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 .
Priority
The instant application does not claim benefit to any preceding applications. Therefore, the effective filing date of claims 1-25 is 6 June 2022.
Information Disclosure Statement
The information disclosure statement (IDS) filed 6 June 2022 fails to comply with 37 CFR 1.98(a)(2)(ii), which requires a legible copy of each non-patent literature publication or that portion which caused it to be listed. Under MPEP § 609.04(a)(II), the visual output of a video, may be submitted if reduced to writing, such as in the form of screen shots and/or a transcript. The reference of “Introduction to Persistent Homology” by M. Wright is lined through because no copy was provided. All references not lined through have been considered by the examiner.
Drawings
The drawings filed on 6 June 2022 and 15 August 2022 have been received and are accepted.
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 1-25 recite patent eligible subject matter. Under MPEP § 2106, subject matter is patent eligible when the claimed invention is to one of the four statutory categories of invention [Step 1], and the claim is not directed to a judicial exception [Step 2A] unless the claim as a whole includes additional limitations amounting to significantly more than the exception [Step 2B].
Step 1
Claims 1-25 describe inventions that are to one of the statutory categories. In Step 1, a claim must fall within one of the four enumerated categories of statutory subject matter (process, machine, manufacture, or composition of matter); a claim falling outside these categories is ineligible without further analysis [MPEP § 2106.03]. Claims 1-7 are properly to one of the four statutory categories because the claimed inventions are methods, which fall into the process category [Step 1: Yes]. Claims 8-25 are properly to one of the four statutory categories because the claimed invention is a computer system or a computer readable storage medium not to be construed as being transitory signals per se (instant spec para. [0117]), which fall into the manufacture category [Step 1: Yes].
Step 2A
Under Step 2A, a claim is directed to a judicial exception if, under the broadest reasonable interpretation, it recites an abstract idea, law of nature, or natural phenomena [Prong One] without the claim as a whole integrating the exception into a practical application [Prong Two]. Abstract ideas include mathematical concepts, mental processes, and certain methods of organizing human activity. Mathematical concepts encompass mathematical relationships, formulas, equations, and mathematical calculations [MPEP § 2106.04(a)(2)(I)]. Mental processes involve concepts that can be performed in the human mind or by a human with the aid of pen and paper, such as observations, evaluations, judgments, or opinions [MPEP § 2106.04(a)(2)(III)]. Certain methods of organizing human activity include fundamental economic principles, commercial or legal interactions, and managing personal behavior or relationships [MPEP § 2106.04(a)(2)(II)]. Laws of nature and natural phenomena, include naturally occurring principles/relations and nature-based products that are naturally occurring or that do not have markedly different characteristics compared to what occurs in nature [MPEP § 2106.04(b)-(c)].
Prong One
A claim recites a judicial exception when it sets forth or describes a law of nature, natural phenomenon, or abstract idea. Claims 1-25 do not recite judicial exceptions. While the invention relates to the use of generative neural networks, which are fundamentally math constructs, the claims do not recite any specific mathematical algorithm or equation. Rather, the claims recite only the application or use of the neural network, which does not fall into any judicial exception [Step 2, Prong One: No]. Therefore, claims 1-25 recite patent eligible subject matter when the inventions are to one of the four statutory categories and the claims do not recite judicial exceptions.
Claim Rejections - 35 USC § 102
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 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, 4-5, 8, 11-12, 15, and 18-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang (ACM Trans. Graph, Vol. 37, (4 December 2018)), as evidenced by TechnoLynx (The Foundation of Generative AI: Neural Networks Explained, (28 April 2025)), C3.ai. (Infrastructure: Machine Learning Hardware Requirements, (15 May 2021)), and AWS (What is a Neural Network?, (29 April 2022)).
Regarding claim 1, Wang discloses a generative neural network to generate a 3D model of a 3D object. At 2 col.2 paras.2-3. Wang teaches applying global and local input representations of the 3D object into the generative neural network to form the 3D model. At 5 col.1 para.4; Fig.2 (applying input representations of a three-dimensional (3D) domain to a generative neural network (GNN); and using the GNN to form a generative model of the 3D domain based at least in part on the input representations; wherein the input representations comprise a global-shape input representation of the 3D domain). While Wang does not explicitly teach computer-implementation, generative neural networks are inherently computer-implemented, as evidenced by TechnoLynx. § Introduction (a computer-implemented method comprising).
Regarding claim 4, Wang teaches applying global and local input representations of the 3D object into the generative neural network to form the 3D model. At 5 col.1 para.4; Fig.2 (the computer-implemented method of claim 1, wherein the input representations further comprise a local point-level input representation of the 3D domain).
Regarding claim 5, Wang discloses that the generative neural network encodes the input parts into latent space and decodes them to form the 3D model. At 4 col.1 para.2; 5 col.1 para.4; Fig.2 (the computer-implemented method of claim 4, wherein using the GNN to form the generative model of the 3D domain comprises: encoding, using the GNN, the input representations to generate latent code; decoding, using the GNN, the latent code to generate a reconstructed version of the input representations). Wang teaches computing a reconstruction loss with the generative neural network by comparing the encoder/decoder output to one of the input’s nearest-neighbors. At 5 col.1 para.5 (generating, using the GNN, a reconstruction loss based at least in part on the reconstructed version of the input representations).
