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 .
Status of the clams
Claims 21-29 are withdrawn.
Claims 1-20 are pending.
Priority
This application claims benefit of U.S Provisional Application 63/030,806 filed 27 May 2020 which claims priority benefit to U.S Provisional Application 63/053,192 filed 17 July 2020 which claims benefit to U.S Provisional Applications 63/190,651, 63/190,656, and 63/190,651 filed 19 May 2019.
Election/Restrictions
Claims 21-28 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 07 January 2025.
Claims 1-20 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05 December 2024, 23 August 2023, 23 May 2022, 06 April 2022, 06 April 2022, and 06 April 2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner.
Drawings
The drawings were received on 27 May 2021. These drawings are accepted. It is noted that black and white drawings were provided and received 27 May 2021 (21 pages).
Specification
The specification received 27 May 2021 has been entered into the application.
Claim Rejections - 35 USC § 112
35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites using “an atomic-orbital-based machine learning (OrbNet) model. The specification states “The OrbNet processes can utilize a trained model that describes relationships between AO based features and properties of molecular systems to perform a ranking and/or categorization (104) of at least the molecules in the input dataset” [paragraph 0091]. The specification further states “A number of embodiments of the invention utilize machine learning models including (but not limited to) Graph Neural Network (GNN) models that receive SAAO features as a direct input and output estimates of molecular properties for the received SAAO features as an output. Several embodiments provide that OrbNet utilizes a GNN architecture with edge and node attention and message passing layers, and a prediction phase to ensure extensivity of the resulting energies” [paragraph 00128]. Furthermore, the specification states [paragraph 0039 fig 3] “illustrates an architecture of an OrbNet process for AO features in accordance with an embodiment of the invention”. The specification merely provides a general description of the function of OrbNet but does not provide structure or an algorithm that constructs the OrbNet model. As such, the specification does not describe algorithms that encompass the OrbNet model, and the structure of the OrbNet model is not disclosed. Additionally, the prior art does not teach an OrbNet model. Therefore, one of ordinary skill in the art would not know what structure and algorithm is encompassed by the OrbNet model because a written description is not provided.
Claims 2-20 are rejected because the claims do not provide structure or algorithm(s) that describe the OrbNet model of claim 1.
35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With respect to claim 1, the limitation “an atomic-orbital-based machine learning (OrbNet) model” renders the claim indefinite. It is unclear what structures or algorithms are encompasses by (OrbNet) model. It is further unclear what part of the OrbNet model is to determine at least one molecular system property based on the set of atomic-orbital-based features. The metes and bounds of (OrbNet)” are not clear. One of ordinary skill would not know what is encompassed by the limitation “(OrbNet)” or “OrbNet.”
Furthermore, claims 5-7, 12 and 14 are indefinite because the claims do not limit or exemplify what structures or algorithms encompass the OrbNet model.
Claims 2-4, 8-11, 13, and 15-20 are rejected because the base claims from which they depend fail to provide limitations to overcome the deficiencies of the base claims.
35 U.S.C 101 § Rejection
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim Analysis
Under broadest reasonable interpretation (BRI), the claims are drawn to using a computer system to obtain and generate data. The data is further processed using a computer implemented OrbNet model to determine molecular system properties. The computer system further processes the molecular system properties to determine if the molecular systems’ properties satisfy a criterion in order to synthesize a molecular system. Under further BRI and in light of the specification, the term “synthesizing” is being interpreted as synthesizing a theoretical molecule(s). Here, the term reads on organizing information or data to “synthesize” a theoretical molecule as the specification does not describe physical and/or laboratory steps for synthesizing the molecule or molecular system.
Step 1 - Process, Machine, Manufacture or Composition
Claims 1-20 are drawn to a drawn to a method, so a process.
