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 .
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.
Applicant's response, filed on 08/25/2025, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Status of claims
Canceled:
3-4, 7, 12, 14, 17, 19-96
Amended:
1-2, 5-6, 11, 13, 18, 98, 102, 104
Pending:
1-2, 5-6, 8-11, 13, 15-16, 18, 97-104
Withdrawn:
none
Examined:
1-2, 5-6, 8-11, 13, 15-16, 18, 97-104
Independent:
1
Allowable:
none
Priority
As detailed on the 10/21/2020 filing receipt, this application claims priority to as early as 04/23/2018.
Information Disclosure Statement
The Information Disclosure Statement filed on 03/18/2025 with 59 references is compliance with the provisions of 37 CFR 1.97 and have been considered in full. The Information Disclosure Statement filed on 03/18/2025 with 50 references is compliance with the provisions of 37 CFR 1.97 and have been considered in part. Reference #20 of the IDS with 50 references have not been considered because of the lack of appropriate dates and/or page numbers as is required under 37 CFR 1.97. Applicant is kindly reminded to provide proper citations in compliance with 37 CFR 1.97 in all future submissions to the office. A signed copy of the list of references cited from each IDS is included with this Office Action.
Withdrawn Rejections/Objections
The rejection of claims 1-2, 5-6, 8-11, 13, 15-16, 18 and 97-104 under 35 U.S.C. §103 over Liu in view of Chinen and Bigdel, in the Office action mailed 03/24/2025 is withdrawn in view of the amendments filed 08/25/2025. However, a new rejection is applied as discussed below.
Claim Rejections - 35 USC § 112
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.
Claim 6 is 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.
Claim 6 recites the limitation "obtaining the one or more complex biological samples from one or more individual subjects". There is insufficient antecedent basis for this limitation in the claim because there is no previous recitation of “one or more complex biological samples”.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 5-6, 8-11, 13, 15-16, 18 and 98-104 are rejected under 35 U.S.C. 103) as being unpatentable by Liu ("Prediction of nanoparticles-cell association based on corona proteins and physicochemical properties." Nanoscale 7.21: 9664-9675; published 2015; cited on the 05/21/2024 “Notice of References Cited” form 892) in view of Gioria ("A combined proteomics and metabolomics approach to assess the effects of gold nanoparticles in vitro." Nanotoxicology 10.6 (2016): 736-748; cited on the attached “Notice of References Cited” form 892).
Regarding claim 1(a), Liu teaches the recited contacting two or more different populations of particles with one or more complex biological samples from one or more individual subjects with “Cellular association of nanoparticles (NPs) in biological fluids is affected by proteins adsorbed onto the NP surface, forming a ‘protein corona’, thereby impacting cellular bioactivity" (Abstract) and with "In parallel, cell association, using A549 human lung epithelial carcinoma cells, was quantified for a compositionally diverse library of 105 NPs (Fig. 1) of 15, 30, or 60 nm Au core with neutral, anionic, or cationic ligands." (Page 9665, Col. 1, Para. 2). Liu also teaches that "The analysis of a cell association dataset of a combinatorial library of 84 gold nanoparticles (NPs) of 15, 30, or 60 nm cores with cationic or anionic surface ligands, included evaluation of a set of 129 PCFs and 19 NPPs as QSAR descriptors. Serum proteins, such as APOB, A1AT, ANT3, and PLMN, were identified, along with NP zeta potential, as being significant PCFs for correlating NP cell association." (Page 9670, Col. 1, Para. 1). Liu teaches forming a protein corona with “Cellular association of nanoparticles (NPs) in biological fluids is affected by proteins adsorbed onto the NP surface, forming a ‘protein corona’, thereby impacting cellular bioactivity" (Abstract).
However, Liu and does not explicitly teach forming two or more different biomolecule coronas. This limitation is taught by Gioria as discussed below in the continuation of claim 1(a) and claim 1(b) section.
Liu teaches the recited each population of the two or more populations of particles has different physicochemical properties and associated recitations at least with "Physicochemical properties used as (Q)SAR descriptors include NP primary and aggregate sizes, zeta potential in the media of exposure concentration (e.g., mass concentration and volume fraction), relativities, energy/enthalpy information (e.g., atomization and band gap energies and formation enthalpies), and structures of NP surface modifying molecules" (Page 9664, Col. 2, Para. 2).
Regarding claim 1(c), Liu teaches the recited applying a model to the multi-omic data set to generate a set of classification model weights for plurality of features and associated recitations at least with "...quantitative structure–activity relationships (QSARs) were developed based on both linear and non-linear support vector regression (SVR) models making use of a sequential forward floating selection of descriptors" (Abstract). "...where (w, x) denotes the inner product of the NP descriptor vector x (e.g., PCFs or NPPs) and weight vector w. The weight vector w and the intercept term b are parameters determined from the data by the least square algorithm." (Page 9671, Col. 1, Para. 3). "...while zi denote NPs (represented by their standardized descriptors) identified as support vectors with their weights given by ai (Fig. 5)" (Page 9664, Col. 1, Para. 2).
Regarding claim 1(d), Liu teaches the recited querying a reference data set for the plurality of features to generate a set of scores for each of the plurality of features at least with "QSAR descriptors were identified by sequential forward floating selection (SFFS)34 with prediction accuracy of developed linear and non-linear models validated by a bootstrapping based approach that is suitable for relatively small datasets." (Page 9665, Col. 2, Para. 4) and with Table 2 "Most suitable descriptors selected for linear and ε-SVR QSARs" (Page 9668, Table 2). Table 2 includes VIP (variable importance (influence) on projection) values for the descriptors listed. The recited "scores" read on "VIP values" of Liu.
Regarding claim 1(e), Liu teaches the recited pairing at least the set of classification model weights with the set of scores to generate a set of feature values at least with "...while zi denote NPs (represented by their standardized descriptors) identified as support vectors with their weights given by ai (Fig. 5)." (Page 9668, Col. 1, Para. 1).
Regarding claim 1(f), Liu teaches the recited detecting that a subset of the set of feature values corresponds to the one or more biomarkers and to the one or more disease states, thereby associating the one or more biomarkers with the one or more disease states. Liu teaches "In order to identify a reasonably small QSAR descriptor subset of relevance to cell association, descriptor selection33,38,39 was accomplished by sequential forward floating selection (SFFS), which represents an improvement of the traditionally used sequential forward selection (SFS)." (Page 9672, Col.1, Para. 2) and with "...it was concluded that the protein corona encodes relevant biological information regarding cell association with Au NPs." (Page 9665, Col. 2, Para. 2).
