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
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDS’) submitted on 06/18/24, 01/23/25, and 06/10/25 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Oath/Declaration
The examiner could not locate the Oath. Please confirm that it has been properly filed.
Drawings
The drawings filed on 06/06/24 are accepted.
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 1-10 and 19-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
With respect to step 1 of the patent subject matter eligibility analysis, the claims are directed to a process, machine, manufacture, or composition of matter. Independent claim 1 is directed to a method for managing molecular prediction, which is a process. Independent claim 19 is directed to an electronic device, which is a machine. Independent claim 20 is directed to a non-transitory computer-readable media, which is a manufacture. All other claims depend on independent claims 1 and 19-20. As such, claims 1-10 and 19-28 are directed to a statutory category.
With respect to step 2A, prong one, the claims recite an abstract idea, law of nature, or natural phenomenon. Specifically, the following limitations recite mathematical concepts and/or mental processes.
Claim 1
the pretrained model describing an association between a molecular structure and molecular energy (The claim does not give any details about what the “describing” entails. A general description of an association between a molecular structure and molecular energy is an observation, evaluation, judgment, and/or opinion that can be performed in the human mind. Also, it would appear that the claimed “association” is a mathematical relationship between two variables. Therefore, this limitation recites an abstract idea in the form of an abstract mental process and/or an abstract mathematical concept.)
determining a downstream model based on a molecular prediction purpose, and an output layer of the downstream model being determined based on the molecular prediction purpose (The claims do not detail the nature of the claimed model, nor does it detail the nature of the claimed molecular prediction purpose. At a general level, a “mental model” can be determined in the human mind, based on a molecular prediction purpose. There are also more complex mathematical models that cannot be determined in the human mind. Therefore, this limitation recites an abstract idea in the form of an abstract mental process and/or an abstract mathematical concept.)
generating a molecular prediction model based on the upstream model and the downstream model, the molecular prediction model describing an association between a molecular structure and a molecular prediction purpose associated with the molecular structure (The general generation of “mental models” are observations, evaluations, judgments, and/or opinions that can be performed in the human mind. More complex mathematical models that cannot be performed in the human mind are defined by abstract mathematical relationships, formulas, equations, and/or calculations. Also, the claimed “association” appears to be a mathematical relationship between two variables. Therefore, this limitation recites an abstract idea in the form of an abstract mental process and/or an abstract mathematical concept. )
Independent claims 19-20 represent variations of claim 1 and recite similar abstract limitations.
Dependent claims 2-10 and 21-28 depend on independent claims 1 and 19-20. They also recite the independent claims’ abstract limitations, by virtue of their dependence. In addition, some of the claims also recite their own abstract mathematical concepts and/or mental processes.
Claims 3-4, 8-9, 22-23, and 27-28 are directed to a loss function, which is a specific mathematical formula/equation/calculation. These claims therefore recite an abstract mathematical concept.
Claims 7 and 26 disclose, “training the molecular prediction model using training data in a training dataset, such that a loss function of the molecular prediction model …” These claims therefore recite a specific mathematical calculation, formula, equation, and/or relationship.
With respect to step 2A, prong two, the claims do not recite additional elements that integrate the judicial exception into a practical application. The following limitations are considered “additional elements” and explanation will be given as to why these “additional elements” do not integrate the judicial exception into a practical application.
Claim 1
A method for managing molecular prediction (This limitation is not indicative of integration into a practical application because it merely serves to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).)
obtaining an upstream model from a portion of network layers in a pretrained model (This limitation is not indicative of integration into a practical application because obtaining data for processing merely adds insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)).)
Claim 19
An electronic device (This limitation is not indicative of integration into a practical application because it merely serves to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).)
at least one processing unit (This is a general and generic recitation of a computer component. Mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, is not indicative of integration into a practical application (see MPEP 2106.05(f)).)
at least one memory coupled to the at least one processing unit and storing instructions executed by the at least one processing unit, the instructions, when executed by the at least one processing unit, causing the electronic device to perform a method for managing molecular prediction (This is a general and generic recitation of a computer component. Mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, is not indicative of integration into a practical application (see MPEP 2106.05(f)).)
obtaining an upstream model from a portion of network layers in a pretrained model (This limitation is not indicative of integration into a practical application because obtaining data for processing merely adds insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)).)
