DETAILED ACTION
Claim Status
Claims 1-25 are rejected.
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 .
Priority
This application claims Domestic Benefit to application #63301443, filed 01/20/2022. Domestic Benefit is acknowledged. Therefore, the effective filing date of claims is 01/20/2022. This application is a 371 of PCT/US2023/011194. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
No IDS was filed herein.
Drawings
Figs. 3, 6A-7A, 7C-9A are executed in color. Color photographs and color drawings are not accepted in utility applications unless a petition filed under 37 CFR 1.84(a)(2) is granted. Any such petition must be accompanied by the appropriate fee set forth in 37 CFR 1.17(h), one set of color drawings or color photographs, as appropriate, if submitted via the USPTO patent electronic filing system or three sets of color drawings or color photographs, as appropriate, if not submitted via the via USPTO patent electronic filing system, and, unless already present, an amendment to include the following language as the first paragraph of the brief description of the drawings section of the specification: The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee. Color photographs will be accepted if the conditions for accepting color drawings and black and white photographs have been satisfied. See 37 CFR 1.84(b)(2).
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 7 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 7 states that the first property of the target protein has to be five distinct values. There is no way in which the first property of the target protein can be all five of these values at once, making it indefinite as to what is claimed as the first property.
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.
Claim 22 is non-statutory as it recites “a computer readable storage medium”. The claims as instantly recited read on carrier waves, which are transitory propagating signals and therefore are not proper patentable subject matter because they do not fit within any of the four statutory categories of invention (In re Nuijten, Federal Circuit, 2007). It is noted that the recitation of a "non-transitory computer readable storage medium" would overcome the rejection with respect to claim 22 reading on signals. However, the amendment to only "non-transitory computer readable medium" would not overcome the rejection under 35 U.S.C. 101 since the claims would still be directed to a judicial exception without significantly more (see below).
Claims 1-25 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:
1, 22, 23: A) obtaining an identity of each single point mutation in a first plurality of single point mutations of the target protein, each respective single point mutation in the first plurality of single point mutations defining a corresponding single point substituted protein characterized by a reference sequence for the target protein with the exception of an alteration at a respective independent position within the reference sequence to an amino acid other than that found in the reference sequence;
1, 22, 23. B) obtaining, for each corresponding point substituted protein defined by the first plurality of single point mutations, a corresponding set of values for a set of properties of the corresponding point substituted protein, wherein the set of properties comprises: (i) a stability of the corresponding point substituted protein, (ii) at least one protein formulation property of the corresponding point substituted protein, and (iii) a determination that the respective single point mutation in the corresponding point substituted protein occurs at a predetermined position that exhibits variability across a plurality of naturally occurring homologs of the target protein;
1, 22, 23. C) filtering the first plurality of single point mutations to form a second plurality of single point mutations based at least upon each corresponding set of values for the set of properties, wherein the filtering includes determining, for each corresponding point substituted protein defined by the first plurality of single point mutations, for each respective property in the set of properties, whether a value of the respective property in the corresponding set of values for the corresponding point substituted protein satisfies a corresponding threshold value requirement for the respective property, wherein the corresponding point substituted protein is included in the second plurality of single point mutations when each corresponding threshold value requirement for each property in the set of properties is satisfied, and the corresponding point substituted protein is not included in the second plurality of single point mutations when any corresponding threshold value requirement of any property in the set of properties is not satisfied;
1, 22, 23. D) obtaining a corresponding measured value of the first property for each combinatorially substituted protein in a first plurality of combinatorially substituted proteins, wherein each respective combinatorially substituted protein in the first plurality of combinatorially substituted proteins is characterized by the reference sequence for the target protein with the exception of the independent inclusion of two or more single point mutations from the second plurality of single point mutations;
1, 22, 23. E) training a surrogate model within an N-dimensional space, wherein N is a positive integer of 10 or greater, using at least, for each respective combinatorially substituted protein in the first plurality of combinatorially substituted proteins, the corresponding measured value of the first property in the respective combinatorially substituted proteins against an identity of each single point mutation in the respective combinatorially substituted protein, wherein the model comprises 20 or more parameters and the first plurality of combinatorially substituted proteins comprises 20 or more proteins;
1, 22, 23. F) using the surrogate model and, for each respective combinatorially substituted protein in the first plurality of combinatorially substituted proteins, the identity of each single point mutation in the respective combinatorially substituted protein, to update a search model; and
1, 22, 23. G) using the updated search model to identify a second plurality of combinatorially substituted proteins within the N-dimensional space, wherein each respective combinatorially substituted protein in the second plurality of combinatorially substituted proteins is characterized by the reference sequence for the target protein with the exception of independent inclusion of two or more single point mutations from the second plurality of single point mutations.
