CTNF 18/146,123 CTNF 97179 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 1-20 are pending and under examination. Claims 1-20 are rejected. Claims 1, 9, and 19 are independent. No claims are allowed, amended, canceled, new, or withdrawn. Office Action Outline Rejections applied Abbreviations x 112/b Indefiniteness PHOSITA "a Person Having Ordinary Skill In The Art before the effective filing date of the claimed invention" 112/b "Means for" BRI Broadest Reasonable Interpretation 112/a Enablement, Written description CRM "Computer-Readable Media" and equivalent language 112 Other IDS Information Disclosure Statement x, x 102, 103 JE Judicial Exception x 101 JE(s) 112/a 35 USC 112(a) and similarly for 112/b, etc. 101 Other N:N page:line Double Patenting MM/DD/YYYY date format Priority As detailed in the 01/19/2023 filing receipt, this application claims priority to U.S. Provisional Application 63/371,508, filed 08/15/2022 . Claim Rejections - 35 USC § 112 07-30-02 AIA 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 1-8 and 17-18 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 depending from rejected claims are rejected similarly, unless otherwise noted, and any amendments in response to the following rejections should be applied throughout the claims, as appropriate. In claim 1 , the relationship is unclear between "receiving an indication of a protein sequence" and all instances of data analysis involving "the protein sequence.". This is because "receiving an indication of a protein sequence" is interpreted to include an embodiment where the sequence is not received, especially when considering Specification [0072], which discloses: "The indication of the protein sequence may be the protein sequence itself or other information such as identification of a food item that contains the protein. " (Emphasis added). Claim 18 is similarly rejected as claim 1 above, for reciting the element "receiving an indication of an uncharacterized protein." Additionally, the claim 18 element does not recite "receiving a...sequence," which results in a lack of antecedent basis problem further in claim 18 (see additional 112(b) rejection of claim 18 below). To overcome the rejection, it is suggested to amend claims 1 and 18 , possibly either by deleting to remove "indication" or amending the subsequent steps to reflect the embodiment where no sequence is received. Additionally, claim 2 recites the "indication of the protein sequence," which should be amended to reflect any amendment of independent claim 1. For compact examination, claim 1 and 18 will be examined as if a sequence is received, with the expectation Applicant will amend appropriately . Note, claim 20 also recites "indication" in the element "receive an indication of an uncharacterized protein," however, there are no clarity issues in claim 20 involving "an indication. In claim 17 , the phrase " is the same as " causes a lack of clarity in the limitation "wherein the second machine learning model is the same as the first machine learning model." This is because in claim 9 (upon which claim 17 depends), the first and second machine models are respectively trained with first and second training sets created from different data, resulting in a clarity issue as to in what way the first and second machine learning models are "the same." Specification [0007] states "This second machine learning model may be the same or different type of model than the machine learning model used earlier," while [0005] discloses "The machine learning model may be any type of machine learning model that can learn a non-linear relationship between a dependent variable and multiple independent variables. For example, the machine learning model may be a regressor..." It is suggested to possibly appropriately amend to make clear that the first and second machine learning models are the same type, while taking care to avoid a lack of antecedent basis for "type." For compact examination, claim 17 will be examined as if the models are the same type, with the expectation Applicant will amend appropriately. In claim 18 , the recited "the sequence of the uncharacterized protein" requires but lacks clear antecedent. If this recitation refers to a previously instantiated instance, then it is not clear which instance that is. If this recitation instantiates this claim element, this is not clear. This rejection might be overcome by for example amending to recite the article "a" instead of "the," or by amending to receive a sequence earlier in the claim. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to one or more judicial exceptions without significantly more. MPEP 2106 details the following framework to analyze Subject Matter Eligibility: • Step 1 : Are the claims directed to a category of statutory subject matter (a process, machine, manufacture, or composition of matter)? (see MPEP § 2106.03) • Step 2A, Prong One : Do the claims recite a judicially recognized exception, i.e. an abstract idea, a law of nature, or a natural phenomenon? (See MPEP § 2106.04(a), 2106.04(a)(2) & 2106.04(b).) • Step 2A, Prong Two : If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application? (see MPEP § 2106.04(d)) • Step 2B : If the claims do not integrate the judicial exception, do the claims provide an inventive concept? (see MPEP § 2106.05) Step 1 : Claims 1-18 are directed to a 101 processes, here methods. Claims 19-20 are directed to a 101 machine or manufacture, here a system comprising processing units and computer-readable media storing instructions. As such, claims 1-20 are directed to related methods and a system, which fall under categories of statutory subject matter. (See MPEP § 2106.03). (Step 1: Yes.) Step 2A, Prong One : The claims recite abstract ideas in the form of mental processes and mathematical concepts , as follows: Independent claim 1 and dependent claims 6-7 recite the following mental processes and mathematical concepts : • determining physiochemical features from the protein sequence (claim 1) • generating embeddings from the protein sequence (claim 1) • providing the other information, the physiochemical features, and the embeddings to a trained machine learning model that is trained on a plurality of proteins with known values for a target feature (claim 1) • generating, by the trained machine learning model, a predicted value of the target feature for the protein sequence (claim 1) • embeddings are created by a transformer (claims 6) • trained machine learning model is a regressor (claims 7) Independent claim 9 and dependent claims 13-18 recite the following mental processes and mathematical concepts : • creating a first training set from physiochemical features (claim 9) • training a first machine learning model (claim 9) • identifying a subset of features (claim 9) • generating embeddings from the protein sequence (claim 9) • creating a second training set from the relevant features and the embeddings • training a second machine learning model (claim 9) • the first machine learning model comprises decision trees, random forest, or gradient boosting (claim 13) • identifying the subset of features that are the relevant features uses feature importance or causal relationships (claim 14) • identifying the subset of features that are the relevant features uses feature importance determined by Shapley values or causal relationships determined by Conditional Average Treatment Effect (CATE) or Individual Treatment Effect (ITE) (claim 15) • embeddings are generated by a transformer model (claim 16) • determining relevant physiochemical features from the sequence of the uncharacterized protein (claim 18) • generating embeddings from the uncharacterized protein (claim 18) • providing the relevant other information, the relevant physiochemical features, and the embeddings to the second machine learning model (claim 18) • generating, by the second machine learning model, a predicted value of the target feature for the uncharacterized protein (claim 18) Independent claim 19 and dependent claim 20 recite the following mental processes and mathematical concepts : • determine physiochemical features from a protein sequence (claim 19) • learn a first correlation between the value for the target feature, the other information, and the physiochemical features (claim 19) • generate embeddings from the protein sequence (claim 19) • identify features used to train the first machine learning model (claim 19) • learn a second correlation between the value for the target feature, the subset of the features, and the embeddings (claim 19) • determined by providing the uncharacterized protein to the second machine learning model (claim 20) Claims 3-5 and 8 further limit the abstract ideas of claim 1. Claims 10-12, and 17 further limit the abstract ideas of claim 9. Step 2A Prong One Summary : The claims recite mental processes and mathematical concepts. When considering the broadest reasonable interpretation (BRI) of the claims, the mental processes recited in independent claims 1, 9 and 19 (e.g., "determining physiochemical features from the protein sequence"; "generating embeddings from the protein sequence"; "creating a first training set", etc.) are directed to processes that may be performed in the human mind, or with pen and paper, as there are no particular limitations recited in claims 1, 9, or 19 which would prevent the mental processes from being performed in the human mind or with pen and paper. The claims recite inherent mathematical processes, e.g., embeddings are created by a transformer, the trained machine learning model is a regressor, and the first machine learning model comprises decision trees, random forest, or gradient boosting, etc., details of which are not explicitly shown, are discussed throughout the Specification, e.g., at [0037, 0051, 0064], etc. Such analysis performed mentally, or with paper and pencil, may take considerable time and effort, and although a general-purpose computer can perform these calculations at a rate and accuracy that can far exceed the mental performance of a skilled artisan, the nature of the activity is essentially the same, and therefore constitutes an abstract idea. Therefore, the claims recite elements that constitute a judicial exception in the form of an abstract idea(s). ( Step 2A, Prong One: Yes.) Step 2A, Prong Two: In Step 2A, Prong One above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs). Here at Step 2A, Prong Two, any remaining steps and/or elements not identified as JEs are therefore in addition to the identified JE(s), and are considered additional elements. Because the claims have been interpreted as being directed to