Liu teaches "In parallel, cell association, using A549 human lung epithelial carcinoma cells, was quantified for a compositionally diverse library of 105 NPs (Fig. 1) of 15, 30, or 60 nm Au core with neutral, anionic, or cationic ligands.27 Liquid chromatography tandem mass spectrometry was employed to detect the adsorbed serum proteins on the NP surface, providing a comprehensive quantitative characterization of the NP protein corona.27 For the entire library of Au NPs, a total of 785 distinct serum proteins were detected with 129 identified as suitable for abundance quantification. The relative abundance27 of the 129 serum proteins adsorbed on the NP surface was used as a “fingerprint” to characterize the protein coronas. QSARs were then developed for 84 NPs based on the 129 protein fingerprints, with the remaining 21 NPs of neutral surface ligands excluded due to their negligible adsorption of serum proteins.27 QSAR for log2-transformed cell association (2.60 × 10−3 to 2.51 mL µg(Mg)−1 ) of Au NPs was developed via partial least squares regression (PLSR)32 using 6 optimal principal components (PCs) calculated from 64 fingerprints identified via sequential forward selection (SFS).33 The developed QSAR demonstrated prediction accuracy in terms of R2 = 0.81 and 0.61 (coefficient of determination between predicted and observed cell association) in leave-one-out (LOO) validation (i.e., R2 LOO = 0.81) and 4-fold cross-validation (i.e., R2 4CV = 0.61), respectively." (Page 9664, col. 1, para. 2). The human lung epithelial carcinoma cells of Liu correspond to the disease state and the absorbed serum proteins of Liu corresponds to the recited biomarkers.
Overall, Liu teaches using A549 human lung epithelial carcinoma cells (page 9665, col. 1, para. 2), which corresponds to the disease state of lung epithelial carcinoma. Liu also teaches performing liquid chromatography tandem mass spectrometry to detect the adsorbed serum proteins on the NP surface (page 9665, col. 1, para. 2). This corresponds to detecting association of biomarkers with the disease state of lung epithelial carcinoma. Fig. 1 also depicts the nanoparticle discussed with 785 distinct serum proteins attached (page 9665). Since Liu is using the A549 human lung epithelial carcinoma cells, the proteins detected by the NP would be associated with the disease state of lung epithelial carcinoma.
As indicated above, Liu teaches contacting two or more different populations of particles with one or more complex biological samples from one or more individual subjects of claim 1(a) with “Cellular association of nanoparticles (NPs) in biological fluids is affected by proteins adsorbed onto the NP surface, forming a ‘protein corona’, thereby impacting cellular bioactivity" (Abstract) and "In parallel, cell association, using A549 human lung epithelial carcinoma cells, was quantified for a compositionally diverse library of 105 NPs (Fig. 1) of 15, 30, or 60 nm Au core with neutral, anionic, or cationic ligands." (Page 9665, Col. 1, Para. 2). Liu also teaches Histidine-rich glycoprotein in Table 1 (page 9666). Liu also teaches "Liquid chromatography tandem mass spectrometry was employed to detect the adsorbed serum proteins on the NP surface, providing a comprehensive quantitative characterization of the NP protein corona. For the entire library of Au NPs, a total of 785 distinct serum proteins were detected with 129 identified as suitable for abundance quantification. The relative abundance27 of the 129 serum proteins adsorbed on the NP surface was used as a “fingerprint” to characterize the protein coronas. (Page 9665, col. 1, para. 2). The recited two or more different populations of particles correspond to the diverse library of 105 NPs (Fig. 1) of 15, 30, or 60 nm Au core with neutral, anionic, or cationic ligands as taught by Liu. The recited one or more complex biological samples from one or more individual subjects corresponds to human lung epithelial carcinoma cells and plasma of Liu. Liu also teaches forming a biomolecule corona with “Cellular association of nanoparticles (NPs) in biological fluids is affected by proteins adsorbed onto the NP surface, forming a ‘protein corona’, thereby impacting cellular bioactivity" (Abstract).
However, Liu does not explicitly teach the claim limitation to form two or more different biomolecule coronas of claim 1(a) and assaying the two or more different biomolecule coronas, to generate a multi-omic data set comprising two or more of proteomic data, genomic data, glycomic data, transcriptomic data, or metabolomics data associated with the one or more biological samples of claim 1(b). However, these limitations are taught by Gioria.
Regarding the continuation of claim 1(a) and claim 1(b), Gioria teaches the claim limitation of to form two or more different biomolecule coronas in claim 1(a) and assaying the two or more different biomolecule coronas, to generate a multi-omic data set comprising two or more of proteomic data, genomic data, glycomic data, transcriptomic data, or metabolomics data associated with the one or more biological samples in claim 1(b) with “For proteomic experiments, 1 106 Caco-2 cells were seeded in 5 mL complete culture medium in 100 20 mm Petri dish (Corning, Valdarno, Italy). After 24 h, the medium was replaced and 5 or 30 nm AuNPs were added to obtain the final concentrations of 300 mM (59 mg/mL). In each experiment, untreated cells were used as control. Six biological replicates were performed for each experimental condition. Proteins extraction from the cytoplasmatic compartment was performed after 72 h of exposure time as described in Gioria et al. (2014).” (page 737, col. 2, para. 3) to (page 738, col. 1, para. 1); “For metabolomics experiments, cells were prepared as described above. At the end of the 72h exposure time, the cell culture medium was removed. Cells were washed with 5 mL of cold phosphate-buffered saline solution (PBS) (Life Technologies, Turin, Italy) and the wash solution discarded. Cell lysates were obtained by adding 500 mL of ice-cold methanol to each well and mechanically harvested with a sterile plastic disposable cell scraper. The lysate was transferred in a 1.5 mL Eppendorf tube. Each dish was then washed with an additional 250 mL ice-cold methanol that was collected into the respective Eppendorf tube. Recovered cell lysate was sonicated at 50 W for 5 min and further centrifuged at 15 000 g for 15 min at 4 C. The supernatant was collected and stored in a new 1.5 mL Eppendorf tube at -80 C. The methanol solution was evaporated to dryness using the centrifugal vacuum evaporator (Univapo 150 ECH, Uniequip, Planegg, Germany) for 30 min with a cooling system at 10 C. The samples were re-suspend in 100 mL of the LC-MS mobile phase (0.1% formic acid (FA) in a solution of milli-Q water: methanol, 95:5) and centrifuged at 15 000g for 10 min at 4 C. Samples were transferred into 96-well plates and then covered with a suitable cover mat for LC-HRMS analyses.” (page 738, col. 1, para. 2) and with Figure 1 (Page 737). Fig. 1 depicts the Experimental design. Fig. 1 caption states “A combination of 2D-gel based proteomic and MS-based metabolomic approaches was used to analyze the differentially expressed proteome and metabolites of the cytoplasmic compartment of Caco-2 cells exposed to 5 or 30 nm AuNPs (300 mM) for 72 h. Data obtained were interpreted using a combination of bioinformatics tools for a combined omics approach.” Gioria also teaches “In this work, a level of confidence of 2 for metabolite identification (putatively annotated compounds) was reached.” (page 738, col. 1, para. 6). In sum, Gioria teaches using AuNPs in proteomic experiments and metabolomics experiments where AuNPs were exposed to the cytoplasmic compartment of Caco-2 cells for 72 hours and identifying the metabolites and identifying proteins as depicted in Fig. 1. This corresponds to the claim limitation of the formation of two or more different biomolecule coronas and assaying the two or more different biomolecule coronas, to generate proteomic data and metabolomics data associated with the one or more biological samples.