2. The computer system of claim 1, wherein the at least one protein formulation property is an electrostatic property of the corresponding point substituted protein, a developability index of the corresponding point substituted protein, a solubility of the corresponding point substituted protein, a measure of aggregation of the corresponding point substituted protein, a viscosity of the corresponding point substituted protein, or a combination thereof.
3. The computer system of claim 1 or 2, wherein the using G) identifies an optimal range of single point mutations, drawn from the second plurality of single point mutations to incorporate into the target protein.
4. The computer system of any one of claims 1-3, wherein the set of properties further comprises a post-translational modification that is predicted to occur to the corresponding point substituted protein.
5. The computer system of any one of claims 1-4, wherein the set of properties further comprises an immunogenicity of the corresponding point substituted protein.
6. The computer system of any one of claims 1-5, wherein the set of properties further comprises a binding energy of the corresponding point substituted protein.
7. The computer system of any one of claims 1-6, wherein the first property of the target protein is a solubility of the target protein, an ability of the target protein to carry out an enzymatic activity in a predetermined pH range, aliphatic index, a molecular weight of the target protein, and a charge of the of the target protein.
8. The computer system of any one of claims 1-7, wherein each combinatorially substituted protein in the first plurality of combinatorially substituted proteins includes three or more, four or more, five or more, or six or more point substitutions.
9. The computer system of any one of claims 1-7, wherein each combinatorially substituted protein in the first plurality of combinatorially substituted proteins includes between three and fifty point substitutions.
10. The computer system of any one of claims 1-9, wherein the target protein is an enzyme and the first property is an enzymatic activity of the target protein.
11. The computer system of claim 10, wherein the enzyme is a hydrolase, oxidoreductase, lyase, transferase, ligase or isomerase.
12. The computer system of any one of claims 1-11, wherein the target protein comprises 50 or more residues, or 100 or more residues.
13. The computer system of any one of claims 1-12, wherein the stability of the corresponding point substituted protein is determined using one or more crystal structures or atomistic models of the target protein.
14. The computer system of any one of claims 1-13, wherein the corresponding threshold value for the stability is a stability of the target protein, wherein, when the corresponding point substituted protein has a stability that is better than the stability of the target protein, the corresponding point substituted protein is included in the second plurality of single point mutations, and when the corresponding point substituted protein has a stability that is worse than the stability of the target protein, the corresponding point substituted protein is not included in the second plurality of single point mutations.
15. The computer system of any one of claims 1-13, wherein the corresponding threshold value for the stability is a stability of the target protein, wherein, when the corresponding point substituted protein has a stability that is at least a threshold percentage or better than the stability of the target protein, the corresponding point substituted protein is included in the second plurality of single point mutations, and when the corresponding point substituted protein has a stability that is less than a threshold percentage of the stability of the target protein, the corresponding point substituted protein is not included in the second plurality of single point mutations.
16. The computer system of any one of claims 1-15, wherein the training E) comprises encoding each respective combinatorially substituted protein in the first plurality of combinatorially substituted proteins as an identity of each single point mutation in the respective combinatorially substituted protein in a first dimension, and a position of each single point mutation in the respective combinatorially substituted protein in a second dimension.