judicial exceptions (abstract ideas in this instance) then Step 2A, Prong Two provides that the claims be examined further to determine whether the judicial exception is integrated into a practical application [see MPEP § 2106.04(d)]. A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. MPEP § 2106.04(d)(I) lists the following five example considerations for evaluating whether a judicial exception is integrated into a practical application: (1) An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a). (2) Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2). (3) Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b). (4) Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c). (5) Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e). The claims recite additional elements as follows: Additional elements of data gathering, inputting, and outputting steps : Receiving data (claims 1-2, 18, and 20); obtaining data (claim 1, 18); providing data and return data (i.e., outputting data to a computing device, as respectively in claim 2 and 20). Data gathering steps are additional elements which perform functions of inputting, collecting, and outputting the data needed to carry out the abstract idea. These steps are considered insignificant extra-solution activity, and are not sufficient to integrate an abstract idea into a practical application as they do not impose any meaningful limitation on the abstract idea or how it is performed, nor do they provide an improvement to technology (see MPEP § 2106.04(d)(I)). Additional elements of computer components : A computing device (claims 2 and 20); processing units (claim 19); computer readable media (claim 19); engines (interpreted as software, in claim 19); and a network interface (claim 20). The claims require only generic computer components, which do not improve computer technology, and do not integrate the recited judicial exception into a practical application (see MPEP § 2106.04(d)(1) and MPEP § 2106.05(f)). Step 2A Prong Two summary : The claims have been further analyzed with respect to Step 2A, Prong Two, and no additional elements have been found, alone or in combination, that would integrate the judicial exception into a practical application. At this point in examination, it is not yet the case that any of the Step 2A Prong Two considerations enumerated above clearly demonstrates integration of the identified JE(s) into a practical application. Referring to the considerations above, none of: (1) an improvement, (2) a treatment, (3) a particular machine, or (4) a transformation is clear in the record. For example, regarding the first consideration for improvement at MPEP 2106.04(d)(1), the record, including the Specification, does not yet clearly disclose an explanation of improvement over the previous state of the technology field, and the claims do not yet clearly result in such an improvement. ( Step 2A, Prong Two: No ). Step 2B: Because the additional claim elements do not integrate the abstract idea into a practical application, the claims are further examined under Step 2B , which evaluates whether the additional elements, individually and in combination, amount to significantly more than the judicial exception itself by providing an inventive concept. An inventive concept is furnished by an element or combination of elements that is recited in the claim in addition to the judicial exception, and is sufficient to ensure that the claim, as a whole, amounts to significantly more than the judicial exception itself (see MPEP § 2106.05). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are well-understood, routine, and conventional. Those additional elements are as follows: Additional elements of data gathering, inputting, and outputting steps : The additional elements of receiving data (claims 1-2, 18, and 20); obtaining data (claim 1, 18); providing data and return(ing) data (i.e., outputting data to a computing device, as respectively in claim 2 and 20) do not cause the claims to rise to the level of significantly more than the judicial exception. The courts have recognized receiving or transmitting data over a network and storing and retrieving information in memory [see MPEP§2106.05(d)(II)], as well-understood, routine, conventional activity when they are claimed in a merely generic manner (e.g., at a high level of generality) or as extra-solution activity. Additional elements of computer components : The additional elements of a computing device (claims 2 and 20); processing units (claim 19); computer readable media (claim 19); engines (interpreted as software, in claim 19); and a network interface (claim 20) do not cause the claims to rise to the level of significantly more than the judicial exception, and as such do not provide an inventive concept; these are conventional computer components. Further regarding the conventionality of additional elements, the MPEP at 2106.05(b) and 2106.05(d) presents several points relevant to conventional computers and data gathering steps in regard to Step 2A Prong 2 and Step 2B, including: • A general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions, does not qualify as a particular machine (see 2106.05(b)(I)), as in the case of claim 2, 19, and 20, which are interpreted to recite conventional computer components. • Integral use of a machine to achieve