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 .
Claim Status
Claims 1-20 are pending.
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
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 9-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention for the following reasons.
Claim 9 recites “the program code being executed by a processor and comprising: program code to…” wherein the “program code” performs no active steps within the claimed invention. As such, it is considered nonfunctional descriptive material and the computer-medium which hosts the program code merely serves as a support for information or data without a functional relationship (see MPEP 211.05 (III)). Claims 10-16 do not remedy the issue. Clarification is required.
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 1-20 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 material sample in the ESAMP framework; (mental process)
learning how one or more processes affect the state of the material sample in the ESAMP framework according to the state vector shared with the other material samples in 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 material samples have undergone (mathematical process)
learning shared and different characteristics of the 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 a material sample as a selected process is applied to the 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 a material sample after a selected process is applied to the 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 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)
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.
Additional elements of the claimed invention include:
Claim 1, 9, and 17: sharing a state vector representing the initial state of the material sample with other material samples in the ESAMP framework;
Claim 4, 12, and 20: The method of claim 1, further comprising training a neural network to predict a state change of a material sample after a selected process is applied to the 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 material samples having a shared initial state vector.
Claims 7 and 15: storing, in the ESAMP database, provenance information regarding the creation of the material samples and processes undergone by each of the material samples.
Claims 8 and 16:
storing, in the ESAMP database, raw process data from processes run on the material samples;
storing, in the ESAMP database, the state information regarding the processes run on the material samples.
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).
[Eligibility Step 2A – Prong One: YES]
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).
[Eligibility Step 2B: NO]
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).
[Eligibility Step 2A – Prong One: YES]
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).
[Eligibility Step 2B: NO]
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.
[Eligibility Step 2A – Prong One: YES]
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.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 9-16 are rejected under U.S.C 102(a)(1) as being anticipated by Jacob et al. (“Memory Systems.” Elsevier; 2008).
Jacob et al. is an excerpt regarding the basics of computer memory architecture.
Claims 9-16 are directed to a non-transitory computer-readable medium that contains code executed by a processer.
Jacob et al. teaches modern processors execute instructions of the memory systems (page 2, column 1) and that several devices suitable for permanent information storage of a general-purpose system (page 5, column 1) have code that provides low-level access to much of the hardware (page 5, column 1). Such devices, like magnetic disks are resistant to transient errors (page 34, column 2).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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), in view of Liu et al. (Journal of Materiomics; Vol. 3:3; 2017) and Ji et al. (Association for Computational Linguistics; Vol. 1, 2015).
Claims 1 and 17 are directed to assembling and storing data pertaining materials and their respective experimental processes into a relational database. The database is used to make observations about a material’s property. Property analysis occurs through the analysis of shared processes.
Puchala describes Materials Commons, a platform that organizes experimental materials data into a searchable infrastructure (page 2035, column 1).
Regarding claims 1 and 17, Puchala teaches combining process models, that contain information regarding how processing affects a material’s structure and properties, and structure–property models, into one data model that can naturally represent both types of materials knowledge (page 2036, column 2).
Claims 2 and 18 are directed to integrating provenance information regarding a material’s origin into the relational database.
Regarding claims 2 and 18, Puchala further teaches representing materials’ associated provenance in the model (page 2036, column 1). Puchala teaches the provenance information conveys the creation of samples, datafiles, and measurements by processes (page 2038, fig. 2).
Claims 3 and 19 are directed to gathering information about a material’s property after selecting a particular process it could undergo.
Regarding claims 3 and 19, Puchala teaches that at each step in the workflow users can select a process from a list of process types. In this way, the system creates a representation, such as shown in Fig. 2a, that details the provenance of all objects in the data framework (page 2037, column 1). Samples in the framework are further associated with attribute information, which measures their properties and features (page 2036, column 2) to show how attributes are transformed by each process (page 2038, fig. 2). Therefore, as a result of user selecting a process, the states of a sample can be analyzed by viewing their attributes.
Claim 6 is directed to sharing information regarding each materials’ initial property with other materials in the database.
Regarding claim 6, Puchala teaches sharing information about a sample’s property ‘as received’, with other samples in the framework (page 2038, fig. 2) and further teaches that as new measurements are added, they are immediately applied to all relevant samples (page 2037, column 2).
Claim 7 is directed to creating a database regarding the provenance of materials samples and storing information regarding the origin and subsequent processes of it.
Regarding claim 7, Puchala teaches building a dataset, which includes an ordered collection of samples, processes, attributes, measurements, and datafiles (page 2037, column 1). Puchala further teaches the platform 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.
Claim 8 is directed to the database storing raw process data, analyzing it to determine property information, then storing the subsequent result 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.
Puchala does not teach a neural network that can make predictions based on the materials science data infrastructure.
However, Puchala does teach that the platform is one essential element of the Predictive Integrated Structural Materials Science’s capability for accelerated predictive structural materials science (page 2036, column 2).
Liu (Journal of Materiomics; Vol. 3:3; 2017) 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.
Ji (Association for Computational Linguistics; Vol. 1, 2015) 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 effectively stores the intricate materials process, structure, provenance, and property relationships as an essential element within a predictive materials science data pipeline.
Liu teaches that Materials Commons is among one of the datasets of materials properties that could provide a powerful impetus to accelerate materials discovery and design (page 161, column 1). Liu further teaches 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, and Ji.
Claims 17-20 are rejected under U.S.C 103 as unpatentable over Puchala in view of Liu and Ji as applied to the claims above, and in further view of Fowers et al (IEEE; 45th Annual International Symposium on Computer Architecture (ISCA); 2018).
Puchala in view of 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 materials and their respective experimental processes into a relational database (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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Milana Thompson whose telephone number is (571) 272-8740. The examiner can normally be reached Monday - Friday, 9:00-6:00 ET.
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/M.K.T./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687