DETAILED ACTION
Applicant's response, filed 21 April 2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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 .
Claim Status
Claims 1-20 are pending.
Claims 1-20 are rejected.
Priority
This application has the effective filing date of 01 June 2022, with no claims to priority.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 31 August 2022 in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Drawings
The drawings, submitted 01 June 2022, have been accepted by the examiner.
Claim Rejections - 35 USC § 112
Applicant’s arguments, see page 1, para. 3, filed 21 April 2026, with respect to failing to particularly point out and distinctly claim the subject matter have been fully considered and are persuasive.
The rejection to claims 9-16 under 35 U.S.C 112(b) is withdrawn.
This following rejection is newly recited and necessitated by claim amendments.
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 9-16 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, as explained below.
Claim 9 recites “a non-transitory computer-readable medium”… “comprising”… “program code to apply the one or more processing to the generated material sample to generate a final material sample,” wherein the disclosure does not provide adequate description of how a computer readable medium is capable of generating a material sample. Claims 10-16 are rejected as dependent claims that fail to remedy the noted deficiency.
Claim Rejections - 35 USC § 101
The previous rejection to claims 1-20 under 35 U.S.C 101 is maintained.
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 abstract ideas without significantly more for the reasons detailed below.
Eligibility Step 1: Subject matter eligibility evaluation in accordance with MPEP § 2106:
Claims 1-8 are directed to a statutory category (method).
Claims 9-16 is directed to a statutory category (product).
Claims 17-20 is directed to a statutory category (system).
Therefore, claims 1-20 have patent eligible subject matter.
[Eligibility Step 1: YES]
Eligibility Step 2A: This step determines whether a claim is directed to a judicial exception in accordance with MPEP § 2106.
Eligibility Step 2A -- Prong One: The limitations below are analyzed to determine if the claims recite any concepts that could equate to a judicial exception (i.e. abstract idea, law of nature, or natural phenomenon).
Claims 1, 9, and 17:
encoding a sequence and interrelationships among events occurring in a simulation and/or experiment in an event-sourced architecture for materials provenance (ESAMP) framework; (mathematical process)
learning an initial state of a generated material sample in the ESAMP framework; (mental process)
learning how one or more processes affect the state of the generated material sample in the ESAMP framework according to the state vector shared with the other material samples in the ESAMP framework. (mental process)
identifying the generated final material sample a material discovery when a final material state is different from a predicted material state based on the ESAMP framework. (mental process)
Claim 2, 10, 18: The method of claim 1, further comprising:
integrate a provenance information regarding how the material samples are created and what processes the generated material samples have undergone (mathematical process)
learning shared and different characteristics of the generated material samples based on the integrated provenance information (mental process)
Claims 3, 11, and 19: The method of claim 1, further comprising predicting a state change of the generated material sample as a selected process is applied to the generated material sample. (mental process)
Claim 4, 12, and 20: The method of claim 1, further comprising training a neural network to predict a state change of the generated material sample after a selected process is applied to the generated material sample. (mathematical concept, additional element)
Claims 5 and 13: The method of claim 4, in which the neural network is trained to predict the state change of each of the generated material samples having a shared initial state vector. (mathematical concept, additional element)
Claims 7 and 15: The method of claim 1, in which encoding further comprises:
assembling an ESAMP database; (mathematical concept)
Claims 8 and 16: The method of claim 7, in which encoding further comprises:
analyzing of the raw process data from the ESAMP database to derive a state information of the raw process data from the ESAMP database; (mental process)
Step 2A – Prong One Analysis:
The limitations that recite encoding data equate to transforming data using mathematical functions. Assembling a database equates to further manipulating the secondary data by organizing the information into a new form. Integrating provenance information, given broadest reasonable interpretation, is directed to transforming the information and adding it to the database in the same method as the encoded events/relational data. As such, these claims describe processes that organize information through mathematical correlations and fall under the mathematical concepts grouping of abstract ideas.
Furthermore, the disclosure provides mathematical relationships and correlations to predict state changes with a neural network, such as a weighted sum of the feature vectors and classifying data based on patterns. Generating secondary data of this nature similarly falls under mathematical concepts grouping of abstract ideas.
Limitations that equate to making observations, connections, or conclusions based on the observable [0079] data properties stored within the table represent analysis techniques in the form of mental determinations of data.