Step 2A Prong One - Identification of an Abstract Idea
Claim 1 recites
obtaining a set of atomic orbitals for a molecular system using a computer system
This step can be performed in the human by following instructions to gather a set of atomic orbitals and is therefore an abstract idea.
generating a set of atomic-orbital-based features based upon the set of atomic orbitals of the molecular system using the computer system
This step can be performed in the human mind by observing and evaluating the set of atomic orbitals of the molecular system to generate a set of atomic-orbital-based features and is therefore an abstract idea.
determining at least one molecular system property based on the set of atomic-orbital-based features using an atomic-orbital-based machine learning (OrbNet) model implemented on the computer system
This step can be performed in the human mind by following instructions to use an atomic-orbital-based machine learning (OrbNet) model to determine a least one molecular system property and is therefore an abstract idea. This step encompasses using a machine learning model to determine molecular system property which encompasses organizing information and manipulating information through mathematical/statistical correlations and is therefore an abstract idea. This step further encompasses using vector and matrix mathematics and is therefore an abstract idea.
determining that at least one molecular system property satisfies at least one criterion by the computer system
This step can be performed in the human mind by observing and evaluating at least one molecular system property to determine if the property satisfies at least one criterion and is therefore an abstract idea.
synthesizing the molecular system
This step can be performed in the human mind by following instructions to combine atomic orbital data to synthesize a molecular system (i.e., molecule or compound) and is therefore an abstract idea. This step encompasses synthesizing a molecular system (i.e., a molecule or compound) by merely combining data by taking atomic orbital information and manipulating the atomic orbital information using mathematical functions (i.e., using OrbNet), and organizing it into a new form such as a molecular system (i.e., molecule or compound) which encompasses using mathematical/statistical correlations and functions and is therefore an abstract idea.
Claims 2-6, and 9-20 are further drawn to limitations that describe the abstract ideas of claim 1.
Step 2A Prong II - Consideration of Practical Application
Claim 1 recites obtaining a set of atomic orbitals, determining at least one molecular system property based on the set of atomic-orbital-based features using an atomic-orbital-based machine learning (OrbNet) model, determining that at least one molecular system property satisfies at least one criterion, and synthesizing the molecular system. Claim 1 does not recite any additional elements which integrate the recited judicial exception into a practical application. Here, in the instant case, the claims merely set forth a method of data analysis for synthesizing the molecular system. As such, practicing the claims merely results in synthesizing a molecular system (i.e., a molecule or compound) which is merely combining data by taking atomic orbital information, manipulating the atomic orbital information using mathematical functions, and organizing the it into a new form such as a molecular system (i.e., molecule or compound). See MPEP 2106.04(a)(2)(I)(A)(iv). Such a result only produces information and does not provide for a practical application in the real-world realm of physical things and acts, i.e., the claims do not utilize the data generated by the judicial exception to affect any type of change. See MPEP 2106.04(a)(2)(A)(iv).
Furthermore, the recitation of the claim limitation (i.e., synthesizing the molecular system) attempts to cover any solution to an identified problem with no restriction on how the synthesis of the molecular system is accomplished and no description of the mechanism for synthesizing the molecular system, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See MPEP 2106.05(f)(1).
Therefore, the claim does not utilize the obtained set of atomic orbitals and the generated set of atomic-orbital-based features, and the abstract ideas to construct a practical application such as treating a subject, making a tangible object, or improving upon an existing technology.
The claims do not recite an additional element that integrates the abstract idea/judicial exception into a practical application.
This judicial exception is not integrated into a practical application because the claims do not meet any of the following criteria:
The claims do not recite an additional element that integrates the abstract judicial exception into a practical application.
This judicial exception is not integrated into a practical application because the claims do not meet any of the following criteria:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than
a drafting effort designed to monopolize the exception.
Step 2B - Consideration of Additional Elements and Significantly More
The claimed method also recites "additional elements" that are not limitations drawn to an abstract idea.
The recited additional element of data gathering by obtaining a set of training atomic orbitals and obtaining a set of training atomic-orbital based features of claim 7 and obtaining a set of symmetry-adapted-atomic-orbitals for each training molecular system and obtaining a set of symmetry-adapted-atomic-orbital-based features of claim 8 does not provide significantly more than the recited judicial exception because obtaining data that is subsequently analyzed by the abstract idea is deemed a well-known and routine extra-solution activity. See MPEP 2106.05(g).
The recited additional element of using computer processes and equipment of claim 1 does not provide significantly more than recited judicial exception because using a computer to analyze the abstract is deemed well-known and conventional. See MPEP 2106.05(b).
Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea recited in the instantly presented claims into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
35 U.S.C § 102 Rejection
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.
Claim(s) 1 and 7 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Schutt et al. (Nature communications, 2019-11, Vol.10 (1), p.5024-10, Article 5024).
