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 Response
Applicant's response, filed 11/12/2025, has been fully considered. Rejections and/or objections not reiterated from previous Office Actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Terminal Disclaimer
The terminal disclaimer filed on 03/17/2025 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of 18/634254 and 18/659964 has been reviewed and is accepted. The terminal disclaimer has been recorded.
Election/Restrictions
Applicant’s election without traverse of Species II encompassing claims 73-86, 96 and 101, drawn to the base portion of the template model has a 3-dimensional structure in the reply filed on 10/24/2024 was acknowledged in the office action mailed 12/18/2024.
Claim Status
Claims 74 and 82 are canceled.
Claims 73, 75-81 and 83-102 are pending.
Claim 95 is withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected species.
Claims 73, 75-81, 83-94 and 96-102 are under examination herein.
Claims 73, 75-81, 83-94 and 96-102 are rejected.
Priority
The instant application filed 05/09/2024 is a Continuation of 18/634254 , filed 04/12/2024, which claims priority from Provisional Application 63/596216 filed 11/03/2023, Provisional Application 634/60985 filed 04/21/2023, and Provisional Application 63/459124 filed 04/13/2023. As such, the effective filing date assigned to each of claims73, 75-81, 83-94 and 96-102 is 04/13/2023.
Drawings
The drawing were accepted in the office action mailed 03/28/2025.
Claim Interpretation
The term “about” as used in claims 87, 96-97 and 102 is interpreted to mean “surrounding, but not including, the one or variable regions such as amino acid sites about antibody CDRs” based on p 14, para 5-p15, para 1 of Applicant’s arguments filed 03/17/2025.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 73, 75-81, 83-94 and 96-102 remain rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea/law of nature/natural phenomenon 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). Newly recited portions are necessitated by claim amendments.
In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claims 73 and 101 recite receiving and/or generating one or more protein fold representations of three-dimensional structural features of a base portion of a reference antibody, said base portion excluding one or more complementarity-determining regions (CDRs) of the reference antibody; (b) receiving and/or generating a seed set of feature vectors, wherein each feature vector of a first portion of the seed set corresponds to a particular site within the base portion of the reference antibody and comprises position and/or orientation components representing a position and/or orientation of the particular site within the base portion, and wherein each feature vector of a second portion of the seed set corresponds to a particular site within one or more variable region(s) of the custom antibody polypeptide and comprises position and/or orientation components representing a position and/or orientation of the particular site within the one or more variable region(s), wherein each of the one or more variable region(s) corresponds to one of the one or more CDR(s) of the reference antibody and represents a to-be-designed custom version thereof; (c)receiving and/or generating, by the processor, a target model representing at least a portion of the target antigen; (d) determining one or more velocity fields based at least in part on the one or more protein fold representations and the target model, and beginning with the seed set, updating values of the feature vectors of both the first portion and the second portion according to the one or more velocity fields, thereby evolving the seed set of feature vectors into a final set of feature vectors representing (i) coordinates of the custom antibody in a docked pose relative to the target model and (ii) backbones of the one or more CDR(s); and (e) generating, using the final set of feature vectors, a scaffold model representing a three-dimensional structure of a de-novo peptide backbone for the custom antibody polypeptide.
Claim 75 recites wherein each feature vector comprises a side chain type component representing one or more likelihoods of one or more particular amino acid side chain types occupying the particular site to which the feature vector corresponds.
Claim 76 recites wherein step (d) comprises determining the one or more velocity fields and updating the values of the feature vectors in an iterative fashion.
Claim 77 recites wherein the machine learning model receives, as input, and conditions generation of the one or more velocity fields on, values of one or more global property variables, each global property variable representing a desired property of a protein or peptide.
Claim 83 recites wherein the target model comprises an identification of a desired epitope of the target antigen.
Claim 84 recites wherein the target model represents a three-dimensional structure of, and/or an amino acid sequence of, the target antigen or portion thereof.
Claim 85 recites wherein the one or more protein fold representation(s) are or comprise a set of secondary structure element (SSE) values, each SSE value associated with a particular position within a polypeptide chain of the custom antibody polypeptide and having a value encoding a particular type of secondary structure at the particular position.
