DETAILED ACTION
Notice of 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 .
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 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.
Status of the Claims
Claims 1-11 are pending.
Claims 1 is objected to.
Claims 1-11 are rejected.
Priority
This application US 18/347,691 (07/06/2023) claims benefit of Foreign Application No. KR10-2023-0053382 (04/24/2023) as reflected in the filing receipt mailed on 07/21/2026. The claims to the benefit of priority are acknowledged and the effective filing date of claims 1-11 is 04/24/2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/06/2023 was considered.
Claim objections
Claim 1 is objected to because of the following informality: the recited "for 03 structure materials and P3 structure materials" should be amended to recite "for a plurality of 03 and P3 structure materials" to maintain consistent claim language. Appropriate correction is required.
Claim Interpretation
35 U.S.C 112(f)
The following is a quotation of 35 U.S.C. l 12(f):
(f) Element in Claim for a Combination. - An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. l 12(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. l 12(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
the claim limitation uses the term "means" or "step" or a term used as a substitute for "means" that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
the term "means" or "step" or the generic placeholder is modified by functional language, typically, but not always linked by the transition word "for" (e.g., "means for") or another linking word or phrase, such as "configured to" or "so that"; and
the term "means" or "step" or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word "means" (or "step") in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C.112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word "means" (or "step” or the generic placeholder) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word "means" (or "step” or the generic placeholder) are being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word "means" (or "step” or the generic placeholder) are not being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
Such claim limitations that use the terms being interpreted under 112(f) are:
an input data generation unit configured to select candidate materials among a plurality of possible materials possible to be used as cathode materials for sodium-ion batteries and generate 03 input data and P3 input data respectively for 03 structure materials and P3 structure materials formed depending on structural transition during charge and discharge from each candidate material (claim 1).
a material classification unit configured to receive the 03 and P3 input data from the input data generation unit and classify the candidate materials depending on stability in a pristine state and desodiated state, respectively, by performing machine learning on data of the plurality of 03 and P3 structure materials using a pristine model and a desodiated model as prediction models (claim 1).
a data sampling unit configured to receive data from the material classification unit and perform data sampling to solve data imbalance between stable and unstable candidate materials in the pristine and desodiated states, respectively (claim 1).
a selection unit configured to receive data from the data sampling unit and selecting a stable material maintaining stable structure during the charge and discharge of sodium-ion batteries among the candidate materials (claim 1).
Because these claim limitations are being interpreted under 35 U.S.C. 112(f), they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. As it appears in the claims, there is insufficient structure/function relationship to accurately describe the recited input data generation unit, material classification unit, data sampling unit and selection unit. The corresponding structure in the instant claims, “at least one processor” is interpreted as computer-implemented. However, the specification must disclose an algorithm for performing the claimed specific computer function, or else the claim lacks description under 35 U.S.C. 112(a) and it is indefinite under 35 U.S.C. 112(b) (MPEP 2181 (II)(B)).
Claim1 recites “an input data generation unit.” Claim 1 recites the processor to select candidate materials among a plurality of possible materials possible to be used as cathode materials for sodium-ion batteries and generate 03 input data and P3 input data respectively for 03 structure materials and P3 structure materials formed depending on structural transition during charge and discharge from each candidate material. However, the disclosure lacks the algorithmic structure for such function [0043].
Claim1 recites “a material classification unit.” Claim 1 recites the processor to receive the 03 and P3 input data from the input data generation unit and classify the candidate materials depending on stability in a pristine state and desodiated state, respectively, by performing machine learning on data of the plurality of 03 and P3 structure materials using a pristine model and a desodiated model as prediction models. However, the disclosure lacks the algorithmic structure for such function [0043].
Claim1 recites “data sampling unit.” Claim 1 recites the processor to select candidate materials among a plurality of possible materials possible to be used as cathode materials for sodium-ion batteries and generate 03 input data and P3 input data respectively for 03 structure materials and P3 structure materials formed depending on structural transition during charge and discharge from each candidate material. However, the disclosure lacks the algorithmic structure for such function [0043].
Claim1 recites “a selection unit.” Claim 1 recites the processor to receive data from the data sampling unit and selecting a stable material maintaining stable structure during the charge and discharge of sodium-ion batteries among the candidate materials. However, the disclosure lacks the algorithmic structure for such function [0043].
If applicant does not intend to have this limitation interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f). The specification discloses “the input data generation unit 100, classification unit 200, data sampling unit 300, and selection unit 400 may each include hardware units such as computers or central processing units (CPUs), software units such as computational programs, and units implemented by a combination of both hardware and software. In addition, one unit may be implemented using two or more hardware components, and two or more units may also be implemented using a single hardware component” at [0043] but does not disclose adequate structure to perform the claimed function. See below regarding issues under 112(a) and 112(b) arising from this claim interpretation.
Claim Rejections - 35 USC § 112
35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
112(a) written description rejections based on 112(f) interpretations above
Claim 1-11 are rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, because the claim purports to invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, but fails to recite a combination of elements as required by that statutory provision and thus cannot rely on the specification to provide the structure, material or acts to support the claimed function. Claims depending from rejected claims are rejected similarly, unless otherwise noted.
The following recitations cause the indicated claims to be rejected under 112(a):
"an input data generation unit … a material classification unit … a data sampling unit … and a selection unit” (claim 1).
