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 .
Applicant’s response filed on 09/11/2025 has been fully considered. The following rejections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application. It is noted that the Examiner of record has changed since the previous office action. It is now examiner Wenyu Yang of art unit 1685.
Claim Status
Claims 1-20 are currently pending and under exam herein.
Claims 1, 2, 10-11, and 19 have been amended.
Claims 3, 4-9, and 12-18 were previously presented.
Claims 1-20 are rejected.
Information Disclosure Statement
The information disclosure statements (IDS) filed 09/11/2025 and 01/12/2026 comply with 37 CFR 1.97(c) as it includes a timing fee as specified in 37 CFR 1.97(e). All the references in the IDS have been considered by the examiner and attached in this office action.
Priority
The instant application claims priority to provisional application No. 63/091,515 filed on 10/14/2020. Domestic benefit is acknowledged. Thus, the effective filing date of claims 1-20 is 10/14/2020.
Claim Rejections - 35 USC § 112
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The rejection of claims 2-9 and 11-18 under 35 U.S.C. § 112(b) are withdrawn in view of the claim amendments
Response to Arguments
Applicant’s arguments, see page 1-2 under Response to Claim Rejections 35 U.S.C § 112 Rejections, filed 09/11/2025 with respect to claims 2-9 and 11-18 have been fully considered. The 112(b)-indefiniteness rejection of 2-9, and 11-18 has been withdrawn due to amendments.
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The rejection of claims 1-20 under 35 U.S.C. § 101 are withdrawn in view of the claim amendments.
Specifically, the new claim amendment to claim 1 of “an optical setup, configured to illuminate at least two laser beams non-collinearly, to create a locally chiral laser field on a target analyte comprising multi-center chiral molecules” is deemed to be a non-conventional additional element that provides an inventive concept to the claim as a whole, as non-collinear laser beams to create a locally chiral field is not conventional. Along the same lines, it was found that “a locally chiral laser field created by illuminating at least two laser beams non-collinearly on a target analyte” (claims 10 and 19) was not a conventional method that was well-established in the field. Hence, claims 1-20 includes additional elements that amount to an inventive concept, and the claims as a whole amount to significantly more. Therefore, claims 1-20 overcome the 25 U.S.C. 101 rejection at Step 2B, and are patent-eligible.
Response to Arguments
Applicant’s arguments, see pages 8-11 under 35 U.S.C § 101 Rejections, filed on 09/11/2025, with respect to claims 1-20 have been fully considered and are persuasive. The 35 U.S.C. 101 rejection of claims 1-20 has been withdrawn.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The previous rejections of claims 1-20 under 35 U.S.C. 103 are withdrawn in view of the claim amendments. However, upon further consideration, a new grounds of rejection is made in view of Ayuso et al. (arXiv preprint arXiv:1809.01632v2, Published Jan 15, 2019) and Del Los Santos et al. (Angewandte Chemie International Edition Vol 59 Issue 6 Pgs. 2440-2448, Published Online Nov 12, 2019). Please see below for more details.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ayuso et al. (arXiv preprint arXiv:1809.01632v2, Published Jan 15, 2019) in view of Del Los Santos et al. (Angewandte Chemie International Edition Vol 59 Issue 6 Pgs. 2440-2448, Published Online Nov 12, 2019). The limitations of the instant application are italicized below. This rejection is newly recited and necessitated by claim amendment.
With respect to claim 1, Ayuso et al. teaches a system that utilizes locally and globally chiral electric fields that interact with randomly oriented chiral molecules for chiral discrimination (pg. 2 para 1, A system for determining chiral characteristic of an analyte). Specifically, Ayuso et al. emphasizes that the finding opened the way for an extremely efficient and ultrafast imaging of chiral structure and dynamics (pg. 2 para 1). To begin, Ayuso et al. teaches an optical setup with two non-collinear beams with wavevectors k1, k2, propagating in the xy plane at angles
±
α
to the y axis, to create a locally chiral field aimed at a chiral molecule (pg. 8 para 2 and pg. 9 Fig. 2, an optical setup, configured to illuminate at least two laser beams non-collinearly, to create a locally chiral laser field on a target analyte). Ayuso et al. then implements this system to observe high harmonic response in randomly oriented propylene oxide (pg. 9 para 3). Next, the response from the interaction of the laser field and the chiral molecules are then collected and analyzed for patterns (pg. 11 para 2). Although not explicitly stated, a detection device, like a photoelectron detector, would have been utilized for the collection of the data, which is inherent in spectroscopy systems like the one in Ayuso et al. (pg. 2 para 3, a detection device). Ayuso et al. also elaborates on the data received as a quantitative model for high harmonic response, implying that high harmonic spectroscopy was utilized, and harmonic emission data was collected (pg. 9 para 3, receive, from the detection device, a target signal representing spectral emission associated with the target analyte). After analysis of the collected emission data, Ayuso et al. realized that the harmonic spectrum measurements could then be utilized to quantify the enantiomer concentrations in a macroscopic mixture with high accuracy and sub femtosecond time resolution (pg. 28 para 1, at an inference stage … to determine chiral characteristic of said target analyte).