17. The computer system of any one of claims 1-15, wherein the training E) comprises encoding each respective combinatorially substituted protein in the first plurality of combinatorially substituted proteins as an identity of each single point mutation in the respective combinatorially substituted protein in a first dimension, a position of each single point mutation in the respective combinatorially substituted protein in a second dimension, and a plurality of amino acid indices, or a low dimension or latent dimension thereof, for each of the naturally occurring amino acids in a third dimension.
18. The computer system of any one of claims 1-17, wherein the surrogate model is a support vector regression with RBF kernel, a random forest, XGBoost, a Gaussian Process, a deep neural network, a convolutional neural network, or a recurrent neural network.
19. The computer system of any one of claims 1-8 or 10-19, wherein the target protein is an enzyme, a co-enzyme, a structural protein, a nutrient protein, a regulatory protein, a defense protein, a transport protein, a storage protein, a contractile protein, or a toxic protein.
20. The computer system of any one of claims 1-19, wherein the using G) identifies optimal single point mutations in the second plurality of single point mutations to incorporate into the target protein.
21. The computer system of claim 20, wherein the using G) rank orders each single point mutation in the second plurality of single point mutations to incorporate into the target protein.
24. The computer system of any one of claims 1-23. wherein the first plurality of combinatorially substituted proteins in D) has mutation rates configured to allow learning of comprehensive interactions between different mutations.
25. The computer system of any one of claims 1-23, wherein the search model is Bayesian optimization.
The limitations for “obtaining,” “filtering,” “training,” and “using” all refer to a series of manipulations made over sequence data, which is a series of variables. While there are limitations for conducting the training and using steps with machine learning models, the claimed models are broad enough to encompass embodiments that could be practically be performed by a human with a pen and paper, such as random forests. While there are limitations for the training within an at least 10-dimensional space, these dimensions could refer to features of the model which could be computed by a human with a pen and paper. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. While claims 1 and 22 recite performing some aspects of the analysis with a “computer”, there are no additional limitations that indicate that this computer requires anything other than carrying out the recited mental process or mathematical concept in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then if falls within the “Mental processes” grouping of abstract ideas. As such, claims 1-25 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). This judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology or applies or uses the recited judicial exception to effect a particular treatment for a condition. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment or mere instructions to apply the recited judicial exception via a generic treatment. Specifically, the claims recite the following additional elements:
1. A computer system comprising: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs for identifying one or more combinatorial substitutions that affect a first property of a target protein,
22. A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by an electronic device with one or more processors and a memory cause the electronic device to identify one or more combinatorial substitutions that affect a first property of a target protein by a method comprising
There are no limitations that indicate that the claimed computer or the formats of the provided data require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. As such, claims 1-25 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 mere instructions to apply the recited exception in a generic way or in a generic computing environment. The instant claims recite additional elements enumerated above, in the section on step 2A.
As discussed above, there are no additional limitations to indicate that the claimed computer requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. The additional elements 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. Therefore, the claims do not amount to significantly more than the judicial exception itself ( Step 2B : No). As such, claims 1-25 are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3, 6-9, 12-13, 17-21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wittman et al (bioRxiv preprint, 2020).
Regarding the computer and program implementation of claim 1, Wittman gives a GITHUB® package for their work (pg 9 right col ¶ 2).
Regarding claim 1 step A, Wittman is directed to simulating the directed evolution of proteins by identifying substitutions that effect protein properties (abstract). Point mutations are obtained for the protein (fig. 1 and description). Regarding the exception about amino acids not present in the reference sequence, Wittman writes: “Importantly, after prediction, only the top-predicted unsampled combinations that could be constructed by recombining combinations in the training data were evaluated” (pg 9 right col ¶ 1).
Concerning claim 1 step B, a formulation property, namely stability, is used as the fitness property in Wittman, reading on steps (i) and (ii) (pg 8 left col ¶ 1-2). Regarding (iii), Wittman discusses drawing the training data from regions already believed to exhibit variability, thus causing the predicted mutations to occur at such predetermined positions (pg 6 right col ¶ 2).