Regarding claim 8, Wang discloses a generative neural network to generate a 3D model of a 3D object. At 2 col.2 paras.2-3. Wang teaches applying global and local input representations of the 3D object into the generative neural network to form the 3D model. At 5 col.1 para.4; Fig.2 (applying input representations of a three-dimensional (3D) domain to a generative neural network (GNN); and using the GNN to form a generative model of the 3D domain based at least in part on the input representations; wherein the input representations comprise a global-shape input representation of the 3D domain). Wang does not explicitly state that the generative neural network is operated on a computer system comprising a memory and a processor communicatively coupled to the memory. However, these elements are inherent in the disclosure of Wang because a computer system comprising a memory and a processor are necessary for the functionality of a generative neural network, as evidenced by C3.ai. § Processors: CPUs, GPUs, TPUs, and FPGAs; § Memory and Storage.
Regarding claim 11, Wang teaches applying global and local input representations of the 3D object into the generative neural network to form the 3D model. At 5 col.1 para.4; Fig.2 (the computer system of claim 8, wherein the input representations further comprise a local point-level input representation of the 3D domain).
Regarding claim 12, Wang discloses that the generative neural network encodes the input parts into latent space and decodes them to form the 3D model. At 4 col.1 para.2; 5 col.1 para.4; Fig.2 (the computer system of claim 11, wherein using the GNN to form the generative model of the 3D domain comprises: encoding, using the GNN, the input representations to generate latent code; decoding, using the GNN, the latent code to generate a reconstructed version of the input representations). Wang teaches computing a reconstruction loss with the generative neural network by comparing the encoder/decoder 3D model output to one of the input’s nearest-neighbors. At 5 col.1 para.5 (generating, using the GNN, a reconstruction loss based at least in part on the reconstructed version of the input representations).
Regarding claim 15, Wang discloses a generative neural network to generate a 3D model of a 3D object. At 2 col.2 paras.2-3. Wang teaches applying global and local input representations of the 3D object into the generative neural network to form the 3D model. At 5 col.1 para.4; Fig.2 (applying input representations of a three-dimensional (3D) domain to a generative neural network (GNN); and using the GNN to form a generative model of the 3D domain based at least in part on the input representations; wherein the input representations comprise a global-shape input representation of the 3D domain). Wang does not explicitly state that the generative neural network is a computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor system to cause the processor system to perform operations. However, a neural network is inherently a computer program product because neural networks are software programs or algorithms that use computing systems to solve mathematical calculations, as evidenced by AWS. § How do neural networks work? para.1. Additionally, Wang inherently discloses the program instructions being executable by a processor system because execution by a processor is necessary for the functionality of a neural network, as evidenced by C3.ai. § Processors: CPUs, GPUs, TPUs, and FPGAs.
Regarding claim 18, Wang teaches applying global and local input representations of the 3D object into the generative neural network to form the 3D model. At 5 col.1 para.4; Fig.2 (the computer program product of claim 15, wherein the input representations further comprise a local point-level input representation of the 3D domain).
Regarding claim 19, Wang discloses that the generative neural network encodes the input parts into latent space and decodes them to form the 3D model. At 4 col.1 para.2; 5 col.1 para.4; Fig.2 (the computer program product of claim 18, wherein using the GNN to form the generative model of the 3D domain comprises: encoding, using the GNN, the input representations to generate latent code; decoding, using the GNN, the latent code to generate a reconstructed version of the input representations). Wang teaches computing a reconstruction loss with the generative neural network by comparing the encoder/decoder 3D model output to one of the input’s nearest-neighbors. At 5 col.1 para.5 (generating, using the GNN, a reconstruction loss based at least in part on the reconstructed version of the input representations).
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.
Claims 1-25 are rejected under 35 U.S.C. 103 as being unpatentable over Oono (US 2017/0161635 A1), in view of Keller (ChemRxiv. (2 October 2018)) and Krishnapriyan (Sci Rep 11, no. 8888 (26 April 2021)), as evidenced by Nicolas (Geometric Deep Learning (15 December 2025)) and Xie (Front Pharmacol. (18 December 2020)).
Regarding claim 1, Oono discloses a computer implemented method where chemical compound representations in the form of fingerprints are input into a generative framework comprising a neural network. Para. [0008] (a computer-implemented method comprising: applying input representations of a three-dimensional (3D) domain to a generative neural network (GNN)). Oono defines chemical compound fingerprints as a string of values of molecular descriptors that contain the information of a compound's chemical structure. Para. [0088]. Oono teaches that the generative framework can generate chemical compounds that have desired characteristics based on the input representations. Abstract; para. [0098] (using the GNN to form a generative model of the 3D domain based at least in part on the input representations).
Oono fails to teach wherein the input representation comprise a global-shape input representation of the 3D domain. However, Keller discloses a molecular representation scheme based on persistent homology. At 2 paras.5-6. Persistent homology offers a means to capture the global geometric and topological structure of complex data, as evidenced by Nicolas. § Persistent Homology. Keller notes that the persistent homology representation is intended to alleviate the significant difficulty in molecular design related to the possibility of multiple confirmations of compounds that have the same molecular formula but important structural differences (i.e. two molecules can share local descriptors, but have distinct global descriptors). At 2 para.3.
To solve a similar issue, Krishnapriyan discloses a machine learning model for metal-organic frameworks (MOFs) that automatically generates feature descriptors based on persistent homology representations and the elemental composition of the molecule. At 2 paras.4-5. Krishnapriyan notes that to comprehensively understand MOFs, it is necessary to recognize geometric and chemical features responsible for their performance in particular applications. At 1 para.3. While input features can be related to a MOF’s performance in a particular application, standard structural descriptors are not able to capture some relevant information, such as the pressure during absorption or local strong absorption sites. At 2 para.2. To overcome these challenges, Krishnapriyan discloses using a topological descriptor called persistent homology and the elemental composition of a molecule as input into a machine learning model. At 2 para.4. The elemental compositions are embedded and the persistent homology representations are translated into persistence images that are suitable as input for machine learning algorithms to generate feature descriptors. At 2 para.11- 3 para.2.