Regarding claim 10, Ayuso et al. teaches a method that utilizes locally and globally chiral electric fields that interact with randomly oriented chiral molecules for chiral discrimination (pg. 2 para 1, A method of determining chiral characteristic of an analyte). Specifically, Ayuso et al. emphasizes that the finding opened the way for an extremely efficient and ultrafast imaging of chiral structure and dynamics (pg. 2 para 1). To begin, Ayuso et al. teaches the use of two non-collinear beams with wavevectors k1, k2, propagating in the xy plane at angles
±
α
to the y axis, to create a locally chiral field aimed at a chiral molecule (pg. 8 para 2 and pg. 9 Fig. 2, illuminating at least two laser beams non-collinearly, to create a locally chiral laser field on a target analyte). Ayuso et al. then implements this system to observe high harmonic response in randomly oriented propylene oxide (pg. 9 para 3). Next, the response from the interaction of the laser field and the chiral molecules are then collected and analyzed for patterns (pg. 11 para 2). Although not explicitly stated, a detection device, like a photoelectron detector, was probably utilized for the collection of the data, which is inherent in spectroscopy systems like the one in Ayuso et al. (pg. 2 para 3). Ayuso et al. also elaborates on the data received as a quantitative model for high harmonic response, implying that high harmonic spectroscopy was utilized, and harmonic emission data was collected (pg. 9 para 3, receive, by a processor, a target signal from a detection device, wherein said target signal represents spectral emission associated with the target analyte). After analysis of the collected emission data, Ayuso et al. realized that the harmonic spectrum measurements could then be utilized to quantify the enantiomer concentrations in a macroscopic mixture with high accuracy and sub femtosecond time resolution (pg. 28 para 1, at an inference stage … to determine chiral characteristic of said target analyte).
Concerning claim 3 and 12, Ayuso et al. teaches that the laser field created by the two non-collinear beams is locally and globally chiral (pg. 8 para 2 and pg.9 Fig. 2, the laser field is locally chiral at said interaction).
With regards to claim 4 and 13, Ayuso et al. teaches that field created freely propagating locally chiral electromagnetic fields, which also maintain their handedness globally in space (pg. 3 para 4 and pg. 9 Fig. 2, laser field maintains said local chirality within all of an interaction region with each of said plurality of analyte and said target analyte).
Regarding claim 5 and 14, Ayuso et al. discloses the laser field is chiral, which implies that it lacks inversion, reflection, and improper rotation symmetry (pg. 3 para 4 and pg. 9 Fig. 2, said laser field exhibits one of the following symmetry properties: static reflection symmetry, dynamical reflection symmetry, dynamical inversion symmetry, dynamical improper rotational symmetry, and lack of inversion, reflection, and improper rotation symmetry).
Concerning claim 6 and 15, Ayuso et al. teaches an optical setup with two non-collinear beams with wavevectors k1, k2, propagating in the xy plane at angles
±
α
to the y axis, to create a locally chiral field aimed at a chiral molecule (pg. 8 para 2 and pg. 9 Fig. 2, said laser field is generated by illuminating at least two laser beams non-collinearly). Ayuso et al. further elaborates that each beam is made of linearly polarized ꞷ and 2ꞷ field with orthogonal polarizations and controlled phase delays (wherein at least one of the following is controlled: (i) one or more of the wavelength of the laser beams, and (ii) one or more of the polarization of the laser beams).