With respect to claim 1 step C, Wittman writes: “To generate training data using a simulated classifier, the GB1 dataset was first filtered to exclude all variants with fitness greater than “limit”. The remaining data was then split into two sets: one set had all variants with fitness greater than or equal to the threshold and the other set had all variants with fitness less than the threshold. Equal numbers of samples were then drawn at random from the two sets without replacement. Training data for the “no classifier” control discussed in the results section and presented in Figure 3C-E (“100% Training Fitness ≥ 0”) was generated by sampling at random from the limit-filtered GB1 dataset.” (pg 12 left col ¶ 3).
Moving on to claim 1 step D, fitness values for the substituted proteins are obtained, corresponding to the measured values (pg 12 left col ¶ 3). Wittman writes that the substituted proteins are based on permutations of a reference sequence: “Importantly, after prediction, only the top-predicted unsampled combinations that could be constructed by recombining combinations in the training data were evaluated” (pg 9 right col ¶ 1).
Regarding step E, Wittman writes: “Many of the learned embeddings used in the previous section are extremely high-dimensional, with the largest (LSTM) describing each combination with 8192 features (Supporting Information: Encoding Preparation).”
Regarding steps F and G, Wittman details a search procedure to rank the permuted proteins by estimated fitness, creating a subset of substituted proteins (pg 4 right col ¶ 1). This is a form of updating the search model to identify a subset of combinatorially substituted proteins.
Claims 22 and 23 are restatements of claim 1, only differing in that they are directed to a computer-readable storage medium and a method, respectively. The arguments against claim 1 apply, mutatis mutandis.
Regarding claim 3, see the discussion (pg 4 right col ¶ 1) on the evaluation of the M-highest variants.
Regarding claim 6, the binding energy of the target protein forms part of the fitness metric of Wittman (pg 4 left col ¶ 3).
Regarding claims 8 and 9, fig. 1 shows an example with four point substitutions. An example about four mutations is also discussed (pg 4 left col ¶ 3).
Regarding claim 12, the example target protein is protein G domain B1 (pg 4 left col ¶ 3), which has 55 residues – see evidentiary reference PDB (https://doi.org/10.2210/pdb3GB1/pdb, 1999).
Regarding claim 13, a crystal structure is used to determine the stability of the target protein in Wittman (pg 8 left col ¶ 2).
Regarding claims 14-15, stability is used as a first property in Wittman, and the substituted proteins are ranked, with only a top set forming a second plurality (pg 8 left col ¶ 1-2, pg 9 left col ¶ 2).
Regarding claims 16-17, the identity of point mutations or variants are encoded as a first dimension, with position as a second dimension (pg 12 left col ¶ 4).
With respect to claim 18, Wittman uses vector regression (pg 3 right col ¶ 2) with XGboost (pg 12 left col ¶ 2).
Concerning claim 19, protein G is a protein that blocks antibodies by binding to them, thus providing defense to the bacterium (pg 4 left col ¶ 3).
Regarding claims 20 and 21, see see the discussion (pg 4 right col ¶ 1) on the evaluation of the M-highest variants.
Regarding claim 24, the mutation rates of the model are configured to learn epistatic interactions between mutations (pg 3 right col ¶ 2).
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.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Wittman as applied to claims 1, 3, 6-9, 12-13, 17-21 above, and further in view of Romero et al (Biochemistry and Molecular Biology Volume 2, Issue 2, 2017).
Romero presents a protein sequence search and design model similar to Wittman, but whose first property is a melting temperature (E195 left col ¶ 1). Melting temperature represents the energy required to overcome electrostatic forces, making it a combination of electrostatic properties.