performance of a method may integrate the recited judicial exception into a practical application or provide significantly more, in contrast to where the machine is merely an object on which the method operates, which does not integrate the exception into a practical application or provide significantly more (see 2106.05(b)(II). In the instant claims, the processor and CRM are used in the data analysis of protein sequence and information; as such, the processor and CRM act only as a tool to perform the steps of data analysis, and do not integrate the exception into a practical application or provide significantly more. • Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not integrate a judicial exception or provide significantly more (see 2106.05(b)(III). The processor and CRM of claim 19 used in performing data analysis does not impose meaningful limitations on the claims. • The courts have recognized “receiving or transmitting data over a network”, “performing repetitive calculations”, and “storing and retrieving information in memory”, as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d)(II)). The receiving and obtaining of data in claim 1-2, 18, and 20 is recited in a generic manner. All limitations of claims 1-20 have been analyzed with respect to Step 2B, and none provides a specific inventive concept, as they all fail to rise to the level of significantly more than the identified judicial exception, and thus do not transform the judicial exception into a patent eligible application of the exceptions. Step2B: NO . Therefore, the claims, when the limitations are considered individually and as a whole, are rejected under 35 U.S.C. § 101 as being directed to non patent-eligible subject matter. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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. 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-15 AIA Claim s 1-2 and 5-8 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Hume (U.S. 2022/0104515 A1, published 04/07/2022; cited on the 11/10/2023 IDS) . Regarding the following elements: • receiving an indication of a protein sequence, and obtaining other information for the protein sequence ( claim 1 ) • determining physiochemical properties ( claim 1 ) • generating embeddings from the protein sequence ( claim 1 ) • providing the other information, the physicochemical features, and the embeddings to a trained machine learning model ( claim 1 ) • receiving an indication of a protein sequence, and providing the target feature, by a computing device ( claim 2 ) • generating a predicted value of a target feature for the protein sequence ( claim 1 ) • a transformer model ( claim 6 ), Hume teaches a computer system that is adapted for machine learning is trained to group similar proteins together and/or predict whether a protein has a selected target function [0009]. Hume teaches identification of potential food ingredients by accessing amino acid sequence databases of naturally occurring proteins [0017]. Hume teaches the selected target function to be predicted includes particular flavors [0025]. Hume teaches computer analysis of protein information from protein databases to predict whether each protein of the database (or a selection of) has the target function [0071]. Hume teaches the protein data is encoded in vector or matrix form, and features are one-hot encoded, binary encoded, or hash encoded. Protein amino acid sequences can be transformed to reduce dimensionality to be processed by the machine learning models . Sequences and additional features for protein of various lengths are encoded in a fixed sized matrix, which can be done by Transformers . Models that generate embeddings are trained on large amounts of unlabeled data [0073]. Regarding the element of claim 5 for the physiochemical features comprise amino acid composition, average weighed atomic number (interpreted as molecular weight), etc., Hume teaches examples of protein continuous data include molecular weight and percentage of each amino acid type [0081]. Regarding the "regressor" of claim 7, Hume teaches classifying protein features, and shows an example of when the prediction task is a calculation of regression losses , e.g. MSE, the regression task is used to predict the continuous value "x" of a new protein [0088]. Regarding the element of claim 8 of a "target feature is...flavor", Hume teaches the selected target function to be predicted includes particular flavors [0025] . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-20-02-aia AIA 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. 