With respect to claim 7 and 16, Ayuso et al. teaches that the global chirality map can be engineered by tuning their handedness locally, at every point (pg. 3 para 4, said laser field has different handedness in different sections of the interaction region)
Regarding to claim 8 and 17, Ayuso et al. discloses a quantitative model for high harmonic response in randomly oriented propylene oxide, implying that high harmonic spectroscopy was utilized (pg. 8 para 3, said spectral emission is a harmonic spectral emission resulting from a high harmonic generation process between said laser field and each of said plurality of analytes and said target analyte).
Concerning claim 9 and 18, Ayuso et al. also discloses second harmonics generation, which is a low harmonic generation process, with the laser field (pg. 11. Para 2 and pg. 13 para 1, said spectral emission is a harmonic spectral emission resulting from a low-order harmonic generation process between said laser field and each of said plurality of analytes and said target analyte).
With respect to claim 19, Ayuso et al. discloses the generation of spectral emission data from the interaction of a locally chiral field with a chiral molecule (pg. 8 para 3, spectral emission resulting from an interaction between a locally chiral laser field and a reference analyte comprising a chiral molecule). In addition, Ayuso et al. discloses that the laser field is generated with two non-collinear beams with wavevectors k1, k2, propagating in the xy plane at angles
±
α
to the y axis, to create a locally chiral field aimed at a chiral molecule (pg. 8 para 2 and pg. 9 Fig. 2, spectral emission resulting from an interaction between a locally chiral laser field and a target analyte comprising said specified chiral molecule, wherein the laser field is created by illuminating at least two laser beams non-collinearly on the target analyte).
However, Ayuso et al. failed to disclose applying a trained machine learning model to target signals to determine the chiral characteristic of a target analyte (claim 1 and 10). Due to this, Ayuso et al. also fails to teach the inclusion of at least hardware processor and a non-transitory computer-readable storage medium having stored thereon program instructions, the program instructions executable by the at least one hardware processor (claim 1). And hence, Ayuso et al. does not teach a trained machine learning model that was previously trained (claim 2 and 11). In a similar sense, this is why Ayuso et al. does not teach a method of analyzing a target analyte through obtaining reference data (with 2n+1 -1 signals) with known labels of molar concentration and target data with unknown concentration in order to reconstruct concentrations of the target analyte (claim 19 and 20). Lastly, while Ayuso et al. does apply the laser field on propylene oxide, propylene oxide is a single chiral center molecule, not a multi-center chiral molecule (claims 1-18). Yet, the concept of using laser fields to determine chiral characteristics of proteins and amino acid molecules (multi-center chiral molecules) was well known in the art before the effective filing date of the instant application as shown by De Los Santos et al. In addition, the concept of a machine learning model to determine enantiomeric and diastereomeric ratios and concentration of chiral samples were also well known in the art before the effective filing date of the application as demonstrated by De Los Santos et al.
With respect to claim 1, De Los Santos et al. teaches applying a linear programming method and a parameter sweeping algorithm to absorption and emission data determine the concentration and relative amount of four different stereoisomer in 20 samples of different stereoisomeric composition (pg. 2440 left col Abstract para, at an inference stage, apply a trained machine learning model to said target signal to determine chiral characteristics of said target analyte). De Los Santos et al. elaborates that the four different stereoisomers were aminoindanol stereoisomers, which typically have at least 2 chiral centers (pg. 2440 left col Abstract para, multi-center chiral molecules). Although not explicitly stated in Del Los Santos et al., the supervised learning approach based on linear programming was carried out on a computer with a processor capable of processing the large body of data generated (pg. 2443 right col para 2, at least one hardware processor). In addition, based on the code lines in the supplementary information of De Los Santos et al. and the references cited for the algorithms, the linear programming was probably encoded into a software on Python (Supplementary Information S8, S11, and S56, a non-transitory computer-readable storage medium having stored thereon program instructions, the program instructions executable by the at least one hardware processor).
Regarding claim 10, De Los Santos et al. teaches applying a linear programming method and a parameter sweeping algorithm to absorption and emission data determine the concentration and relative amount of four different stereoisomer in 20 samples of different stereoisomeric composition (pg. 2440 left col Abstract para, at an inference stage, applying, by a processor, a trained machine learning (ML) model to said target signal to determine chiral characteristics of said target analyte). De Los Santos et al. elaborates that the four different stereoisomers were aminoindanol stereoisomers, which typically have at least 2 chiral centers (pg. 2440 left col Abstract para, multi-center chiral molecules). Although not explicitly stated in Del Los Santos et al., the supervised learning approach based on linear programming was carried out on a computer with a processor capable of processing the large body of data generated (pg. 2443 right col para 2, by the processor).
