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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 18 November 2025 has been entered.
Status of the Claims
The claim set received on 18 November 2025 has been entered into the application.
Claims 1-2, 15, and 22-23 are amended.
Claims 3 and 24 are withdrawn.
Claim(s) 1-2 and 4-24 are pending.
Election/Restrictions
After further search and consideration, and in view of the Applicants traversal and amendments, the restriction and election of species requirement for Groups I-III of claims 1-23 are withdrawn.
However, claim 24 is withdrawn from consideration because the Applicant acknowledges that claim 24 includes steps different from claims 1 and 22-23.
Claims 1-2 and 4-23 are pending examination.
Priority
No priority is claimed.
Specification
The objection to the specification because using fully or partially semi-supervise learning in the Office Action mailed 20 May 2025 is withdrawn in view of the amendments received 18 November 2025.
The objection to the specification because wherein the prediction comprises specifications for synthesizing and characterizing new materials in the Office Action mailed 20 May 2025 is withdrawn in view of the amendments received 18 November 2025.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06 September 2024 and 06 September 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Specification
The amendment filed 18 November 2025 is objected to under 35 U.S.C. 132(a) because it introduces new matter into the disclosure. 35 U.S.C. 132(a) states that no amendment shall introduce new matter into the disclosure of the invention. The added material which is not supported by the original disclosure is as follows:
The added material which is not supported by the original disclosure is “synthesizing the material having preconfigured properties based on the prediction” of claims 22-23.
Applicant is required to cancel the new matter in the reply to this Office Action.
Claim Rejections - 35 USC § 112
35 USC § 112(a)
It is noted the amendments received 18 November 2025 necessitated new ground(s) of rejection.
The rejection of claim 1 and 22-23 because creating a dataset using at least semi-supervised learning under 35 U.S.C § 112(a) in the Office Action mailed 20 May 2025 is withdrawn in view of the amendments received 18 November 2025.
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.
Claims 22-23 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.
New Matter
Claims 22-23 were amended to recite “synthesizing the material having preconfigured properties based on the prediction”.
The specification discloses “[0021] In some aspects, the techniques described herein relate to a method, further including synthesizing the desired material based on the prediction.” The specification discloses “[0074] Moreover, the machine learning process optimize experimental design, efficiently suggesting conditions for material synthesis and characterization, minimizing resource-intensive trial-and-error approaches. “ The specification discloses “[0076] In various embodiments, it is recognized that conventional experimental research can be expensive and time-intensive. The disclosure herein may allow for computer simulations for modeling and predicting material behavior in a manner that novel materials can be predicted, refined, outputted, and then synthesized.“ The specification discloses “[00165] In one embodiment, the GAD application may synthesize the desired material(s) based on the prediction. Outputting a prioritized collection of candidate atomic structures may involve further verification of the top <n> candidates within the final output set, and may entail then selecting a subset of candidates that may be chosen for actual physical synthesis and experimentation. Additionally, the generation process may further comprise synthesizing the desired material based on prediction(s). [00166] Within the context of the present description, synthesis may refer to the process of creating new materials. In one embodiment, synthesis may be achieved by inducing chemical reactions between different substances. Synthesis may involve carefully selecting and combining precursors, as well as applying external factors (such as heat, pressure, and/or light), to instigate desired reactions, where the goal may be the production of novel compounds or materials with specific properties or structures. For example, a researcher may synthesize a new ceramic material designed to withstand extreme temperatures, or may create a new polymer with unique mechanical properties. Thus, synthesis may be a foundation of materials discovery, enabling the creation of new materials that might not exist naturally. It is to be appreciated that synthesis (the process of actually creating the material) builds upon the machine learning system output, which includes the actual atomic structure.” The specification discloses “[00351] Within the context of synthesis, fabrication may involve the creation of the desired material. For example, fabrication may include shaping, assembly, and/or construction of materials into functional devices or structures. While synthesis may focus on creating the material itself, fabrication may involve using that material to build something else (i.e. a device, an end product, etc.). For example, fabrication processes may range from depositing thin films for electronics, and/or carving nanostructures using techniques like lithography, to assembling larger components for mechanical systems. Whether constructing a new type of solar cell, developing a nanoscale sensor, or building a robust mechanical part, fabrication may tum raw materials into functional components that may be integrated into real-world applications.” The specification discloses “[00366] In some aspects, the techniques described herein relate to a method, where the new dataset for the desired material includes at least two of electronic properties, charge density, force vectors, interatomic potential, or a scalar value property associated with the desired material. In some aspects, the techniques described herein relate to a method, further including synthesizing the desired material based on the prediction. “Furthermore, the specification does not disclose methods (i.e., clinical/laboratory methods/techniques) for synthesizing the material having preconfigured properties based on the prediction but merely discloses that a material can be synthesize using the predicted preconfigured properties.