Gioria also teaches contacting two or more different populations of particles with one or more complex biological samples from one or more individual subjects of claim 1(a) in Figure 2 (page 739). Figure 2 caption states “Proteomic analysis of the cytoplasmic extract of Caco-2 cells exposed to AuNPs. Representative two-dimensional gel protein maps of cytoplasmic fractions of (A) untreated (Ctrl), (B) treated with 5 nm AuNPs, and (C) treated with 30 nm AuNPs cells for 72 h. (D) The Venn diagram is showing the distribution of differentially expressed proteins: 5 nm AuNPs versus Ctrl (red circle, 36 proteins), 30 nm AuNPs versus (blue circle, 33 proteins) or 5 versus 30 nm AuNPs (green circle, 23 proteins). Number inside overlapping region of two circles refers to the spots common to different groups (For color figure refer to the online version.).” The recited 2 populations of particles correspond to 5nm and 30 nm AuNPs as taught by Gioria and the biological sample corresponds to cytoplasmic extract of Caco-2 cells as taught by Gioria.
It would have been prima facia obvious to combine the teachings of Liu and Gioria to arrive at the claimed invention. Gioria teaches that the proteome and metabolome are directly interconnected as protein levels influence the metabolic profile of a cell system and metabolites’ concentration may affect protein expression (page 736, col. 2, para. 1). A person of ordinary skill in the art would have been motivated to modify the method of Liu to generate a multi-omic data set comprising of proteomic data and metabolomics data as taught by Gioria to better analyze protein expression. Furthermore, there would have been a reasonable expectation of success, since Liu and Gioria teach methods that pertain to the use of AuNPs to analyze biological samples.
Regarding claim 2, Liu teaches the recited wherein detecting the subset in (f) comprises filtering the set of feature values for feature values in which (i) the classification model weights meet a first threshold and (ii) the scores meet a second threshold, and wherein the one or more biomarkers comprise the filtered set of feature values with Figure 5 and Table 2. Figure 5 depicts "Selected descriptors (standardized per the z-score) for the linear and the ε-SVR QSARs where by the Au NPs are ordered on the basis of their cellular association. A brief explanation about these descriptors is provided in Table 1. The values given following “/” are the weight factors of the NPs identified as support vectors." (Page 9669, Fig. 5). Table 2 "Most suitable descriptors selected for linear and ε-SVR QSARs" (Page 9668, Table 2). Table 2 includes VIP (variable importance (influence) on projection) values for the descriptors listed. The recited "threshold" reads on the VIP values for the descriptors selected of Liu.
Regarding claim 5, Liu teaches the recited wherein the model is trained using a set of labeled multi-omic data of a plurality of reference biological samples, wherein the labeled multi-omic data set comprises data features of one or more proteins corresponding to disease states, wherein the set of features are proteins and associated recitations at least with "In the above formulation, n denotes the total number of training samples, while C and ε are known as regularization factor and tube size, respectively." (Page 9671, Col. 2, Para. 1) and with Figure 2 "Workflow for QSAR development" (Page 9666, Figure 2). Figure 2 depicts using a training set to train the model of linear regression and nonlinear (ε-SVR) regressions. Liu also teaches in Table 1 that the descriptors are protein (Page 9667, Table 1). Liu teaches "In parallel, cell association, using A549 human lung epithelial carcinoma cells, was quantified for a compositionally diverse library of 105 NPs (Fig. 1) of 15, 30, or 60 nm Au core with neutral, anionic, or cationic ligands.27 Liquid chromatography tandem mass spectrometry was employed to detect the adsorbed serum proteins on the NP surface, providing a comprehensive quantitative characterization of the NP protein corona." (Page 9664, col. 1, para. 2).
Regarding claim 6, Liu teaches the recited obtaining the one or more complex biological samples from one or more individual subjects and associated recitations at least with "In parallel, cell association, using A549 human lung epithelial carcinoma cells, was quantified for a compositionally diverse library of 105 NPs (Fig. 1) of 15, 30, or 60 nm Au core with neutral, anionic, or cationic ligands." (Page 9665, Col. 1, Para. 2).
Regarding claim 8, Liu teaches the recited generating an output corresponding to a disease state of the one or more disease states and associated recitations at least with "The best performing QSAR developed in the above work was using 7 PCs calculated from 52 descriptors that included both protein corona fingerprints and NP physicochemical properties; however, only marginal performance improvement was attained (R2 LOO = 0.86 and R2 4CV = 0.63) relative to the QSAR utilizing only the protein corona fingerprints. Given the above, it was concluded that the protein corona encodes relevant biological information regarding cell association with Au NPs." (Page 9665, Col. 2, Para. 2) and with "Although definitive correlations of protein corona fingerprints with cell association were demonstrated, the QSAR descriptors were selected based on SFS process that provided limited exploration of the descriptor space" (Page 9665, Col. 2, Para. 3).