Claim 1 recites obtaining a set of atomical orbital for a molecular system using a computer. Claim 1 recites generating a set of atomic-orbitals-based features based upon the set of atomic orbitals of the molecular system using a computer. Claim 1 recites determining at least one molecular system based on the atomic-orbital-based features using an atomic-orbital-based machine learning (OrbNet) model using a computer. Claim 1 recites determining that at least one molecular property that satisfies at least one criterion by the computer system. Claim 1 recites synthesizing the molecular system.
Schutt teaches SchNOrb architecture that is trained using neural networks that use several data sets of water as well as ethanol, malondialdehyde, and uracil from the MD17 dataset [page 4 left col Learning electronic structure and derived properties], as in claim 1 obtaining a set of atomical orbital for a molecular system using a computer.
Schutt teaches a SchNOrb neural network architecture of atomic-orbital-based features is constructed as symmetry-adapted pairwise features Ω1ij to represent the block of the Hamiltonian matrix corresponding to atoms i, j [page 3 fig 2 and left col SchNOrb deep learning framework], as in claim 1 generating a set of atomic-orbitals-based features based upon the set of atomic orbitals of the molecular system using a computer.
Schutt teaches using SchNOrb (SchNet for Orbitals) which presents a framework that captures the electronic structure in a local representation of atomic orbitals (i.e., determining molecular system properties) that is common in quantum chemistry [page 3 left col], as in claim 1 determining at least one molecular system based on the atomic-orbital-based features using an atomic-orbital-based machine learning (OrbNet) model using a computer. Here, the SchNOrb is a machine learning neural network used to capture the electronic structure in a local representation of atomic orbitals which teaches a machine learning model that functions as an atomic-orbital-based machine learning (OrbNet) model.
Schutt teaches that figure 2f depicts the predicted and reference orbital energies for the frontier MOs of ethanol (solid and dotted lines, respectively), as well as the orbital shapes derived from the coefficients. Schutt teaches that the occupied and unoccupied energy levels are reproduced with high accuracy, including the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbitals (LUMO) which teach that the orbital energies meet a criterion as the predicted and molecular orbitals were compared for four selected energy levels [page 4 right col first paragraph], as in claim 1 determining that at least one molecular property that satisfies at least one criterion by the computer system.
Schutt teaches predicted (e.g., synthesized) molecules were compared to reference orbital energies [page 3 fig 2d and 2f], as in claim 1 synthesizing the molecular system.
Schutt teaches using trained separate neural networks for several data sets of water as well as ethanol, malondialdehyde, and uracil from the MD17 dataset [page 4 left col Learning electronic structure and derived properties]. Schutt teaches augmenting the training data by adding rotated geometries and correspondingly rotated Hamiltonian and overlap matrices to learn the correct rotational symmetries [page 4 Learning electronic structure and derived properties], as in claim 7
35 U.S.C § 103 Rejection
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 2 is rejected under 35 U.S.C. 103 as being unpatentable over Schutt et al., as applied to claims 1 and 7 above, and in further view of Chen et al. (Chemistry of materials, 2019-05, Vol.31 (9), p.3564-3572).
Schutt et al. teach claims 1 and 7, as set forth above.
Schutt does not teach claim 2
Chen et al. (Chen) teaches the graph representation concept has been successfully applied to predict molecular properties [page 3564 left col introduction]. Chen teaches the final result is a new graph representation [page 3565 figure 1], as in claim 2.
It would be obvious to one of ordinary skill in the art by the effective filing date of the claimed invention to modify Schutt in further view of Chen because Chen teaches using the modular system MEGNet, comprising a graph neural network, for representing sets of atomic attributes. One of ordinary skill in the art would be motivated to combine Schutt view of Chen because Chen teaches processing vector representations of molecules using the MEGNet system [page 3566 fig 2]. One of ordinary skill in the art would except a reasonable success in combining the teachings of Schutt in further view of Chen because Chen teaches using HOMO and LUMO properties for determining lowest and highest occupied molecular orbitals [page 3567 table 2]. Here, the combination of Schutt in further view of Chen would construct and yield a predictable modular system for synthesizing a molecule based on atomic and molecular orbitals.
Conclusion
Claims 1-20 are rejected.
No claims are allowed.
This Office action is a Non-Final action. A shortened statutory period for reply to this action is set to expire THREE MONTHS from the mailing date of this action.
Inquires
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH C PULLIAM whose telephone number is (571)272-8696. The examiner can normally be reached 0730-1700 M-F.
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/J.C.P./Examiner, Art Unit 1687
/Anna Skibinsky/
Primary Examiner, AU 1635