Claim 86 recites wherein the one or more protein fold representation(s) are or comprise a block adjacency matrix, said block adjacency matrix comprising a plurality of elements, each element of the block adjacency matrix associated with a particular pair of positions within a polypeptide chain of the custom antibody polypeptide and having one or more values representing a relative position and/or orientation of secondary structural elements (SSEs) at the particular pair of positions.
Claims 87 and 102 recite (a) receiving and/or generating, by a processor of a computing device, a target model representing at least a portion of the target antigen;(b) receiving and/or generating, by the processor, a template model representing a sequence of, and/or a three-dimensional structure of, at least a portion of a reference biologic, the template model comprising a base portion representing a portion of the reference biologic located about one or more variable regions of the reference biologic designated as modifiable for binding to the target;(c) receiving and/or generating, by the processor, a seed set of feature vectors, wherein each feature vector of a first portion of the seed set of feature vectors: corresponds to a particular site within the base portion of the template model, and comprises position and/or orientation components representing a position and/or orientation of the particular site within the base portion of the template model, and wherein each feature vector of a second portion of the seed set of feature vectors: corresponds to a particular site within the one or more variable region(s) of the template model, and comprises (i) position and/or orientation components representing a position and/or orientation of the particular site, and/or (ii) a side chain type component, representing likelihood(s) of one or more particular amino acid side chain types occupying the particular site within the one or more variable region(s);(d) determining, by the processor, using a machine learning model, one or more velocity fields based at least in part on (i) the target model and (ii) the base portion of the template model and, beginning with the seed set, updating, by the processor, values of the plurality of feature vectors of both the first portion and the second portion according to the one or more velocity fields, thereby evolving the seed set of feature vectors into a final set of feature vectors representing (i) coordinates of the custom biologic in a docked pose relative to the target and (ii) backbones and/or sequences of the one or more variable regions;(e) generating, by the processor, using, the final set of feature vectors, one or both of (A) and (B) as follows:(A) a scaffold model representing a de-novo peptide backbone for the one or more variable region(s) of the custom biologic; and(B) an amino acid sequence of the one or more variable region(s) of the custom biologic.
Claim 93 recites wherein the target model comprises an identification of a desired epitope of the target antigen.
Claim 94 recites wherein the target model represents a three-dimensional structure and/or an amino acid sequence of the target antigen or portion thereof.
Claim 96 recites wherein the base portion of the template model is or comprises a scaffold model representing a peptide backbone of the reference biologic at locations about the one or more variable region(s) and wherein step (d) comprises using, by the machine learning model, the scaffold model to determine the one or more velocity fields.
Claim 97 recites further comprising determining, for the base portion of the template model, one or more protein fold representations encoding three-dimensional structural features of a peptide backbone of the reference biologic at locations about the one or more variable regions and wherein step (d) comprises using, by the machine learning model, the one or more protein fold representation(s) to determine the one or more velocity fields.
Claim 98 recites wherein the one or more protein fold representation(s) are or comprise a set of secondary structure element (SSE) values, each SSE value associated with a particular position within a polypeptide chain of the custom biologic and having a value encoding a particular type of secondary structure at the particular position.
Claim 99 recites wherein the one or more protein fold representation(s) are or comprise a block adjacency matrix, said block adjacency matrix comprising a plurality of elements, each element of the block adjacency matrix associated with a particular pair of positions within a polypeptide chain of the custom biologic and having one or more values representing a relative position and/or orientation secondary structural elements (SSEs) at the particular pair of positions.
Claim 100 recites wherein the template model is an antibody template representing a sequence of, and/or three-dimensional structure of, a reference antibody and comprises one or more complementarity determining region (CDR) portions, each associated with and comprising a CDR of the reference antibody.