Therefore, the synthesizing steps of claims 22-23 are new matter because claims 22 and 23 are drawn to processors that execute instructions for synthesizing a material, and in light of the specification, it does not support a computer processor, computer, and/or machine (e.g., automated robot(s)) that can physically perform material synthesis (i.e., synthesizing ceramics, solid state electrolytes, biopolymers [Specification page 63 para 00247]) but provides support for material property data analysis and simulation for modeling and predicting material for subsequent synthesis.
35 USC § 112(b)
The rejection of claims 1 and 22-23 because using fully or partially semi-supervised learning under 35 U.S.C § 112(b) in the Office Action mailed 20 May 2025 is withdrawn in view of the amendments received 18 November 2025.
The rejection of claims 1 and 22-23 because wherein the creating reduces computational resources compared to conventional materials discovery methods under 35 U.S.C § 112(b) in the Office Action mailed 20 May 2025 is withdrawn in view of the amendments received 18 November 2025.
The rejection of claims 1 and 22-23 because the creating under 35 U.S.C § 112(b) in the Office Action mailed 20 May 2025 is withdrawn in view of the amendments received 18 November 2025.
The rejection of claims 2 and 4-21 under 35 U.S.C § 112(b) in the Office Action mailed 20 May 2025 is withdrawn in view of the amendments received 18 November 2025.
The rejection of claims 2 and 15 under 35 U.S.C § 112(b) in the Office Action mailed 20 May 2025 is withdrawn in view of the amendments received 18 November 2025.
The rejection of claims 15 because wherein at least one under 35 U.S.C § 112(b) in the Office Action mailed 20 May 2025 is withdrawn in view of the amendments received 18 November 2025.
The rejection of claims 15 because the creating integrates classical machine learning and quantum machine learning under 35 U.S.C § 112(b) in the Office Action mailed 20 May 2025 is withdrawn in view of the amendments received 18 November 2025.
The rejection of claims 15 because the creating includes using Bayesian Optimization (BO) to generate data for the new dataset for the desired material under 35 U.S.C § 112(b) in the Office Action mailed 20 May 2025 is withdrawn in view of the amendments received 18 November 2025.
It is noted the amendments received 18 November 2025 necessitated new ground(s) of rejection.
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 and 22-23 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.
Claims 1 and 22-23 were amended to recite “wherein creating the new dataset reduces computational resources compared to conventional materials discovery methods.” The claimed limitations are indefinite because the metes and bounds of the claimed element “conventional materials discovery methods” are not clear. It is further not clear because the previous and subsequent steps do not provide as to what methods are included or excluded in this comparison. Moreover, claims 1 and 22-23 are further indefinite because it is not clear which steps are intended to be performed by the recited “reduces computational resources.” Here, the claimed step is drawn to creating a new data set. However, it is not clear as to what are the computational resources and how are they reduced. These steps are not recited. It is not clear if this limitation is an intended use of the dataset or an actual step to be performed.
Claims 2 and 4-21 are rejected because they fail to provide limitations to overcome the deficiencies of the base claim(s).