Oono discloses a base method where molecular fingerprints are input into a generative framework. Keller discloses a persistent homology representation aiming to alleviate major difficulty in molecular design regarding multiple confirmations of compounds that have the same molecular formula but important structural differences. Krishnapriyan discloses a solution to a similar issue in the context of MOFs where persistence images (i.e. global representation) and elemental compositions (i.e. local representation) are used as inputs for machine learning algorithms. One of ordinary skill in the art could apply Krishnapriyan’s technique of inputting global and local representations to the method of Oono by additionally inputting the persistent homology representation of Keller. This would predictably result in an improved method of molecular design because, similar to the results in Krishnapriyan, the incorporation of Keller’s persistent homology representation as input into Oono’s generative framework will remedy the issue of molecules consisting of the same chemical formula but different structures. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to improve the method of Oono by additionally inputting the persistent homology representations of Keller as taught by Krishnapriyan. Use of known technique to improve similar devices (methods, or products) in the same way is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007) (see MPEP § 2143, C).
Regarding claim 2, Oono discloses that the generative framework may be implemented as part of a cloud computer system. Para. [0146] (the computer-implemented method of claim 1, wherein the GNN is part of a cloud computing system).
Regarding claim 3, Krishnapriyan discloses that persistent homology representations must be translated into vectors suitable as input for machine learning. At 3 para.1. Specifically, Krishnapriyan translates the persistent homology representations into persistence images. Id.
Keller discloses a persistent homology representation that one of ordinary skill in the art would know to include as an additional input into Oono’s base method of inputting molecular fingerprints into a generative framework (see 103 rejection of claim 1 above). Krishnapriyan discloses vectorizing persistent homology representations via persistence images to be usable as input for machine learning. A person having ordinary skill in the art would know to translate the persistent homology representations of Keller into persistence images because the resulting vectorized persistent homology representation would be suitable as input for the generative framework of Oono. This modification would yield predicable results, in that the persistent homology representations of Keller would be translated into persistence images, which is proper format for machine learning input. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to improve the method of Oono by additionally inputting the persistent homology representations of Keller in the form of persistence images as taught by Krishnapriyan. Use of known technique to improve similar devices (methods, or products) in the same way is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007) (see MPEP § 2143, C).
Regarding claim 4, Oono discloses inputting chemical compound fingerprints into the generative framework. Para. [0009]. As evidenced by Xie, molecular fingerprints describe the local aspect of chemical structures. At 3 col.1 para.1 (the computer-implemented method of claim 1, wherein the input representations further comprise a local point-level input representation of the 3D domain).
Regarding claim 5, Oono discloses that the generative framework can be trained to form a generative model, para. [0040], by encoding the input representations into latent representations and decoding the latent representations into reconstructions of the input representations, para. [0041] (the computer-implemented method of claim 4, wherein using the GNN to form the generative model of the 3D domain comprises: encoding, using the GNN, the input representations to generate latent code; decoding, using the GNN, the latent code to generate a reconstructed version of the input representations). Oono further discloses that the generative framework calculates a reconstruction loss function based on the input representations and the reconstructed input representations. Para. [0046]; Fig.2A (generating, using the GNN, a reconstruction loss based at least in part on the reconstructed version of the input representations).
Regarding claim 6, Oono discloses inputting chemical compound fingerprints into a generative framework, and defines chemical compound fingerprints as a string of values of molecular descriptors that contain the information of a compound's chemical structure. Para. [0088] (the computer-implemented method of claim 5, wherein: the local point-level input representation comprises a string input representation). Krishnapriyan discloses that persistent homology representations must be translated into vectors suitable as input for machine learning via persistence images. At 3 para.1 (the global-shape input representation comprises a persistence image input representation).
Keller discloses a persistent homology representation that one of ordinary skill in the art would know to include as an additional input into Oono’s base method of inputting molecular fingerprints into a generative framework (see 103 rejection of claim 1 above). Krishnapriyan discloses vectorizing persistent homology representations via persistence images to be usable as input for machine learning. A person having ordinary skill in the art would know to translate the persistent homology representations of Keller into persistence images because the resulting vectorized persistent homology representation would be suitable as an additional input for the generative framework of Oono. This modification would yield predicable results, in that the persistent homology representations of Keller would be vectorized into persistence images, which is proper format for machine learning input. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to improve the method of Oono by additionally inputting the persistent homology representations of Keller in the form of persistence images as taught by Krishnapriyan. Use of known technique to improve similar devices (methods, or products) in the same way is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007) (see MPEP § 2143, C).
Regarding claim 7, Keller discloses a multi-parameter persistent homology representation that captures the important properties of the shapes of molecules and incorporates non-shape information in a coherent and effective manner. At 4 para.2. Keller teaches that the first parameter of the representation captures a set of points in Euclidean space representing atom centers in a molecule, at 5 para.4, while the second parameter may capture partial charge, at 9 para.1; Figure 3 caption, atomic mass, at 13 para.1; Figure 5 caption, hydrogen donor/acceptor status, or some other non-shape molecular characteristic, at 12 para.4. One of ordinary skill in the art would know to include Keller’s multi-parameter persistent homology representation as an additional input into Oono’s base method of inputting molecular fingerprints into a generative framework (see 103 rejection of claim 1 above). Krishnapriyan discloses that persistent homology representations must be translated into vectors suitable as input for machine learning via persistence images. At 3 para.1. A person having ordinary skill in the art would know to translate the persistent homology representations of Keller into persistence images because the resulting vectorized persistent homology representation would be suitable as an additional input for the generative framework of Oono. This modification would yield predicable results, in that the persistent homology representations of Keller would be vectorized into persistence images, which is proper format for machine learning input. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to improve the method of Oono by additionally inputting the persistent homology representations of Keller in the form of persistence images as taught by Krishnapriyan. Use of known technique to improve similar devices (methods, or products) in the same way is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007) (see MPEP § 2143, C).