These recitations equate to steps of collecting information, analyzing data and making observations, evaluations and judgements that can be carried out in the human mind. Specifically, generating one or more protein fold representations of three-dimensional structural features of a base portion of a reference antibody, generating a seed set of feature vectors, each feature vector of a first and second portion corresponding to a particular site within the base portion of the reference antibody or one or more variable region(s) of the custom antibody polypeptide, respectively, and comprises position and/or orientation components, generating a target model representing at least a portion of the target antigen, determining using a machine learning model, one or more velocity fields based at least in part on the one or more protein fold representations and the target model, and beginning with the seed set, updating values of the feature vectors of the first and second potion according to the one or more velocity fields to evolve the seed set of feature vectors into a final set of feature vectors (i) coordinates of the custom antibody in a docked pose relative to the target model and (ii) backbones of the one or more CDR(s), generating a scaffold model representing a three-dimensional structure of a de-novo peptide backbone for the custom antibody polypeptide and/or an amino acid sequence for the one or more variable regions of the custom antibody polypeptide, determining the one or more velocity fields based further on the target model, determining one or more protein fold representations encoding three-dimensional structural features of a peptide backbone of the reference biologic at locations about the one or more variable regions, and using the one or more protein fold representation(s) to determine the one or more velocity fields can be practically performing the human mind as claimed and are similar to the concepts of collecting and comparing known information in Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011) and collecting information, analyzing it, and reporting certain results of the collection and analysis in Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016) that the courts have identified as concepts that can be practically performed in the human mind. Therefore, each of the above recited limitations fall under the “Mental Processes” grouping of abstract ideas. Furthermore, the steps as claimed for generating a seed set of feature vectors, determining one or more velocity fields based at least in part on the one or more protein fold representations, and beginning with the seed set, updating values of the feature vectors according to the one or more velocity fields to evolve the seed set of feature vectors into a final set of feature vectors, determining the one or more velocity fields based further on the target model and using the one or more protein fold representation(s) to determine the one or more velocity fields equate to organizing information and manipulating information through mathematical correlations and reciting a mathematical equation, similar to the concepts of taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form in Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). Therefore, these limitations also fall under the “Mathematical Concepts” grouping of abstract ideas. With respect to using a machine learning model and a scaffold model being a computer representation as disclose din para 0232 of the instant specification, claims are still considered to recite an abstract idea even if the claims use a computer as a tool to perform the concept (i.e. using a computer as a tool to determine velocity fields and update feature vectors, and generating a model representing a 3-D structure of a de-novo peptide backbone for the custom antibody polypeptide. Claims 75-76, 83-86, 93-94, 96 and 98-100 further quantify the judicial exceptions. As such, claims 73, 75-81, 83-94 and 96-102 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 affect 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. Specifically, the claims recite the following additional elements:
Claim 73 recites by a processor of a computing device, using a machine learning model; scaffold model.
Claim 77 recites wherein the machine learning model receives, as input, and conditions generation of the one or more velocity fields on, values of one or more global property variables, each global property variable representing a desired property of a protein or peptide.
Claim 78 recites wherein the one or more global property variables comprise a thermostability variable whose value categorizes and/or measures protein thermostability.
Claim 79 recites wherein the one or more global property variables comprise an immunogenicity variable whose value classifies and/or measures a propensity and/or likelihood of provoking an immune response.
Claim 80 recites wherein the machine learning model receives, as input, and conditions generation of the one or more velocity fields on, values of one or more node property variables, each node property variable associated with and representing a particular property of a particular amino acid site.
Claim 81 recites wherein the one or more node property variables comprise a side chain type variable that identifies a particular type of amino acid side chain.
Claim 87 recites a processor of a computing device; using a machine learning model; and (f) storing and/or providing, by the processor, the generated scaffold model and/or amino acid sequence for display and/or further processing.
Claim 88 recites wherein the machine learning model receives, as input, and conditions generation of the one or more velocity fields on, values of one or more global property variables, each global property variable representing a desired property of a protein or peptide.
Claim 89 recites wherein the one or more global property variables comprise a thermostability variable whose value categorizes and/or measures protein thermostability.
Claim 90 recites wherein the one or more global property variables comprise an immunogenicity variable whose value classifies and/or measures a propensity and/or likelihood of provoking an immune response.
