DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The Information Disclosure Statement (IDS) submitted on 07/24/2026 is in compliance with the provisions of 37 CFR 1.97, 37 CFR 1.98, and MPEP § 609. It has been placed in the application file, and the information referred to therein has been considered as to the merits.
Claim Objections
Claim 1 is objected to because of the following informality:
Change to “…a classifier encoded with a classifying machine learning model including a machine learning library for classification, and configured to…” (page 2).
Appropriate correction 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 (an) abstract idea(s) without significantly more.
Claims 1 and 11 recite:
processing test failure information tracked during evaluation of a manufactured storage device
to generate text features, categorical features, and numerical features,
the test failure information associated with test failures for the manufactured storage device;
generating an embedding vector based on the text features;
concatenating the embedding vector, the categorical features, and the numerical features
using a concatenation machine learning model
to generate concatenated features as a training dataset; and
classifying the training dataset
using a classifying machine learning model including a machine learning library for classification
to generate prediction information indicating probabilities for each category affecting the test failures, the probabilities for each category identifying particular features to change for a reduction in the test failures in the manufactured storage device,
wherein the identifying the particular features to change comprises providing values which show influences of the particular features on the test failures.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes:
Claim 1 is a machine.
Claim 11 is a process.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The ‘processing’ limitation in # 1 above, as claimed and under broadest reasonable interpretation (BRI), is a mental process that covers performance of the limitation in the mind. For example, “processing” in the context of this claim encompasses a person evaluating data and writing down simple data elements on paper or via a generic keyboard and display.
The ‘…to generate… and generating’ limitations in # 2, 4, 7, and 10 above, as claimed and under BRI, are mental processes that cover performance of the limitation in the mind. For example, “generating” in the context of this claim encompasses the person writing down simple data elements on paper or via a generic keyboard and display.
The ‘concatenating’ limitation in # 5 above, as claimed and under BRI, is a mental process that covers performance of the limitation in the mind. For example, “concatenating” in the context of this claim encompasses the person writing down simple data elements on paper or via a generic keyboard and display.
The ‘classifying’ limitation in # 8 above, as claimed and under BRI, is a mental process that covers performance of the limitation in the mind. For example, “classifying” in the context of this claim encompasses the person performing an evaluation.
Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
In # 3 above, the claimed test failure information is merely further described in the context of a field of use. See MPEP 2106.05(h).
The ‘…using / encoded with a concatenation machine learning model…’ limitation in # 6 above, as claimed and under BRI, is an additional element that is mere instructions to apply an exception. For example, “using / encoded with” in the context of this claim encompasses applying generic computer instructions and/or algorithms to a mental process (concatenating). See MPEP 2106.05(f).
The ‘…using / encoded with a classifying machine learning model including a machine learning library for classification…’ limitation in # 9 above, as claimed and under BRI, is an additional element that is mere instructions to apply an exception. For example, “using” in the context of this claim encompasses applying generic computer instructions and/or algorithms to a mental process (classifying). See MPEP 2106.05(f).
The ‘…providing…which show’ limitation in # 11 above, as claimed and under BRI, is an additional element that is insignificant extra-solution activity. For example, “providing” in the context of this claim encompasses mere data output / manipulation. See MPEP 2106.05(g).
Additionally, the claims recite the following additional elements:
a preprocessor circuit (Claim 1),
a manufactured storage device (Claims 1 and 11),
a processor (Claim 1),
an embedding vector generator (Claim 1),
a features concatenator (Claim 1),
a classifier (Claim 1), and
storage devices (Claim 11).
These additional elements are recited at a high level of generality (i.e. as generic computer components) such that they amount to no more than components comprising mere instructions to apply an exception. Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
As discussed above with respect to integration of the abstract idea(s) into a practical application, the aforementioned additional elements amount to no more than components comprising mere instructions to apply the exception. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
Additionally, with regards to # 11 above, per MPEP 2106.05(d)(Il), the courts have recognized the following computer function(s) as well-understood, routine, and conventional when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity:
iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93.