The recited " an input data generation unit … a material classification unit … a data sampling unit … and a selection unit” (claim 1) are interpreted as computer-implemented. The instant specification [0043] provides support for “the input data generation unit 100, classification unit 200, data sampling unit 300, and selection unit 400 may each include hardware units such as computers or central processing units (CPUs), software units such as computational programs, and units implemented by a combination of both hardware and software. In addition, one unit may be implemented using two or more hardware components, and two or more units may also be implemented using a single hardware component.” However, there is not support within the specification, nor has Applicant provided such support, for an algorithm for performing the claimed specific computer function. The written description requirement may be satisfied through disclosure of function and minimal structure when there is a well-established correlation between structure and function. See MPEP 2163. MPEP 2161.01.I "Determining Whether There Is Adequate Written Description For A Computer-Implemented Functional Claim Limitation" also pertains. As appropriate, these rejections may be overcome, for example, (i) by amending so as not to invoke 112(f) and/or (ii) by clarifying on the record where support can be found and how that support relates to the recitations in order to satisfy 112(f).
35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION —The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
112(b) written description rejections based on 112(f) interpretations above
Claims 1-11 are rejected under 35 U.S.C. 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor regards as the invention at least due to the 112(f) issues identified in the above interpretations section. The claims read on subject matter which is not described in the specification in such a way as satisfy 112(f). Claims depending from rejected claims are rejected similarly, unless otherwise noted.
The following recitations cause the indicated claims to be rejected under 112(b):
"an input data generation unit … a material classification unit … a data sampling unit … and a selection unit” (claim 1).
The recited "an input data generation unit … a material classification unit … a data sampling unit … and a selection unit” (claim 1) are interpreted as computer-implemented. The instant specification [0043] provides support for “the input data generation unit 100, classification unit 200, data sampling unit 300, and selection unit 400 may each include hardware units such as computers or central processing units (CPUs), software units such as computational programs, and units implemented by a combination of both hardware and software. In addition, one unit may be implemented using two or more hardware components, and two or more units may also be implemented using a single hardware component.” However, it is unclear what is the physical structure related to an algorithm for performing the claimed specific computer function. As appropriate, these rejections may be overcome, for example, (i) by amending so as not to invoke 112(f) and/or (ii) by clarifying on the record where support can be found and how that support relates to the recitations in order to satisfy 112(f). For more information, see MPEP 2181.
Other 112(b) rejections
Claims 3-4 are rejected under 35 U.S.C. 112(b)as being indefinite for failing to particularly point out and distinctly claim the subject matter the invention. Dependent claims are rejected similarly, unless otherwise noted below. The following issues cause the respective claims to be rejected under 112(b) as indefinite:
Claim 3 recites "wherein the plurality of possible materials are represented by a formula NaxNi1DayDbzO2 (where Da and Db are arbitrary elements, 0.5 ≤ x ≤ 1, and y : z = 0.25:0.25, 0.42:0.08, or 0:0.5)" which is indefinite because it recites exemplary claim language (see MPEP 2173.05(d)). It is unclear if “(where Da and Db are arbitrary elements, 0.5 ≤ x ≤ 1, and y : z = 0.25:0.25, 0.42:0.08, or 0:0.5)" refers to the preceding term as a further limiting terms or if it is just exemplary.
Claim 4 recites “wherein each of Da and Db is at least one element selected from” in which “each of Da and Db” lacks antecedent basis since there is no “Da and Db“ recited in claim 1. To overcome this rejection, claim 4 may be amended to depend on claim 3.
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 1-11 are rejected under 35 USC § 101 because the claimed inventions are directed to one or more Judicial Exceptions (JEs) without significantly more. Regarding JEs, "Claims directed to nothing more than abstract ideas..., natural phenomena, and laws of nature are not eligible for patent protection" (MPEP 2106.04 §I). Abstract ideas include mathematical concepts and procedures for evaluating, analyzing or organizing information, which are a type of mental process (MPEP 2106.04(a)(2)).
101 background
MPEP 2106 organizes JE analysis into Steps 1, 2A (Prong One & Prong Two), and 2B as analyzed below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter (MPEP 2106.03)?
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
Analysis of instant claims
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
The instant claims are directed to a system (claims 1-10) and a method (claim 11); each of which falls within one of the categories of statutory subject matter. [Step 1: claims 1-11: Yes]
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Background
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. MPEP § 2106.04(a)(2) further explains that abstract ideas are defined as:
• mathematical concepts (mathematical formulas or equations, mathematical relationships
and mathematical calculations) (MPEP 2106.04(a)(2)(I));
• certain methods of organizing human activity (fundamental economic principles or practices, managing personal behavior or relationships or interactions between people) (MPEP 2106.04(a)(2)(II)); and/or
• mental processes (concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) (MPEP 2106.04(a)(2)(III)).
Analysis of instant claims
With respect to the instant claims, under Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the grouping of mathematical concepts (in particular mathematical relationships and formulas) and mental processes (in particular procedures for observing, analyzing and organizing information) are as follows.
