether they integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application for the following reasons.
Claims 2, 4-9, and 11-13 do not recite any elements in addition to the judicial exception and therefore are part of the judicial exception.
The additional element of claim 3 includes:
using the classified extract for at least one of: manufacturing a medical product with treatment properties corresponding to the respective class.
The additional element of claim 3 is not sufficient to provide integration because the additional element is not required under the broadest reasonable interpretation of the claims. Claim 3 requires “one of” either manufacturing or standardization of medical extracts, the latter of which is a mental process as discussed above. Therefore, the manufacturing is not required by the claim. Regardless, even if claim 3 did require the manufacturing step, the limitation would amount to mere instructions to apply the exception because the claim only recites the idea of a solution, but fails to recite details of how a solution to a problem is accomplished. See MPEP 2106.05(f). In the instant case, claim 3 recites manufacturing a medical product “with treatment properties corresponding to the respective class”, but provides not details regarding what the treatment is, what the treatment properties are, or how they are determined based on some class.
The additional elements of claim 10 include:
a) providing for a plurality of training samples a bioassay among the one or more bioassays to obtain bioassay results informative of modulating properties of the respective samples; repeating operation a) for all the one or more bioassays thereby obtaining bioassay data for the training samples from the plurality of training samples; and
providing the one or more bioassays for a new sample, thereby generating bioassay data informative of modulating properties thereof; and
The above additional elements of providing bioassays to training samples to obtain bioassay results informative of modulating properties and providing the one or more bioassays for a new sample to generate bioassay data for the new sample, as recited in claim 10, only serves to collect data for use by the abstract idea to make a classification or provide a score, respectively. Therefore the additional elements amount to insignificant extra-solution activity that does not integrate the recited judicial exception into a practical application. See MPEP 2106.05(g). Furthermore, these additional element of providing a bioassay for the plurality of training samples is not required under the broadest reasonable interpretation of the claims (see claim interpretation), and therefore this additional element cannot provide integration.
The additional elements of claims 1, 10, and 16-18 include:
a computer (claims 1 and 10);
one or more computing devices comprising processors and memory (claim 16);
a classification system (interpreted as a computer in the 112(b) above) (claim 17); and
a non-transitory computer-readable medium (claim 18).
The additional elements of a computing device with processors and memory, a computer, and a memory are generic computer components that are merely used as a tool to carry out the abstract idea identified above. The courts have found the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
Therefore, the additionally recited elements amount to insignificant extra-solution activity and, as such, the claims as a whole do not integrate the abstract idea into practical application. Thus, claims 1-13 and 16-18 are directed to an abstract idea. [Step 2A, Prong 2: NO]
Step 2B: In the second step it is determined whether the claimed subject matter includes additional elements that amount to significantly more than the judicial exception. See MPEP § 2106.05.
The claims do not include any additional steps appended to the judicial exception that are sufficient to amount to significantly more than the judicial exception.
Claims 2, 4-9, and 11-13 do not recite any elements in addition to the judicial exception and therefore are part of the judicial exception.
The additional element of claim 3 includes:
using the classified extract for at least one of: manufacturing a medical product with treatment properties corresponding to the respective class.
The additional element of claim 3 is not sufficient to provide significantly more because the additional element is not required under the broadest reasonable interpretation of the claims as discussed above under Step 2A, Prong 2.
The additional elements of claim 10 include:
a) providing for a plurality of training samples a bioassay among the one or more bioassays to obtain bioassay results informative of modulating properties of the respective samples; repeating operation a) for all the one or more bioassays thereby obtaining bioassay data for the training samples from the plurality of training samples; and
providing the one or more bioassays for a new sample, thereby generating bioassay data informative of modulating properties thereof; and
The above additional elements of providing bioassays to training samples to obtain bioassay results informative of modulating properties and providing the one or more bioassays for a new sample to generate bioassay data for the new sample, as recited in claims 7 and 10, are well-understood, routine, and conventional. Applicant’s specification at para. [0003] discloses extracts may be prepared by any suitable process known in the art. Applicant’s specification at para. [0005]-[0008] further discloses a plurality of publications which perform bioassays on extracts to determine their quality and efficacy (i.e. bioassay results informative of modulating properties), and discloses standardization of extracts for the treatment of diseases have been recognized in the conventional art, and various techniques have been developed to provide solutions, including evaluating efficacy and/or potency of the product. Applicant’s specification at para. [0087]-[0089] further discloses the bioassay may be any of the bioassays known in the art, and then discloses various bioassays that may be used without providing details how they are carried out, demonstrating the conventionality of such bioassays. Furthermore, the additional element of providing a bioassay for the plurality of training samples is not required under the broadest reasonable interpretation of the claims (see claim interpretation), and therefore cannot provide significantly more.