Claim 91 recites wherein the machine learning model receives, as input, and conditions generation of the one or more velocity fields on, values of one or more node property variables, each node property variable associated with and representing a particular property of a particular amino acid site.
Claim 92 recites wherein the one or more node property variables comprise a side chain type variable that identifies a particular type of amino acid side chain.
Claim 101 recites a processor of a computing device; and memory having instructions stored thereon; using a machine learning model.
Claim 102 recites a processor of a computing device; and memory having instructions stored thereon; using a machine learning model; and (f) storing and/or providing, by the processor, the generated scaffold model and/or amino acid sequence for display and/or further processing.
Claims 75-75, 83-86, 93-94, and 96-100 do not recite elements in addition to the recited judicial exceptions. Claims 73, 76-81, 87-92 and 101-102 also merely recite using generic computing systems and computer program products, including using a generically recited machine learning model (defined as referring to a computer implemented process such as a software function in para 0230 of the instant specification) and outputting computer representations, to receive and output data, and to carry out instructions to implement an abstract idea on a computer. The computer system and computer program product as claimed fails to recite details of how a solution to a problem is accomplished and only recites the idea of a solution or outcome. There are no limitations that indicate that the claimed steps require anything other than generic computing systems. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). 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. There is no indication that any of these additional elements provide a practical application of the recited judicial exception outside of the judicial exception itself. As such, claims 73, 75-81, 83-94 and 96-102 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). Further analyzing the additional elements under step 2B, the additional elements as described above do not rise to the level of significantly more than the judicial exception. As directed in the Berkheimer memorandum of 19 April 2018 and set forth in the MPEP, determinations of whether or not additional elements (or a combination of additional elements) may provide significantly more and/or an inventive concept rests in whether or not the additional elements (or combination of elements) represents well-understood, routine, conventional activity. Said assessment is made by a factual determination stemming from a conclusion that an element (or combination of elements) is widely prevalent or in common use in the relevant industry, which is determined by either a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to the instant claims under the 2B analysis, the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Therefore, 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, and the claims do not amount to significantly more than the judicial exception itself (Step 2B: NO). As such, claims 73, 75-81, 83-94 and 96-102 are not patent eligible.
Response to applicant’s arguments
Applicant states the independent claims integrate any recited judicial exceptions into practical application, , as each claim is directed to the generation of a custom biologic for binding to a target antigen using a flow-matching approach (which is described even if not explicitly named) and offers a detailed and specific series of steps which reflect an improvement in the functioning of a computer or other technology or other technical field, and refers to the “2024 Guidance Update on Patent Subject Matter Eligibility Including on Artificial Intelligence” and Ex parte Desjardins decision (hereafter referred to as “the decision”) (Applicant’s Arguments, p 13, para 5 – p 15, para 3). Applicant further states the previous office action appears to equate any machine learning or software with an abstract idea and this kind of overbroad reasoning is cautioned against by the decision, and that the detailed procedures and structures in steps (a)-(e) cannot be dismissed as abstract ideas simply because they are steps performed in a computer-implemented procedure as they steps improve the ability to generate custom antibody polypeptides in silico (Applicant’s Arguments, p 15, para 4-p 17, para 2). Applicant further asserts the steps cannot be practically performed in the human mind and is not similar to concepts of taking analyzing it and reporting certain results, but rather performs a specific and new process to generate new output that reflect an improvement to computer technology, and therefore the instant claims are not like the claims in Classen Immunotherapies, Electric Power Group, and Digital Image Techs, and requests the rejection be withdrawn (Applicant’s Arguments, p 17, para 3- p 19, para 3)
It is respectfully submitted that this is not persuasive. As recited, the instant claims do not recite any elements that could not be performed by the human mind, as it does not recite any details that indicate the data and steps would be too complicated to be performed mentally (or using a pencil and paper as a tool). While the instant specification may provide details that could perhaps practically not be performed in the human mind, the instant claims are devoid of such details. As recited, the human mind is capable of generating a basic representation of 3-D structural features in a base portion, generating a set of feature vectors (which could be as small as 2 vectors), generating a basic target model, determining velocity fields and updating the feature vectors, and then using the updated vectors to draw another 3-D model. The machine learning model is also recited generically, and simply amounts to applying the abstract ideas using a computer, without placing any limits on how the machine learning model functions. Furthermore, even if the instant claims do not recite mental processes, it still recites mathematical processes (e.g., generating and updating seed vectors and determining velocity fields), and therefore the claims would still recite judicial exceptions under Step 2A, Prong 1.