103-1 (of 3): 07-22-aia AIA Claim s 9-10, 12-14, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Hume as applied to claim s 1-2 and 5-8 above, and further in view of Feala (U.S. 2022/0122692 A1, published 04/21/2022; cited on the attached form PTO-892) . The following elements are taught by Hume below: • obtaining a protein sequences, values for target features, other information ( claim 9 ) • creating a first training set from physiochemical properties the obtained sequence, value, and information ( claim 9 ) • training a first machine learning model ( claim 9 ) • identifying subset of features as relevant features ( claim 9 and 19 ) • generating embeddings from the protein sequence ( claim 9 and 18 ) • a transformer model ( claim 16 ) • receiving an indication and other information of a protein sequence ( claim 18 ) • determining physiochemical properties ( claims 18 and 19 ) • threshold importance of features ( claim 19 ) • machine learning models to learn correlations ( claim 19 ) Hume shows a computer system that is adapted for machine learning is trained to group similar proteins together and/or predict whether a protein has a selected target function [0009]. Hume shows identification of potential food ingredients by accessing amino acid sequence databases of naturally occurring proteins [0017]. Hume shows the selected target function to be predicted includes particular flavors [0025]. Hume shows computer analysis of protein information from protein databases to predict whether each protein of the database (or a selection of) has the target function [0071]. Hume shows the protein data is encoded in vector or matrix form, and features are one-hot encoded, binary encoded, or hash encoded. Protein amino acid sequences can be transformed to reduce dimensionality to be processed by the machine learning models. Sequences and additional features for protein of various lengths are encoded in a fixed sized matrix, which can be done by Transformers ; models that generate embeddings are trained on large amounts of unlabeled data [0073]. Hume shows each target protein function is associated with a specific set of function specific properties that can be used to determine whether a protein candidate is nominated as a potential food ingredient; function specific properties of a candidate protein are compared with benchmark thresholds ; compared values are used to determine whether each protein candidate has sufficient target function [0115]. Regarding a feature importance engine to identifying features to train the model and threshold importance to a target feature ( claim 9 ); and identifying the subset of features that are the relevant features uses feature importance ( claim 14 ), Hume shows by integrating machine learning, vector representation of protein features, and laboratory assays, the system learns on an ongoing basis what features are important for a particular target function [0043]. Regarding the element of claim 10 of a "target feature is...flavor", Hume teaches the selected target function to be predicted includes particular flavors [0025]. Regarding the element of claim 12 for the physiochemical features comprise amino acid composition, average weighed atomic number (interpreted as molecular weight), etc. Hume teaches examples of protein continuous data include molecular weight and percentage of each amino acid type [0081] (as taught above in the 102 rejection of claim 5). Regarding the processing units, computer readable media, and engines ( claim 19 ); and the network interface and computing device ( claim 20 ), Hume shows the computer components at [0142-0145]. Hume does not show the elements of a second machine learning model and/or a second training set of independent claims 9 and 17-20 , and the first and second models are the same, of claim 17 (taught by Feala). Hume does not show the first machine learning model comprises decision trees, random forest, or gradient boosting of claim 13 (taught by Feala). Regarding the elements of a second machine learning model and a second training set ( claims 9 and 17-20) , Feala shows a method modeling a desired protein property comprising: training a first system with a first set of data, the first system comprising a first neural net transformer encoder and a first decoder, the first decoder of the pretrained system being configured to generate an output different from the desired protein property; transferring at least a part of the first transformer encoder of the pretrained system to a second system, the second system comprising a second transformer encoder and a second decoder; training the second system with a second set of data, the second set of data comprising a set of proteins representing a smaller number of classes of proteins than the first set , wherein the classes of proteins include one or more of: ( a ) classes of proteins within the first set of data , and ( b ) classes of proteins excluded from the first set of data ; and analyzing , by the second system , a primary amino acid sequence of a protein analyte , thereby generating a prediction of the desired protein property for the protein analyte. Feala shows in some embodiments, a second model of the second system comprises a first model of the first system in which the last layer is removed [0009]. Regarding the decision trees, random forest, or gradient boosting ( claim 13 ), Feala shows the machine learning method is selected from the group including a random forest or a decision tree [0057]; and boosting [0050]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of Hume for predicting a target protein feature from protein information using machine learning, by using a second machine learning model as in Feala, because Feala shows the second model has an improved performance metric relative to a model trained [0009]. One would have had a reasonable expectation of success in modifying Hume with Feala, because Hume and Feala are generally drawn to related teaching using machine learning in predicting properties of proteins by analysis of protein information (including sequence data), and as such, the combination would have been obvious. 