These processes can be completed using nothing more than the human mind or pen/paper, and as such fall into the mental process grouping of abstract ideas.
The amended limitation of “identifying the generated final material sample a material discovery when a final material state is different from a predicted material state based on the ESAMP framework” falls under mental processes as it reads on using the human mind to draw a conclusion based on the comparison of two observable data entries.
Additional elements include:
Claim 1, 9, and 17: sharing a state vector representing the initial state of the generated material sample with other generated material samples in the ESAMP framework;
applying the one or more processing to the generated material sample to generate a final material sample;
Claim 4, 12, and 20: The method of claim 1, further comprising training a neural network to predict a state change of the generated material sample after a selected process is applied to the generated material sample.
Claims 5 and 13: The method of claim 4, in which the neural network is trained to predict the state change of each of the generated material samples having a shared initial state vector.
Claims 7 and 15: storing, in the ESAMP database, provenance information regarding the creation of the generated material samples and processes undergone by each of the generated material samples.
Claims 8 and 16:
storing, in the ESAMP database, raw process data from processes run on the generated material samples;
storing, in the ESAMP database, the state information regarding the processes run on the generated material samples.
Step 2A – Prong Two Analysis:
The disclosure states that training the neural network model may occur through using observables and sharing state vectors with other material samples [0076]. It further states that there are output prediction functions that can be learned from the vectors [0077]. Therefore, in light of the specification, training the neural network to predict state changes requires accessing/retrieving data in the form of vectors within the database and transforming the vectors and observables into a prediction via a mathematical function.
Sharing vectors, given the broadest reasonable interpretation, allows entities stored within a database to access and retrieve data stored under different classifications within the same structure. This process equates to a mere data gathering activity that does not integrate the judicial exception into practical application per MPEP 2106.05(g).
The amended limitation “applying one or more processing to the generated material sample to generate a final material sample” is evaluated for significantly more below:
As, the limitation reads on a transformation of a generated material sample into a generated final sample, the examiner considers if the transformation is particular using the 5 relevant analysis factors provided by MPEP 2106.05 (c):
(1) The particularity or generality of the transformation: The transformation in this limitation is a “process” in which the specification discloses to be embodied by an event that occurs to one or more samples; for example, the process is associated with an experiment in a laboratory, such as annealing in a sample furnace or performing spectroscopic characterization [0053].
Since an event occurring to the sample includes those “occurring in a simulation and/or experiment” (claims 1, 9, and 17) without other meaningful limits, the transformation encompasses in silico processes and merely moving locations of the material. Therefore, the transformation is not particular enough to equate to significantly more.
(2) The degree to which the recited article is particular: The recited article in the limitation is a “generated material,” which does not place meaningful limits on the article that could be have the transformation applied; and is thus not particular.
(3) The nature of the transformation in terms of the type or extent of change in state or thing: The specification discloses the state is defined by two entities of the sample process that share the sample and do not have an entity of the sample process chronologically between the two entities of the sample process; and by managing the state under the most conservative assumption that every process alters the sample's state, any state equivalency rules (e.g., whether a certain type of process alters the state or not) may be applied in a transparent manner [0070].
As such, a “state”, as generically recited in the claims and in light of the specification, does not limit the process to result in the transformed article having a different function or use, needed to integrate the judicial exception or equate to significantly more.
(4) The nature of the article transformed: Though a “sample” can be embodied as a tangible/physical object or substance, the specification discloses: Some aspects of the present disclosure learn the initial state of a particular sample that is stored as experimental materials science data in a database [0049]. Therefore, the claims do not limit “material sample” enough to not provide significantly more.
(5) Whether the transformation is extra-solution activity or a field-of-use: Since the “process” and “sample” are generically recited, they do not place limits on the judicial exceptions to particular machines, manufactures, transformations, articles, or states and merely recite equivalents to “apply it” regarding the judicial exceptions (2106.04(d)).
Additional elements that may be categorized differently include:
Claims 6 and 14: sharing the state vector representing the initial state of the material sample with the other material samples in the ESAMP framework is performed for each of the material samples having the initial state.
This limitation specifies what type of data undergoes manipulation via analysis techniques. Activities of this nature are classified as insignificant extra solution activity and do not integrate the judicial exception into practical application per MPEP 210.05(g).
