DETAILED ACTION
This is responsive to the amendment filed 19 December 2025.
Claims 1, 5-15, 17, 19-20 and 23 are currently pending and considered below.
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 .
Response to Arguments
Applicant's arguments filed 19 December 2025 regarding the 35 USC 101 rejections have been fully considered but they are not persuasive.
Applicant argues:
Claim 1 addresses the major challenge of addressing the time-consuming and labor- intensive process of analyzing and resolving trouble reports (TRs), also known as trouble tickets or bug reports, which TRs engineers traditionally have manually reviewed to identify possible solutions, which is slow, costly, and prone to inefficiencies (Specification, pg. 1, lines 13-20). Claim 1 recites a specific technical solution rather than a results-oriented statement by requiring that "pre-processing" the "query" include "(a) tokening text comprised in the query, (b) detecting abbreviations in the text comprised in the query and replacing the detected abbreviations with complete words, (c) removing numerical data, (d) handling one or more special tokens, or (e) any combination of any two or more of (a) - (d)," which is a technical preparation of the "query" for additional technical features. The "the pre-processed query" is applied to "a first representation-based model" where the "first representation-based model" is "(i) is a model that is able to create a semantic representation of a sentence that captures its meaning in a dense vector, (ii) is a model that uses an attention mechanism for understanding and encoding sentences as a whole, (iii) is a bi-directional model that looks at all words in a sentence to encode the sentence, or (iv) is any combination of any two or more of (i)-(iii)." Claim 1 as a whole is directed to a computer-implemented solution that performs "pre- processing" to create a technical representation of the "query" and applies this "query" to a specific technical "first representation-based model" with attention to "semantic representations" captured in "dense vectors," "attention mechanisms" for "understanding and encoding sentences" or "a bi-direction model" that encodes a sentence.
The Examiner respectfully disagrees. Claims are not rendered patent eligible by the fact that (using existing machine learning technology) they perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved. Courts have consistently held, in the context of computer-assisted methods, that such claims are not made patent eligible under § 101 simply because they speed up human activity. See, e.g., Content Extraction, 776 F.3d at 1347; DealerTrack, 674 F.3d at 1333. Whether the issue is raised at step one or step two, the increased speed and efficiency resulting from use of computers (with no improved computer techniques) do not themselves create eligibility. See, e.g., Trinity Info Media, LLC v. Covalent, Inc., 72 F.4th 1355, 1363 (Fed. Cir. 2023) (rejecting argument that “humans could not mentally engage in the ‘same claimed process' because they could not perform ‘nanosecond comparisons' and aggregate ‘result values with huge numbers of polls and members' ”) (internal citation omitted); Customedia Techs., LLC v. Dish Network Corp., 951 F.3d 1359, 1365 (Fed. Cir. 2020) (holding claims abstract where “[t]he only improvements identified in the specification are generic speed and efficiency improvements inherent in applying the use of a computer to any task”).
A human may "pre-process" a "query" by "(a) tokening text comprised in the query, (b) detecting abbreviations in the text comprised in the query and replacing the detected abbreviations with complete words, (c) removing numerical data, (d) handling one or more special tokens. Applying this exception using a generic computer components (first representation-based model) does not integrate the abstract idea into a practical application.
Applicant further argues:
Furthermore, "similarity metrics" are computed "such that the initial list bridges semantic gaps between the query and answers rather than relying on exact text matching." We respectfully submit that the amendments clearly show that what is being claimed is a technical process and not a mental process. Furthermore, claim 1 recites a concrete technological solution to a technical problem in computer-based technology with constrained components and defined formats, including formatting acceptable to the particular "first representation-based model," which ties the features of claim 1 to particular machine implementation. Claim 1 is not results-oriented since it mandates concrete pre-processing operations, limits "first representation-based model," and requires "similarity metrics" are that "bridge semantic gaps between the query and answers rather than relying on exact text matching."