Claim Rejections - 35 USC § 101
The instant rejection is maintained for reason for record in the Office Action mailed 20 May 2025 and modified in view of the amendments filed 18 November 2025. Additionally, it is noted the amendments received 18 November 2025 necessitated new ground(s) of 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 1-2 and 4-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Following the Flowchart of MPEP 2106
Step I - Process, Machine, Manufacture or Composition
Claims 1-2 and 4-21 are drawn to a method, so process.
Claim 22 is drawn to a system, so a machine.
Claim 23 is drawn to a computer program product comprising stored on a non-transitory computer-readable medium (CRM), so a manufacture.
2A Prong I - Identification of an Abstract Idea
Claims 1 and 22-23 recited similar limitation. Therefore, claims 1 and 22-23 will be examined similarly.
creating, using at least two machine learning models associated with the at least one computing device, a new dataset for the material having preconfigured properties
This step can be performed in the human mind by organizing data to create a dataset for a material having preconfigured properties and is therefore an abstract idea. This step recites using at least two machine learning models which encompasses performing mathematical/statistical computations for creating a data set and is therefore an abstract idea. Here, the machine learning model (i.e., semi-supervised learning) is generically recited and reads on mathematical calculations iteratively calculated by the human mind or by a tangential computerized algorithm for programming efficiency. Furthermore, the step encompasses organizing information and manipulating the information through mathematical correlations (i.e., using machine learning models) and is therefore an abstract idea. See MPEP 2106.04(a)(2)(A)(iv).
wherein the at least two machine learning models are trained using at least semi-supervised learning, based on the one or more datasets, to model properties of the material having preconfigured properties
Here, training machine learning models read on organizing/creating mathematical relationships and organizing data which can be performed in the human mind. This step recites using at least semi-supervised machine learning models which encompasses performing mathematical/statistical computations to model the properties of a material and is therefore an abstract idea. For example, semi-supervised machine learning models encompasses utilizing graph-based label propagation and/or property prediction model (i.e., energy mapping) which are mathematical techniques/methods. Furthermore, the step encompasses organizing information and manipulating the information through mathematical correlations (i.e., using fully or partially semi-supervised learning) and is therefore an abstract idea. See MPEP 2106.04(a)(2)(A)(iv).
wherein at least two machine learning models comprise a machine learning interatomic potential (MLIP) model that predicts interatomic forces and global energy values, and a machine learning charge density (MLCD) model that predicts charge density values as a discretized scalar density field spanning a volume of an atomic structure
This step encompasses using machine learning models (i.e., MLIP) to predict interatomic forces and global energy values which encompasses performing mathematical computations for quantifying information (i.e., predicting interatomic forces and global energy values) for making predictions which reads on abstract ideas/mathematical concepts. For example, it is noted that using an MLIP (e.g., machine learning potential (MLP)) entails using high-dimensional regression (e.g., kernel ridge regression, neural networks) to learn the mapping between atomic descriptors (representing the local atomic environment) and the corresponding total energy/atomic forces and entails using interatomic potentials (force fields) or electronic structure methods to model the potential energy surface (PES) of a system of atoms which are mathematical methods/techniques. This step further encompasses using other machine learning models (i.e., MLCD) for predicting density values as a discretized scalar density field spanning a volume of an atomic structure which encompasses performing mathematical computations for quantifying information (i.e., charge density values as a discretized scalar density field spanning a volume of an atomic structure) for making predictions. which reads on reads on abstract ideas/mathematical concepts. Here, it is noted that discretized scalar density field spanning the volume of an atomic structure is a computational representation where a physical property, such as electron density or atomic mass density, is calculated and stored as a set of discrete values associated with specific points in a 3D grid or mesh. As such, this step encompasses using taking information (i.e., datasets with materials having preconfigured properties), manipulating the information using mathematical correlations (i.e., machine learning charge density (MLCD) model), and organizing/converting the data into a new form (i.e., charge density values as a discretized scalar density field spanning a volume of an atomic structure) which reads on abstract ideas. See MPEP 2106.04(a)(2)(I)(A)(iv).