Additional elements that may be categorized differently include:
Claim 1: A method for neural network material state prediction, comprising:
Claim 9: non-transitory computer-readable medium having program code recorded thereon fort neural network material state prediction, the program code being executed by a processor and comprising,
Claim 17:
a neural processing unit (NPU);
a memory coupled to the NPU, and instructions stored in the memory and operable, when executed by the NPU, cause the system:
A memory and non-transitory computer readable medium qualify as components of generic computing environments/implementations of a method onto a generic computer environment. Though a neural processing unit can be seen as a specialized type of hardware, it does not contain the specificity required to qualify as a component of a particular machine per MPEP 2106.05(b).
Furthermore, these components when viewed separately and in the context of a whole claimed invention merely act as tools to apply the judicial exceptions. As such, they do not integrate the judicial exception into practical application, as exemplified by Genetic Techs. Ltd., 818 F.3d at 1377; 118 USPQ2d at 1546.
As such, the additional elements when viewed separately and in the context of whole do not integrate the judicial exceptions into practical application (Step 2A, Prong Two: NO), and therefore are directed to judicial exceptions.
[Eligibility Step 2A: YES]
Step 2B Analysis:
Wan et al. (Nano Letters; Vol. 19; 2019), reviews materials discovery and properties prediction, and provides evidence that the following techniques are well-understood, routine, and conventional: First, a large amount of experimental data is needed; second, based on data mining, second machine learning models could be built to search for or predict new materials with desired properties; and finally, if material properties meet the necessary requirements, the materials can be synthesized in terms of machine learning predictions (page 3393, column 2).
Furthermore, the courts have found storing and retrieving information in memory to be routine, well-understood, and conventional in the art as exemplified by Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Selecting information, based on types of information and availability of information, for analysis is also well known, routine, and conventional within the art, as exemplified by Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
Additionally, the generic computer components recited are well-understood, routine, and conventional within the art. Though the neural processing unit represents a piece of specialized hardware that does improve the efficiency of the claimed invention, NPUs are well-known and conventional for increasing general computer efficiency as evidenced by Chen et al. (Engineering; Vol. 6 (3); 2020). Furthermore, including the NPU itself does not provide an inventive solution to the problem presented by materials state prediction and thus does not provide evidence of inventive concept per TLI Communications, 823 F.3d at 611-12, 118 USPQ2d at 1747.
[Eligibility Step 2B: NO]
As such claims 1-20 are directed to judicial exceptions and rejected under 35 U.S.C 101, in accordance with Alice/Mayo, MPEP 2143 evaluation.
Response to Arguments
Applicant's arguments, filed 21 April 2026, with respect to 35 U.S.C 101, have been fully considered but they are not persuasive.
Applicant argues encoding interrelationships, sharing vectors, learning states, and identifying a sample cannot be performed mentally or manually (page 2, para. 2).
Examiner responds that encoding is/was classified as a mathematical concept, which does not require being able to be performed mentally or manually; and encoding, as instantly recited, reads on organizing information and manipulating information through mathematical correlations.
MPEP § 2106.04(a)(I)(A)(iv) recites: The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form. The court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014).
“Learning states,” as generically recited, reads on identifying properties using observations, evaluations, judgments, and opinions. Such techniques can be performed mentally and manually; and MPEP 2106.04(a)(2)(C) recites that such concepts performed 1) on a generic computer, or 2) in a computer environment, or 3) merely using a computer as a tool to perform the concept are still considered to recite mental processes.
As such, the noted limitations are still drawn to judicial exceptions (abstract ideas: mental processes and/or mathematical concepts).
Applicant argues amended limitation "identifying the generated final material sample as a material discovery when a final material state is different from a predicted material state based on the ESAMP framework," recites a specific technological solution to a technological problem in materials science research, found within the specification; and does not merely collect, analyze, and display data (page 2, para. 3-4).
Examiner responds comparing a predicted, observable property/state to an expected property/state and identifying a new material sample based on the analysis is commensurate in scope with a claim to collecting and comparing known information, which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011) and using observations, evaluations, judgments, and opinions MPEP 2106.04(a)(2)(A). Therefore, the amended limitation is drawn to a judicial exception in the form of an abstract idea (mental process).