Claim 20
A non-transitory computer-readable storage medium, storing a computer program thereon, the computer program, when executed by a processor, causing the processor to implement a method for managing molecular prediction (The general disclosure of a non-transitory computer-readable storage medium merely uses a computer as a tool to perform an abstract idea. The general intended use of “for managing molecular prediction” merely serves to generally link the use of the judicial exception to a particular technological environment or field of use.)
obtaining an upstream model from a portion of network layers in a pretrained model (This limitation is not indicative of integration into a practical application because obtaining data for processing merely adds insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)).)
Dependent claims 2-10 and 21-28 depend on independent claims 1 and 19-20. They also recite the independent claims’ limitations that are not indicative of integration into a practical application, by virtue of their dependence. In addition, some of the claims also recite their own limitations that are not indicative of integration into a practical application.
Claims 2 and 21 further narrow the obtaining of the upstream model. However, as discussed above, obtaining of the upstream model merely serves as insignificant extra-solution activity to obtain the data that is processed by the “solution”.
Claims 5 and 24 contextualize the molecular prediction purpose, but this only serves to generally link the use of the judicial exception to a particular technological environment or field of use.
Claims 6 and 25 describe the downstream model as comprising at least one downstream network layer, and the last downstream network layer in the at least one downstream network layer is the output layer of the downstream model. This is a general and generic description of models. The claims merely serve to generally link the use of the judicial exception to a particular technological environment or field of use.
Claims 7 and 26 disclose connecting the upstream model and the downstream model to form the molecular prediction model. However, no details are given as to how the connection is performed. The claims merely serve to generally link the use of the judicial exception to a particular technological environment or field of use.
With respect to step 2B, the claims do not recite additional elements that amount to significantly more than the judicial exception. The claimed invention does not add significantly more because, as discussed above in step 2A, prong two, the claims do nothing more than merely use a computer as a tool to perform an abstract idea; add insignificant extra-solution activity to the judicial exception; and/or generally link the use of the judicial exception to a particular technological environment or field of use. The claims are directed to receiving and processing data. This is well-understood, routine, and conventional. Simply appending well-understood, routine, and conventional activities previously known to the industry, and specified at a high level of generality, to the judicial exception is not indicative of an inventive concept (aka “significantly more”) (see MPEP 2106.05(d) and Berkheimer Memo).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-2, 5-6, 10, 19-21, and 24-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al (US PgPub 20220122697).
With respect to claim 1, Liu et al discloses:
A method for managing molecular prediction (abstract states, “A method for predicting a compound property.”; paragraph 0003 states, “Therefore, using deep learning methods to facilitate accurate prediction of drug molecules has become more and more important.”)
obtaining an upstream model from a portion of network layers in a pretrained model, the pretrained model describing an association between a molecular structure and molecular energy (Paragraph 0067 states, “in the model training phase, the technical solution provided by the present disclosure firstly uses large-scale compound molecules that are not labeled with corresponding property information to perform pre-training to learn spatial structure-related knowledge …” Paragraph 0031 states, “the spatial structure information mainly relates to a spatial structure formed by atoms and chemical bonds, such as bond angles and bond lengths of the chemical bonds, three-dimensional coordinates of respective atoms, an overall potential energy of compound molecule, atomic distances, and so on.” Paragraph 0054 states, “a first-layer spatial structure prediction model may model features and spatial structures of first-order neighbors, and a second-layer spatial structure prediction model may model features and spatial structures of second-order neighbors, and so on. When superimposing is performed to obtain an n-layer spatial structure prediction model, features and spatial structures of n-order neighbors may be modeled …”)
determining a downstream model based on a molecular prediction purpose, and an output layer of the downstream model being determined based on the molecular prediction purpose (Paragraph 0067 states, “… then uses a trained spatial structure prediction model as the basis, and uses a small sample quantity of compound molecules labels with pieces of corresponding property information for fine-tuning.” Please also note paragraph 0033, which states, “A reason for acquiring the spatial structure information is that from a microscopic point of view, downstream tasks such as a property prediction of compound molecules and an interaction between a drug and a target are essentially results of intermolecular interactions …” Finally, please note the abstract, which states, “using the first sample compounds as input samples and pieces of corresponding spatial structure information as output samples, to obtain a spatial structure prediction model; and continuing training, using second sample compounds as input samples and pieces of corresponding property information as output samples, to obtain the compound property prediction model on the basis of the spatial structure prediction model.”)