The additional elements of claims 1, 10, and 16-18 include:
a computer (claims 1 and 10);
one or more computing devices comprising processors and memory (claim 16);
a classification system (interpreted as a computer in the 112(b) above) (claim 17); and
a non-transitory computer-readable medium (claim 18).
The additional elements of a computing device with processors and memory, a computer, and a memory are conventional computer components that are merely used as a tool to carry out the abstract idea identified above. The courts have found the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Applicant’s specification at para. [0006] and [0008] also provide examples for assaying extracts in combination with computer software, demonstrating the conventionality of assays and computers.
Therefore, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself. [Step 2B: NO]
Therefore, the instantly rejected claims are not drawn to eligible subject matter as they are directed to an abstract idea (and/or natural correlation) without significantly more. For additional guidance, applicant is directed generally to the MPEP § 2106.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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 1-2, 4-5, and 7-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yoo (2020).
Cited reference: Yoo et al., A Deep Learning-Based Approach for Identifying the Medicinal Uses of Plant-Derived Natural Compounds, 30 Nov. 2020, Frontiers in Pharmacology, 11, pg. 1-15; cited in IDS filed 29 Feb. 2024.
Regarding claim 1, Yoo discloses a method for identifying medicinal uses of plant-derived natural compounds, including plant extracts (i.e. a medical extract from a non-animal eukaryotic source), using a deep learning model (i.e. computer-based classifying) (Abstract), where the method comprises the following:
Yoo discloses obtaining chemical property features comprising pharmacokinetic (PK) properties of a natural product, including intestinal absorption, blood-brain barrier permeability skin permeability, and drug-likeness determined from bioavailability (i.e. results of a bioassay informative of modulating properties of the sample, given pharmacokinetic data is determined by measuring drug concentration over time and drug concentration modules drug efficacy/toxicity) (pg. 4, col. 1, para. 2 to col. 2, para. 1), thereby generating bioassay data (e.g. the PK data) informative of values of attributes related to the modulating properties.
Yoo discloses applying a trained deep learning model (i.e. a trained MLM) to the chemical property features (i.e. bioassay data) to classify the natural product has having a medicinal effect from a set of 15 predefined medicinal effect classes (Figures 2-3; Table 1, e.g. classification using all features or chemical property features only; pg. 2, col. 2, para. 1, pg. 6, col. 2, para. 2, and Table 2, e.g. class labels are medicinal effect for certain disease terms). Yoo discloses the deep learning model was trained based on a training set comprises a plurality of training drugs, each characterized by chemical property features and associated with a particular effect (i.e. class label) (Figure 2, e.g. training drugs; pg. 6, col. 1, para. 3 to col. 2, para. 1, e.g. output layer needs data indicating effects of training drugs; pg. 8, para. 2, col. 2).
Regarding claim 2, Yoo discloses classifying the natural product based on the class label output from the deep learning model (Table 5).
Regarding claim 4, Yoo discloses screening multiple natural products to reveal multiple natural products in a given class (Figure 2 and Table 5).
Regarding claim 5, Yoo discloses the classifying assigns the natural product to a given class Figures 2-3 and Table 2). Yoo further discloses predefining a set of features derived from bioassays related to the given class (Figure 1, e.g. chemical property features; Figure 2; pg. 4, col. 1, para. 2 to col 2, para. 3, e.g. selected features used to predict class).
Regarding claims 7-9, as discussed above in claim interpretation, the limitations serve to define the process in which the machine learning model was previously trained by generating the training set. However, a step of generating the training set is not required by the claims. MPEP 2113 I. states "[E]ven though product-by-process claims are limited by and defined by the process, determination of patentability is based on the product itself. The patentability of a product does not depend on its method of production. If the product in the product-by-process claim is the same as or obvious from a product of the prior art, the claim is unpatentable even though the prior product was made by a different process." In re Thorpe, 777 F.2d 695, 698, 227 USPQ 964, 966 (Fed. Cir. 1985). In the instant case, Yoo discloses the trained deep learning model classifies a natural product has having a medicinal effect from a set of predefined classes based on bioassay data, and further discloses the deep learning model was trained based on a training set comprises a plurality of training drugs, each characterized by chemical property features and associated with a particular effect (i.e. class label) (Figure 2, e.g. training drugs; pg. 6, col. 1, para. 3 to col. 2, para. 1, e.g. output layer needs data indicating effects of training drugs; pg. 8, para. 2, col. 2). Therefore, the trained machine learning model of Yoo is the same as that of the instant claims, even if the set of training samples of Yoo was selected by a different process than the process recited in claims 7-9. Therefore, Yoo discloses claims 7-9.