Mathematical concepts (in particular mathematical relationships and formulas) include:
• "performing machine learning on data of the plurality of 03 and P3 structure materials using a pristine model and a desodiated model as prediction models" (claim 1);
• "perform data sampling to solve data imbalance between stable and unstable candidate materials in the pristine and desodiated states, respectively" (claim 1);
• "performs density functional theory (DFT) calculations on the plurality of possible materials to obtain an energy difference value (ED) between the 03 structure material and the P3 structure material" (claim 2);
• "generates the pristine and desodiated models by training a classification model” (claim 6);
• "performing, by the data sampling unit, oversampling and undersampling sequentially to solve data imbalance between the stable and unstable candidate materials in the pristine and desodiated states, respectively” (claim 11).
The claims identified above read on math. The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation and determined each element performed by mathematical operation. The step directed to “executing data sampling via oversampling and undersampling algorithms” requires mathematical techniques as the only supported embodiments because it describes a mathematical technique (MPEP 2106.04(a)(2) pertains). Further support for the mathematical techniques used in the claims is provided in the specification at [0094] which discloses a machine learning model that incorporates the Extra Trees Classifier along with the SMOTE oversampling model and the ENN undersampling model. Thus, the recited terms correspond to verbal equivalents of mathematical concepts because they constitute actions executed by a group of mathematical steps in a form of a mathematical algorithm; thus mathematical concepts (MPEP 2106.04(a)(2)). A mathematical concept need not be expressed in mathematical symbols, because "words used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). MPEP 2106.04(a)(2) pertains.
Mental processes, defined as concepts or steps practically performed in the human mind such as steps of observations, evaluations, judgments, analysis, opinions or organizing information include:
• "select candidate materials among a plurality of possible materials possible to be used as cathode materials for sodium-ion batteries” (claim 1);
• "generate 03 input data and P3 input data respectively for 03 structure materials and P3 structure materials formed depending on structural transition during charge and discharge from each candidate material” (claim 1);
• "classify the candidate materials depending on stability in a pristine state and desodiated state, respectively" (claim 1);
• “selecting a stable material maintaining stable structure during the charge and discharge of sodium-ion batteries among the candidate materials" (claim 1);
• "selects the candidate materials by excluding materials unable to achieve structural stabilization among the plurality of possible” (claim 5);
• "generating, … the 03 and P3 input data” (claim 11);
• "classifying, … the candidate materials depending on stability in the pristine state and desodiated state, respectively, generated by training a classification model” (claim 11) and
• "deriving, … the stable material by selecting the candidate material stable in both the pristine and desodiated states” (claim 11).
The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined to each cover performance either in the mind (i.e. concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) or because the method only requires a user to manually determine action based on an added number. Under the BRI, the recited limitations are mental processes because a human mind is also sufficiently capable of evaluating data and select possible materials, generating data based on structural transitions using pen and paper, judging data to classify the candidate materials and apply evaluations made to select stable material from the list of candidate materials.
Dependent claims 3-4 and 7-10 recite further steps that limit the judicial exceptions in independent claim 1 and, as such, also are directed to those abstract ideas. For example, claims 3-4 recite further details about the plurality of possible materials; claim 7 recites further details about the classification model; and claims 8-10 recite further details about the data sampling step.
[Step 2A Prong One: claims 1-11: Yes ]
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Background
MPEP 2106.04(d).I lists the following example considerations for evaluating whether a judicial exception is integrated into a practical application:
An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using 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, as discussed in MPEP § 2106.05(e).
Analysis of instant claims
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2).
Instant claims 1 and 11 recite additional elements that are not abstract ideas:
• "input data generation unit" (claims 1 and 11);
• "material classification unit" (claims 1 and 11);
• "data sampling unit" (claims 1 and 11);
• "selection unit" (claims 1 and 11);
• "receive the 03 and P3 input data from the input data generation unit" (claim 1);
• "receive data from the material classification unit" (claim 1);
• "receive data from the data sampling unit" (claim 1) and
• "at least one processor” (claim 1).
Considerations under Step 2A, Prong Two
The limitations recited in claims 1 and 11 are interpreted as requiring the use of a computer. Hence, the claims explicitly recite steps executed by computers and therefore can be described as computer functions or instructions to implement on a generic computer.
The recited "input data generation unit" (claims 1 and 11); "material classification unit" (claims 1 and 11); "data sampling unit" (claims 1 and 11); and "selection unit" (claims 1 and 11) are interpreted as instructions to perform the judicial exceptions and are considered elements of a computing device. Further steps directed to additional non-abstract elements of a computing device/computer do not describe any specific computational steps by which the "computer parts" perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute judicial exceptions.
The judicial exceptions in the claims are considered to perform the claimed abstract idea with a computer, which is not sufficient to integrate an abstract idea into a practical application (see MPEP 2106.05(f)); since steps that can be performed mentally and merely performing the mental process in a computer environment do not negate the fact that something that can be carried out in the human mind. See MPEP 2106.04(a)(2).III.C.
Claims directed to "receive…data" read on receiving or transmitting data over a network -Symantec, 838 F.3d at 1321 - MPEP 2106.05(a) pertains; which constitutes just necessary data gathering and therefore correspond to insignificant extra-solution activity.
Hence, these are mere instructions to apply the abstract idea using a computer and insignificant extra-solution activity and therefore the claims do not integrate that abstract idea into a practical application (see MPEP 2106.04(d) § I; 2106.05(f); and 2106.05(g)).
In Step 2A, Prong One above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs).
In this Step 2A, Prong Two immediately above claim steps and/or elements were identified as part of one or more additional elements. Additional elements are further discussed in Step 2B below.