The method resulting from the combination of Oono, Keller, and Krishnapriyan would input chemical composition fingerprints and multi-parameter persistence images capturing shape and non-shape molecular information into a generative framework (the input representations further comprise an input representation of a characteristic of the 3D domain). The first parameter of the persistence image captures a set of points in Euclidean space representing atom centers in a molecule (the global-shape input representation comprises a first parameter of a multi-parameter persistence image), while the second parameter may capture partial charge, atomic mass, hydrogen donor/acceptor status, or some other non-shape molecular characteristic (the input representation of the characteristic of the 3D domain comprises a second parameter of the multi-parameter persistence image).
Regarding claim 8, Oono discloses a computer system, para. [0041], comprising a memory coupled to a bus for communicating information to a processor, which executes instructions, para. [0145], to input chemical compound fingerprints into a generative framework comprising a neural network, para. [0008] (a computer system comprising a memory and a processor communicatively coupled to the memory, wherein the processor is operable to perform operations comprising: applying input representations of a three-dimensional (3D) domain to a generative neural network (GNN)). Oono teaches that the generative framework can generate chemical compounds that have desired characteristics based on the input representations. Abstract; para. [0098] (using the GNN to form a generative model of the 3D domain based at least in part on the input representations).
Oono fails to teach wherein the input representation comprise a global-shape input representation of the 3D domain. However, Keller discloses a molecular representation scheme based on persistent homology. At 2 paras.5-6. Keller notes that the persistent homology representation is intended to alleviate the significant difficulty in molecular design related to the possibility of multiple confirmations of compounds that have the same molecular formula but important structural differences (i.e. two molecules can share local descriptors, but have distinct global descriptors). At 2 para.3.
To solve a similar issue, Krishnapriyan discloses a machine learning model for metal-organic frameworks (MOFs) that automatically generates feature descriptors based on persistent homology representations and the elemental composition of the molecule. At 2 paras.4-5. Krishnapriyan notes that to comprehensively understand MOFs, it is necessary to recognize geometric and chemical features responsible for their performance in particular applications. At 1 para.3. While input features can be related to a MOF’s performance in a particular application, standard structural descriptors are not able to capture some relevant information, such as the pressure during absorption or local strong absorption sites. At 2 para.2. To overcome these challenges, Krishnapriyan discloses using a topological descriptor called persistent homology and the elemental composition of a molecule as input into a machine learning model. At 2 para.4. The elemental compositions are embedded and the persistent homology representations are translated into persistence images that are suitable as input for machine learning algorithms to generate feature descriptors. At 2 para.11- 3 para.2.
Oono discloses a base method where molecular fingerprints are input into a generative framework. Keller discloses a persistent homology representation aiming to alleviate major difficulty in molecular design regarding multiple confirmations of compounds that have the same molecular formula but important structural differences. Krishnapriyan discloses a solution to a similar issue in the context of MOFs where persistence images (i.e. global representation) and elemental compositions (i.e. local representation) are used as inputs for machine learning algorithms. One of ordinary skill in the art could apply Krishnapriyan’s technique of inputting global and local representations to the method of Oono by additionally inputting the persistent homology representation of Keller. This would predictably result in an improved method of molecular design because, similar to the results in Krishnapriyan, the incorporation of Keller’s persistent homology representation as input into Oono’s generative framework will remedy the issue of molecules consisting of the same chemical formula but different structures. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to improve the method of Oono by additionally inputting the persistent homology representations of Keller as taught by Krishnapriyan. Use of known technique to improve similar devices (methods, or products) in the same way is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007) (see MPEP § 2143, C).
Regarding claim 9, Oono discloses that the generative framework may be implemented as part of a cloud computer system. Para. [0146] (the computer system of claim 8, wherein the GNN is part of a cloud computing system).
Regarding claim 10, Krishnapriyan discloses that persistent homology representations must be translated into vectors suitable as input for machine learning via persistence images. At 3 para.1. Keller discloses a persistent homology representation that one of ordinary skill in the art would know to include as an additional input into Oono’s base method of inputting molecular fingerprints into a generative framework (see 103 rejection of claim 8 above). Krishnapriyan discloses vectorizing persistent homology representations via persistence images to be usable as input for machine learning. A person having ordinary skill in the art would know to translate the persistent homology representations of Keller into persistence images because the resulting vectorized persistent homology representation would be suitable as input for the generative framework of Oono. This modification would yield predicable results, in that the persistent homology representations of Keller would be translated into persistence images, which is proper format for machine learning input. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to improve the method of Oono by additionally inputting the persistent homology representations of Keller in the form of persistence images as taught by Krishnapriyan. Use of known technique to improve similar devices (methods, or products) in the same way is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007) (see MPEP § 2143, C).
Regarding claim 11, Oono discloses inputting chemical compound fingerprints into the generative framework. Para. [0009]. As evidenced by Xie, molecular fingerprints describe the local aspect of chemical structures. At 3 col.1 para.1 (the computer system of claim 8, wherein the input representations further comprise a local point-level input representation of the 3D domain).