As the amended limitation appears to recite a judicial exception and the claim as a whole does not include additional elements that impart this improvement to the technical field into the claimed invention, MPEP 2106.05(a) recites: The judicial exception alone cannot provide the improvement. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, or the additional element(s) in combination with the recited judicial exception (MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018)).
In response to applicant's argument that the specification discloses an improvement to technology, it is noted that the features upon which applicant relies on are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant argues claims improve computer functionality by enabling materials state-aware machine learning and providing an ESAMP database that (1) captures information about the samples in the database including storing provenance regarding how they were created and what processes they have undergone, (2) raw data from processes run on the samples, and (3) information derived from analyses of these raw data (page 3, para. 4).
Examiner responds that the courts have indicated claims commensurate in scope with “accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer”; “mere automation of manual processes, such as using a generic computer to process an application for financing a purchase”; or “speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application”, may not be sufficient to show an improvement in computer-functionality per MPEP 2106.05(a)(I) and FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016); Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017), and LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016), respectively.
Applicant argues claims apply the recited operations to the particular technological environment of experimental materials science research (page 4, para. 2) because the claimed method encodes sequences, learns initial states of generated material samples, shares state vectors with other generated material samples in the ESAMP framework, learns how processes affect material states according to shared state vectors, and identifies material discoveries when final material states differ from predicted states (page 4, para. 2).
Examiner responds the courts all noted limitations except for “shares state vectors with other generated material samples in the ESAMP framework” are directed to judicial exception which cannot alone provide the improvement to the field. The “sharing” limitation is commensurate in scope with “Requiring that the abstract idea of creating a contractual relationship that guarantees performance of a transaction (a) be performed using a computer that receives and sends information over a network, which merely indicating a field of use or technological environment in which to apply a judicial exception” (buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1354, 112 USPQ2d 1093, 1095-96 (Fed. Cir. 2014); and/or receiving or “transmitting data over a network, which is insignificant extra-solution activity” (Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)).
Applicant argues claims affect the transformation of experimental materials science data into material discovery identifications by transforming raw process data and sample provenance information into learned state representations, transforming those state representations into predictions of how processes will affect material states, and transforming deviations between predicted and actual states into identifications of material discoveries (page 4, para. 3).
Examiner responds that the transformation is not particular, based on analysis of the five relevant factors for particular transformations per MPEP 2106.05 (c), recited in the instant office action of paragraphs 40-45.
Applicant argues that examiner conflates the Step 2A analysis of generic computer components and neural processing units with a Step 2B analysis in Office Action, paragraphs 43-44 (page 4, para. 4).
Examiner responds that previous Office action, paragraphs 40-41 are directed to the Step 2A – Prong Two analysis of the indicated computer components, in which integration of the judicial exceptions into practical application is considered; and Office Action, paragraphs 43-44 are directed to their respective 2B analysis, which considers conventionality, as applicant indicates. Examiner respectfully clarifies analysis steps in the rejection herein.
Applicant argues examiner does not identify evidence showing that encoding event sequences in an ESAMP framework, learning material states, sharing state vectors with other generated material samples, learning how processes affect states according to shared state vectors, and identifying material discoveries when final states differ from predicted states as well-understood, routine, or conventional (page 5, para. 4).
Examiner responds that the noted limitations were identified as judicial exceptions in the form of abstract ideas in Step 2A; and therefore, are not evaluated for inventive concept in Step 2B, per MPEP 2106.05 (I), which recites: 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). See also Alice Corp., 573 U.S. at 21-18, 110 USPQ2d at 1981.
Applicant argues the ordered combination of limitations recites an unconventional arrangement (page 5, para. 5) and thus, significantly more (page 5, para. 6).
Examiner responds the order and arrangement of limitations is not specifically claimed. Furthermore, the combination of elements when considered as a whole in the recited analysis does not equate to significantly more in the form of improvements to a computer, a technological field, or transformation of a particle article, as discussed per MPEP 2106.05(I)(A).
Applicant concludes that the foregoing reasons, amended claim 1 is patent-eligible under 35 U.S.C. § 101 and does not recite a judicial exception (Step 2A, Prong One: NO). Alternatively, the claim is integrated into a practical application (Step 2A, Prong Two: NO). In the further alternative, the claim recites an inventive concept (Step 2B: YES) (page 6, para. 3-5).