Claims 2 and 12 recite:
generating explanation information indicating which of the particular features have influenced the prediction information.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes:
Claim 2 is a machine.
Claim 12 is a process.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The ‘generating’ limitation in # 12 above, as claimed and under BRI, is a mental process that covers performance of the limitation in the mind. For example, “generating” in the context of this claim encompasses the person writing down simple data elements on paper or via a generic keyboard and display.
Claims 3 and 13 recite:
wherein the test failure information is retrieved from a failure database in response to a failure identifier received from an issue tracker.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes:
Claim 3 is a machine.
Claim 13 is a process.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s). The abstract idea(s) of Claims 2 and 12 is/are the same as the abstract idea(s) of Claims 3 and 13, respectively.
Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The ‘is retrieved’ limitation in # 13 above, as claimed and under BRI, is an additional element that is insignificant extra-solution activity. For example, “retrieving” in the context of this claim encompasses mere data gathering. See MPEP 2106.05(g).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
With regards to # 13 above, per MPEP 2106.05(d)(Il), the courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity:
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Claims 4 and 14 recite:
providing the prediction information and the explanation information to the issue tracker.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes:
Claim 4 is a machine.
Claim 14 is a process.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s). The abstract idea(s) of Claims 3 and 13 is/are the same as the abstract idea(s) of Claims 4 and 14, respectively.
Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The ‘providing’ limitation in # 14 above, as claimed and under BRI, is an additional element that is insignificant extra-solution activity. For example, “providing” in the context of this claim encompasses mere data transmission. See MPEP 2106.05(g).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
With regards to # 14 above, per MPEP 2106.05(d)(Il), the courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity:
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); 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).
Claims 5 and 15 merely further describe the claimed test failure information of Claims 1 and 11, respectively, in the context of mere fields of use. See MPEP 2106.05(h).
Claims 6 and 16 recite:
wherein the processing of the processing test failure information includes:
processing the raw texts to generate the text features;
processing the log information to generate statistics information as the categorical features;
performing one-hot encoding on the test labels to generate the numerical features; and
normalizing the numeric values to generate the normalized values as the numerical features.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes:
Claim 6 is a machine.
Claim 16 is a process.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The ‘processing’ limitations in # 15 and 16 above, as claimed and under BRI, are mental processes that cover performance of the limitation in the mind. For example, “processing” in the context of this claim encompasses the person thinking about data and writing down simple data elements on paper or via a generic keyboard and display.
The ‘performing’ limitation in # 17 above, as claimed and under BRI, is a mental process that covers performance of the limitation in the mind. For example, “performing” in the context of this claim encompasses the person writing down simple binary data elements on paper or via a generic keyboard and display.
The ‘normalizing’ limitation in # 18 above, as claimed and under BRI, is a mathematical concept defined as one or more mathematical relationships, mathematical formulas or equations, and mathematical calculations. For example, “normalizing” in the context of this claim encompasses applying a logarithmic algorithm (see, e.g., ¶ 0074 of the instant specification).
Claims 7 and 17 merely further describe the claimed text feature(s) of Claims 6 and 16, respectively, in the context of mere fields of use. See MPEP 2106.05(h).
Claims 8 and 18 recite:
wherein the text features have a JavaScript Object Notation (JSON) format, and
the text features are parsed to generate the numeric features.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes:
Claim 8 is a machine.
Claim 18 is a process.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The ‘…are parsed…’ limitation in # 20 above, as claimed and under BRI, is a mental process that covers performance of the limitation in the mind. For example, “parsing” in the context of this claim encompasses the person marking extractions of data on paper or via a generic keyboard and display.
Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
In # 19 above, the claimed text features of Claims 6 and 16, respectively, are merely further described in the context of fields of use. See MPEP 2106.05(h).
Claims 9 and 19 recite:
wherein the generating of the embedding vector includes
concatenating the text features, and
generating a fixed-length numerical vector as the embedding vector.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes:
Claim 9 is a machine.