wherein creating the new dataset reduces computational resources compared to conventional materials discovery methods
This step describes the “creating” of a new dataset as reducing computational resources compared to other methods.
wherein the prediction enables physical synthesis of the material by providing atomic structure configurations with stable interatomic forces below a predetermined threshold
This step of predicting reads on utilizing the mathematical concepts of inequalities and equalities for providing atomic structure configurations below a predetermined threshold which reads on an abstract idea. .
Claims 2 and 4-21 are further drawn to limitations that describe the abstract ideas of claim 1.
2A Prong II - Consideration of Practical Application
Claims 1 and 22-23 were amended to recite “synthesizing the material having preconfigured properties based on the prediction.”
Here, with respect to the synthesizing step of claims 1 and 22-23, the claimed steps do not integrate the recited judicial exception into a practical application because the recitation of the synthesizing steps of claims 1 and 22-23 attempts to cover any solution for synthesizing a material using the predicted preconfigured properties with no restriction on how the synthesizing is accomplished and no description of the methods/techniques for synthesizing a material using the predicted preconfigured properties which 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). Here, the abstract idea results in an output of a prediction of a material having preconfigured properties and provides atomic structure configurations with stable interatomic forces. The claim then goes on to ‘synthesize the material.” However, this is not an application of the abstract idea to a particular transformation of mater. The claims do not recite what the transformation of matter is, what the preconfigured properties are and how they are applied and/or whether the predicted atomic structure configurations are applied to synthesizing. The limitation of synthesizing is equivalent to just “apply it.”
The recited additional element of using computer system of claims 22 does not integrate the recited judicial exception into a practical application because using a computer to process and store abstract ideas is merely tangential to the claimed steps. See MPEP 2106.05(d) and 2106.05 (g).
The recited additional element of using storing instructions using non-transitory memory of claim 22 and non-transitory computer readable media of claim 23 does not integrate the recited judicial exception into a practical application because using a computer to process and store the abstract ideas is merely tangential to the claimed steps. See MPEP 2106.05(d) and 2106.05(g).
The recited additional element of data gathering by receiving datasets of claims 1, 14, 22, and 23 does not integrate the recited judicial exception into a practical application because receiving data that is analyzed by abstract ideas is deemed an extra-solution activity. See MPEP 2106.05 (g).
The recited additional element of outputting data by outputting predictions of claims 1 and 22-23 and outputting a generative model of claims 8-10 does not integrate the recited judicial exception into a practical application because outputting data that is analyzed by abstract ideas is deemed an extra solution activity. See MPEP 2106.05 (g).
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.
2B Analysis - 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 using computer system of claim 22 does not add significantly more than the recited judicial exception because using a computer to process and store abstract ideas is well-known and conventional. See MPEP 2106.05(d) and 2106.05 (g).
The recited additional element of using storing instructions using non-transitory memory of claim 22 and non-transitory computer readable media of claim 23 does not add significantly more than the recited judicial exception because using a computer to process and store the abstract ideas is well-known and conventional. See MPEP 2106.05(d) and 2106.05(g).
The recited additional element of data gathering by receiving datasets of claims 1, 14, 22, and 23 does not add significantly more than the recited judicial exception because receiving data that is analyzed by abstract ideas is deemed a well-known and conventional extra solution activity. See MPEP 2106.05 (g).
The recited additional element of outputting data by outputting predictions of claims 1 and 22-23 and outputting a generative model of claims 8-10 does not add significantly more than the recited judicial exception because outputting data that is analyzed by abstract ideas is deemed a well-known and conventional extra solution activity. See MPEP 2106.05 (g).