With respect to Step 2A, Prong 2, as discussed in the guidance and MPEP 2106.05(a), a key point of distinction to be made for AI inventions is between a claim that reflects an improvement to a computer or other technology described in the specification (which is eligible) and a claim in which the additional elements amount to no more than (1) a recitation of the words “apply it” (or an equivalent) or are no more than instructions to implement a judicial exception on a computer, or (2) a general linking of the use of a judicial exception to a particular technological environment or field of use (which is ineligible), and that an improvement in the judicial exception itself is not an improvement in the technology.
The instant specification does not appear to provide evidence the alleged improvement in computer technology. With respect to the instant claims, the improvements appear to be directed at improving the recited specific series of steps in steps (a)-(e) and the improvement appears to be solely provided by these steps (a)-(e) in which velocity fields are determined based on protein fold representations of an antibody, and beginning with a seed set of feature vectors corresponding to base portion and CDR(s) of the antibody, values of the feature vectors are updated according to the velocity fields, thereby evolving the seed set of feature vectors into a final set of feature vectors, which are used to generate a de-novo peptide backbone and/or amino acid sequence for the CDRs of a custom antibody polypeptide, which were found to be abstract ideas. And, as discussed in MPEP 2106.05(a), the improvement cannot be to the judicial exception itself and the judicial exception alone cannot provide the improvement.
As discussed above, the additional elements of the claims 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, and there is no indication that any of these additional elements provide a practical application of the recited judicial exception outside of the judicial exception itself. Therefore, the rejection is maintained.
Claim Rejections - 35 USC § 102
The rejection of claims 73, 75-77, 80-81, 83-88, 91-94 and 96-102 under 35 U.S.C. 102(a)(1) as being anticipated by Luo et al. (Advances in Neural Information Processing Systems 2022, 35, pp.9754-9767; previously cited; hereafter referred to as Luo) are withdrawn in view of applicant arguments filed 11/12/2025 describing how the instant application teaches flow-matching even if the term is not explicitly used in the claims, and further asserting the flow-matching steps recited in the instant claims are not disclosed by the prior art to Luo, as Luo uses a different, diffusion-based generative model.
Claim Rejections - 35 USC § 103
The rejection of claims 78-79 and 89-90 under 35 U.S.C. 103 as being unpatentable over Luo et al. (Advances in Neural Information Processing Systems 2022, 35, pp.9754-9767; previously cited; hereafter referred to as Luo) as applied to claims 73, 77, 87 and 88 above, and further in view of Quijano-Rubio et al. (Chemical Biology 2020, 56, pp.119-128; previously cited; hereafter referred to as Quijano-Rubio) are withdrawn in view of applicant arguments filed 11/12/2025 describing how the instant application teaches flow-matching even if the term is not explicitly used in the claims, and further asserting the flow-matching steps recited in the instant claims are not disclosed by the prior art to Luo, as Luo uses a different, diffusion-based generative model.
Conclusion
No claims allowed.
The claims appear to be free from prior art, as the closest prior art to Luo et al. (Advances in Neural Information Processing Systems 2022, 35, pp.9754-9767; previously cited; hereafter referred to as Luo), does not appear to disclose the flow-matching techniques of evolving feature vectors using velocity fields based on protein fold representations.
The prior art to Lipman et al. arXiv preprint 2022, 28 pages; newly cited) introduces flow matching for generative design, but does not disclose how it is can be specifically used for the design of custom biologics as disclosed in the instant claims.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Inquires
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/N.D./ Examiner, Art Unit 1686
/Karlheinz R. Skowronek/ Supervisory Patent Examiner, Art Unit 1687