Examiner responds that the previous and amended claims recite judicial exceptions (Step 2A, Prong One: YES), do not integrate the judicial exceptions into practical application (Step 2A, Prong Two: NO), and do not provide inventive concept (Step 2B: NO), as reflected in the 101 analysis herein.
Claim Rejections - 35 USC § 102
Applicant’s arguments, see page 7, para. 1, filed 21 April 2026, with respect to 35 USC § 102 been fully considered and are persuasive.
Examiner clarifies for the record that no prior art references or 102 rejections were discussed during the telephonic interview conducted 16 April 2026, as detailed on the examiner interview summary.
The rejection under U.S.C 102(a)(1) to claims 9-16 is withdrawn.
Claim Rejections - 35 USC § 103
Applicant’s arguments, that Puchala, Liu, and Ji individually and combined fail to teach or suggest the amended limitations (page 8, para. 2-4), have been fully considered and are persuasive.
As such, the previous rejections to claims 1-20 are withdrawn.
The following rejections are newly recited and necessitated by claim amendments.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-16 are rejected under 35 U.S.C. 103 as being unpatentable over Puchala et al. (JOM; Vol. 68: 8; 2016; previously cited), in view of Galtois et al. (APL Materials; Vol. 4, 053213; 2016, newly cited), Liu et al. (Journal of Materiomics; Vol. 3:3; 2017; previously cited) and in further view of Ji et al. (Association for Computational Linguistics; Vol. 1, 2015; previously cited).
Puchala describes Materials Commons, a platform that organizes experimental materials data into a searchable infrastructure (page 2035, column 1).
Claims 1 and 9 are directed to assembling and storing data pertaining generated materials and relationships with events occurring in a simulation or experiment into a relational database.
Puchala teaches The Materials Commons is a new materials information repository (page 2044, column 1); and all files and measurements in the system are versioned and relationships between data model objects are stored as provenance information which allows scientists to recover and view the state of their experiments at different points in their workflow (page 2041, column 2).
Claims 1 and 9 are further directed to learning an initial state of the generated sample.
Puchala teaches illustrating, in Fig. 3, a subset of possible results and provenance information for DFT calculations, cluster expansion effective Hamiltonian fitting, grand canonical Monte Carlo calculations, and phase field calculations represented in Materials Commons (page 2038, column 1). Puchala further shows storing the initial atomic configuration in the database (page 2038, fig. 3).
Claims 1 and 9 are further directed to sharing vectors representing the initial state of the generated material sample with other generated material samples in the database.
Puchala teaches as new measurements are added, they are immediately applied to all relevant samples, and as new samples are created, they can immediately be associated with existing measurements (page 2037, column 2).
Claims 1 and 9 are further directed to using the state vector to learn how one or more processes affect the generated material sample’s state.
Puchala teaches using process models, that describe how processing affects materials’ structure and properties (page 2036, column 2).
Claims 1 and 9 are further directed to applying the one or more processes to the generated material sample to create a second generated material sample.
Puchala teaches researchers receives some material, section and heat-treats it for different amounts of time, and continually interact with the Materials Commons to store each individual step in the process (page 2037, column 1). Puchala further teaches users select a process from a list of process types and existing samples as input to the process, and create or upload new or transformed samples, files, and measurements as output from the process (page 2037, column 1).
Claims 1 and 9 are further directed to identifying the generated final material sample as a material discovery when the final material state is different from a predicted material state, based on the database.
Puchala teaches that such an information infrastructure will lead to acceleration in discovering and engineering new materials by ensuring that information is readily available and shared (page 2035, column 1); can be used to identify samples of the material of interest (page 2039, column 1); and is one essential element of the capability for accelerated predictive structural materials science (page 2036, column 2).
Claims 2 and 10 are directed to integrating provenance information regarding a generated material’s origin into the relational database.
Puchala teaches representing materials’ associated provenance in the model (page 2036, column 1), in which the provenance information conveys the creation of samples, datafiles, and measurements by processes (page 2038, fig. 2).
Claims 3 and 11 are directed to gathering information about a generated material’s property after a particular process is applied.
Puchala teaches users can select a process from a list of process types which creates a representation, such as shown in Fig. 2a, details the provenance of all objects in the data framework (page 2037, column 1); and for transformational processes, such as heat treatment, the list of attributes associated with the transformed sample is updated (page 2037, column 1).