Regarding claim 9, Liu teaches the recited reference data set is a database comprising features related to disease states by an association score. Liu teaches "...cell association, using A549 human lung epithelial carcinoma cells, was quantified for a compositionally diverse library of 105 NPs (Fig. 1) of 15, 30, or 60 nm Au core with neutral, anionic, or cationic ligands." (Page 9665, Col. 1, Para. 2) and "QSAR descriptors were identified by sequential forward floating selection (SFFS)34 with prediction accuracy of developed linear and non-linear models validated by a bootstrapping based approach that is suitable for relatively small datasets." (Page 9665, Col. 2, Para. 4) and with Figure 5, "Selected descriptors (standardized per the z-score) for the linear and the ε-SVR QSARs where by the Au NPs are ordered on the basis of their cellular association. A brief explanation about these descriptors is provided in Table 1. The values given following “/” are the weight factors of the NPs identified as support vectors" (Page 9669, Figure 5). Figure 5 depicts z-scores for the descriptors and log2-transformed values for cellular association. Liu teaches using human lung epithelial carcinoma cells that corresponds to the recited disease state and the score obtained would be related to the disease. The recited "association scores" correspond to the QSAR for log2-transformed values for cellular association of Liu.
Regarding claim 10, Liu teaches the recited the set of scores are association scores between the set of features and the one or more disease states and associated recitations at least with Figure 5, "Selected descriptors (standardized per the z-score) for the linear and the ε-SVR QSARs where by the Au NPs are ordered on the basis of their cellular association. A brief explanation about these descriptors is provided in Table 1. The values given following “/” are the weight factors of the NPs identified as support vectors" (Page 9669, Figure 5). Figure 5 depicts z-scores for the descriptors and log2-transformed values for cellular association. The recited "association scores" corresponds to QSAR for log2-transformed values for cellular association as taught by Liu.
Regarding claim 11, Liu teaches the recited the one or more biological samples are selected from the group consisting of: plasma, serum, whole blood, amniotic fluid, cerebral spinal fluid, urine, saliva, tears, and feces and associated recitations at least with "In a physiological environment, NPs suspended in a biological fluid (e.g., blood, plasma, or interstitial fluid) will adsorb proteins that form a “protein corona” on the NP outer surface." (Page 9664, Col. 2, Para. 2).
Regarding claim 13, Liu teaches the recited the multi-omic data comprises proteomic data comprising (i) protein identifiers and (ii) disease states of one or more individual subjects and associated recitations at least with Figure 1 "For the entire NP library, a total of 785 distinct serum proteins were detected on their surfaces with 129 identified as suitable for relative abundance as “fingerprints” for protein corona characterization." (Figure 1, Caption, Page 9665).
Regarding claim 15, Liu teaches the recited wherein the multi-omic data set is generated by assaying a complex biological sample of an individual subject of the one or more individual subjects and associated recitations at least with "The 39 physicochemical properties included TEM and DLS size characterization, zeta potential, absorbance spectrophotometry, and the amount of adsorbed serum protein on the NP surface obtained from the bicinchoninic acid (BCA) assay." (Page 9665, Col. 2, Para. 2). Liu also teaches "Liquid chromatography tandem mass spectrometry was employed to detect the adsorbed serum proteins on the NP surface, providing a comprehensive quantitative characterization of the NP protein corona.27 For the entire library of Au NPs, a total of 785 distinct serum proteins were detected with 129 identified as suitable for abundance quantification. The relative abundance27 of the 129 serum proteins adsorbed on the NP surface was used as a “fingerprint” to characterize the protein coronas. (Page 9665, col. 1, para. 2). Histidine-rich glycoprotein of Liu corresponds to the recited glycomic data and proteins of Liu correspond to the recited proteomic data. Fig. 2 also teaches assaying the biomolecule coronas (page 9666).
Regarding claim 16, Liu teaches the recited wherein the set of features represent different proteins and associated recitations at least with Table 2 "Most suitable descriptors selected for linear and ε-SVR QSARs." (Page 9668, Table 2). Table 2 lists different descriptors that represent different proteins.
Regarding claim 18, Liu teaches the recited the one or more biological samples are subjected to prior protein depletion and associated recitations at least with "At each selection step, SFFS first conducts a forward selection to identify the descriptor that leads to the greatest increase in model performance, then backward elimination to evaluate whether previously selected descriptors should be removed due to the addition of the newly selected one." (Page 9672, Col. 1, Para. 2).
Regarding claim 98, Liu teaches the recited performing mass spectrometry on biomolecules of the two or more different biomolecule coronas and associated recitations at least with "Liquid chromatography tandem mass spectrometry was employed to detect the adsorbed serum proteins on the NP surface, providing a comprehensive quantitative characterization of the NP protein corona." (Page 9665, Col. 1, Para. 2). Gioria also teaches performing mass spectrometry on biomolecules of the two or more different biomolecule coronas with “Qualitative and quantitative data of de-regulated proteins and metabolites obtained using two-dimensional gel electrophoresis (2DE) and liquid chromatography high resolution tandem mass spectrometry (LC-HRMS/MS) were combined and interpreted using systems biology analysis.” (Page 373, Col. 1, Para. 1).
Regarding claim 99, Liu teaches the recited a particle of the two or more different populations of particles comprises a magnetite particle and associated recitations at least with "Based on the assumption that ENMs of similar physicochemical properties will have similar bioactivity, a number of (quantitative) structure–activity relationships ((Q)SARs) have been successfully developed for various ENMs, such as metal oxide NPs, surface modified iron oxide NPs, and carbon nanotubes." (Page 9664, Col. 2, Para. 2).
Regarding claim 100, Liu teaches the recited a particle of the two or more different populations of particles is superparamagnetic and associated recitations at least with "Based on the assumption that ENMs of similar physicochemical properties will have similar bioactivity, a number of (quantitative) structure–activity relationships ((Q)SARs) have been successfully developed for various ENMs, such as metal oxide NPs, surface modified iron oxide NPs, and carbon nanotubes." (Page 9664, Col. 2, Para. 2).
Regarding claim 101, Liu teaches the recited a particle of the two or more different populations of particles comprises a nanoparticle and associated recitations at least with "Cellular association of nanoparticles (NPs) in biological fluids is affected by proteins adsorbed onto the NP surface, forming a “protein corona”, thereby impacting cellular bioactivity." (Abstract).