The recited additional element of synthesizing materials of claims 1 and 22-23 does not add more than the recited judicial exception because synthesizing a material is well-known, routine, and conventional. To provide evidence of conventionality, Huang et al. (Huang) reviews using machine learning methods/techniques in material prediction and material synthesis [title]. Huang teaches a ML-aided method for predicting the crystallization tendency of metal–organic nano-capsules (MONCs) that was used to synthesize new crystalline MONC’s using the derived features and chemical hypothesis of the machine learning model (i.e., XGBoost model) [page 21 section 4.2.4] (Materials, 2023-08, Vol.16 (17), p.5977).
In conclusion, and when 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.
Response to Arguments
Applicant’s arguments, filed 18 November 2025, have been fully considered and the rejection is maintained.
The Applicant states points to the 2019 USPTO Revised Patent Subject Matter Eligibility Guidance for guidance. The Applicant points to the amended claims limitations of "wherein the at least two machine learning models comprise a machine learning interatomic potential (MLIP) model that predicts interatomic forces and global energy values, and a machine learning charge density (MLCD) model that predicts charge density values as a discretized scalar density field spanning a volume of an atomic structure" and "wherein the prediction enables physical synthesis of the material by providing atomic structure configurations with stable interatomic forces below a predetermined threshold." The Applicant states the limitation do not fall with abstract idea grouping as they are directed to a specific technical implementation for predicting physical properties of atomic structures and enabling physical synthesis of materials.” [remarks, page 9].
In response, as described in the Step 2A Prong of the 101 analyses above, claims 1 and 22-23 wherein at least two machine learning models and wherein the prediction enables physical synthesis steps encompass abstract ideas. For example, the “wherein at least two machine learning models” step encompasses using machine learning models (i.e., MLIP and MLCD) to predict interatomic forces, global energy, and density values which encompasses performing mathematical computations for quantifying information (i.e., predicting interatomic forces and global energy values and density values) which reads on abstract ideas/mathematical concepts. See MPEP 2106.04(a)(2)(I)(A)(iv). Furthermore, the “wherein the prediction enables physical synthesis” step describes the “prediction” (i.e., the data) as enabling physical synthesis of the material by providing atomic structure configurations with stable interatomic forces below a predetermined threshold which reads on abstract ideas. With respect to providing a specific technical implementation for predicting physical properties of atomic structures and enabling physical synthesis of materials, the claims are entirely drawn to data analysis that can be performed by the human mind or math, or with the aid of a generic computer merely for programming efficiency. Novel or improved abstract idea steps alone are not deemed to be an improvement to technology. Here, the predicted material with pre-configured properties is not integrated with the synthesis step because the synthesis step was found to be mere instructions to apply the exception. Therefore, as noted in Step 2A Prong I of the 101 analyses above, claims 1 and 22-23 encompass abstract ideas.
The Applicant states “The Examiner's characterization that the claims merely involve "creating datasets" and "outputting predictions" fails to account for the specific technical nature of the claimed machine learning models and their integration with physical material synthesis processes.” The Applicant states the amended limitations represent a specific technical implementation that go beyond abstract mathematical concepts [remarks, page 9].
