Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-20 are pending and examined herein.
No claims are canceled.
Priority
As detailed on the 05 April 2023 filing receipt, the application claims priority as early as 25 March 2022 to provisional application 63/323,540. At this point in examination, all claims have been interpreted as being accorded this priority date as the effective filing date.
Information Disclosure Statement
Information disclosure statement (IDS) was filed on 27 February 2023. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the references are being considered by the examiner.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
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.
The term “difficult” in claims 6, 13, and 20 is a relative term which renders the claim indefinite. The term “difficult” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear what is meant by difficult and therefore unclear when and how the step of re-optimization should occur, or what it is.
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 1-20 are rejected under 35 USC § 101 because the claimed inventions are directed to an abstract idea without significantly more. "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" (MPEP 2106.04 § I). Abstract ideas include mathematical concepts, and procedures for evaluating, analyzing or organizing information, which are a type of mental process (MPEP 2106.04(a)(2)). The claims as a whole, considering all claim elements individually and in combination, are directed to a judicial exception at Step 2A, Prong 2, and the additional elements of the claims, considered individually and in combination, do not provide significantly more at Step 2B than the abstract idea of generating binding TCRs recognizing target peptides.
MPEP 2106 organizes JE analysis into Steps 1, 2A (Prong One & Prong Two), and 2B as analyzed below.
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter (MPEP 2106.03)?
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of
nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
The claims are directed to a method (claims 1-7), a non-transitory computer-readable medium (claim 8-14), and a computer system (claim 15-20), each of which falls within one of the categories of statutory subject matter. [Step 1: Yes]
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. MPEP § 2106.04(a)(2) further explains that abstract ideas are defined as:
• mathematical concepts (mathematical formulas or equations, mathematical relationships
and mathematical calculations) (MPEP 2106.04(a)(2)(I));
• certain methods of organizing human activity (fundamental economic principles or practices, managing personal behavior or relationships or interactions between people) (MPEP 2106.04(a)(2)(II)); and/or
• mental processes (concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) (MPEP 2106.04(a)(2)(III)).
Mathematical concepts recited in the independent claims include: developing a framework to generating maximum scores, where the framework is disclosed as using a reward function to measure likelihoods and probabilities of recognizing peptides, where functions to measuring likelihood and probability are verbal descriptions of mathematical functions; defining reward functions based on scores is a verbal description of a mathematical concept where a reward function is considered a verbal description of a mathematical concept; following the policy network is interpreted as performing the calculations to modify the TCR based on their probabilities and thus a verbal description of a mathematical concept; ranking may be understood as a mathematical step of evaluating the binding scores and ordering them numerically; and optimizing candidates greedily by maximizing and minimizing sums until a criterion is met is a verbal description of a mathematical concept of greedy optimization.
Mental processes, defined as concepts practically performed in the human mind such as steps of observing, evaluating, or judging information, recited in the independent claims include: extracting peptides from sequence data, where extraction is considered to be a data selection or evaluation step of determining which sequences are relevant as peptides and thus a mental process; predicting interaction scores, where predicting a score is interpreted as an evaluation of the peptide and TCRs to qualify the likelihood of interaction between them; developing a framework to generating maximum scores, where the framework uses rules which may be understood as mental steps for evaluating the data; randomly sampling is a mental process of data selection and following the policies could be interpreted as a mental step for the reasons outlined above; outputting could be recording the results with pen and paper and thus is a mental step; ranking the outputted TCRs is interpreted as ordering them based on a value, which is data evaluation and thus a mental step; identifying self-peptides, where identification is data evaluation and thus a mental step.
Claims 2, 9, and 16 recite the reward functions measure both a likelihood of mutated sequences being valid TCRs and probabilities of the TCRs recognizing peptides, where measuring likelihoods and probabilities are verbal descriptions of mathematical concepts.
Claims 4, 11, and 18 recite density estimation over a latent space within the TCR-AE is evaluated by using a Gaussian Mixture Model (GMM), where density estimation and GMM are mathematical concepts.
Claims 6, 13, and 20 recite a buffering and re-optimizing framework including a buffer is employed to handle TCRs difficult to optimize and to generalize optimization capacity to more diverse TCRs, where at least re-optimizing is interpreted as a mathematical concept given its broadest reasonable interpretation in light of the specification, where Equation 10 is disclosed as related to the optimization (pg. 15, paragraph [60]).