Claim 6 is directed to sharing information regarding each generated materials’ initial property with other generated materials in the database.
Puchala teaches sharing information about a sample’s property ‘as received’, with other samples in the framework (page 2038, fig. 2); and as new measurements are added, they are immediately applied to all relevant samples (page 2037, column 2).
Claims 7 and 15 are directed to creating a database regarding the provenance of generated materials samples and storing information regarding their origin and subsequent processes.
Puchala teaches building a dataset, which includes an ordered collection of samples, processes, attributes, measurements, and datafiles (page 2037, column 1) that is intended to store each individual step in the process done to the sample and that the provenance information ensures that all original sources of data get proper credit in the curated project (page 2041, column 1), which requires the provenance data include sample origin information.
Claims 8 and 16 are directed to the database storing raw process data run on the generated samples, analyzing it to determine property information, then storing the subsequent results from analysis.
Regarding claim 8, Puchala teaches that as a researcher performs an experiment, completes analyses, and draws conclusions from it (via models), they should continually interact with the Materials Commons to store each individual step in the process (page 2037, column 1).
Regarding claims 9-16, Puchala further teaches that the Materials Commons software is as open-source code (page 2036, column 1) and therefore completes all the steps described previously through program code/machine-readable instructions executed via a processor.
Therefore, Puchala teaches a database capable of gathering provenance information about materials, learning the initial state of generated materials, and generating new materials based on the state of other generated materials stored in the database.
Puchala does not teach the amended limitation of identifying the generated final material sample as a materials discovery when a final material state is different from a predicted material state based on the database (claims 1, 9, and 17).
Gaultois et al. describes web-based machine learning models for real-time screening of thermoelectric materials properties.
Gaultois et al. teaches illustrating the remarkable chemical homogeneity of most thermoelectric materials investigated to date by plotting each material from a thermoelectric database on the periodic table, based on the composition-weighted average of the positions of elements in the material, in which the tight cluster of previously investigated chemistries is, as expected, dominated by chalcogenides and p-block elements such as Sn and Sb (page 3, column 1).
Gaultois et al. teaches in contrast, we also show the positions of Gd12Co5Bi and Er12Co5Bi, materials derived from our recommendation engine, which we characterize as a new class of thermoelectrics in this work; in sharp contrast to thermoelectric compounds investigated to date (page 3, column 1).
Gaultois et al. further teaches for each range of thermoelectric property, the engine gives a confidence score between 0% and 100% that a given material’s measured value for that property at room temperature will fall within the targeted range; and classifies any material for which the answer to all these questions is likely “yes” as a potentially promising thermoelectric that may warrant further study (page 4, column 1).
Gaultois et al. teaches the objective of this recommendation engine is to directly enable experimental researchers to rapidly identify new materials, such as RE12Co5Bi, that are very distinct from known compound classes, and worthy of further study (page 3, column 1) and augment the chemical intuition of experimental researchers working on materials discovery, but not definitively identify record-setting compounds, which remains open challenges for future work (page 4, column 1).
Therefore Gaultois et al. teaches using a materials science database in combination with a predictive algorithm to compare expected properties to predicted properties for the identification of materials discovery, when they are different. As such, one of ordinary skill in the art has sufficient motivation to substitute the materials science database, taught by Gaultois et al., with the another, designed to aid synthesis, engineering, and identification of new materials, with a reasonable expectation of success, as taught by Puchala (page 2035, column 1) (page 2039, column 1).Gaultois et al. further provides motivation for one of ordinary skill in the art to verify the new material compounds in order to provide a definitive identification.
Puchala in view of Gaultois et al. teach a materials science database that learns and predicts generated materials properties and provenance during every experimentation stage and applies the processes and prediction analysis in order to generate and identify materials discoveries.
Puchala in view of Gaultois et al. do not teach using a neural network for material state prediction.
Liu describes methods of modelling quantitative-structure activity relationships (QSARS) and machine learning techniques that aid the materials discovery process.
Liu teaches that the regression and clustering algorithms (page 164, column 2), in neural networks (page 164, fig. 3), are the most suitable machine learning techniques for material property prediction tasks on the macro and micro levels (page 164, column 2).