103-2 (of 3): 07-22-aia AIA Claim s 3, 4, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Hume as applied to claims 1-2 and 5-8 above, in view of Feala as applied to claim s 9-10, 12-13, and 16-18 above, and further in view of Al-Snafi (IOSR Journal of Pharmacy, vol. 8(11), pages 1-21 (2018); cited on the attached form PTO-892) Hume in view of Feala does not show the elements of nutritional information of a food item containing protein ( claims 3 and 11 ); nor the nutritional information comprising energy content, dietary fiber amount, fat quantity, ash quantity, total sugar, calcium content, phosphorus content, sodium content, zinc content, copper content, and iron content ( claim 4 ); (taught by Al-Snafi). Regarding the element for nutritional information of a food item that contains a protein of claims 3, 4, and 11 , Al-Snafi shows chemical analysis of Juglans regia (walnut), to include ash quantity (p.2, ¶ 11); and energy content, dietary fiber amount, fat quantity, total sugar, and calcium, phosphorus, sodium, zinc, copper, and iron content (p.3, ¶ 1). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the models, methods, and systems of predicting a target protein feature from protein information using machine learning models of Hume, in view of Feala, to include nutritional information from biochemical composition analysis of food containing protein of Al-Snafi, because Al-Snafi shows biochemical analysis of walnuts yields extensive pharmacological, nutritional, and therapeutic information. A PHOSITA would have understood this information to be potentially important and useful in modeling prediction of a target feature from a protein contained in a food item, and as such the modification would have been obvious. 103-3 (of 3): 07-22-aia AIA Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Hume as applied to claims 1-2 and 5-8 above, in view of Feala as applied to claim s 9-10, 12-14, and 16-20 above, and further in view of Lundberg (Advances in neural information processing systems, vol.30, pages 1-10 (2017); cited on the 08/03/2023 IDS) . Hume shows identifying feature importance of claim 15, however, Hume in view of Feala does not show feature importance determined by Shapley values of claim 15 (taught by Lundberg). Regarding the feature importance determined by Shapley values ( claim 15 ) Lundberg shows a unified framework for interpreting a model's predictions, SHAP (SHapley Additive exPlanations); SHAP assigns each feature an importance value for a particular prediction (p.1, abstract and entire document). Lundberg shows SHAP values as a unified measure of feature importance (p.4, Section 4 titled "SHAP (Shapley Additive explanation) Values"). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the models, methods, and systems of predicting a target protein feature from protein information using machine learning models of Hume, in view of Feala, to include the Shapley values of feature importance of Lundberg, because a PHOSITA would have been motivated to modify Hume and Feala with Lundberg, as Lundberg reveals new SHAP value estimation methods show improved computational performance and/or better consistency with human intuition than previous approaches. A PHOSITA would have understood inclusion of Shapley values of feature importance to be a useful improvement in prediction model interpretation of feature importance, and as such the modification would have been obvious. Conclusion No claims are allowed. This Office action is a Non-Final action. A shortened statutory period for reply to this action is set to expire THREE MONTHS from the mailing date of this action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Meredith A Vassell whose telephone number is (571)272-1771. The examiner can normally be reached 8:30 - 4:30. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, KARLHEINZ SKOWRONEK can be reached at (571)272-9047. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /M.A.V./Examiner, Art Unit 1687 /G. STEVEN VANNI/Primary patents examiner, Art Unit 1686 Application/Control Number: 18/146,123 Page 2 Art Unit: 1687 Application/Control Number: 18/146,123 Page 3 Art Unit: 1687 Application/Control Number: 18/146,123 Page 4 Art Unit: 1687 Application/Control Number: 18/146,123 Page 5 Art Unit: 1687 Application/Control Number: 18/146,123 Page 6 Art Unit: 1687 Application/Control Number: 18/146,123 Page 7 Art Unit: 1687 Application/Control Number: 18/146,123 Page 8 Art Unit: 1687 Application/Control Number: 18/146,123 Page 9 Art Unit: 1687 Application/Control Number: 18/146,123 Page 11 Art Unit: 1687 Application/Control Number: 18/146,123 Page 12 Art Unit: 1687 Application/Control Number: 18/146,123 Page 13 Art Unit: 1687 Application/Control Number: 18/146,123 Page 14 Art Unit: 1687 Application/Control Number: 18/146,123 Page 15 Art Unit: 1687 Application/Control Number: 18/146,123 Page 16 Art Unit: 1687 Application/Control Number: 18/146,123 Page 17 Art Unit: 1687 Application/Control Number: 18/146,123 Page 18 Art Unit: 1687 Application/Control Number: 18/146,123 Page 19 Art Unit: 1687 Application/Control Number: 18/146,123 Page 20 Art Unit: 1687 Application/Control Number: 18/146,123 Page 21 Art Unit: 1687
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