Here in Step 2A, Prong Two, no additional step or element clearly demonstrates integration of the JE(s) into a practical application.
[Step 2A Prong Two: claims 1-11: No]
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during examination that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
Claims 1-11 recite a computer or computer functions, interpreted as instructions to apply the abstract idea using a computer, where the computer does not impose meaningful limitations on the judicial exceptions; which can be performed without the use of a computer (MPEP 2106.04(d) § I; and MPEP 2106.05(f)).
Further, the courts have found that receiving data is a well-understood, routine, and conventional function of a computer when claimed in a generic manner or as insignificant extra-solution activity (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), Versa ta Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, as discussed in MPEP 2106.05(d)(Il)(i)).
When the claims are considered as a whole, they do not integrate the abstract idea into a practical application; they do not confine the use of the abstract idea to a particular technology; they do not solve a problem rooted in or arising from the use of a particular technology; they do not improve a technology by allowing the technology to perform a function that it previously was not capable of performing; and they do not provide any limitations beyond generally linking the use of the abstract idea to a broad technological environment. See MPEP 2106.05(a) and 2106.05(h).
The instant claims constitute insignificant extra solution activity, and when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(g)). Hence, these elements, when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(d)).
[Step 2B: claims 1-11: No]
Conclusion: Instant claims are directed to non-statutory subject matter
For the reasons above, the claims in this instant application, when the limitations are considered individually and as a whole, are directed to an abstract idea and lack an inventive concept not clearly anything significantly more.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter 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 pre-AIA 35 U.S.C. 103(a) 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.
A. Claims 1-2 and 5-6 are rejected under 35 U.S.C. 103(a) as being unpatentable over Moses ("Machine learning screening of metal-ion battery electrode materials." ACS applied materials & interfaces 13.45: 53355-53362 (2021)) in view of Min ("Computational screening of dopants for mitigating degradation behaviors in sodium-ion layered oxide cathode material." Journal of Alloys and Compounds 859:157785 (2021)) as cited on the attached Form PTO-892.
Claim 1 recites:
an input data generation unit configured to select candidate materials among a plurality of possible materials possible to be used as cathode materials for sodium-ion batteries and generate 03 input data and P3 input data respectively for 03 structure materials and P3 structure materials formed depending on structural transition during charge and discharge from each candidate material
• Moses teaches a deep neural network regression machine learning models trained on data obtained from the Materials Project database for predicting average voltages and volume change upon charging and discharging of electrode materials for metal-ion batteries (pg. A Abstract); wherein Na-ion electrodes were produced by systematically replacing Li-ions in the original database by Na-ions (pg. A Abstract); wherein a Materials Application Programming Interface and the Python Materials Genomics (i.e. an input data generation unit) were used to retrieve data from the Materials Project database and extract unique instances of computed data (i.e. an input data generation unit configured to select candidate materials among a plurality of possible materials possible to be used as cathode materials for sodium-ion batteries) (pg. B col. 1 para. 3); wherein by utilizing the stoichiometries of both charged and discharged electrodes and a software, additional features to form a set of 306 features to uniquely represent each reaction in the ML models were generated (i.e. generate input data depending on structural transition during charge and discharge from each candidate material) (pg. B col. 2 para. 2).
• Moses does not teach “03 input data and P3 input data respectively for 03 structure materials and P3 structure materials.” However, Min teaches computational screening of selection dopants (i.e. selection unit) for mitigating degradation behaviors in sodium-ion layered oxide cathode material using calculations based on first-principles to shortlist ideal dopants - Hf, Zr, Y, Ti, and Ru aiming to improve the structural and chemical stabilities of sodium-ion layered cathode materials (i.e. data sampling unit) (pg. 1 Abstract); wherein the stability analysis during phase transition from O3 to P3 during electrochemical cycling is based on the comparison of the total energy difference between the two phases (i.e. 03 and P3 input data and O3 and P3 structure materials) (pg. 3 col. 1 para. 2).
a material classification unit configured to receive the 03 and P3 input data from the input data generation unit and classify the candidate materials depending on stability in a pristine state and desodiated state, respectively, by performing machine learning on data of the plurality of 03 and P3 structure materials using a pristine model and a desodiated model as prediction models
• Moses teaches extracting features from input data based the chemical formula of the electrodes with low and high concentration of the working ion, the working ion (Li, Na, K, Rb, Cs, Mg, Ca, Al, Zn, or Y), the type of the electrode (either intercalation or conversion), the Bravais lattice type, the space group, the average voltage, and the percentage change in the volume of the electrodes with low and high concentration of working ions (pg. B col. 1 para. 3); wherein Na-ion electrodes were produced by systematically replacing Li-ions in the original database by Na-ions and then, selecting a set of 22 electrodes that exhibit a good performance (i.e. stability is an inherent trait for achieving “good performance”) in energy density, as well as small volume variations upon charging and discharging (i.e. smaller variations in said volume leads to more stable materials), as predicted by the machine learning model (i.e. receive the input data generation unit and classify the candidate materials depending on stability) (pg. A Abstract).