The Examiner respectfully disagrees. A human may compute similarity metrics between the representation of the pre-processed query and a plurality of representations of a plurality of pre-processed answers of a plurality of existing, previously processed, trouble reports (e.g. a human may process calculate similarity metrics as claimed); and creating an initial list of candidate answers based on the similarity metrics, the initial list of candidate answers comprising a plurality of candidate answers selected from among a plurality of answers of the plurality of existing trouble reports based on the similarity metrics such that the initial list bridges semantic gaps between the query and answers rather than relying on exact text matching (e.g. a human may derive candidate answers from a similar database such that the initial list bridges semantic gaps between the query and answers rather than relying on exact text matching). Therefore, the claims don’t recite a concrete technological solution to a technical problem in computer-based technology. Further, the claims are not tied to a particular machine implementation. The abstract idea is merely applied using generic computer products (first representation-based model).
Therefore, all of Applicant’s arguments regarding the 35 USC 101 have been addressed and they are not persuasive.
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, 5-15, 17, 19-20 and 23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Further, the judicial exception is not integrated into a practical application.
In claims 1 and 23 the limitations a method performed by previously processed, trouble reports; and creating an initial list of candidate answers based on the similarity metrics, the initial list of candidate answers comprising a plurality of candidate answers selected from among a plurality of answers of the plurality of existing trouble reports based on the similarity metrics such that the initial list bridges semantic gaps between the query and answers rather than relying on exact text matching, as drafted, are processes that, under their broadest reasonable interpretation, cover performance of the limitations in the mind but for the recitation of generic computer components.
That is, other than reciting a “computing device” and a “first representation based model … wherein the first representation-based model: (i) is a model that is able to create a semantic representation of a sentence that captures its meaning in a dense vector, (ii) is a model that uses an attention mechanism for understanding and encoding sentences as a whole, (iii) is a bi-directional model that looks at all words in the sentence to encode the sentence, or (iv) is any combination of any two or more of (i)-(iii)” (claim 1) and a “computing device comprising processing circuitry” and a “first representation based model … wherein the first representation-based model: (i) is a model that is able to create a semantic representation of a sentence that captures its meaning in a dense vector, (ii) is a model that uses an attention mechanism for understanding and encoding sentences as a whole, (iii) is a bi-directional model that looks at all words in the sentence to encode the sentence, or (iv) is any combination of any two or more of (i)-(iii)” (claim 23) nothing in the claims precludes the steps from practically being performed in the mind.
For example, a person may obtain a query from a trouble report, the query comprising text; wherein obtaining the query comprises determining a faulty area as a synthesization of a faulty product by mapping the faulty product to the faulty area and including the faulty area within the query as a token that represents a general technology area that the trouble report affects (e.g. a human may obtain a query from a trouble report by determining faulty area derived from mapping faulty product to the faulty area); pre-processing the query to provide a pre-processed query; wherein pre-processing the query comprises (a) tokening text comprised in the query, (b) detecting abbreviations in the text comprised in the query and replacing the detected abbreviations with complete words, (c) removing numerical data, (d) handling one or more special tokens, or (e) any combination of any two or more of (a) - (d) (e.g. a human may pre-process the query for example detecting and replacing abbreviations); apply the pre-processed query to provide a representation of the pre-processed query, wherein the pre-processing of the query is such that the query is formatted in a way that is acceptable to the first representation-based model (e.g. a human may process the pre-processed to derive a format acceptable by a certain model); computing similarity metrics between the representation of the pre-processed query and a plurality of representations of a plurality of pre-processed answers of a plurality of existing, previously processed, trouble reports (e.g. a human may process calculate similarity metrics as claimed); and creating an initial list of candidate answers based on the similarity metrics, the initial list of candidate answers comprising a plurality of candidate answers selected from among a plurality of answers of the plurality of existing trouble reports based on the similarity metrics such that the initial list bridges semantic gaps between the query and answers rather than relying on exact text matching (e.g. a human may derive candidate answers from a similar database such that the initial list bridges semantic gaps between the query and answers rather than relying on exact text matching).