Regarding claim 102, Liu teaches the recited the one or more biological samples comprise plasma or serum and associated recitations at least with "In a physiological environment, NPs suspended in a biological fluid (e.g., blood, plasma, or interstitial fluid) will adsorb proteins that form a “protein corona” on the NP outer surface." (Page 9664, Col. 2, Para. 2).
Regarding claim 103, Liu teaches the recited the multi-omic data comprises proteomic data and associated recitations at least with "For the entire library of Au NPs, a total of 785 distinct serum proteins were detected with 129 identified as suitable for abundance quantification. The relative abundance of the 129 serum proteins adsorbed on the NP surface was used as a “fingerprint” to characterize the protein coronas." (Page 9665, Col. 1, Para. 2).
Regarding claim 104, Liu teaches wherein a first composition of biomolecules in the two or more different biomolecule coronas is different from a second composition of the biomolecules in the one or more biological samples. Liu teaches "...cell association, using A549 human
lung epithelial carcinoma cells, was quantified for a compositionally diverse library of 105 NPs (Fig. 1) of 15, 30, or 60 nm Au core with neutral, anionic, or cationic ligands. Liquid chromatography tandem mass spectrometry was employed to detect the adsorbed serum proteins on the NP surface, providing a comprehensive quantitative characterization of the NP protein corona. For the entire library of Au NPs, a total of 785 distinct serum proteins were detected..." (Page 9665, Col. 1, Para. 2).
Claim 97 is rejected under 35 U.S.C. 103 as being unpatentable over Liu ("Prediction of nanoparticles-cell association based on corona proteins and physicochemical properties." Nanoscale 7.21: 9664-9675; published 2015; cited on the 05/21/2024 “Notice of References Cited” form 892) in view of Gioria ("A combined proteomics and metabolomics approach to assess the effects of gold nanoparticles in vitro." Nanotoxicology 10.6 (2016): 736-748; cited on the attached“Notice of References Cited” form 892) as applied to claims 1-2, 5-6, 8-11, 13, 15-16, 18 and 98-104 above; and further in view of Bigdeli ("Exploring cellular interactions of liposomes using protein corona fingerprints and physicochemical properties." ACS nano 10.3: 3723-3737, published 2016; cited on the 05/15/2023 IDS Document).
Liu and Gioria are applied to claims 1-2, 5-6, 8-11, 13, 15-16, 18 and 98-104 as discussed above.
Liu does not teach the model comprises a neural network of claim 97. However, this limitation was known in the art at the time of the effective filing date of the invention, as taught by Bigdeli.
Regarding claim 97, Bigdeli teaches the recited the model comprises a neural network and associated recitations at least with "To develop linear and nonlinear correlations between the determined descriptors (x) and the measured biological end points (y), multiple linear regressions (MLR) and artificial neural networks (ANN) were accomplished to model y as a function of x, y = f(x)." (Page 3729, Col. 2, Para. 2).
Rationale for combining
It would have been prima facia obvious to combine the teachings of Liu with the teachings of Bigdeli to achieve the claim invention because Bigdeli’s ANN techniques are beneficial for discovering the possible relationship between the input descriptors and the output bioresponses (Page 3734, Col. 1, Para. 3). Therefore, it would have been obvious to include ANN as taught by Bigdeli to discover the relationship between input descriptors and the output bioresponses. Furthermore, there would have been a reasonable expectation of success, since both Liu and Bigdeli teach methods that pertain to the analysis protein coronas.
Response to 35 USC §103 Remarks (08/25/2025, Pages 10-13 of remarks)
Applicant amended claims 1-2, 5-6, 11, 13, 18, 98, 102 and 104.
Applicant provides a copy of amended claim 1.
It is noted that Applicant’s remarks are based on amended claims.
Applicant argues that Liu, Chinen, or Bigdeli do not disclose "A method for detecting association of one or more biomarkers with one or more disease states, comprising . .. assaying the two or more different biomolecule coronas to generate a multi-omic data set comprising two or more of: proteomic data, genomic data, glycomic data, transcriptomic data, or metabolomics data associated with the one or more biological samples" as recited in amended claim 1. Applicant also argues that Liu does not disclose one or more disease states or a method for detecting association of one or more biomarkers with one or more disease states comprising the recited features of the pending claims.
Applicant also disagrees with the Office that Liu teaches generating a multi-omic data set from the two or more different biomolecule coronas, wherein the multi-omic data set comprises two or more data sets selected from the group consisting of: proteomic data, genomic data, glycomic data, transcriptomic data, and metabolomics data. Applicant also argues that a person of ordinary skill in the art would not understand Liu as involving glycomic data, but a person of ordinary skill in the art would understand Liu to be focused on proteomic data alone.
In response, Applicant’s remarks have been fully considered and are persuasive. Therefore, the rejections have been withdrawn. However, upon further consideration, a new ground of rejection is made in view of claim amendments. As discussed above, Liu teaches contacting two or more different populations of particles with one or more complex biological samples from one or more individual subjects of claim 1(a) with “Cellular association of nanoparticles (NPs) in biological fluids is affected by proteins adsorbed onto the NP surface, forming a ‘protein corona’, thereby impacting cellular bioactivity" (Abstract) and "In parallel, cell association, using A549 human lung epithelial carcinoma cells, was quantified for a compositionally diverse library of 105 NPs (Fig. 1) of 15, 30, or 60 nm Au core with neutral, anionic, or cationic ligands." (Page 9665, Col. 1, Para. 2). Liu also teaches Histidine-rich glycoprotein in Table 1 (page 9666). Liu also teaches "Liquid chromatography tandem mass spectrometry was employed to detect the adsorbed serum proteins on the NP surface, providing a comprehensive quantitative characterization of the NP protein corona. For the entire library of Au NPs, a total of 785 distinct serum proteins were detected with 129 identified as suitable for abundance quantification. The relative abundance27 of the 129 serum proteins adsorbed on the NP surface was used as a “fingerprint” to characterize the protein coronas. (Page 9665, col. 1, para. 2). The recited two or more different populations of particles correspond to the diverse library of 105 NPs (Fig. 1) of 15, 30, or 60 nm Au core with neutral, anionic, or cationic ligands as taught by Liu. The recited one or more complex biological samples from one or more individual subjects corresponds to human lung epithelial carcinoma cells and plasma of Liu. Liu also teaches forming a biomolecule corona with “Cellular association of nanoparticles (NPs) in biological fluids is affected by proteins adsorbed onto the NP surface, forming a ‘protein corona’, thereby impacting cellular bioactivity" (Abstract). However, Liu does not explicitly teach the claim limitation to form two or more different biomolecule coronas of claim 1(a) and assaying the two or more different biomolecule coronas, to generate a multi-omic data set comprising two or more of proteomic data, genomic data, glycomic data, transcriptomic data, or metabolomics data associated with the one or more biological samples of claim 1(b). However, these limitations are taught by Gioria.