• Moses does not teach “03 and P3 input data” and “O3 and P3 structure materials”, “pristine state and desodiated state” and “using a pristine model and a desodiated model.” However, Min teaches computational screening of selection dopants (i.e. selection unit) for mitigating degradation behaviors in sodium-ion layered oxide cathode material using calculations based on first-principles to shortlist ideal dopants - Hf, Zr, Y, Ti, and Ru aiming to improve the structural and chemical stabilities of sodium-ion layered cathode materials (i.e. data sampling unit) (pg. 1 Abstract); wherein the stability analysis during phase transition from O3 to P3 during electrochemical cycling is based on the comparison of the total energy difference between the two phases (i.e. 03 and P3 input data and O3 and P3 structure materials) (pg. 3 col. 1 para. 2); wherein the phase stability at the pristine state was modeled (pg. 3 col. 2 para. 5) and, to mimic the desodiation process during charging, Na ions were extracted one by one from each Na-layer in serial based on the Ewald summation method (i.e. desodiated state modeling) (pg. 2 col. 1 para. 3).
a data sampling unit configured to receive data from the material classification unit and perform data sampling to solve data imbalance between stable and unstable candidate materials in the pristine and desodiated states, respectively; and
a selection unit configured to receive data from the data sampling unit and selecting a stable material maintaining stable structure during the charge and discharge of sodium-ion batteries among the candidate materials
• Moses does teach the recitation above. However, Min teaches computational screening of selection dopants (i.e. selection unit) for mitigating degradation behaviors in sodium-ion layered oxide cathode material (i.e. perform data sampling to solve data imbalance between stable and unstable candidate materials) using calculations based on first-principles to shortlist ideal dopants - Hf, Zr, Y, Ti, and Ru aiming to improve the structural and chemical stabilities of sodium-ion layered cathode materials (i.e. data sampling unit) (pg. 1 Abstract); wherein doping is used to remedy to alleviate the degradation behaviors (pg. 1 col. 2 para. 2); wherein screening of ideal dopants during the charge and discharge phases – sodiation/desodiation - and selected ideal dopants were reported (i.e. selecting a stable material maintaining stable structure during the charge and discharge of sodium-ion batteries among the candidate materials) (pg. 2 Fig. 1). Here, the instant specification at [0075] supports this interpretation for data sampling which discloses data sampling being performed for removing data interfering with predictions.
Here, the “to solve data imbalance” is being interpreted as improving stability in the dataset as supported by [0020]. The “to solve data imbalance” is taught by Min via the method for mitigating degradation behaviors using calculations aiming to improve the structural and chemical stabilities of sodium-ion layered cathode materials (pg. 1 Abstract).
Claim 2 recites:
wherein the input data generation unit performs density functional theory (DFT) calculations on the plurality of possible materials to obtain an energy difference value (ED) between the 03 structure material and the P3 structure material
• Moses does not teach the recitation above. However, Min teaches “input data generation unit performs density functional theory (DFT) calculations on the plurality of possible materials” as computational screening for selecting dopants for mitigating degradation behaviors in sodium-ion layered oxide cathode material using calculations based on first-principles to shortlist ideal dopants - Hf, Zr, Y, Ti, and Ru aiming to improve the structural and chemical stabilities of sodium-ion layered cathode materials (pg. 1 Abstract); wherein the behavior analysis during phase transition from O3 to P3 during electrochemical cycling is based on the comparison of the total energy difference between the two phases (i.e. to obtain an energy difference value (ED) between the 03 structure material and the P3 structure material) (pg. 3 col. 1 para. 2).
Claim 5 recites:
wherein the input data generation unit selects the candidate materials by excluding materials unable to achieve structural stabilization among the plurality of possible materials
• Moses does not teach the recitation above. However, Min teaches computational screening for selecting dopants for mitigating degradation behaviors in sodium-ion layered oxide cathode material using calculations based on first-principles to shortlist ideal dopants - Hf, Zr, Y, Ti, and Ru aiming to improve the structural and chemical stabilities of sodium-ion layered cathode materials (pg. 1 Abstract); wherein the stability analysis during phase transition from O3 to P3 during electrochemical cycling is based on the comparison of the total energy difference between the two phases and doped materials with a lower O3-phase energy (more stable than P3-phase) are selected (i.e. hence when the criteria is not met said materials would be excluded – reading on wherein the input data generation unit selects the candidate materials by excluding materials unable to achieve structural stabilization among the plurality of possible materials) (pg. 3 col. 1 para. 2).
Claim 6 recites:
wherein the material classification unit generates the pristine and desodiated models by training a classification model
• Moses teaches a deep neural network regression machine learning models trained on data obtained from the Materials Project database for predicting average voltages and volume change upon charging and discharging of electrode materials for metal-ion batteries (pg. A Abstract); wherein models were trained with three different sets of data: (i) on the entire data set consisting of electrode materials for 10different active metal-ions (labeled all), (ii) on all alkali-ion based electrode materials (labeled Alkali), and (iii) on only Li ion based electrode materials (labeled Li) (pg. B col. 2 para. 3) and applied metrics to measure battery performance include specific capacity, voltage, energy density, thermal stability, Coulombic efficiency, safety, cyclability, electrical conductivity of electrodes, and lifetime (i.e. here described metrics are used to classify battery performance – hence a “classification model”) (pg B col. 1para. 2).