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements – a “computing device” and a “first representation based model … wherein the first representation-based model: (i) is a model that is able to create a semantic representation of a sentence that captures its meaning in a dense vector, (ii) is a model that uses an attention mechanism for understanding and encoding sentences as a whole, (iii) is a bi-directional model that looks at all words in the sentence to encode the sentence, or (iv) is any combination of any two or more of (i)-(iii)” (claim 1) and a “computing device comprising processing circuitry” and a “first representation based model … wherein the first representation-based model: (i) is a model that is able to create a semantic representation of a sentence that captures its meaning in a dense vector, (ii) is a model that uses an attention mechanism for understanding and encoding sentences as a whole, (iii) is a bi-directional model that looks at all words in the sentence to encode the sentence, or (iv) is any combination of any two or more of (i)-(iii)” (claim 23) which are recited at a high-level of generality (i.e., as generic processors performing generic computer functions) such that they amount to no more than mere instructions to apply the exception using a generic computer components.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are therefore directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As stated above, the claims recite the additional limitations of a “computing device” and a “first representation based model … wherein the first representation-based model: (i) is a model that is able to create a semantic representation of a sentence that captures its meaning in a dense vector, (ii) is a model that uses an attention mechanism for understanding and encoding sentences as a whole, (iii) is a bi-directional model that looks at all words in the sentence to encode the sentence, or (iv) is any combination of any two or more of (i)-(iii)” (claim 1) and a “computing device comprising processing circuitry” and a “first representation based model … wherein the first representation-based model: (i) is a model that is able to create a semantic representation of a sentence that captures its meaning in a dense vector, (ii) is a model that uses an attention mechanism for understanding and encoding sentences as a whole, (iii) is a bi-directional model that looks at all words in the sentence to encode the sentence, or (iv) is any combination of any two or more of (i)-(iii)” (claim 23). However, these are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications (see Applicant’s specification page 29, lines 18-38 and page 30, line 24 to page 31, line 2). Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
The dependent claims, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea.
The dependent claims recite:
wherein the first representation-based model is a first representation-based Bidirectional Encoder Representation from Transformer, BERT, model.
wherein the first representation-based model is a first sentence-Bidirectional Encoder Representation from Transformer, BERT, model; a first Expansion via Prediction of Importance with Contextualization, EPIC, model; a first Representation-focused BERT, RepBERT, model; a first Approximate nearest neighbor Negative Contrastive Learning, ANCE, model, or a first Contextualized Late interaction over BERT, ColBERT, model.
further comprising: pre-processing a plurality of answers of the plurality of existing trouble reports to provide the plurality of pre-processed answers; and applying the plurality of pre-processed answers to a second representation-based model to provide the plurality of representations of the plurality of pre-processed answers;
wherein: pre-processing the plurality of answers and applying the plurality of pre-processed answers to the second representation-based model are performed prior to obtaining the query; and the method further comprises storing the plurality of representations of the plurality of pre-processed answers;
wherein the first representation-based model and the second representation-based model are the same representation-based BERT model.
wherein the first representation-based model and the second representation-based model are the same sentence-BERT model, the same EPIC model, the same RepBERT model, the same ANCE model, or the same ColBERT model;
further comprising performing a re-ranking scheme that selects a subset of the plurality of candidate answers comprised in the initial list of candidate answers to provide a ranked list of candidate answers;
wherein performing the re-ranking scheme comprises applying the pre-processed query and the initial list of candidate answers to a BERT-based re-ranker model to provide the ranked list of candidate answers;
wherein the BERT-based re-ranker model is a monoBERT model, a duoBERT model, or a Contextualized Embeddings for Document Ranking, CEDR, model;
wherein the BERT-based re-ranker model comprises an ensemble of BERT-based models;
determining whether the trouble report is a duplicate trouble report based on the ranked list of candidate answers;
wherein the query comprises text from an observation of the trouble report or text from a header of the trouble report;
wherein obtaining the query comprises determining a faulty area based on information about a product involved in the trouble report and including the faulty area within the query;
wherein the similarity metrics are cosine similarity metrics, inner product metrics, or Euclidean distance metrics.
The additional recited limitations further narrow the steps of the independent claims without however providing “a practical application of” or "significantly more than" the underlying “Mental Processes” abstract idea. Therefore, the dependent claims are also not patent eligible.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAMUEL G NEWAY whose telephone number is (571)270-1058. The examiner can normally be reached Monday-Friday 9:00am-5:00pm EST.
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/SAMUEL G NEWAY/ Primary Examiner, Art Unit 2657