Claims 7 and 14 recite the TCRs and the extracted peptides are encoded by a TCR-AE in a distributed embedding space, and a mapping is learnt between the embedding space and the TCR mutation policies. Embedding is a mathematical concept of conversion of data to vectors while mapping is a mental step of relating the sequences to policies.
Thus, the claims recite abstract ideas and thus must be examined further to determine whether elements in addition to the abstract ideas integrate the judicial exceptions into a practical application (MPEP 2106.04(d)). [Step 2A Prong One: Yes]
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Because the claims recite judicial exceptions, direction under Step 2A Prong Two provides that the claims must be examined further to determine whether they recite elements in addition to the abstract ideas which integrate the judicial exceptions into a practical application (MPEP 2106.04(d)). A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. This is performed by analyzing the additional elements of the claim to determine if the judicial exceptions are integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the judicial exceptions, the claim is said to fail to integrate the judicial exceptions into a practical application (MPEP 2106.04(d)(III)).
Elements in addition to the abstract ideas recited in the claims are a deep neural network (claims 1, 8, and 15), a TCR autoencoder, which is an autoencoder trained only on TCRs (claims 4, 11, and 18), which uses a bidirectional LSTM to encode an input sequence into a hidden vector by concatenating the last hidden vectors from two LSTM directions (claims 5, 12, and 19), a non-transitory computer-readable medium executed on a computer (claim 8), and a system comprising memory and processors (claim 15).
The machine learning elements are considered to be generally linking the use of the judicial exceptions to the recited particular field of use or technological environment of neural networks, which does not integrate the abstract ideas into a practical application (MPEP 2106.05(h)).
The claims comprising computer components do not describe any specific computational steps by which the computer performs or carries out the abstract idea, nor do they provide any details of how specific structures of the computer are used to implement these functions. The claims state nothing more than that a generic computer performs the functions that constitute the abstract idea. Hence, these are mere instructions to apply the abstract idea using a computer, and therefore the claim does not integrate that abstract idea into a practical application (see MPEP 2106.04(d) § I; and MPEP 2106.05(f)).
Because the claims recite elements in addition to the abstract ideas which are not considered to integrate the abstract ideas into a practical application, the elements in addition to the abstract ideas are further analyzed to determine if they provide significantly more than the abstract ideas. [Step 2A Prong Two: No]
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
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 of 101 analysis determines whether the claims contain additional elements that amount to an inventive concept, and an inventive concept cannot be furnished by an abstract idea itself (MPEP 2106.05).
Elements in addition to the abstract ideas recited in the claims are a deep neural network (claims 1, 8, and 15), a TCR autoencoder, which is an autoencoder trained only on TCRs (claims 4, 11, and 18), which uses a bidirectional LSTM to encode an input sequence into a hidden vector by concatenating the last hidden vectors from two LSTM directions (claims 5, 12, and 19), a non-transitory computer-readable medium executed on a computer (claim 8), and a system comprising memory and processors (claim 15).
Chen (Molecular Systems Design & Engineering 6(6): 406-428, 2021; newly cited) teaches autoencoders to encode raw data (Section 3.6.1), a deep neural network for bioinformatic purposes to learn interactions (Section 3.1), bidirectional long short term memory got peptide evaluation (Section 2.2.2), and application of neural networks to TCR binding prediction (Section 2.1.2).
Therefore, the recited additional elements, alone or in combination, do not appear to provide an inventive concept. [Step 2B: No]
Conclusion: Claims are Directed to Non-statutory Subject Matter
For these reasons, the claims, when the limitations are considered individually and as a whole,
are directed to an abstract idea and lack an inventive concept. Hence, the claimed invention does not
constitute significantly more than the abstract idea, so the claims are rejected under 35 USC § 101 as
being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 6-10, 13-17, and 20
Claims 1-3, 6-10, 13-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Springer (Frontiers in Immunology 12(664514): 11 pgs., 2021; previously cited on the 27 February 2023 IDS form) in view of Riley (Protein Engineering, Design & Selection 29(12): 595-606, 2016; newly cited) and King (doctoral dissertation, University of Washington, 76 pgs., 2014; newly cited).
Claim 1 recites extracting peptides to identify a virus or tumor cells and collecting a library of TCRs from target patients.
Springer teaches dual encoding of TCR and peptides (pg. 2, col. 2, last paragraph).
Claim 1 recites predicting, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients and a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores.