With respect to claim 1, Liu et al differs from the claimed invention in that is does not explicitly disclose:
generating a molecular prediction model based on the upstream model and the downstream model, the molecular prediction model describing an association between a molecular structure and a molecular prediction purpose associated with the molecular structure
With respect to claim 1, the following limitation(s) is/are obvious in view of the total teachings of Liu et al.
generating a molecular prediction model based on the upstream model and the downstream model, the molecular prediction model describing an association between a molecular structure and a molecular prediction purpose associated with the molecular structure (Liu et al does not explicitly use language that distinguishes between three different models: 1) upstream model, 2) downstream model, and 3) molecular prediction model. However, this is effectively what Liu et al teaches, using slightly different language. The abstract of Liu et al states, “using the first sample compounds as input samples and pieces of corresponding spatial structure information as output samples, to obtain a spatial structure prediction model; and continuing training, using second sample compounds as input samples and pieces of corresponding property information as output samples, to obtain the compound property prediction model on the basis of the spatial structure prediction model.” The upstream model is analogous to the pre-training for the spatial structure prediction model. The downstream model is analogous to the fine-tuning for the output samples. The compound property prediction model, that is the final output of the upstream and downstream models, is analogous to the claimed molecular prediction model. Although Liu et al does not specifically separate these different models into three different models, they serve the same function as the claimed upstream, downstream, and molecular prediction models. The claimed limitation is therefore obvious, in view of the total teachings of Liu et al.)
With respect to claim 1, it would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Liu et al. The motivation for the skilled artisan in doing so is to gain the benefit of accurate prediction of molecules.
Claims 19-20 represent variations of claim 1. They are rejected for similar reasons. The main difference between claims 19-20 and claim 1 is in the preamble, where claims 19-20 disclose generic computer components, such as processing unit, memory, and non-transitory computer-readable storage media. Liu et al also discloses these computer elements (abstract)
With respect to claims 2 and 21, Liu et al, as modified, discloses:
wherein obtaining the upstream model comprises: obtaining the pretrained model, which comprises a plurality of network layers (paragraph 0054 discloses various layers for the spatial structure prediction model)
selecting the upstream model from a group of network layers other than an output layer of the pretrained model from the plurality of network layers (obvious in view of teachings of Liu; As seen in the abstract and paragraph 0067, the output of the pre-training is the spatial structure prediction model that is used as the basis for fine-tuning. Selecting the other layers for pre-training is an obvious application of working with models.)
With respect to claims 5 and 24, Liu et al, as modified, discloses:
wherein the molecular prediction purpose comprises at least any of: a molecular property and a molecular force field, and the pretrained model is selected based on the molecular prediction purpose (Paragraph 0033 states, “A reason for acquiring the spatial structure information is that from a microscopic point of view, downstream tasks such as a property prediction of compound molecules and an interaction between a drug and a target are essentially results of intermolecular interactions …”)
With respect to claims 6 and 25, Liu et al, as modified, discloses:
wherein the downstream model comprises at least one downstream network layer, and the last downstream network layer in the at least one downstream network layer is the output layer of the downstream model (obvious in view of total teachings of Liu et al; The output layer is implied by the abstract teachings of “using second sample compounds as input samples and pieces of corresponding property information as output samples, to obtain the compound property prediction model …”)
With respect to claim 10, Liu et al, as modified, discloses:
further comprising: in response to receiving a target molecular structure, determining a predicted value corresponding to the molecular prediction purpose based on the molecular prediction model (Liu et al paragraphs 0033, 0054, and 0067)
Claim(s) 3-4, 7-9, 22-23, and 26-28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al (US PgPub 20220122697) in view of Kim et al NPL (Kim, H.; Lee, J.; Ahn, S.; and Lee, J. – “A merged molecular representation learning for molecular properties prediction with a web-based service”; Scientific Reports (2021) 11:11028).