• Moses does not teach “pristine and desodiated states.” However, Min teaches computational screening of selection dopants (i.e. selection unit) for mitigating degradation behaviors in sodium-ion layered oxide cathode material using calculations based on first-principles to shortlist ideal dopants - Hf, Zr, Y, Ti, and Ru aiming to improve the structural and chemical stabilities of sodium-ion layered cathode materials (i.e. data sampling unit) (pg. 1 Abstract); wherein the stability analysis during phase transition from O3 to P3 during electrochemical cycling is based on the comparison of the total energy difference between the two phases (i.e. 03 and P3 input data and O3 and P3 structure materials) (pg. 3 col. 1 para. 2); wherein the phase stability at the pristine state was modeled (pg. 3 col. 2 para. 5) and, to mimic the desodiation process during charging, Na ions were extracted one by one from each Na-layer in serial based on the Ewald summation method (i.e. desodiated state) (pg. 2 col. 1 para. 3)
Rationale for combining (MPEP §2142-2143)
Regarding claims 1-2 and 5-6, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Moses in view of Min because all references disclose methods for improving the structural and chemical stabilities of sodium-ion layered cathode. The motivation would have been to find ideal dopants for optimizing the performance of O3 type cathodes - NaNi0.42Mn0.5D0.08O2 (D = dopant) in terms of structural and chemical stability during electrochemical cycling (pg. 2 col. 2 para. 2 Min).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the method for screening metal-ion battery electrode materials of Moses to the methods by Min because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for improving the structural and chemical stabilities of sodium-ion layered cathode.
B. Claim 3 is rejected under 35 U.S.C. 103(a) as being unpatentable over Moses and Min as applied to claim 1 above further in view of Wang ("Ti‐substituted NaNi0. 5Mn0. 5‐xTixO2 cathodes with reversible O3− P3 phase transition for high‐performance sodium‐ion batteries." Advanced Materials 29.19:1700210 (2017)) in view of Belcher (US Patent Application No. 20190288326A1), as cited on the attached Form PTO-892.
Claim 3 recites:
wherein the plurality of possible materials are represented by a formula NaxNi1DayDbzO2 (where Da and Db are arbitrary elements, 0.5 ≤ x ≤ 1, and y : z = 0.25:0.25, 0.42:0.08, or 0:0.5)
• Neither Moses or Min teach the recitation above. However, However, Wang teaches the Ti substituted NaNi0. 5Mn0. 5‐xTixO2 cathode (i.e. NaxNi1DayDbzO2 formula) with reversible O3-P3 phase transition for high-performance sodium-ion batteries (pg. 1 Title); wherein 0 ≤ x ≤ 0.5 (pg. 1 Abstract); wherein the recited “y” is taught as 0.5 – x with 0 ≤ x ≤ 0.5 (i.e. hence 0 ≤ y ≤ 0.5) and z is taught as 0 ≤ z ≤ 0.5 (pg. 1 Title) (i.e. hence the various combinations of y and z read on all possible recited y:z values).
• Wang teaches the 0 ≤ x ≤ 0.5 range which makes obvious the instantly claimed range of 0.5<x<1. It would have been prima facie obvious to one of ordinary skill in the art to select any portions of the disclosed ranges including the instantly claimed ranges from the ranges disclosed in the prior art references, particularly in view of the fact that: "The normal desire of scientists or artisans to improve upon what is already generally known provides the motivation to determine where in a disclosed set percentage ranges is the optimum combination of percentages" In re Peterson 65 USPQ2d 1379 (CAFC 2003). See also In re Malagari, 182 USPQ 549,533 (CCPA 1974) and MPEP 2144.05 modifying the values for identity, coverage and e-value would improve the quality of the data being filtered for the database created since it would yield more complete sequences (with less sequences overlaps when it comes to e-values) being identified by the method.
• Regarding the recited stoichiometric value for Ni in the NaxNi1DayDbzO2 formula, Wang teaches a 0.5 stoichiometric value for Ni in NaNi0. 5Mn0. 5‐xTixO2. Neither Moses or Wang or Min teach a 1 stoichiometric value for Ni. However, Belcher teaches a sodium ion battery cathode composition wherein the NaTMO2 layered oxide is NaNiMnXO2 where X is Ti, Fe, Sn or Si (claim 11) (i.e. reading on the formula NaxNi1DayDbzO2).
Rationale for combining (MPEP §2142-2143)
Regarding claim 3, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Moses and Min in view of Wang and Belcher because all references disclose methods for improving the structural and chemical stabilities of sodium-ion layered cathode. The motivation would have been to:
• incorporate O3 and P3 input data materials to obtain a product of improved performance as taught by Wang (pg. 1 Abstract) and
• design high energy and high rate cathode materials for sodium ion batteries ([0023] Belcher).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the method for screening metal-ion battery electrode materials of Moses and Min to the methods by Wang and Belcher because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for improving the structural and chemical stabilities of sodium-ion layered cathode.
C. Claim 4 is rejected under 35 U.S.C. 103(a) as being unpatentable over Moses and Min as applied to claim 1 above further in view of Wang ("Ti‐substituted NaNi0. 5Mn0. 5‐xTixO2 cathodes with reversible O3− P3 phase transition for high‐performance sodium‐ion batteries." Advanced Materials 29.19:1700210 (2017)), as cited on the attached Form PTO-892.