Regarding claim 10, Yoo discloses a method for identifying medicinal uses of plant-derived natural compounds, including plant extracts (i.e. a medical extract from a non-animal eukaryotic source), using a deep learning model (i.e. computer-based) (Abstract), where the method comprises the following:
Yoo discloses obtaining chemical property features comprising pharmacokinetic (PK) properties of a test natural product (i.e. a new sample), including intestinal absorption, blood-brain barrier permeability skin permeability, and drug-likeness determined from bioavailability (i.e. results of a bioassay informative of modulating properties of the sample, given pharmacokinetic data is determined by measuring drug concentration over time and drug concentration modules drug efficacy/toxicity) (pg. 4, col. 1, para. 2 to col. 2, para. 1), thereby obtaining bioassay data (e.g. the PK data) for a new sample informative of values of attributes related to the modulating properties (see 112(b) interpretation above). Alternatively, Yoo discloses performing in vitro assessments on natural compounds for the assessment of biological activities of natural compounds (pg. 2, col. 1, para. 2).
Regarding the step of “providing for a plurality of training samples a bioassay….; repeating operation a) for all the one or more bioassays…thereby obtaining bioassay data for the training samples”, the limitation is not required under the broadest reasonable interpretation of the claims because the step is contingent “upon specifying one or more bioassays relevant for the given class”, which is not required by the claim. As a result, the subsequent step of “generating a training set comprising, for each training sample…data informative of its bioassay data…; using the training set to obtain a machine-learning model trained…; and applying the trained machine learning model…” are not required under the broadest reasonable interpretation of the claim because the condition precedent of having bioassay data for training samples is not met.
Regarding claims 11-13¸ as discussed above for claim 10, under the broadest reasonable interpretation of the claims, the step of generating a training set comprising the bioassay data of training samples is not required under the broadest reasonable interpretation of the claim because claim 10 does not require the condition precedent of obtaining bioassay data of the plurality of training samples. Claims 11-13 only serve to further limit the generation of the training set, which is not required by the claim. Therefore, Yoo discloses the methods of claims 11-13 for the reasons discussed above with respect to claim 10.
Regarding claims 14-15, Yoo discloses in vitro screening tests (i.e. bioassays) were performed for the assessment of biological activities of natural compounds, including plant extracts (pg. 2, col. 1, para. 1; Abstract). Biological activities of natural compounds are usable for making classifications of the natural product
Therefore Yoo anticipates the claimed invention.
Claims 14-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Idowu (2020).
Cited reference: Idowu et al., Artificial Intelligence (AI) to the Rescue: Deploying Machine Learning to Bridge the Biorelevance Gap in Antioxidant Assays, 2021, SLAS Technology, 26(1), pg. 16-25 (Published online Oct. 2020).
Regarding claims 14-15, Idowu discloses a lipholicicity assay and a planar artificial membrane permeability assay usable for making classifications of a medicinal effect of natural compounds including extracts from plant sources (pg. 21, col. 1, para. 2-4; Abstract; pg. 16, col. 1, para. 2 to col. 2, para. 1).
Therefore, Idowu anticipates the claimed invention.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Yoo (2020) in view of Idowu (2020).
Cited references:
Yoo et al., A Deep Learning-Based Approach for Identifying the Medicinal Uses of Plant-Derived Natural Compounds, 30 Nov. 2020, Frontiers in Pharmacology, 11, pg. 1-15; cited in IDS filed 29 Feb. 2024; and
Idowu et al., Artificial Intelligence (AI) to the Rescue: Deploying Machine Learning to Bridge the Biorelevance Gap in Antioxidant Assays, 2021, SLAS Technology, 26(1), pg. 16-25 (Published online Oct. 2020).