Regarding claim 12, Oono discloses that the generative framework can be trained to form a generative model, para. [0040], by encoding the input representations into latent representations and decoding the latent representations into reconstructions of the input representations, para. [0041] (the computer system of claim 11, wherein using the GNN to form the generative model of the 3D domain comprises: encoding, using the GNN, the input representations to generate latent code; decoding, using the GNN, the latent code to generate a reconstructed version of the input representations). Oono further discloses that the generative framework calculates a reconstruction loss function based on the input representations and the reconstructed input representations. Para. [0046]; Fig.2A (generating, using the GNN, a reconstruction loss based at least in part on the reconstructed version of the input representations).
Regarding claim 13, Oono discloses inputting chemical compound fingerprints into a generative framework, para. [0008], and defines chemical compound fingerprints as a string of values of molecular descriptors that contain the information of a compound's chemical structure, para. [0088] (the computer system of claim 12, wherein: the local point-level input representation comprises a string input representation). Krishnapriyan discloses that persistent homology representations must be translated into vectors suitable as input for machine learning via persistence images. At 3 para.1 (the global-shape input representation comprises a persistence image input representation).
Keller discloses a persistent homology representation that one of ordinary skill in the art would know to include as an additional input into Oono’s base method of inputting molecular fingerprints into a generative framework (see 103 rejection of claim 8 above). Krishnapriyan discloses vectorizing persistent homology representations via persistence images to be usable as input for machine learning. A person having ordinary skill in the art would know to translate the persistent homology representations of Keller into persistence images because the resulting vectorized persistent homology representation would be suitable as an additional input for the generative framework of Oono. This modification would yield predicable results, in that the persistent homology representations of Keller would be vectorized into persistence images, which is proper format for machine learning input. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to improve the method of Oono by additionally inputting the persistent homology representations of Keller in the form of persistence images as taught by Krishnapriyan. Use of known technique to improve similar devices (methods, or products) in the same way is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007) (see MPEP § 2143, C).
Regarding claim 14, Keller discloses a multi-parameter persistent homology representation that captures the important properties of the shapes of molecules and incorporates non-shape information in a coherent and effective manner. At 4 para.2. Keller teaches that the first parameter of the representation captures a set of points in Euclidean space representing atom centers in a molecule, at 5 para.4, while the second parameter may capture partial charge, at 9 para.1; Figure 3 caption, atomic mass, at 13 para.1; Figure 5 caption, hydrogen donor/acceptor status, or some other non-shape molecular characteristic, at 12 para.4. One of ordinary skill in the art would know to include Keller’s multi-parameter persistent homology representation as an additional input into Oono’s base method of inputting molecular fingerprints into a generative framework (see 103 rejection of claim 8 above). Krishnapriyan discloses that persistent homology representations must be translated into vectors suitable as input for machine learning via persistence images. At 3 para.1. A person having ordinary skill in the art would know to translate the persistent homology representations of Keller into persistence images because the resulting vectorized persistent homology representation would be suitable as an additional input for the generative framework of Oono. This modification would yield predicable results, in that the persistent homology representations of Keller would be vectorized into persistence images, which is proper format for machine learning input. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to improve the method of Oono by additionally inputting the persistent homology representations of Keller in the form of persistence images as taught by Krishnapriyan. Use of known technique to improve similar devices (methods, or products) in the same way is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007) (see MPEP § 2143, C).
The method resulting from the combination of Oono, Keller, and Krishnapriyan would input chemical composition fingerprints and multi-parameter persistence images capturing shape and non-shape molecular information into a generative framework (the computer system of claim 12, wherein: the input representations further comprise an input representation of a characteristic of the 3D domain). The first parameter of the persistence image captures a set of points in Euclidean space representing atom centers in a molecule (the global-shape input representation comprises a first parameter of a multi-parameter persistence image), while the second parameter may capture partial charge, atomic mass, hydrogen donor/acceptor status, or some other non-shape molecular characteristic (the input representation of the characteristic of the 3D domain comprises a second parameter of the multi-parameter persistence image).
Regarding claim 15, Oono discloses a computer program stored in a computer readable storage medium with instructions, para. [0143], that are executable by a communicably coupled processor, which executes instructions, para. [0145], to input chemical compound fingerprints into a generative framework comprising a neural network, para. [0008] (a computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor system to cause the processor system to perform operations comprising: applying input representations of a three-dimensional (3D) domain to a generative neural network (GNN)). Oono teaches that the generative framework can generate chemical compounds that have desired characteristics based on the input representations. Abstract; para. [0098] (using the GNN to form a generative model of the 3D domain based at least in part on the input representations).
Oono fails to teach wherein the input representation comprise a global-shape input representation of the 3D domain. However, Keller discloses a molecular representation scheme based on persistent homology. At 2 paras.5-6. Keller notes that the persistent homology representation is intended to alleviate the significant difficulty in molecular design related to the possibility of multiple confirmations of compounds that have the same molecular formula but important structural differences (i.e. two molecules can share local descriptors, but have distinct global descriptors). At 2 para.3.
To solve a similar issue, Krishnapriyan discloses a machine learning model for metal-organic frameworks (MOFs) that automatically generates feature descriptors based on persistent homology representations and the elemental composition of the molecule. At 2 paras.4-5. Krishnapriyan notes that to comprehensively understand MOFs, it is necessary to recognize geometric and chemical features responsible for their performance in particular applications. At 1 para.3. While input features can be related to a MOF’s performance in a particular application, standard structural descriptors are not able to capture some relevant information, such as the pressure during absorption or local strong absorption sites. At 2 para.2. To overcome these challenges, Krishnapriyan discloses using a topological descriptor called persistent homology and the elemental composition of a molecule as input into a machine learning model. At 2 para.4. The elemental compositions are embedded and the persistent homology representations are translated into persistence images that are suitable as input for machine learning algorithms to generate feature descriptors. At 2 para.11- 3 para.2.