Springer teaches deep learning methods and a multilayer perceptron (pg. 2, col. 2, last paragraph), where a multilayer perceptron is a type of deep neural network.
Springer does not teach steps related to maximizing binding scores by mutating TCRs.
Riley teaches a scoring function and a model for affinity enhancing mutations in TCRs (abstract) and training a TCR prediction model (pg. 598, col. 1, last paragraph).
Claim 1 recites defining reward functions based on a reconstruction-based score and a density
estimation-based score and sampling TCRs to mutate them.
Reconstruction and density estimation scores are not defined and are interpreted to be validity scores for mutations/modifications of TCRs. Riley teaches screening and scoring new TCR mutations (pg. 601, col. 2, second paragraph).
Claim 1 recites outputting mutated TCRs and ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy.
Riley teaches ranking of mutations (pg. 601, col. 2, last paragraph) and enhancing TCRs for immunotherapy (pg. 603, col. 2, last paragraph) as well as favorable ranking, where favor is interpreted as a top ranking.
Claim 1 recites, for each top-ranked TCR candidate, repeatedly identifying a set of self-peptides that the top-ranked TCR candidate binds to and further optimizing the top-ranked TCR candidate greedily by maximizing a sum of its interaction scores with a given set of peptide antigens while minimizing a sum of its interaction scores with the set of self-peptides until one or more stopping criteria of efficacy and safety are met.
Riley teaches modification of TCRs and detecting self-antigens leading to off-target recognition (pg. 596, col. 1, third paragraph). Riley teaches, related to modulating binding properties, scoring variants for controlling and identifying reactivity to self-antigens (pg. 605, col. 1, last paragraph).
King teaches greedy optimization design in point mutations, designing based on MHC binding affinity, where scores about a threshold were selected (pg. 57-58). King teaches scoring and rescoring for binding affinity to improve specificity (pg. 15, second paragraph) and only keeping sequences with substitutions scoring above a certain threshold (pg. 60, second paragraph).
Claim 8 recites a non-transitory computer-readable storage medium executed on a computer performing the steps of claim 1.
King teaches at least programs (pg. 4, second paragraph) as well as use of processors (pg. 15, third paragraph) on a computer, which suggests a computational environment.
Claim 15 recites a system comprising memory and processor(s) performing the steps of claim 1.
King teaches at least use of processors (pg. 15, third paragraph) on a computer, which would also require memory, which suggests a computational environment.
Claims 2, 9, and 16 recite the reward functions measure both a likelihood of mutated sequences being valid TCRs and probabilities of the TCRs recognizing peptides.
Riley teaches validation based on peptide recognition (pg. 602, col. 1, last paragraph) and predicting energetics associated with mutations (pg. 603, col. 1, first paragraph) while Springer teaches predicting binding probability (pg. 2, col. 2, last paragraph).
Claims 3, 10, and 17 recite the measurement of the likelihood of the mutated sequences being valid TCRs is enabled by a TCR autoencoder (TCR-AE) trained only by TCRs.
Springer teaches autoencoder trained on TCR data (pg. 2, col. 2, last paragraph).
Claims 6, 13, and 20 recite a buffering and re-optimizing framework including a buffer is employed to handle TCRs difficult to optimize and to generalize optimization capacity to more diverse TCRs.
It is unclear what “difficult” connotes in terms of TCR optimization (see rejection under 35 USC 112(b) above). However, Riley teaches optimization improvements compared to previous poor accuracy optimization and therefore may teach re-optimization.
Claims 7 and 14 recite the TCRs and the extracted peptides are encoded by a TCR-AE in a distributed embedding space, and a mapping is learnt between the embedding space and the TCR mutation policies.
Springer teaches dual embedding of TCR and peptides (pg. 8, col. 1, last paragraph) while Riley teaches molecular modification and flexibility in affinity-enhancing mutations (abstract).
Combining Springer, Riley, and King
An invention would have been obvious to one of ordinary skill in the art if some motivation in the prior art would have led that person to modify prior art reference teachings to arrive at the claimed invention prior to the effective filing date of the invention. One would have been motivated to combine the works of Springer and Riley because Springer teaches determining peptide binding prediction based on peptides and TCR (abstract) and use in unseen peptides (abstract). Riley teaches medication of peptides to have enhanced binding (abstract) by novel mutations, resulting in novel, unseen sequences which require similar validation as taught by Springer. Both Springer and Riley are directed to the shared field of endeavor of TCR binding affinity research. Further combination with King would be motivated by King’s greedy optimization for sequence design because Riley is directed to sequence design to optimize binding and King teaches an optimization design which reliably converges on the near-optimal substitutions to produce output above an acceptable threshold (pg. 60, second paragraph). King is directed to sequence design optimization in computational protein design (pg. 6, second paragraph). Therefore, the invention is prima facie obvious.