Regarding claim 6, Yoo discloses a method for identifying medicinal uses of plant-derived natural compounds, including plant extracts (i.e. a medical extract from a non-animal eukaryotic source), using a deep learning model (i.e. computer-based classifying) (Abstract), where the method comprises the following:
Yoo discloses obtaining chemical property features comprising pharmacokinetic (PK) properties of a natural product, including intestinal absorption, blood-brain barrier permeability skin permeability, and drug-likeness determined from bioavailability (i.e. results of a bioassay informative of modulating properties of the sample, given pharmacokinetic data is determined by measuring drug concentration over time and drug concentration modules drug efficacy/toxicity) (pg. 4, col. 1, para. 2 to col. 2, para. 1), thereby generating bioassay data (e.g. the PK data) informative of values of attributes related to the modulating properties.
Yoo discloses applying a trained deep learning model (i.e. a trained MLM) to the chemical property features (i.e. bioassay data) to classify the natural product has having a medicinal effect from a set of 15 predefined medicinal effect classes (Figures 2-3; Table 1, e.g. classification using all features or chemical property features only; pg. 2, col. 2, para. 1, pg. 6, col. 2, para. 2, and Table 2, e.g. class labels are medicinal effect for certain disease terms). Yoo discloses the deep learning model was trained based on a training set comprises a plurality of training drugs, each characterized by chemical property features and associated with a particular effect (i.e. class label) (Figure 2, e.g. training drugs; pg. 6, col. 1, para. 3 to col. 2, para. 1, e.g. output layer needs data indicating effects of training drugs; pg. 8, para. 2, col. 2).
Further regarding claim 6, Yoo does not disclose applying to the bioassay data a trained machine learning model (MLM) to obtain a score indicative of efficacy of the sample for the class it has been classified, wherein the MLM is trained to score samples for respective class in accordance with bioassay data thereof, the training provided with the help of a training set comprising a plurality of training samples, each characterized by a respective bioassay data and a score for the respective class.
However, Idowu discloses a method for using artificial intelligence to predict an antioxidant effect of phytochemical antioxidants found in seeds, fruits, and vegetables, including polyphenols (Abstract; pg. 16, col. 1, para. 2 to col. 2, para. 2), which includes applying a trained machine learning model (MLM) to predict an antioxidant capacity (i.e. a score indicative of efficacy) (Figure 2; pg. 19, col. 2, para. 2-4; pg. 23, col. 1, para. 2 to col. 2, para. 2). Idowu discloses the MLM is trained to predict an antioxidant capacity using input and output variables from a dataset from a training set of phytochemical antioxidants (pg. 23, col. 1, para. 2-3), wherein each training sample is characterized by molecular descriptors determined experimentally, including drug permeability, clearance, and kinetics of hydrogen atom transfer (HAT) as well as an output variable (pg. 19, col. 2, para. 2-3; pg. 19, col. 2, para. 4 to pg. 21, col. 1, para. 4). Idowu further discloses that no current antioxidant assay captures the multiple dimensions required to determine antioxidant effect, as they are unidimensional whereas biorelevance requires integration of several inputs, and therefore, the use of machine learning to relate the polyphenol’s antioxidant action to various molecular descripts addresses this deficiency (Abstract). Idowu further discloses the method represents a robust predictive tool in assessing biorelevant antioxidant capacity of polyphenols, thus facilitating the identification or design of antioxidant molecules (Abstract).
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of Yoo to have further applied a trained machine learning model to the bioassay data to predict a score indicative of efficacy of the sample, as shown by Idowu above. One of ordinary skill in the art would have been motivated to combine the methods of Yoo with Idowu in order to provide robust predictions of biorelevant antioxidant capacity of polyphenols that account for multiple input dimensions, thus facilitating the identification or design of antioxidant molecules (Abstract), given Yoo also discloses plant-derived natural compounds include antioxidants (Abstract). There would have been a reasonable expectation of success given the bioassay data of Yoo includes pharmacokinetic data including permeability information and chemical properties, and Idowu uses PK information including permeability information and chemical descriptors to make the score predictions, and furthermore both Yoo and Idowu analyze the medicinal capability of natural products.
Therefore, the invention is prima facie obvious.
Claims 3 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Yoo (2020).
Cited reference: Yoo et al., A Deep Learning-Based Approach for Identifying the Medicinal Uses of Plant-Derived Natural Compounds, 30 Nov. 2020, Frontiers in Pharmacology, 11, pg. 1-15; cited in IDS filed 29 Feb. 2024.