In response, creating datasets reads on mathematically organizing data (i.e., via using equalities and inequalities and machine learning platform) to create a dataset for a material having preconfigured properties which reads on abstract ideas. Here, this step uses at least two machine learning models which encompasses taking existing information (i.e., material chemical property data) and manipulating the data/information via mathematical computations (i.e., machine learning platform, semi-supervised) for creating a data set which further reads on abstract ideas. See MPEP 2106.04(a)(2)(A)(iv). Moreover, with respect to outputting data, this step is evaluated under Step 2A Prong II and Step 2B to determine if the additional element of “data outputting” integrates the judicial exception into a practical application and/or provides significantly more. Here, the additional element of “data outputting” is an extra-solution activity that does not integrate the judicial exception into a practical application. MPEP 2106.05(g). With respect to the specific technical implementation of the claimed machine learning models and their integration with physical material synthesis processes, the claims are entirely drawn to data analysis that can be performed by the human mind or math, or with the aid of a generic computer merely for programming efficiency. Here, the steps are not applied to any additional elements so as to result in a practical application or an improvement to technology. For example, the claims do not recite what synthesis is taking place, what material are being synthesized, what are the preconfigured properties and how they are applied and/or whether the predicted atomic structure configurations are applied to synthesizing. Furthermore, the claims do not recite what the transformation of matter is. For example, taking a known material or raw material and transforming it via chemical or physical acts (i.e., reductions/oxidation reactions, temperature control) such that it has been changed/manipulated to encompass the predicted pre-configured properties. See MPEP 2106.05(c). Thus, novel or improved abstract idea steps alone are not deemed to be an improvement to technology. Here, the data produced from the machine learning models (i.e., predict material chemical properties) is not integrated with the synthesizing step because the synthesizing step, as noted in Step 2A Prong II of the 101 analyses, is equivalent to mere instructions to apply the exception. See MPEP 2106.05(f).
The Applicant states the claims encompasses additional elements that extend beyond the judicial exception and integrate the recited judicial exception into practical application. The Applicant points to the MPEP 2106.04(II)(2) for guidance. The Applicant states the amended claims include elements that represent an improvement in the functioning of a computer or technical field so as to render the same a particular machine in a particular technological environment. The Applicant states “Specifically, "wherein the at least two machine learning models comprise a machine learning interatomic potential (MLIP) model that predicts interatomic forces and global energy values, and a machine learning charge density (MLCD) model that predicts charge density values as a discretized scalar density field spanning a volume of an atomic structure" as recited by claims 1, 22, and 23 reflects an improvement in the functioning of computer systems for materials discovery.” [remarks, page 10].
In response, the argument is not persuasive because the Applicant provides a conclusionary statement that does not provide any support or evidence (i.e., qualitative/statistical and qualitative data) showing claims 1, 22, and 23 reflects an improvement in the functioning of computer systems for materials discovery. In further light of the specification, it does not set forth evidence that the claimed process causes a computer to operate differently than it ordinarily would. It appears that the claims are drawn to an abstract idea that results in a computational process that is more efficient than others known in the art. However, the computer is not improved by way to the claimed process. Moreover, the argument is not persuasive because rather than improving the functioning of a computer itself, the claimed invention amounts to improved instructions for a computer. Merely utilizing a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection (see Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 223-24, 110 USPQ2d 1976, 1983-84 (2014); In re Alappat, 33 F.3d 1526, 1545, 31 USPQ2d 1545, 1558 (Fed. Cir. 1994); In re Bilski, 545 F.3d 943, 88 USPQ2d 1385 (Fed. Cir. 2008)).
The Applicant states “Furthermore, "wherein the prediction enables physical synthesis of the material by providing atomic structure configurations with stable interatomic forces below a predetermined threshold" as recited by claims 1, 22, and 23 effects a transformation of particular articles (raw materials) to a different state (synthesized materials with specific configurations). This limitation demonstrates that the claimed method goes beyond mere data manipulation to enable actual physical synthesis of materials.” [remarks, page 10].
In response, the argument is not persuasive because the limitation merely describes the prediction (i.e., information/data) that can be used to physically synthesize a material based on data (i.e., atomic structure configurations) below a threshold which reads on abstract as noted in Step 2A Prong I of the 101 analyses above. Here, there is not transformation of matter because the claimed step is drawn to abstract ideas with respect to describing data (i.e., prediction) and describing the data (i.e., prediction) as being below a threshold. Here, the prediction is mere data, and data is an abstract concept not a physical structure/object. Additionally, the claimed step is interpreted as using the information “prediction” for physically synthesizing a material which does not provide physical steps of synthesizing but merely references using the information “prediction” for subsequent synthesis of a new material. With respect to the synthesizing the material with preconfigured properties step, the claimed step was found to be equivalent to the words "apply it". See MPEP 2106.05(f).