Oono discloses a base method where molecular fingerprints are input into a generative framework. Keller discloses a persistent homology representation aiming to alleviate major difficulty in molecular design regarding multiple confirmations of compounds that have the same molecular formula but important structural differences. Krishnapriyan discloses a solution to a similar issue in the context of MOFs where persistence images (i.e. global representation) and elemental compositions (i.e. local representation) are used as inputs for machine learning algorithms. One of ordinary skill in the art could apply Krishnapriyan’s technique of inputting global and local representations to the method of Oono by additionally inputting the persistent homology representation of Keller. This would predictably result in an improved method of molecular design because, similar to the results in Krishnapriyan, the incorporation of Keller’s persistent homology representation as input into Oono’s generative framework will remedy the issue of molecules consisting of the same chemical formula but different structures. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to improve the method of Oono by additionally inputting the persistent homology representations of Keller as taught by Krishnapriyan. Use of known technique to improve similar devices (methods, or products) in the same way is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007) (see MPEP § 2143, C).
Regarding claim 16, Oono discloses that the generative framework may be implemented as part of a cloud computer system. Para. [0146] (the computer program product of claim 15, wherein the GNN is part of a cloud computing system).
Regarding claim 17, Krishnapriyan discloses that persistent homology representations must be translated into vectors suitable as input for machine learning via persistence images. At 3 para.1. Keller discloses a persistent homology representation that one of ordinary skill in the art would know to include as an additional input into Oono’s base method of inputting molecular fingerprints into a generative framework (see 103 rejection of claim 15 above). Krishnapriyan discloses vectorizing persistent homology representations via persistence images to be usable as input for machine learning. A person having ordinary skill in the art would know to translate the persistent homology representations of Keller into persistence images because the resulting vectorized persistent homology representation would be suitable as input for the generative framework of Oono. This modification would yield predicable results, in that the persistent homology representations of Keller would be translated into persistence images, which is proper format for machine learning input (the computer program product of claim 15, wherein the global-shape input representation of the 3D domain comprises a persistence image). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to improve the method of Oono by additionally inputting the persistent homology representations of Keller in the form of persistence images as taught by Krishnapriyan. Use of known technique to improve similar devices (methods, or products) in the same way is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007) (see MPEP § 2143, C).
Regarding claim 18, Oono discloses inputting chemical compound fingerprints into the generative framework. Para. [0009]. As evidenced by Xie, molecular fingerprints describe the local aspect of chemical structures. At 3 col.1 para.1 (the computer program product of claim 15, wherein the input representations further comprise a local point-level input representation of the 3D domain).
Regarding claim 19, Oono discloses that the generative framework can be trained to form a generative model, para. [0040], by encoding the input representations into latent representations and decoding the latent representations into reconstructions of the input representations, para. [0041] (the computer program product of claim 18, wherein using the GNN to form the generative model of the 3D domain comprises: encoding, using the GNN, the input representations to generate latent code; decoding, using the GNN, the latent code to generate a reconstructed version of the input representations). Oono further discloses that the generative framework calculates a reconstruction loss function based on the input representations and the reconstructed input representations. Para. [0046]; Fig.2A (generating, using the GNN, a reconstruction loss based at least in part on the reconstructed version of the input representations).
Regarding claim 20, Oono discloses inputting chemical compound fingerprints into a generative framework, para. [0008], and defines chemical compound fingerprints as a string of values of molecular descriptors that contain the information of a compound's chemical structure, para. [0088] (the computer program product of claim 19, wherein: the local point-level input representation comprises a string input representation). Krishnapriyan discloses that persistent homology representations must be translated into vectors suitable as input for machine learning via persistence images. At 3 para.1 (the global-shape input representation comprises a persistence image input representation).
Keller discloses a persistent homology representation that one of ordinary skill in the art would know to include as an additional input into Oono’s base method of inputting molecular fingerprints into a generative framework (see 103 rejection of claim 15 above). Krishnapriyan discloses vectorizing persistent homology representations via persistence images to be usable as input for machine learning. A person having ordinary skill in the art would know to translate the persistent homology representations of Keller into persistence images because the resulting vectorized persistent homology representation would be suitable as an additional input for the generative framework of Oono. This modification would yield predicable results, in that the persistent homology representations of Keller would be vectorized into persistence images, which is proper format for machine learning input. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to improve the method of Oono by additionally inputting the persistent homology representations of Keller in the form of persistence images as taught by Krishnapriyan. Use of known technique to improve similar devices (methods, or products) in the same way is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007) (see MPEP § 2143, C).
Regarding claim 21, Keller discloses a multi-parameter persistent homology representation that captures the important properties of the shapes of molecules and incorporates non-shape information in a coherent and effective manner. At 4 para.2. Keller teaches that the first parameter of the representation captures a set of points in Euclidean space representing atom centers in a molecule, at 5 para.4, while the second parameter may capture partial charge, at 9 para.1; Figure 3 caption, atomic mass, at 13 para.1; Figure 5 caption, hydrogen donor/acceptor status, or some other non-shape molecular characteristic, at 12 para.4. One of ordinary skill in the art would know to include Keller’s multi-parameter persistent homology representation as an additional input into Oono’s base method of inputting molecular fingerprints into a generative framework (see 103 rejection of claim 15 above). Krishnapriyan discloses that persistent homology representations must be translated into vectors suitable as input for machine learning via persistence images. At 3 para.1. A person having ordinary skill in the art would know to translate the persistent homology representations of Keller into persistence images because the resulting vectorized persistent homology representation would be suitable as an additional input for the generative framework of Oono. This modification would yield predicable results, in that the persistent homology representations of Keller would be vectorized into persistence images, which is proper format for machine learning input. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to improve the method of Oono by additionally inputting the persistent homology representations of Keller in the form of persistence images as taught by Krishnapriyan. Use of known technique to improve similar devices (methods, or products) in the same way is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007) (see MPEP § 2143, C).