Regarding claims 3 and 16-18, Yoo discloses a method for identifying medicinal uses of plant-derived natural compounds, including plant extracts (i.e. a medical extract from a non-animal eukaryotic source), using a deep learning model (i.e. computer-based classifying) (Abstract), where the method comprises the following:
Yoo discloses obtaining chemical property features comprising pharmacokinetic (PK) properties of a natural product, including intestinal absorption, blood-brain barrier permeability skin permeability, and drug-likeness determined from bioavailability (i.e. results of a bioassay informative of modulating properties of the sample, given pharmacokinetic data is determined by measuring drug concentration over time and drug concentration modules drug efficacy/toxicity) (pg. 4, col. 1, para. 2 to col. 2, para. 1), thereby generating bioassay data (e.g. the PK data) informative of values of attributes related to the modulating properties.
Yoo discloses applying a trained deep learning model (i.e. a trained MLM) to the chemical property features (i.e. bioassay data) to classify the natural product has having a medicinal effect from a set of 15 predefined medicinal effect classes (Figures 2-3; Table 1, e.g. classification using all features or chemical property features only; pg. 2, col. 2, para. 1, pg. 6, col. 2, para. 2, and Table 2, e.g. class labels are medicinal effect for certain disease terms). Yoo discloses the deep learning model was trained based on a training set comprises a plurality of training drugs, each characterized by chemical property features and associated with a particular effect (i.e. class label) (Figure 2, e.g. training drugs; pg. 6, col. 1, para. 3 to col. 2, para. 1, e.g. output layer needs data indicating effects of training drugs; pg. 8, para. 2, col. 2).
Yoo does not disclose the following limitations:
Further regarding claim 3, Yoo does not explicitly disclose using the classified extract for manufacturing a medical product with treatment properties corresponding to the respective class.
However, Yoo does disclose medicinal plants and extracts have been used as important sources for drug discovery (Abstract), and that a large number of medicinal plants possess diverse natural compounds, contributing to drug development by providing novel candidate therapeutic agents against various diseases (pg. 1, para. 1). As discussed above, Yoo discloses applying a trained deep learning model (i.e. a trained MLM) to chemical property features (i.e. bioassay data) to classify the natural product has having a particular medicinal effect (Figures 2-3; Table 1; pg. 2, col. 2, para. 1, pg. 6, col. 2, para. 2, and Table 2).
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of Yoo to have further manufactured a medical product with treatment properties corresponding to the respective class, because Yoo discloses medicinal plants provide novel candidate therapeutic agents against various diseases, which would require manufacturing the candidate therapeutic agent (Abstract; pg. 1, para. 1). One of ordinary skill in the art would have been motivated to manufacture the candidate therapeutic agent in order to provide a novel candidate therapeutic agent for consideration in drug development, as shown by Yoo (Abstract; pg. 1, para. 1). This modification would have had a reasonable expectation of success given Yoo suggests using natural compounds as candidate therapeutic agents (pg. 1, para. 1).
Further regarding claims 16-18, Yoo does not disclose the method is carried out by one or more computing devices comprising processors and memory, a classification system, or a non-transitory computer-readable medium with instructions for carrying out the method.
However, the court held that broadly providing an automatic or mechanical means to replace a manual activity which accomplished the same result is not sufficient to distinguish over the prior art. See MPEP 2144.04 III. Implementing a known function on a computer has been deemed obvious to one of ordinary skill in the art if the automation of the known function on a general purpose computer is nothing more than the predictable use of prior art elements according to their established functions. KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 417, 82 USPQ2d 1385, 1396 (2007). See MPEP 2114 IV.
Therefore, the invention is prima facie obvious.
Citation of pertinent prior art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Boothroyd et al., US 2022/0223246 A1; and
Zhang et al., Clustering-based undersampling with random over sampling examples and support vector machine for imbalanced classification of breast cancer diagnosis, 2019, Computer Assisted Surgery, 24(S2), pg. 62-72.
Boothroyd discloses machine learning methods which predict effects of cannabis extracts on subjects using bioassay data (Abstract; [0006]; [0011]; [0015]; [0103]; [0128]). Boothroyd also discloses predicting efficacy scores of cannabis extracts for subjects (claim 2; [0119]).
Zhang discloses method for using machine learning to make classifications (Abstract), which includes clustering data of potential training samples and selecting a set of training samples to include in the training set based on distances of samples to a centroid of a cluster in order to improve class imbalance (Abstract; Figure 1).
Conclusion
No claims are allowed.
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/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685
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