Concerning claim 2 and 11, De Los Santos et al. teaches a trained linear programming model previously trained on 20 samples of various stereoisomeric composition (pg. 2440 left col Abstract para, wherein the machine learning model was previously trained). De Los Santos et al. teaches that UV (ultraviolet) and CD (circular dichroism) spectroscopy data was obtained for 20 samples with known compositions of quaternary isomeric mixtures, and utilized as the training input (pg. 2443 right col para 3, receiving a plurality of signals representing spectral emission resulting from an interaction between a laser field and a respective plurality of analytes, wherein at least some of said analytes comprise multi-center chiral molecules). De Los Santos et al. further elaborates that the 20 samples, T1-T20 with varying diastereomeric ratios (dr) and enantiomeric ratios (er) along with their UV and CD spectra were fed into the linear programming algorithm for training to obtain individual percent compositions of (R,S)-1, (S,R)-1, (R,R)-1, (S,S)-1 (er values), and [(R,S)-1 + (S,R)-1] and [(R,R)-1 + (S,S)-1] (dr values) (pg. 2443 right col para 3, at a training stage, training the ML model on a training set comprising: (i) plurality of signals, and (ii) labels associated with a configuration of a chirality in each of said plurality of analytes, wherein said plurality of signals are labeled with said labels).
Responding to claim 19, De Los Santos et al. teaches an optical method for accurate concentration, er and dr analysis of amino alcohols that utilizes machine learning on a computer (pg. 2240 left col Abstract para and Title, A method of analyzing a target analyte by at least one processor). To begin, De Los Santos et al. obtains UV and CD spectral emission data from 20 samples of quaternary isomeric mixtures with known concentrations as the training input for the linear programming model (pg. 2443 right col para 3, obtaining reference data comprising a plurality of reference signals representing spectral emissions resulting from an interaction between a … laser field and a reference analyte). Then, De Los Santos et al. obtains UV and CD spectral emission data for another 20 samples (S1-S20) of scalemic mixtures with varying unknown concentrations as the testing input into the model (pg. 2444 left col para 2, obtaining target data comprising a plurality of target signals representing spectral emissions resulting from an interaction between … a laser field and a target analyte comprising said specified chiral molecule). Next, De Los Santos et al. explains, that after the reference/training input is fed into the model, all possible 10, 50, 100, 150, and 200 nm ranges were evaluated with 10 nm steps across the whole spectrum width before the optimal wavelength span was selected by identifying the range that produces the lowest averaged absolute error across all compositions (pg. 2443 left col para 3, calculating reference phase data with respect to each of said reference signals). Finally, after feeding the testing input into the model, De Los Santos et al. showcases how the model mapped the optimized ranges derived from the training set onto the testing set in order to predict the individual concentrations of each of the four stereoisomers in the samples (pg. 2444 left col para 2 and Supplementary Information S16, deriving target phase data with respect to said target signals, by applying an optimization algorithm which minimizes an error between said target signals and said reference signals, based at least in part, on said calculated reference phase data; and reconstructing molar concentrations of stereoisomers in said target analyte, based at least in part on said target phase data).
With respect to claim 20, De Los Santos et al. explains how the four different stereoisomers were aminoindanol stereoisomers, which typically have at least 2 chiral centers (pg. 2440 left col Abstract para, said specified chiral molecules has n chiral centers). In addition, De Los Santos et al. showcases at least 11 spectrum signals were obtained just for the UV spectra of the 2 chiral center molecules (Pg. 2442 Scheme 2., wherein said reference data comprises at least 2n+1 -1 signals).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to implement the machine learning model of De Los Santos et al. with the spectroscopy system of Ayuso et al. to create a robust end-to-end system capable of processing large datasets of raw, detailed spectral emissions into quantitative variables such molar concentrations. One of ordinary skill in the art would have been motivated to incorporate the linear programming model of De Los Santos et al. to process the highly sensitive spectral emission data derived from the spectroscopy system of Ayuso et al. to efficiently extract important information such as enantiomer concentration in a racemic mixtures, helping to further the drug development and stereochemical analysis as stated in De Los Santos et al. (pg. 2440 right col para 2). In addition, one of skill in the art before the effective filing date of the claimed invention would have a reasonable expectation of success at analyzing the spectral emission data of Ayuso et al. with the linear programming model of De Los Santos et al., as the spectroscopy data is similar to the original spectroscopy data of De Los Santos et al. and the analysis/processing of complex spectral emission data relating to chiral molecules has already been demonstrated to be successful through De Los Santos et al.’s experimentations. It would be merely implementing the model with a similar data set of spectral emissions from other chiral molecules in racemic mixtures.