Claim 4 recites:
wherein each of Da and Db is at least one element selected from the group consisting of Zr, Se, Fe, Zn, Sc, Cu, Y, Sb, Cr, W, Nb, Co, V, Mo, B, Ti, Mn, As, Te, Mg, Al, Ta, La, Sn, Ge, Si, and Ga
• Neither Moses or Min teach the recitation above. However, Wang teaches the Ti substituted NaNi0. 5Mn0. 5‐xTixO2 cathode (i.e. NaxNi1DayDbzO2 formula wherein each of Da and Db is at least one element selected from the group consisting of Zr, Se, Fe, Zn, Sc, Cu, Y, Sb, Cr, W, Nb, Co, V, Mo, B, Ti, Mn, As, Te, Mg, Al, Ta, La, Sn, Ge, Si, and Ga) with reversible O3-P3 phase transition for high-performance sodium-ion batteries (pg. 1 Title).
Rationale for combining (MPEP §2142-2143)
Regarding claim 4, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Moses and Min in view of Wang because all references disclose methods for improving the structural and chemical stabilities of sodium-ion layered cathode. The motivation would have been to:
• incorporate O3 and P3 input data materials to obtain a product of improved performance as taught by Wang (pg. 1 Abstract).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the method for screening metal-ion battery electrode materials of Moses and Min to the methods by Wang because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for improving the structural and chemical stabilities of sodium-ion layered cathode.
D. Claims 7-8 and 11 are rejected under 35 U.S.C. 103(a) as being unpatentable over Moses and Min as applied to claims 1 and 6 above further in view of Mohammed ("Machine learning with oversampling and undersampling techniques: overview study and experimental results." 2020 11th international conference on information and communication systems (ICICS). IEEE, 2020), as cited on the attached Form PTO-892.
Claim 7 recites:
wherein the classification model is one of Extra Trees Classifier model, Random Forest model, K-Nearest Neighbors Classifier model, Light Gradient Boosting Machine (LightGBM) model, and Logistic Regression model
Claim 8 recites:
wherein the data sampling unit performs oversampling and undersampling sequentially for the data sampling
• Neither Moses or Min teach the recitation above. However, Mohammed teaches a review of machine learning algorithms for classification tasks in which the distribution of classes or labels in a given dataset is not uniform by exploring comparison and evaluations of oversampling and undersampling techniques (pg. 1 Abstract) in data sampling sequentially in Fig. 4 (pg. 3) for oversampling and Fig. 6 for undersampling (pg. 5) (i.e. wherein the data sampling unit performs oversampling and undersampling sequentially for the data sampling as in claim 8); wherein Logistic Regression (i.e. as in claim 7) is listed as one of the classifier models reviewed (pg. 3 Table 1).
Claim 11 recites:
generating, by the input data generation unit, the 03 and P3 input data; classifying, by the material classification unit, the candidate materials depending on stability in the pristine state and desodiated state, respectively, generated by training a classification model …
deriving, by the selection unit, the stable material by selecting the candidate material stable in both the pristine and desodiated states
• Moses teaches a deep neural network regression machine learning models trained on data obtained from the Materials Project database for predicting average voltages and volume change upon charging and discharging of electrode materials for metal-ion batteries (pg. A Abstract); wherein Na-ion electrodes were produced by systematically replacing Li-ions in the original database by Na-ions (pg. A Abstract); wherein a Materials Application Programming Interface and the Python Materials Genomics (i.e. an input data generation unit) were used to retrieve data from the Materials Project database and extract unique instances of computed data (i.e. an input data generation unit) (pg. B col. 1 para. 3); wherein by utilizing the stoichiometries of both charged and discharged electrodes and a software, additional features to form a set of 306 features to uniquely represent each reaction in the ML models were generated (i.e. generate input data) (pg. B col. 2 para. 2); wherein Na-ion electrodes were produced by systematically replacing Li-ions in the original database by Na-ions and then, selecting a set of 22 electrodes that exhibit a good performance (i.e. “selecting the candidate material stable” - here stability is an inherent trait for achieving “good performance”) in energy density, as well as small volume variations upon charging and discharging (i.e. smaller variations in said volume leads to more stable materials), as predicted by the machine learning model (i.e. receive the input data generation unit and classify the candidate materials depending on stability) (pg. A Abstract).
• Moses does not teach “03 and P3 input data” and “O3 and P3 structure materials”, “pristine state and desodiated state” and “using a pristine model and a desodiated model.” However, Min teaches it as computational screening of selection dopants (i.e. selection unit) for mitigating degradation behaviors in sodium-ion layered oxide cathode material using calculations based on first-principles to shortlist ideal dopants - Hf, Zr, Y, Ti, and Ru aiming to improve the structural and chemical stabilities of sodium-ion layered cathode materials (i.e. data sampling unit) (pg. 1 Abstract); wherein the stability analysis during phase transition from O3 to P3 during electrochemical cycling is based on the comparison of the total energy difference between the two phases (i.e. 03 and P3 input data and O3 and P3 structure materials) (pg. 3 col. 1 para. 2); wherein the phase stability at the pristine state was modeled (pg. 3 col. 2 para. 5) and, to mimic the desodiation process during charging, Na ions were extracted one by one from each Na-layer in serial based on the Ewald summation method (i.e. desodiated state modeling) (pg. 2 col. 1 para. 3).
performing, by the data sampling unit, oversampling and undersampling sequentially to solve data imbalance between the stable and unstable candidate materials in the pristine and desodiated states, respectively
• Moses teaches extracting features from input data based the chemical formula of the electrodes with low and high concentration of the working ion, the working ion (Li, Na, K, Rb, Cs, Mg, Ca, Al, Zn, or Y), the type of the electrode (either intercalation or conversion), the Bravais lattice type, the space group, the average voltage, and the percentage change in the volume of the electrodes with low and high concentration of working ions (pg. B col. 1 para. 3); wherein Na-ion electrodes were produced by systematically replacing Li-ions in the original database by Na-ions and then, selecting a set of 22 electrodes that exhibit a good performance (i.e. stability is an inherent trait for achieving “good performance”) in energy density, as well as small volume variations upon charging and discharging (i.e. smaller variations in said volume leads to more stable materials), as predicted by the machine learning model (i.e. receive the input data generation unit and classify the candidate materials depending on stability) (pg. A Abstract).