Regarding the continuation of claim 1(a) and claim 1(b), Gioria teaches the claim limitation of to form two or more different biomolecule coronas in claim 1(a) and assaying the two or more different biomolecule coronas, to generate a multi-omic data set comprising two or more of proteomic data, genomic data, glycomic data, transcriptomic data, or metabolomics data associated with the one or more biological samples in claim 1(b) with “For proteomic experiments, 1 106 Caco-2 cells were seeded in 5 mL complete culture medium in 100 20 mm Petri dish (Corning, Valdarno, Italy). After 24 h, the medium was replaced and 5 or 30 nm AuNPs were added to obtain the final concentrations of 300 mM (59 mg/mL). In each experiment, untreated cells were used as control. Six biological replicates were performed for each experimental condition. Proteins extraction from the cytoplasmatic compartment was performed after 72 h of exposure time as described in Gioria et al. (2014).” (page 737, col. 2, para. 3) to (page 738, col. 1, para. 1); “For metabolomics experiments, cells were prepared as described above. At the end of the 72h exposure time, the cell culture medium was removed. Cells were washed with 5 mL of cold phosphate-buffered saline solution (PBS) (Life Technologies, Turin, Italy) and the wash solution discarded. Cell lysates were obtained by adding 500 mL of ice-cold methanol to each well and mechanically harvested with a sterile plastic disposable cell scraper. The lysate was transferred in a 1.5 mL Eppendorf tube. Each dish was then washed with an additional 250 mL ice-cold methanol that was collected into the respective Eppendorf tube. Recovered cell lysate was sonicated at 50 W for 5 min and further centrifuged at 15 000 g for 15 min at 4 C. The supernatant was collected and stored in a new 1.5 mL Eppendorf tube at -80 C. The methanol solution was evaporated to dryness using the centrifugal vacuum evaporator (Univapo 150 ECH, Uniequip, Planegg, Germany) for 30 min with a cooling system at 10 C. The samples were re-suspend in 100 mL of the LC-MS mobile phase (0.1% formic acid (FA) in a solution of milli-Q water: methanol, 95:5) and centrifuged at 15 000g for 10 min at 4 C. Samples were transferred into 96-well plates and then covered with a suitable cover mat for LC-HRMS analyses.” (page 738, col. 1, para. 2) and with Figure 1 (Page 737). Fig. 1 depicts the Experimental design. Fig. 1 caption states “A combination of 2D-gel based proteomic and MS-based metabolomic approaches was used to analyze the differentially expressed proteome and metabolites of the cytoplasmic compartment of Caco-2 cells exposed to 5 or 30 nm AuNPs (300 mM) for 72 h. Data obtained were interpreted using a combination of bioinformatics tools for a combined omics approach.” Gioria also teaches “In this work, a level of confidence of 2 for metabolite identification (putatively annotated compounds) was reached.” (page 738, col. 1, para. 6). In sum, Gioria teaches using AuNPs in proteomic experiments and metabolomics experiments where AuNPs were exposed to the cytoplasmic compartment of Caco-2 cells for 72 hours and identifying the metabolites and identifying proteins as depicted in Fig. 1. This corresponds to the claim limitation of the formation of two or more different biomolecule coronas and assaying the two or more different biomolecule coronas, to generate proteomic data and metabolomics data associated with the one or more biological samples.
Gioria also teaches contacting two or more different populations of particles with one or more complex biological samples from one or more individual subjects of claim 1(a) in Figure 2 (page 739). Figure 2 caption states “Proteomic analysis of the cytoplasmic extract of Caco-2 cells exposed to AuNPs. Representative two-dimensional gel protein maps of cytoplasmic fractions of (A) untreated (Ctrl), (B) treated with 5 nm AuNPs, and (C) treated with 30 nm AuNPs cells for 72 h. (D) The Venn diagram is showing the distribution of differentially expressed proteins: 5 nm AuNPs versus Ctrl (red circle, 36 proteins), 30 nm AuNPs versus (blue circle, 33 proteins) or 5 versus 30 nm AuNPs (green circle, 23 proteins). Number inside overlapping region of two circles refers to the spots common to different groups (For color figure refer to the online version.).” The recited 2 populations of particles correspond to 5nm and 30 nm AuNPs as taught by Gioria and the biological sample corresponds to cytoplasmic extract of Caco-2 cells as taught by Gioria.
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 1-2, 5-6, 8-11, 13, 15-16, 18, 97-104 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Law of nature recited include:
Claim 1 recites: “detecting that a subset of the set of feature values corresponds to the one or more biomarkers and to the one or more disease states, thereby associating the one or more biomarkers with the one or more disease states.” This claim limitation involves correlating biomarkers with disease states which is a law of nature.
Mental processes recited include:
Claim 1 recites: “assaying the two or more different biomolecule coronas, to generate a multi-omic data set comprising two or more of: proteomic data, genomic data, glycomic data, transcriptomic data, or metabolomics data associated with the one or more biological samples; querying a reference data set for the set of features to generate a set of scores; pairing at least the set of classification model weights with the set of scores to generate a set of feature values; selecting a subset of the set of feature values... and detecting that a subset of the set of feature values corresponds to the one or more biomarkers and to the one or more disease... The process of selecting involves analyzing and evaluating data and choosing from a list that can be practically performed either in the mind or with pen and paper. Assaying, querying and detecting involves analyzing, evaluating, observing and identifying the data are data analytics steps that could also be performed with the human mind and/or with pen and paper. Pairing model weights with scores are methods of organizing data that could be performed with the human mind and/or with pen and paper.
Claim 2 recites wherein detecting the subset in (f) comprises filtering the set of feature values for feature values in which (i) the classification model weights meet a first threshold and (ii)the scores meet a second threshold such that the one or more biomarkers comprise the filtered set of feature values.” Detecting involves analyzing, evaluating, observing and identifying the data that can be practically performed either in the mind or with pen and paper.