Claim 19 is a process.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The ‘concatenating’ limitation in # 21 above, as claimed and under BRI, is a mental process that covers performance of the limitation in the mind. For example, “concatenating” in the context of this claim encompasses the person writing down simple data elements on paper or via a generic keyboard and display.
The ‘generating’ limitation in # 22 above, as claimed and under BRI, is a mental process that covers performance of the limitation in the mind. For example, “generating” in the context of this claim encompasses the person writing down simple data elements on paper or via a generic keyboard and display.
Claims 10 and 20 recite:
wherein the concatenating of the embedding vector and the categorical and numerical features includes concatenating the embedding vector and a fixed set of the categorical and numerical features to generate the training dataset.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes:
Claim 10 is a machine.
Claim 20 is a process.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The ‘concatenating’ limitation in # 23 above, as claimed and under BRI, is a mental process that covers performance of the limitation in the mind. For example, “concatenating” in the context of this claim encompasses the person writing down simple data elements on paper or via a generic keyboard and display.
Allowable Subject Matter
Claims 1-20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter:
The elements of independent Claims 1 and 11 were neither found through a search of the prior art nor considered obvious by the Examiner. In particular, the prior art of record does not teach or suggest, in combination with the remaining limitations and in the context of their claims as a whole:
Claim 1: “…a classifier encoded with a classifying machine learning library including a machine learning library for classification, and configured to classify the training dataset, and generate prediction information indicating probabilities for each category affecting the test failures, the probabilities for each category identifying particular features to change for a reduction in the test failures in the manufactured storage device, wherein the identifying the particular features to change provides values which show influences of the particular features on the test failures.”
Claim 11: “…classifying the training dataset using a classifying machine learning model including a machine learning library for classification to generate prediction information indicating probabilities for each category affecting the test failures, the probabilities for each category identifying particular features to change for a reduction in the test failures in the manufactured storage device, wherein the identifying the particular features to change comprises providing values which show influences of the particular features on the test failures.”
Response to Arguments
Applicant's arguments filed 07/24/2026, with regards to 35 U.S.C. 101, have been fully considered, but they are not persuasive.
The Remarks argue that:
With the present amendments to the independent claims, the processing is clearly beyond performance of the limitations in the mind as by definition a machine learning model and a machine learning library requires aspects of a machine to learn.
Moreover, the claims as amended define a practical application of the system and method in that the claimed system and method are used "during evaluation of a manufactured storage device" for "identifying particular features to change for a reduction in the test failures in the manufactured storage device" and "provides values which show influences of the particular features on the test failures." As noted above, in Applicant's [0081], "the importance values cannot be analyzed by a human as is," and the "final importance plot shows the absolute importance value of a feature for a given class." Thus, the claimed inventions represent a technological improvement in the field of processing test failure information for a manufactured storage device.
Furthermore, the presently claimed inventions recite subject matter beyond that "conventional in the art," as evidenced by there being no prior art rejections. The Federal Circuit in Cooperative Entertainment Inc. V. Kollective Technology, 50 F.4th 127 (Fed. Cir. Sept. 28, 2022) overruled a District Court's decision that Cooperative's 452 patent were ineligible under 35 U.S.C. §101. In that case, the District Court had ruled that the '452 patent failed step two of Alice because the patent was "merely implementing the abstract idea of preparing and transmitting data over a computer network with generic computer components using conventional technology." Yet, the Federal Circuit disagreed with the lower court and agreed with Cooperative, "we conclude that claim 1 recites a specific technical solution it recites a particular arrangement of peer nodes for distributing content which did not exist in the prior art." Here, Applicant's pending claims are not rejected under 35 U.S.C. § 102 or § 103. It is thus apparent that Applicant's elements recited in claim 1 do not exist in the prior art and also are not obvious in view of the prior art. Accordingly, like Cooperative Entertainment Inc., pending claim 1 recites "a specific technical solution that is an inventive concept" and therefore "is not an abstract idea implemented on a generic computer." Moreover, as noted above, like Cooperative Entertainment Inc., Applicant's invention improves the test failure analysis by providing values which show influences of the particular features on the test failures.
However, the Examiner respectfully disagrees.