The Applicant states the amended synthesizing step of claims 1 and 22-23 applied the judicial exception. The Applicant states the amended “synthesizing” step of claims 1 and 22-23 requires physical synthesis of the material not merely data manipulation [remarks, page 10].
In response, as noted in Step 2A Prong II of the 101 analysis above, the synthesizing step of claims 1 and 22-23 was found to be equivalent to the words "apply it" because, even though synthesizing a material is a physical step that does not contain abstract ideas, the recitation of the synthesizing step of claims 1 and 22-23 attempts to cover any solution for synthesizing a material using the predicted preconfigured properties with no restriction on how the synthesizing is accomplished and no description of the methods/techniques for synthesizing a material using the predicted preconfigured properties which does not integrate a judicial exception into a practical application or provide significantly more. See MPEP 2106.05(f).
The Applicant states “the specific technical implementation recited in the amended claims, including the MLIP model that "predicts interatomic forces and global energy values" and the MLCD model that "predicts charge density values as a discretized scalar density field spanning a volume of an atomic structure" as recited by claims 1, 22, and 23, represents a particular technological solution to the technical problem of materials discovery and synthesis. These models provide specific technical outputs (interatomic forces, global energy values, charge density values) that enable the physical synthesis of materials with stable atomic configurations.” [remarks, page 10-11].
In response, and with respect to the machine learning models, the claimed models are utilized for creating datasets. Claims 1 and 22-23 do not utilize the models for making predictions but are used for creating datasets which reads on abstract as noted in Step 2A Prong I of the 101 analysis above. With respect to the specific technical outputs (interatomic forces, global energy values, charge density values) that enable the physical synthesis of materials with stable atomic configurations, “data outputting” (i.e., additional element) is an extra-solution activity that does not integrate the recited judicial exception into a practical application.
The Applicant states “The claim elements recited in the amended claims 1, 22, and 23, individually and in combination, represent unconventional and non-routine implementations in the field of materials discovery. The specific combination of MLIP models predicting "interatomic forces and global energy values" with MLCD models predicting "charge density values as a discretized scalar density field spanning a volume of an atomic structure" to enable "physical synthesis of the material by providing atomic structure configurations with stable interatomic forces below a predetermined threshold" represents a specific technical solution that goes beyond well-understood, routine, conventional activity in the field. The integration of these specific machine learning models with physical material synthesis processes, as recited by the amended claims, provides a technological improvement over conventional materials discovery methods by reducing computational resources while enabling accurate prediction and synthesis of materials with desired properties.” [remarks, page 11].
It is noted that under Step 2B of the 101 analyses, “An inventive concept "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself." Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016). ... Evaluating additional elements to determine whether they amount to an inventive concept requires considering them both individually and in combination to ensure that they amount to significantly more than the judicial exception itself. (MPEP 2106.05 § I). In the instant case, the additional elements of synthesizing a material with pre-configured properties of claims 1 and 22-23 is well-known and conventional, which individually and in any combination, does not add significantly more than the recited judicial exception. Thus, the combination of data gathering (e.g., receiving data) and data outputting (e.g., outputting a prediction) elements [ See MPEP 2106.05(g)], computer elements [See MPEP 2106.05(b) and 2106.05(d)(I)], and synthesizing material steps does not add significant more because the claimed elements are routine and conventional. Additionally, the argument is not persuasive because in light of the specification it does not provide evidence or support (i.e., quantitative/statistical or qualitative data) comparing other known material discovery methods with the methods of the instant claims to show that the instant claims reduce computational methods compared to conventional material discovery methods. Therefore, the claims are not patent eligible under Step 2B of the 35 U.S.C § 101 analysis.
Conclusion
Claims 1-2 and 4-23 are rejected.
No claims are allowed.
Finality
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
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. /J.C.P./Examiner, Art Unit 1687
/Anna Skibinsky/
Primary Examiner, AU 1635