The method resulting from the combination of Oono, Keller, and Krishnapriyan would input chemical composition fingerprints and multi-parameter persistence images capturing shape and non-shape molecular information into a generative framework (the computer program product of claim 19, wherein: the input representations further comprise an input representation of a characteristic of the 3D domain). The first parameter of the persistence image captures a set of points in Euclidean space representing atom centers in a molecule (the global-shape input representation comprises a first parameter of a multi-parameter persistence image), while the second parameter may capture partial charge, atomic mass, hydrogen donor/acceptor status, or some other non-shape molecular characteristic (the input representation of the characteristic of the 3D domain comprises a second parameter of the multi-parameter persistence image).
Regarding claim 22, Oono discloses a computer system, para. [0041], comprising a memory coupled to a bus for communicating information to a processor, which executes instructions, para. [0145], that generate chemical compound models having desired characteristics based on the input representations. Abstract; para. [0098] (a computer system comprising a memory and a processor communicatively coupled to the memory, wherein the processor is operable to form a generative model of a three-dimensional (3D) domain by performing operations comprising). Oono teaches that the generative framework encodes input representations into latent representations. Para. [0041] (encoding, using a generative neural network (GNN), input representations to generate latent code). Oono discloses that the latent representations are decoded in the generative framework to reconstructions of the input representations. Id (decoding, using the GNN, the latent code to generate a reconstructed version of the input representations). Oono further discloses that the generative framework calculates a reconstruction loss function based on the input representations and the reconstructed input representations. Para. [0046]; Fig.2A (generating, using the GNN, a reconstruction loss based at least in part on the reconstructed version of the input representations). Oono discloses the input representations comprise chemical compound fingerprints, para. [0008], and defines fingerprints as a string of values of molecular descriptors that contain the information of a compound's chemical structure, para. [0088] (wherein the input representations comprise: a string input representation of the 3D domain).
Oono fails to teach the input representations comprising a 3D coordinate input representation of the 3D domain. However, Keller discloses a multi-parameter persistent homology representation that captures a set of points in Euclidean space representing atom centers in a molecule, at 5 para.4, and some other non-shape molecular characteristic, at 12 para.4. Keller notes that the persistent homology representation is intended to alleviate the significant difficulty in molecular design related to the possibility of multiple confirmations of compounds that have the same molecular formula but important structural differences (i.e. two molecules can share local descriptors, but have distinct global descriptors). At 2 para.3.
To solve a similar issue, Krishnapriyan discloses a machine learning model for metal-organic frameworks (MOFs) that automatically generates feature descriptors based on persistent homology representations and the elemental composition of the molecule. At 2 paras.4-5. Krishnapriyan notes that to comprehensively understand MOFs, it is necessary to recognize geometric and chemical features responsible for their performance in particular applications. At 1 para.3. While input features can be related to a MOF’s performance in a particular application, standard structural descriptors are not able to capture some relevant information, such as the pressure during absorption or local strong absorption sites. At 2 para.2. To overcome these challenges, Krishnapriyan discloses using a topological descriptor called persistent homology and the elemental composition of a molecule as input into a machine learning model. At 2 para.4. The elemental compositions are embedded and the persistent homology representations are translated into persistence images that are suitable as input for machine learning algorithms to generate feature descriptors. At 2 para.11- 3 para.2.
Oono discloses a base method where molecular fingerprints are input into a generative framework to form a generative model of the molecule. Keller discloses a persistent homology representation that captures a set of points in Euclidean space and aims to alleviate major difficulty in molecular design regarding multiple confirmations of compounds that have the same molecular formula but important structural differences. Krishnapriyan discloses a solution to a similar issue in the context of MOFs where persistence images (i.e. global representation) and elemental compositions (i.e. local representation) are used as inputs for machine learning algorithms. One of ordinary skill in the art could apply Krishnapriyan’s technique of inputting global and local representations to the method of Oono by additionally inputting the persistent homology representation of Keller. This would predictably result in an improved method of molecular design because, similar to the results in Krishnapriyan, the incorporation of Keller’s persistent homology representation as input into Oono’s generative framework will remedy the issue of molecules consisting of the same chemical formula but different structures. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to improve the method of Oono by additionally inputting the persistent homology representations of Keller as taught by Krishnapriyan. Use of known technique to improve similar devices (methods, or products) in the same way is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007) (see MPEP § 2143, C).
Regarding claim 23, Keller discloses a multi-parameter persistent homology representation that captures a set of points in Euclidean space representing atom centers in a molecule, at 5 para.4, and some other non-shape molecular characteristic, at 12 para.4 (the computer system of claim 22, wherein: the input representations further comprise an input representation of a characteristic of the 3D domain). Keller teaches that the first parameter of the representation captures a set of points in Euclidean space representing atom centers in a molecule, at 5 para.4, while the second parameter may capture partial charge, at 9 para.1; Figure 3 caption, atomic mass, at 13 para.1; Figure 5 caption, hydrogen donor/acceptor status, or some other non-shape molecular characteristic, at 12 para.4. One of ordinary skill in the art would know to include Keller’s multi-parameter persistent homology representation as an additional input into Oono’s base method of inputting molecular fingerprints into a generative framework (see 103 rejection of claim 22 above). Krishnapriyan discloses that persistent homology representations must be translated into vectors suitable as input for machine learning via persistence images. At 3 para.1. A person having ordinary skill in the art would know to translate the persistent homology representations of Keller into persistence images because the resulting vectorized persistent homology representation would be suitable as an additional input for the generative framework of Oono. This modification would yield predicable results, in that the persistent homology representations of Keller would be vectorized into persistence images, which is proper format for machine learning input. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to improve the method of Oono by additionally inputting the persistent homology representations of Keller in the form of persistence images as taught by Krishnapriyan. Use of known technique to improve similar devices (methods, or products) in the same way is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007) (see MPEP § 2143, C).