With regards to A above, the mere use of “machine learning” with a “machine” does not immediately make a claim patent eligible under 35 U.S.C. 101. See, e.g., MPEP 2106.04(a)(2)(III)(C): A Claim That Requires a Computer May Still Recite a Mental Process.
In at least Claims 1 and 11, the Examiner respectfully asserts that the mental processes are merely applied using generic computer components. The claims do not specify anything outside of high-level machine learning and/or software to perform these mental processes.
With regards to B above, as to "the importance values cannot be analyzed by a human as is," Applicant has omitted much of ¶ 0081 which describes using a specific explanation model used for tree-based gradient boosting inside a SHAP package. The Examiner respectfully submits that the claim limitations incorporate nothing associated with tree-based gradient boosting inside a SHAP package. As such, the much-broader claims are rejected according to the 35 U.S.C. 101 rejections above in this Office action, and the abstract idea steps, as claimed and under BRI, are capable of being performed by a human.
With regards to C above, the Cooperative case decision appears to have been reversed because the disputed claim recited a specific technical solution that did not exist in the prior art, not because the claim had no prior art rejections from the beginning of prosecution.
The Examiner notes MPEP 2106.05(I): “Although the courts often evaluate considerations such as the conventionality of an additional element in the eligibility analysis, the search for an inventive concept should not be confused with a novelty or non-obviousness determination. See Mayo, 566 U.S. at 91, 101 USPQ2d at 1973 (rejecting "the Government’s invitation to substitute §§ 102, 103, and 112 inquiries for the better established inquiry under § 101"). As made clear by the courts, the "‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter." Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1315, 120 USPQ2d 1353, 1358 (Fed. Cir. 2016) (quoting Diamond v. Diehr, 450 U.S. at 188–89, 209 USPQ at 9). See also Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) ("a claim for a new abstract idea is still an abstract idea. The search for a § 101 inventive concept is thus distinct from demonstrating § 102 novelty."). … Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101.”
For at least the reasoning provided above, Claims 1-20 remain rejected.
Conclusion
THIS ACTION IS MADE FINAL. 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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Qiu et al. (U.S. Patent Application Publication No. US 2026/0194890 A1); teaching per, e.g., Fig. 2A and ¶ 0035-0037: “Before feeding the measurement values to the self-attention modules 211, the system may embed the categorical values and continuous values to embedding vectors z ∈ IRd. The system may continue embed continuous values with a learnable linear layer with the output dimension of d. Inspired by word2vec, the system may utilize a lookup table that stores learnable embedding vectors with the dimension of d for all categorical values. In summary, the upstream measurements x.sub.1:T are first embedded to vectors z1:T0. The system may obtain the various measurements and values from the stations and generate corresponding categorical input embeddings 203 and a numeric input embeddings 205. Categorical input embeddings 203 may be derived utilizing a technique used in machine learning, particularly in natural language processing (NLP) and deep learning, to transform categorical data into a numerical format that can be used as input for machine learning models. In one embodiment, One-Hot Encoding may be utilized and each category may be represented as a binary vector, where only one element is “1” (indicating the presence of that category) and all other elements are “0.” This approach can lead to high-dimensional data, especially with a large number of categories. In another embodiment, label encoding may be utilized. In label encoding, each category may be assigned a unique integer value. To enable the self-attention modules 211 to capture the sequence's order, it becomes necessary to incorporate information concerning the relative or absolute positions of the input features within the sequence. To achieve this, the system and method may add positional embeddings 209 to the embeddings z.sub.1:T.sup.0 prior to entering the self-attention modules 211. The positional embeddings 209 may be a vector representing the position of the token in the sequence that is added to the token embedding. This helps the model distinguish between words based on their positions.”
This teaching is similar to some of the claimed features and concatenation as claimed in at least the independent claims.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH KUDIRKA whose telephone number is (571)270-7126. The examiner can normally be reached M-F 7:30am - 5pm ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ashish Thomas can be reached at (571) 272-0631. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOSEPH R KUDIRKA/Primary Patent Examiner, Art Unit 2114
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