Response to Amendment
Applicant’s arguments, see pages 5-8 section titled 35 USC § 103 Rejections, filed 09/11/2025 with respect to the rejections of claims 1-20 under 35 USC § 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of Ayuso et al. and De Los Santos et al. as explained above.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,546,704 B2 in view of De Los Santos et al. The claim limitations of the instant application are italicized below. This rejection is newly recited and necessitated by claim amendment.
With respect to claim 1 of the instant application, claim 19 of U.S. Patent No. 18,546,704 B2 teaches a system for detecting a chiral characteristic of a randomly oriented analyte (a system for determining chiral characteristics of an analyte). Claim 19 elaborates that the system comprises of: at least one laser source, configured to illuminate the analyte with at least two laser beams non-collinearly to generate a laser field at an interaction region (an optical setup configured to illuminate at least two laser beams non-collinearly to create a locally chiral laser field on a target analyte that is a multi-center chiral molecule), a detection device coupled with at least one hardware processor (a detection device; at least one hardware processor), and a non-transitory computer-readable storage medium having stored thereon program instructions, where the program instructions are executable by the hardware processor (a non-transitory computer-readable storage medium having stored thereon program instruction, the program instructions executable by the at least one hardware processor). Claim 19 then goes on to recite receiving, via the detection device, at least one spectral line of a harmonic emission signal indicative of chirality (receive, from the detection device, a target signal representing spectral emission associate with the target analyte). Finally, claim 19 determines the chiral characteristic of the analyte based on measure characteristics of the spectral emission (at an inference stage … determine chiral characteristics of said target analyte).
Concerning claim 3 and 11, claim 12 of U.S. Patent No. 18,546,704 B2 recites that the laser field is locally chiral at said interaction (wherein the laser field is locally chiral at said interaction).
With respect to claim 4 and 13, claim 12 of U.S. Patent No. 18,546,704 B2 also recites that the locally chiral mains its local chirality and handedness within all the interaction regions with the analyte (wherein the laser field maintains said local chirality within all of an interaction region with each of said plurality of analytes and said target analyte).
Regarding claim 5 and 14, claim 2 of U.S. Patent No. 18,546,704 B2 recites that the laser field can exhibit any one of the following symmetry properties: static reflection symmetry; dynamical reflection symmetry; dynamical inversion symmetry; dynamical improper rotational symmetry; and lack of inversion, reflection, and improper-rotation symmetry, wherein the laser field is locally chiral at the interaction between the laser field and analyte (wherein said laser field exhibits one of the following symmetry properties: static reflection symmetry; dynamical reflection symmetry; dynamical inversion symmetry; dynamical improper rotational symmetry; and lack of inversion, reflection, and improper-rotation symmetry).
Concerning claim 6 and 15, claim 14 of U.S. Patent No. 18,546,704 B2 recites that one or more of the wavelengths of the at least two laser beams and/or one or more of the polarization of the at least two laser beams is controlled such that the laser field is configured to exhibit local chirality at said interaction region (wherein at least one of the following is controlled: (i) one or more wavelengths of the laser beams, and (ii) one or more of the polarizations of the laser beams). And claim 10, which claim 14 is dependent on, recites that at least two laser beams are used to illuminate the analyte non collinearly to generate a laser field (wherein said laser field is generated by illuminating at least two laser beams non-collinearly).
With respect to claim 7 and 16, claim 12 of U.S. Patent No. 18,546,704 B2 also recites that the locally chiral laser field has different handedness in different sections of the interaction region (wherein the laser field has different handedness in different sections of the interaction region).