With respect to claims 3 and 22, Liu et al, as modified, discloses:
The method of claim 1 (as applied to claim 1 above)
The device of claim 19 (as applied to claim 19 above)
With respect to claims 3 and 22, Liu et al, as modified, differs from the claimed invention in that is does not explicitly disclose:
wherein obtaining the pretrained model comprises: training the pretrained model using pretraining data in a pretraining dataset, such that a loss function associated with the pretrained model satisfies a predetermined condition, the pretraining data comprising a sample molecular structure and sample molecular energy
With respect to claims 3 and 22, Kim et al NPL discloses:
wherein obtaining the pretrained model comprises: training the pretrained model using pretraining data in a pretraining dataset, such that a loss function associated with the pretrained model satisfies a predetermined condition, the pretraining data comprising a sample molecular structure and sample molecular energy (Page 4, first sentences after equation (5) states, “The function for pre-training is the sum of Mean Squared Error and Cross-entropy …” Page 5, paragraph 2 in “Results and discussion” section states, “Loss and ROC-AUC curves on classification and regression tasks are reported.”)
With respect to claims 3 and 22, it would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Kim et al NPL into the invention of Liu et al. The motivation for the skilled artisan in doing so is to gain the benefit of minimizing mean square error.
With respect to claims 4 and 23, Liu et al, as modified, discloses:
wherein the loss function comprises at least any of: energy loss, the energy loss representing a difference between the sample molecular energy and a predicted value of the sample molecular energy based on the sample molecular structure; estimated energy loss, the estimated energy loss representing a difference between the sample molecular energy and a predicted value of the sample molecular energy based on the sample molecular structure, the sample molecular structure being estimated; and force loss, the force loss representing a difference between a predetermined gradient and a gradient of a predicted value of the sample molecular energy obtained based on the sample molecular structure relative to the sample molecular structure (obvious in view of combination; As discussed above, Liu et al’s spatial structure model is defined by energy (paragraphs 0031, 0033, and 0035). Applying a loss function to one of its core parameters would be obvious to one of ordinary skill in the art.)
With respect to claims 7 and 26, Liu et al, as modified, discloses:
The method of claim 5 (as applied to claim 5 above)
The device of claim 24 (as applied to claim 24 above)
wherein generating the molecular prediction model based on the upstream model and the downstream model comprises: connecting the upstream model and the downstream model to form the molecular prediction model (see abstract of Liu et al)
With respect to claims 7 and 26, Liu et al, as modified, differs from the claimed invention in that is does not explicitly disclose:
training the molecular prediction model using training data in a training dataset, such that a loss function of the molecular prediction model satisfies a predetermined condition, the training data comprising a sample molecular structure and a sample target measurement value corresponding to the molecular prediction purpose
With respect to claims 7 and 26, Kim et al NPL discloses:
training the molecular prediction model using training data in a training dataset, such that a loss function of the molecular prediction model satisfies a predetermined condition, the training data comprising a sample molecular structure and a sample target measurement value corresponding to the molecular prediction purpose (obvious in view of combination; As discussed above, Kim et al NPL discloses applying loss function to pre-training stage. Liu et al teaches molecular parameters and molecular prediction model.)
With respect to claims 7 and 26, it would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Kim et al NPL into the invention of Liu et al. The motivation for the skilled artisan in doing so is to gain the benefit of minimizing mean square error.
With respect to claims 8 and 27, Liu et al, as modified, discloses:
wherein the loss function of the molecular prediction model comprises the difference between the sample target measurement value and a predicted value of the sample target measurement value obtained based on the sample molecular structure (obvious in view of applying loss function teachings of Kim et al NPL to molecular prediction model of Liu et al)
With respect to claims 9 and 28, Liu et al, as modified, discloses:
wherein in response to determining the molecular force field as the molecular prediction purpose, the loss function of the molecular prediction model further comprises: a difference between a predetermined gradient and a gradient of a predicted value of the sample molecular energy obtained based on the sample molecular structure relative to the sample molecular structure (obvious in view of applying loss function teachings of Kim et al NPL to molecular prediction model of Liu et al)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Feinberg et al (US PgPub 20190272468) discloses systems and methods for spatial graph convolutions with applications to drug discovery and molecular simulation.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LEONARD S LIANG whose telephone number is (571)272-2148. The examiner can normally be reached M-F 10:00 AM - 7 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ARLEEN M VAZQUEZ can be reached at (571)272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/LEONARD S LIANG/ Examiner, Art Unit 2857 09/19/26
Prosecution Insights