Claims 4, 11, and 18
Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Springer, Riley, and King as applied to claims 1-3, 6-10, 13-17, and 20 above and further in view of Friedensohn (bioRxiv 2020.02.25.965673, 17 pgs., 2020; newly cited).
Claims 4, 11, and 18 recite density estimation over a latent space within the TCR-AE is evaluated by using a Gaussian Mixture Model (GMM).
Spring, Riley, and King do not teach a density estimation evaluated using a GMM.
Friedensohn teaches a GMM in latent space (abstract) and an autoencoder to estimate densities in a deep neural network (pg. 2, second paragraph).
Combining Springer, Riley, King, and Friedensohn
An invention would have been obvious to one of ordinary skill in the art if some motivation in the prior art would have led that person to modify prior art reference teachings to arrive at the claimed invention prior to the effective filing date of the invention. One would have been motivated to combine the previously combined works with those of Friedensohn because Friedensohn teaches the combination of the GMM and autoencoder allows more accurate density estimates (pg. 2, second paragraph). The combined works are directed to the shared field of endeavor of deep learning and immunology, and therefore the invention is prima facie obvious.
Claims 5, 12, and 19
Claims 5, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Springer, Riley, and King as applied to claims 1-3, 6-10, 13-17, and 20 above and further in view of Chen.
Claims 5, 12, and 19 recite the TCR-AE uses a bidirectional long short-term memory (LSTM) to encode an input sequence into a hidden vector by concatenating last hidden vectors from two LSTM directions.
Springer teaches long short memory encoding (pg. 2, col. 2, last paragraph) and a hidden layer with output from the previous layer (pg. 4, col. 1, second paragraph) but not a bidirectional LSTM.
Chen teaches a bidirectional long short term memory got peptide evaluation (Section 2.2.2).
Combining Springer, Riley, King, and Chen
An invention would have been obvious to one of ordinary skill in the art if some motivation in the prior art would have led that person to modify prior art reference teachings to arrive at the claimed invention prior to the effective filing date of the invention. One would have been motivated to combine the previously combined works with those of Chen because Chen teaches peptide identification, generation, and property prediction using deep learning, and specifically teaches bidirectional LSTM networks for peptide design and evaluation which yield well-trained and task-specific embeddings adaptive to the model (Section 2.2.2). Chen also teaches application to TCR peptide binding (Table 1) and thus a shared field of endeavor with at least Springer and Riley. Therefore, the invention is prima facie obvious.
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 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 18/151,686 in view of Riley and King.
This is a provisional nonstatutory double patenting rejection.
The instant claims 1-20 are taught by the reference claims 1-20 except for the following limitation of claims 1, 8, and 15: for each top-ranked TCR candidate, repeatedly identify a set of self-peptides that the top-ranked TCR candidate binds to and further optimize the top-ranked TCR candidate greedily by maximizing a sum of its interaction scores with a given set of peptide antigens while minimizing a sum of its interaction scores with the set of self-peptides until one or more stopping criteria of efficacy and safety are met. Riley teaches modification of TCRs and detecting self-antigens leading to off-target recognition (pg. 596, col. 1, third paragraph). Riley teaches, related to modulating binding properties, scoring variants for controlling and identifying reactivity to self-antigens (pg. 605, col. 1, last paragraph). King teaches greedy optimization design in point mutations, designing based on MHC binding affinity, where scores about a threshold were selected (pg. 57-58). King teaches scoring and rescoring for binding affinity to improve specificity (pg. 15, second paragraph) and only keeping sequences with substitutions scoring above a certain threshold (pg. 60, second paragraph). Riley is directed to sequence design to optimize binding and King teaches an optimization design which reliably converges on the near-optimal substitutions to produce output above an acceptable threshold (pg. 60, second paragraph). The reference application and prior art are directed to sequence design optimization in computational protein design (pg. 6, second paragraph). Therefore, the invention is prima facie obvious.
Conclusion
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/Robert J. Kallal/Examiner, Art Unit 1685
Prosecution Insights