Regarding claim 8 and 17, claim 5 of U.S. Patent No. 18,546,704 B2 recites that the harmonic emission can result from a high harmonic generation process between the laser field and the analyte (wherein said spectral emission is a harmonic spectral emission resulting from a high harmonic generation process between said laser field and each of said plurality of analytes and said target analyte).
Concerning claim 9 and 18, claim 5 of U.S. Patent No. 18,546,704 B2 recites that the harmonic emission can result from a low-order harmonic generation process between the laser field and the analyte (wherein said spectral emission is a harmonic spectral emission resulting from a low-order harmonic generation process between said laser field and each of said plurality of analytes and said target analyte).
With respect to claim 10, claim 1 of U.S. Patent No. 18,546,704 B2 recites a method of detecting a chiral characteristic of a randomly oriented analyte (a method of determining chiral characteristic of an analyte). Claim 1’s method starts by illuminating the analyte with at least two laser beams non-collinearly to generate a laser field at an interaction region (illuminating at least two laser beams non-collinearly, to create a locally chiral laser field on a target analyte comprising multi-center chiral molecules). Then claim 1 recites receiving at least one spectral line of a harmonic emission signal indicative of chirality resulting from an electric dipole interaction between the laser field and the analyte at the interaction region (receiving, by a processor, a target signal from a detection device, wherein said target signal represents spectral emission associated with the target analyte). Lastly, claim 1 determines the chiral characteristic of said analyte based on said measured characteristics of the at least one spectral line (at an inference stage … to determine chiral characteristic of said target analyte).
While the claim set of U.S. Patent No. 18,546,704 B2 does not recite utilizing a trained machine learning model to determine the chiral characteristic, it would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to implement a trained machine learning in order to determine the chiral characteristics based on spectral emissions for faster and more accurate predictions as shown by De Los Santos et al.
With respect to claim 1, De Los Santos et al. teaches applying a linear programming method and a parameter sweeping algorithm to absorption and emission data determine the concentration and relative amount of four different stereoisomer in 20 samples of different stereoisomeric composition (pg. 2440 left col Abstract para, at an inference stage, apply a trained machine learning model to said target signal to determine chiral characteristics of said target analyte). De Los Santos et al. elaborates that the four different stereoisomers were aminoindanol stereoisomers, which typically have at least 2 chiral centers (pg. 2440 left col Abstract para, multi-center chiral molecules). Although not explicitly stated in Del Los Santos et al., the supervised learning approach based on linear programming was carried out on a computer with a processor capable of processing the large body of data generated (pg. 2443 right col para 2, at least one hardware processor). In addition, based on the code lines in the supplementary information of De Los Santos et al. and the references cited for the algorithms, the linear programming was probably encoded into a software on Python (Supplementary Information S8, S11, and S56, a non-transitory computer-readable storage medium having stored thereon program instructions, the program instructions executable by the at least one hardware processor).
Regarding claim 10, De Los Santos et al. teaches applying a linear programming method and a parameter sweeping algorithm to absorption and emission data determine the concentration and relative amount of four different stereoisomer in 20 samples of different stereoisomeric composition (pg. 2440 left col Abstract para, at an inference stage, applying, by a processor, a trained machine learning (ML) model to said target signal to determine chiral characteristics of said target analyte). De Los Santos et al. elaborates that the four different stereoisomers were aminoindanol stereoisomers, which typically have at least 2 chiral centers (pg. 2440 left col Abstract para, multi-center chiral molecules). Although not explicitly stated in Del Los Santos et al., the supervised learning approach based on linear programming was carried out on a computer with a processor capable of processing the large body of data generated (pg. 2443 right col para 2, by the processor).
Concerning claim 2 and 11, De Los Santos et al. teaches a trained linear programming model previously trained on 20 samples of various stereoisomeric composition (pg. 2440 left col Abstract para, wherein the machine learning model was previously trained). De Los Santos et al. teaches that UV (ultraviolet) and CD (circular dichroism) spectroscopy data was obtained for 20 samples with known compositions of quaternary isomeric mixtures, and utilized as the training input (pg. 2443 right col para 3, receiving a plurality of signals representing spectral emission resulting from an interaction between a laser field and a respective plurality of analytes, wherein at least some of said analytes comprise multi-center chiral molecules). De Los Santos et al. further elaborates that the 20 samples, T1-T20 with varying diastereomeric ratios (dr) and enantiomeric ratios (er) along with their UV and CD spectra were fed into the linear programming algorithm for training to obtain individual percent compositions of (R,S)-1, (S,R)-1, (R,R)-1, (S,S)-1 (er values), and [(R,S)-1 + (S,R)-1] and [(R,R)-1 + (S,S)-1] (dr values) (pg. 2443 right col para 3, at a training stage, training the ML model on a training set comprising: (i) plurality of signals, and (ii) labels associated with a configuration of a chirality in each of said plurality of analytes, wherein said plurality of signals are labeled with said labels).