The system resulting from the combination of Oono, Keller, and Krishnapriyan would input chemical composition fingerprints and multi-parameter persistence images capturing shape and non-shape molecular information into a generative framework. The first parameter of the persistence image captures a set of points in Euclidean space representing atom centers in a molecule (the 3D coordinates input representation of the 3D domain is represented as a first parameter of a multi-parameter persistence image), while the second parameter may capture partial charge, atomic mass, hydrogen donor/acceptor status, or some other non-shape molecular characteristic (the input representation of the characteristic of the 3D domain is represented as a second parameter of the multi-parameter persistence image).
Regarding claim 24, Oono discloses a computer system, para. [0041], comprising a memory coupled to a bus for communicating information to a processor, which executes instructions, para. [0145], that generate chemical compound models having desired characteristics based on the input representations. Abstract; para. [0098] (a computer system comprising a memory and a processor communicatively coupled to the memory, wherein the processor is operable to form a generative model of a three-dimensional (3D) domain by performing operations comprising). Oono teaches that the generative framework encodes input representations into latent representations. Para. [0041] (encoding, using a generative neural network (GNN), input representations to generate latent code). Oono discloses that the latent representations are decoded in the generative framework to reconstructions of the input representations. Id (decoding, using the GNN, the latent code to generate a reconstructed version of the input representations). Oono further discloses that the generative framework calculates a reconstruction loss function based on the input representations and the reconstructed input representations. Para. [0046]; Fig.2A (generating, using the GNN, a reconstruction loss based at least in part on the reconstructed version of the input representations). Oono discloses the input representations comprise chemical compound fingerprints, para. [0008], and defines fingerprints as a string of values of molecular descriptors that contain the information of a compound's chemical structure, para. [0088] (wherein the input representations comprise: a string input representation of the 3D domain).
Oono fails to teach the input representations comprising a 3D coordinates input representation of the 3D domain and an input representation of a characteristic of the 3D domain. However, Keller discloses a multi-parameter persistent homology representation that captures a set of points in Euclidean space representing atom centers in a molecule, at 5 para.4, and partial charge, at 9 para.1; Figure 3 caption, atomic mass, at 13 para.1; Figure 5 caption, hydrogen donor/acceptor status, or some other non-shape molecular characteristic, at 12 para.4. Keller notes that the persistent homology representation is intended to alleviate the significant difficulty in molecular design related to the possibility of multiple confirmations of compounds that have the same molecular formula but important structural differences (i.e. two molecules can share local descriptors, but have distinct global descriptors). At 2 para.3.
To solve a similar issue, Krishnapriyan discloses a machine learning model for metal-organic frameworks (MOFs) that automatically generates feature descriptors based on persistent homology representations and the elemental composition of the molecule. At 2 paras.4-5. Krishnapriyan notes that to comprehensively understand MOFs, it is necessary to recognize geometric and chemical features responsible for their performance in particular applications. At 1 para.3. While input features can be related to a MOF’s performance in a particular application, standard structural descriptors are not able to capture some relevant information, such as the pressure during absorption or local strong absorption sites. At 2 para.2. To overcome these challenges, Krishnapriyan discloses using a topological descriptor called persistent homology and the elemental composition of a molecule as input into a machine learning model. At 2 para.4. The elemental compositions are embedded and the persistent homology representations are translated into persistence images that are suitable as input for machine learning algorithms to generate feature descriptors. At 2 para.11- 3 para.2.
Oono discloses a base method where molecular fingerprints are input into a generative framework to form a generative model of the molecule. Keller discloses a persistent homology representation that captures a set of points in Euclidean space and some non-shape molecular characteristic with the goal of alleviating major difficulty in molecular design regarding multiple confirmations of compounds that have the same molecular formula but important structural differences. Krishnapriyan discloses a solution to a similar issue in the context of MOFs where persistence images (i.e. global representation) and elemental compositions (i.e. local representation) are used as inputs for machine learning algorithms. One of ordinary skill in the art could apply Krishnapriyan’s technique of inputting global and local representations to the method of Oono by additionally inputting the persistent homology representation of Keller. This would predictably result in an improved method of molecular design because, similar to the results in Krishnapriyan, the incorporation of Keller’s persistent homology representation as input into Oono’s generative framework will remedy the issue of molecules consisting of the same chemical formula but different structures. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to improve the method of Oono by additionally inputting the persistent homology representations of Keller as taught by Krishnapriyan. Use of known technique to improve similar devices (methods, or products) in the same way is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007) (see MPEP § 2143, C).
Regarding claim 25, Oono discloses that the object being analyzed and modeled is a molecule or chemical compound. Para. [0090] (the computer system of claim 24, wherein: the 3D domain comprises a molecule). Keller discloses that the non-shape characteristic captured in the persistent homology representation can be partial charge, at 9 para.1; Figure 3 caption, atomic mass, at 13 para.1; Figure 5 caption, hydrogen donor/acceptor status, or some other non-shape molecular characteristic, at 12 para.4 (the characteristic of the molecule is selected from the group consisting of an atomic charge and an atomic weight).
Conclusion
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/E.A.D./Examiner, Art Unit 1686
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685