Claim 11 recites: “…wherein the one or more biological samples are selected from the group consisting of are plasma, serum, whole blood, amniotic fluid, cerebral spinal fluid, urine, saliva, tears, and feces.” The process of selecting involves selecting from a list that can be practically performed either in the mind or with pen and paper.
Mathematical concepts recited include:
Claim 1 recites: "applying a model to the multi-omic data set to generate a set of classification model weights, wherein the set of classification model weights comprises a classification model weight for each feature in a set of features." The process of applying a model to generate weights requires carrying out a series of mathematical calculations.
Claim 9 recites "...association score..." Association scores are mathematical concepts and requires performing a series of mathematical calculations to determine the score.
Claim 10 recites wherein the set of scores are association scores between the set of features and the one or more disease states. Association scores are mathematical concepts and requires performing a series of mathematical calculations to determine the score.
Claim 97 recites wherein the model comprises a neural network. The neural network is a mathematical concept and formula that requires carrying out a series of mathematical calculations to implement the model.
The processes of claim 1 includes the process of assaying, querying and detecting that are involved with analyzing, evaluating, observing and identifying data. Detecting of claim 2 is also involved with analyzing and identifying a specific data and are data analytics steps. Claim 11 includes selecting that involves choosing from a list. These are all acts of analyzing, evaluating, organizing and judging information that could be practically performed in the human mind and/or with pen and paper as discussed above. Acts of evaluating and analyzing data could be practically performed in the human mind and/or with pen and paper because they merely require making observations, evaluations, judgments, and opinions (See MPEP 2106.04(a)(2) subsection III). Therefore, under the broadest reasonable interpretation, claims 1-2 and 11 can be practically carried out in the human mind or with pen and paper as claimed, which falls under the "Mental processes" grouping of abstract ideas.
Claim 1 recites mathematical concepts and formulas as discussed above. The process of applying a model to generate weights requires carrying out a series of mathematical calculations and the neural network is a mathematical concept and formula that requires carrying out a series of mathematical calculations to implement the model. Claims 9 and 10 association scores that are mathematical concepts and requires performing a series of mathematical calculations to determine the score. The neural network of claim 97 is a mathematical concept and formula that requires carrying out a series of mathematical calculations to implement the model. Therefore, claims 1, 9, 10 and 97 recite claim elements that falls under the “mathematical concepts” grouping of abstract ideas.
As such, claims 1-2, 5-6, 8-11, 13, 15-16, 18, 97-104 recite an abstract idea (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The above indicated judicial exceptions are not integrated into a practical application because the claims do not recite an additional elements that apply, rely on or use the judicial exception in such a manner to amount to integration into a practical application. For example, there are no limitations that reflect an improvement to technology or applies or uses the recited judicial exception in some other meaningful way. Rather, the instant claims recite additional elements that equate to mere instructions to implement an abstract idea or insignificant extra solution activity. Specifically, the instant claims recite the following additional elements:
Claim 1 recites …to generate a multi-omic data set comprising two or more of: proteomic data, genomic data, glycomic data, transcriptomic data, and metabolomics data...
Claim 5 recites the model is trained using a set of labeled multi-omic data of a plurality of reference biological samples, wherein the labeled multi-omic data set comprises data features of one or more proteins corresponding to one or more disease states
Claim 6 recites obtaining the one or more complex biological samples from one or more individual subjects.
Claim 8 recites generating an output corresponding to a disease state of the one or more disease states.
Claim 9 recites wherein the reference data set is a database comprising features related to disease states by an association score
Claim 15 recites wherein the multi-omic data set is generated by assaying a complex biological sample of an individual subject of the one or more individual subjects.
Claim 98 recites …performing mass spectrometry on biomolecules of the two or more different biomolecule coronas.
These elements of claims 1, 5-6, 8-9, 15 and 98 equate to insignificant extra solutional activities of data gathering, data inputting and data outputting. The limitations of obtaining data serves as input to the recited judicial exception in the claims.
Claims 2, 10-11, 13, 16, 18, 97, and 99-104 do not recite any elements in addition to the judicial exception. As such, as currently recited, the claims do not appear to recite an improvement to technology or apply or use the recited judicial exception in some other meaningful way. Therefore, claims 1-2, 5-6, 8-11, 13, 15-16, 18, 97-104 are directed to an abstract idea (Step 2A, Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to well-understood, routine and conventional activities, insignificant extra-solution activity or mere instructions to implement the abstract idea on a generic computer. The instant claims recite the following additional elements:
Claim 1 recites …to generate a multi-omic data set comprising two or more of: proteomic data, genomic data, glycomic data, transcriptomic data, and metabolomics data...
Claim 5 recites the model is trained using a set of labeled multi-omic data of a plurality of reference biological samples, wherein the labeled multi-omic data set comprises data features of one or more proteins corresponding to one or more disease states
Claim 6 recites obtaining the one or more complex biological samples from one or more individual subjects.
Claim 8 recites generating an output corresponding to a disease state of the one or more disease states.
Claim 9 recites wherein the reference data set is a database comprising features related to disease states by an association score
Claim 15 recites wherein the multi-omic data set is generated by assaying a complex biological sample of an individual subject of the one or more individual subjects.
Claim 98 recites …performing mass spectrometry on biomolecules of the two or more different biomolecule coronas.
The additional elements indicated above do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. The limitations indicated above equate to mere data gathering and outputting activities. Limitations that equate to mere data gathering and outputting are insignificant extra solutional activities. As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional. (see MPEP 2106.05(g)). The courts have also recognized that techniques for determining the level of a biomarker in blood by any means as well-understood, routine, conventional activity in the life science arts 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)). Additionally, Sapsford (as cited on the 03/24/2025 "Notice of References Cited" form 892) discloses that the use of mass spectrometry to characterize nanoparticle samples are known (page 4459, col. 2, para.1 to page 4460, col. 1, para. 1). Sapsford also discloses in Figure 1 (Page 4455) the use of nanoparticles to bind to biomolecules, which is also a known method. Overall, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-2, 5-6, 8-11, 13, 15-16, 18, 97-104 are not patent eligible.
Response to 35 USC §101 Remarks (08/25/2025, Pages 6-10 of remarks)
Applicant amended claims 1-2, 5-6, 11, 13, 18, 98, 102 and 104.
Applicant provides a copy of amended claim 1.