Responding to claim 19, De Los Santos et al. teaches an optical method for accurate concentration, er and dr analysis of amino alcohols that utilizes machine learning on a computer (pg. 2240 left col Abstract para and Title, A method of analyzing a target analyte by at least one processor). To begin, De Los Santos et al. obtains UV and CD spectral emission data from 20 samples of quaternary isomeric mixtures with known concentrations as the training input for the linear programming model (pg. 2443 right col para 3, obtaining reference data comprising a plurality of reference signals representing spectral emissions resulting from an interaction between a … laser field and a reference analyte). Then, De Los Santos et al. obtains UV and CD spectral emission data for another 20 samples (S1-S20) of scalemic mixtures with varying unknown concentrations as the testing input into the model (pg. 2444 left col para 2, obtaining target data comprising a plurality of target signals representing spectral emissions resulting from an interaction between … a laser field and a target analyte comprising said specified chiral molecule). Next, De Los Santos et al. explains, that after the reference/training input is fed into the model, all possible 10, 50, 100, 150, and 200 nm ranges were evaluated with 10 nm steps across the whole spectrum width before the optimal wavelength span was selected by identifying the range that produces the lowest averaged absolute error across all compositions (pg. 2443 left col para 3, calculating reference phase data with respect to each of said reference signals). Finally, after feeding the testing input into the model, De Los Santos et al. showcases how the model mapped the optimized ranges derived from the training set onto the testing set in order to predict the individual concentrations of each of the four stereoisomers in the samples (pg. 2444 left col para 2 and Supplementary Information S16, deriving target phase data with respect to said target signals, by applying an optimization algorithm which minimizes an error between said target signals and said reference signals, based at least in part, on said calculated reference phase data; and reconstructing molar concentrations of stereoisomers in said target analyte, based at least in part on said target phase data).
With respect to claim 20, De Los Santos et al. explains how the four different stereoisomers were aminoindanol stereoisomers, which typically have at least 2 chiral centers (pg. 2440 left col Abstract para, said specified chiral molecules has n chiral centers). In addition, De Los Santos et al. showcases at least 11 spectrum signals were obtained just for the UV spectra of the 2 chiral center molecules (Pg. 2442 Scheme 2., wherein said reference data comprises at least 2n+1 -1 signals).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to implement the machine learning model of De Los Santos et al. with the spectroscopy system of U.S. Patent No. 18,546,704 B2 to create a robust end-to-end system capable of processing large datasets of raw, detailed spectral emissions into quantitative variables such molar concentrations. One of ordinary skill in the art would have been motivated to incorporate the linear programming model of De Los Santos et al. to process the highly sensitive spectral emission data derived from the spectroscopy system of U.S. Patent No. 18,546,704 B2 to efficiently extract important information such as enantiomer concentration in a racemic mixtures, helping to further the drug development and stereochemical analysis as stated in De Los Santos et al. (pg. 2440 right col para 2). In addition, one of skill in the art before the effective filing date of the claimed invention would have a reasonable expectation of success at analyzing the spectral emission data of U.S. Patent No. 18,546,704 B2 with the linear programming model of De Los Santos et al., as the spectroscopy data is similar to the original spectroscopy data of De Los Santos et al. and the analysis/processing of complex spectral emission data relating to chiral molecules has already been demonstrated to be successful through De Los Santos et al.’s experimentations. It would be merely implementing the model with a similar data set of spectral emissions from other chiral molecules in racemic mixtures.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
David Ayuso, Andres Ordonez, Piero Decleva, Misha Ivanov, Olga Smirnova, "Polarization of chirality", arXiv:2004.05191v1 [physics.optics] 10 Apr 2020
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).
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/W.Y./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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