• Moses does not teach “the pristine and desodiated states.” However, Min teaches it as computational screening of selection dopants (i.e. selection unit) for mitigating degradation behaviors in sodium-ion layered oxide cathode material using calculations based on first-principles to shortlist ideal dopants - Hf, Zr, Y, Ti, and Ru aiming to improve the structural and chemical stabilities of sodium-ion layered cathode materials (i.e. data sampling unit) (pg. 1 Abstract); wherein the stability analysis during phase transition from O3 to P3 during electrochemical cycling is based on the comparison of the total energy difference between the two phases (i.e. 03 and P3 input data and O3 and P3 structure materials) (pg. 3 col. 1 para. 2); wherein the phase stability at the pristine state was modeled (pg. 3 col. 2 para. 5) and, to mimic the desodiation process during charging, Na ions were extracted one by one from each Na-layer in serial based on the Ewald summation method (i.e. desodiated state modeling) (pg. 2 col. 1 para. 3).
• Neither Moses or Min teach “oversampling and undersampling sequentially to solve data imbalance between the stable and unstable candidate materials.” However, Mohammed teaches a review of machine learning algorithms for classification tasks in which the distribution of classes or labels in a given dataset is not uniform by exploring comparison and evaluations of oversampling and undersampling techniques (pg. 1 Abstract) in data sampling sequentially in Fig. 4 (pg. 3) for oversampling and Fig. 6 for undersampling (pg. 5) (i.e. wherein the data sampling unit performs oversampling and undersampling sequentially for the data sampling as in claim 8); wherein Logistic Regression (i.e. as in claim 7) is listed as one of the classifier models reviewed (pg. 3 Table 1).
Rationale for combining (MPEP §2142-2143)
Regarding claims 7-8 and 11, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Moses and Min in view of Mohammed because all references disclose methods for implementing computational data screening techniques. The motivation would have been to solve classification tasks in which the distribution of classes or labels in a given dataset is not uniform by adding records to the minority class or deleting ones from the majority class (pg. 1 Abstract Mohammed).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the method for screening data of Moses and Min to the methods by Mohammed because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for implementing computational data screening techniques.
E. Claim 9 is rejected under 35 U.S.C. 103(a) as being unpatentable over Moses, Min and Mohammed as applied to claims 1 and 8 above further in view of Zhu ("Synthetic minority oversampling technique for multiclass imbalance problems." Pattern recognition 72:327-340 (2017)), as cited on the attached Form PTO-892.
Claim 9 recites:
wherein the data sampling unit uses Synthetic Minority Oversampling Technique (SMOTE) for performing the oversampling.
• Neither Moses or Min or Mohammed teach the recitation above. However, Zhu teaches k-nearest neighbors (k-NN)-based synthetic minority oversampling algorithm, termed SMOM, to handle multiclass imbalance problems (pg. 327 Abstract).
Rationale for combining (MPEP §2142-2143)
Regarding claim 9, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Moses, Min and Mohammed in view of Zhu because all references disclose methods for implementing computational data screening techniques. The motivation would have been to aggressively broaden the regions of minority classes to better protect the minority class instances while decreasing significantly the negative effects in the oversampling process of minority classes (pg. 328 col. 1 para. 1 Zhu).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the method for screening data of Moses, Min and Mohammed to the methods by Zhu because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for implementing computational data screening techniques.
F. Claim 10 is rejected under 35 U.S.C. 103(a) as being unpatentable over Moses, Min and Mohammed as applied to claims 1 and 8 above further in view of Bach ("The proposal of undersampling method for learning from imbalanced datasets." Procedia Computer Science 159:125-134 (2019)), as cited on the attached Form PTO-892.
Claim 10 recites:
wherein the data sampling unit uses Tomek Links and Edited Nearest Neighbors (ENN) for performing the undersampling
• Neither Moses or Min or Mohammed teach the recitation above. However, Bach teaches the analysis of different methods of class balancing obtained by undersampling (pg. 125 Abstract); where some of the representative undersampling algorithms chosen were Tomek Links and Edited Nearest Neighbors (pg. 126 para. 3).
Rationale for combining (MPEP §2142-2143)
Regarding claim 10, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Moses, Min and Mohammed in view of Bach because all references disclose methods for implementing computational data screening techniques. The motivation would have been to aggressively broaden the regions of minority classes to removing information from high-density areas versus less-density areas leading to less loss of information and achieve better performance (pg. 125 Abstract Bach).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the method for screening data of Moses, Min and Mohammed to the methods by Bach because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for implementing computational data screening techniques.
Conclusion
No claims are allowed.
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/F.F.L./Examiner, Art Unit 1685
/JANNA NICOLE SCHULTZHAUS/Examiner, Art Unit 1685
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