Regarding claim 2, An invention would have been prima facie obvious to one of ordinary
skill in the art at the time of the effective filing date of the invention if some teaching, suggestion, or motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. There is a teaching to use melting temperature as the first property in the text of Romero (E195 left col ¶ 1), in order to find more thermostable proteins (abstract). There would be a
reasonable expectation of success in making this combination to a person of ordinary skill in the art, as the two models have the same general structure, only differing in the optimized property. Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the method of Wittman by using melting temperature as a first property, in order to find more thermostable proteins.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Wittman as applied to claims 1, 3, 6-9, 12-13, 17-21 above, and further in view of Rocha et al (Biochemistry and Molecular Biology Volume 2, Issue 2, 2017).
With respect to claim 4, Rocha teaches that shifts in steric bulk and hydrophobicity predict post-translational modifications (Rocha abstract), and uses these values to quantify them, providing a suggestion to use this value in sequence design (Rocha pg 19 right col ¶ 1). The Georgiev parameters used in Wittman (fig. 1) include hydrophobicity and steric bulk – see evidentiary reference Georgiev (J Comput Biol. 2009 May;16(5):703-23.), fig. 1.
Regarding claim 4, An invention would have been prima facie obvious to one of ordinary
skill in the art at the time of the effective filing date of the invention if some teaching, suggestion, or motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. There is a suggestion to use prediction of post-translational modifications for sequence design in the text of Rocha, in order to improve the design of sequences towards desired properties (Rocha pg 19 right col ¶ 1). There would be a reasonable expectation of success in making this combination to a person of ordinary skill in the art, as the parameters to form the predictive quantifier are part of the parameters of Wittman. Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the method of Wittman by including the post-translational modification quantifier of Rocha, in order to improve sequence design.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Wittman as applied to claims 1, 3, 6-9, 12-13, 17-21 above, and further in view of Liu et al. (Bioinformatics, Volume 36, Issue 7, April 2020).
Regarding claim 5, Liu teaches immunoglobin G expression and phage display panning against target as parameters of the training data of a similar model to Wittman’s (pg 2 left col ¶ 4).
Regarding claim 5, An invention would have been prima facie obvious to one of ordinary
skill in the art at the time of the effective filing date of the invention if some teaching, suggestion, or motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. There is a teaching to use immunogenicity data in the text of Liu, in order to better predict sequence design for sequences involving immunological reactions (pg 2 left col ¶ 4, abstract). There would be a reasonable expectation of success in making this combination to a person of ordinary skill in the art, as Wittman and Liu are directed to the same structure of sequence design algorithm. Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the method of Wittman by including the parameters of Liu, in order to adapt the method to immunological concerns.
Claims 7, 10-11, 13 are rejected under 35 U.S.C. 103 as being unpatentable over Wittman as applied to claims 1, 3, 6-9, 12-13, 17-21 above, and further in view of Greenhalgh et al (NATURE COMMUNICATIONS | (2021) 12:5825).
Moving on to claim 7, enzymatic activity in a PH range is the first property of Greenhalgh (Greenhalgh abstract).
Regarding claim 10, enzymatic activity is the first property of the target protein in Greenhalgh (Greenhalgh abstract).
Regarding claim 11, Ligases are targeted by Greenhalgh (Greenhalgh pg 2 right col ¶ 2).
With respect to claim 13, crystal structures are suggested as a source for the determination of structure stability in Greenhalgh (pg 2 left col ¶ 3).
Regarding claim 25, Bayesian optimization, namely upper confidence bound optimization, is used as the search model in Greenhalgh (pg 9 left col ¶ 1).
Regarding claims 7, 10-11, and 13, An invention would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date of the invention if some teaching, suggestion, or motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. There is a teaching to use enzymatic activity as a first property and a crystal structure as a stability reference in the text of Greenhalgh, in order to find sequences with the best enzymatic activity (abstract). There would be a reasonable expectation of success in making this combination to a person of ordinary skill in the art, as the algorithms of Wittman and Greenhalgh have the same general structure. Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the method of Wittman by including the variables and structures of Greenhalgh, in order to adapt the method to produce new enzymes.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GRACELYN M HILL whose telephone number is (571)272-9871. The examiner can normally be reached Monday-Friday 8:30-5pm.
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/G.M.H./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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