It is noted that Applicant’s remarks are based on amended claims.
Under Step 2A Prong 1, Applicant argues that the amended claims, as a whole, are directed to significantly more than any alleged judicial exception. Applicant states that independent claim 1 includes at least the wet lab steps of "(a) contacting ...", "(b) assaying ... ", and "(f) detecting . . .", which require physical operations to be performed, which necessarily cannot be performed in the mind.
In response, Applicants' remarks have been fully considered and are not persuasive. The argued wet-lab steps correspond to the recited method of claim 1 and would fall under the category of “process” in Step 1 of the 35 U.S.C. 101 analysis of patentable subject matter and does not end the eligibility analysis. As disclosed in MPEP 2106.04 (I), determining that a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 U.S.C. 101 (i.e., process, machine, manufacture, or composition of matter) in Step 1 does not end the eligibility analysis, because claims directed to nothing more than abstract ideas (such as a mathematical formula or equation), natural phenomena, and laws of nature are not eligible for patent protection. Also, the process of assaying and detecting involves analyzing, evaluating, observing and identifying are data analytics steps, which could be performed with the human mind and/or with pen and paper as claimed. Acts of evaluating and analyzing data could be practically performed in the human mind and/or with pen and paper because they merely require making observations, evaluations, judgments, and opinions and would fall under the “mental processes” grouping of abstract ideas (See MPEP 2106.04(a)(2) subsection III).
Also, the process of performing assays and the particles contacting biological samples resulting in the formation of biomolecule coronas of proteins is a field of use or insignificant extra solution activity. The particles are used to determine biomarkers in the biological sample that amounts to mere data gathering and equates to insignificant extra-solution activity and a field of use. As indicated in MPEP 2106.05(g), data gathering is insignificant extra solution activity and a data gathering step that is limited to a particular data source or a particular type of data could be considered to be both insignificant extra-solution activity and a field of use limitation. Also, the courts have also recognized that techniques for determining the level of a biomarker in blood by any means as well-understood, routine, conventional activity in the life science arts 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)).
Under Step 2A Prong 1, Applicant argues that Claim 1 is patent eligible because it is directed to an improved practical application of detecting potential biomarkers for various disease states by multimodal assaying relying on a particular transformation of a complex biological sample into a biomolecule corona. Applicant asserts that the formation of biomolecule coronas constitutes a transformation or reduction of a particular article to a different state or thing that integrates any alleged judicial exception into a practical application. Applicant disagrees with the Office previous response that rejected an analogous argument as allegedly failing to recite a physical transformation. Applicant states that attaching one substance to another via a physical process, such as via adsorption, yields a transformation of matter for at least the reason that no proteins are attached to the particles prior to the contacting with the sample, and the adsorption of proteins to the particles after the contacting necessarily results in a reduction of a total number of non-attached proteins in a given sample, particularly when the particles are removed from the sample, e.g., for assaying, as recited in step (b).
In response, Applicants' remarks have been fully considered and are not persuasive. Applicant’s arguments under Step 2A Prong 2, that the formation of biomolecule coronas constitutes a transformation or reduction of a particular article to a different state or thing that integrates any alleged judicial exception into a practical application is not persuasive. As mentioned in the Response to 35 USC §101 section, page 22, of the office action dated 03/24/2025, the particle is used to absorb biomolecules from the biological sample. Both the particle and the biological sample did not change to a different state or thing. The particle is still a particle with biomolecules attached and the biological sample is still the same sample. Also, the particle's function or use for absorbing biomolecules from its environment did not change from its original function. Similarly, the function of the biological sample is not new and did not change. Also, the reduced quantity of proteins in the sample is equivalent to changing the location of the proteins. As stated in MPEP 2106.05(c), "An 'article' includes a physical object or substance. The physical object or substance must be particular, meaning it can be specifically identified. 'Transformation' of an article means that the 'article' has changed to a different state or thing. Changing to a different state or thing usually means more than simply using an article or changing the location of an article. A new or different function or use can be evidence that an article has been transformed. The courts have also recognized that techniques for determining the level of a biomarker in blood by any means as well-understood, routine, conventional activity in the life science arts 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)).
Applicant further argues that the formation of biomolecule coronas from contacting populations of particles with the biological samples provides an improvement to the practical application of detecting biomarkers for disease states, as forming the biomolecule coronas allows consideration of proteins, that would otherwise not be detected, as potential biomarkers and can improve the analysis of biomolecules in a biological state for the purposes of identifying and employing biomarkers in the detection of a disease state.
In response, the arguments under Step 2A Prong 2 regarding improvement have been fully considered and are not persuasive. It is understood that the asserted improvement is in the analysis of biomolecules in a biological state for the purposes of identifying and employing biomarkers in the detection of a disease state. The asserted improvement is in an improvement to the judicial exception (JE) of mental processes. As discussed in the 101 rejection section above, assaying, querying and detecting involves analyzing, evaluating, observing and identifying data are data analytics steps that could also be performed with the human mind and/or with pen and paper and falls under the mental processes grouping of abstract ideas. For the JEs to be integrated into a practical application, the additional elements have to apply, rely on or use the JE in way that imposes a meaningful limit on the claims to provide an improvement. As stated in MPEP 2106.05(a), the judicial exception alone cannot provide the technical improvement. The improvement can be provided by one or more additional elements as seen in Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II.
Applicant argues under step 2B of the 101 analysis that using biomolecule coronas and multi-omic data to detect biomarkers for a disease is not conventional, as evidenced by Liu and Bigdeli. Applicant also argues that the claims include the recitation of assaying the two or more different biomolecule coronas to generate a multi-omic data set comprising two or more of: proteomic data, genomic data, glycomic data, transcriptomic data, or metabolomics data associated with the one or more biological samples" are not routine or conventional.
In response, Applicants' remarks have been fully considered and are not persuasive because Liu teaches using A549 human lung epithelial carcinoma cells to detect absorbed serum proteins onto the nanoparticle surface. Chinen (as cited on the attached "Notice of References Cited" form 892) also teaches that the use of nanoparticles for the detection of extracellular cancer biomarkers and cancer cells are known methods (Page 10531, col. 1, para. 3). Both Liu and Chinen teaches using nanoparticles to detect biomarkers. The courts have also recognized that techniques for determining the level of a biomarker in blood by any means as well-understood, routine, conventional activity in the life science arts 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)).
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/K.K./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686