Therefore, Liu provides sufficient motivation for one of ordinary skill in the art to apply a neural network for the prediction of a materials property (state) to the ‘big data’ framework – Materials Commons – taught by Puchala, with a reasonable expectation of success and improvement to the system.
Puchala in view of Gaultois et al. and Liu teach using a materials science framework and neural network that learns and predicts generated materials properties and provenance during every experimentation stage and applies the processes and prediction analysis in order to generate and identify materials discoveries.
Puchala in view of Gaultois et al. and Liu do not teach explicit use of vectors to store and share state, property, or process information within the database.
Ji describes knowledge graphs modelling via neural networks.
Ji teaches that knowledge graphs typically contain large amounts of structured data in the form of triplets (head entity, relation, tail entity), where relation models the relationship between the two entities (page 687, column 1).
Ji further teaches a task of the model is to encode every element, entity, and relation of a knowledge graph into a low-dimensional embedding vector space (page 687, column 2) and then learn each embedding (page 687, column 2).
Regarding claims 4, 5, and 20, Ji teaches knowledge graph completion is the ability to predict relations between entities based on existing triplets in the knowledge graph (page 687, column 2).
Ji further teaches that a neural network can capture the correlations between entities and relations via matrix operations, where parameters of the neural network are shared by all relations (page 689, column 2).
Therefore, Puchala teaches a structured data framework that can effectively store generated materials process, structure, provenance, and property relationships as an essential element within a predictive materials science data pipeline and means of engineering/generating new materials.
Gaultois et al. teaches combining such a materials database with the technique of comparing predicted material property with the expected material property, in order to identify material discoveries.
Liu teaches that Materials Commons is a dataset of materials properties that could provide a powerful impetus to accelerate materials discovery and design (page 161, column 1) and that combining such ‘big data’ frameworks with machine learning techniques can successfully resolve the difficulties of modelling the relationships between materials properties and complex physical factors (page 161, column 1).
Lastly, Ji teaches neural network techniques that can accurately represent complex entity and relation data. The method includes encoding, sharing, and learning vectors representing the data. As such, it would be obvious to one of ordinary skill in the art to automate the Materials Commons framework into a generic neural network capable of predicting the state/property changes of materials samples, according to the claimed invention, based on the teachings of Puchala, Liu, Gaultois et al., and Ji.
Claims 17-20 are rejected under U.S.C 103 as unpatentable over Puchala (previously cited) in view of Gaultois et al.(newly cited), Liu (previously cited), and Ji (previously cited), as applied to the claims above, and in further view of Fowers et al. (IEEE; 45th Annual International Symposium on Computer Architecture (ISCA); 2018; previously cited).
Puchala in view of Gaultois et al. Liu and Ji, as applied to the claims above, teach a system with machine readable instructions, that when executed, cause a processor to assemble and store data pertaining generated materials and their respective experimental processes into a relational database; apply processes to the generated material sample to generate a final material sample; identify the generated material sample as a material discovery when a final material state is different from a predicted material state (claim 17); integrate provenance information (claim 18); and train a neural network (claim 20) to gather information about a material’s property after selecting a particular process it could undergo (claim 19).
Neither Puchala, which teaches the materials data infrastructure, nor Liu, which motivates one of ordinary skill in the art to convert the database to a neural network prediction model, explicitly teach use of a neural processing unit (NPU).
However, Liu teaches that there is in urgent need to develop intelligent and high-performance prediction models that can correctly predict the properties of materials at a low temporal and computational cost (page 164, column 2). Fowers describes Neural Processing Unit architecture for a large-scale deep learning platform. Fowers teaches that neural processing units (NPUs) provide execution of DNN models with low latency, high throughput, and high efficiency, and (2) flexibility to accommodate evolving state-of-the-art models (e.g., RNNs, CNNs, MLPs) without costly silicon updates (page 1, column 1).
Therefore, Fowers suggests integration of a neural processing unit to neural network architecture in order to address the temporal and computational limitations of the neural networks, explained by Liu. The change is equivalent to a simple combination of one known element (CPU) with another (NPU) to obtain predictable results (acceleration of neural prediction). One of ordinary skill in the art would have sufficient motivation to incorporate NPUs to their neural network system in order to address the known computational limitations of a general processor for the claimed neural network/deep learning task with a reasonable expectation of success.
Conclusion
No claims are currently allowed.
Correspondence
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/M.K.T./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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