DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments with respect to 35 U.S.C. 101 in regards to claims 18-29 have been considered, however are not found to be persuasive due to the following reasons.
Claims 18-29 are still rejected under 35 U.S.C. 101 because, under USPTO Step 2A, it is directed to an abstract idea—specifically, a mathematical concept and information analysis. The claim’s core operation is converting text into vectors and training a model by minimizing vector angles (i.e., optimizing a mathematical distance/similarity relationship). The USPTO’s 2019 Revised Guidance expressly identifies “mathematical concepts—mathematical relationships, mathematical formulas or equations, and mathematical calculations” as an abstract idea. In addition, the claim’s overall focus is on using data (claim/spec text), transforming it into numerical representations, and optimizing a similarity measure, which aligns with cases characterizing claims as abstract when they focus on “collecting information, analyzing it” and producing results, rather than any specific inventive technology for doing so.
Claims also fail USPTO Step 2A (Prong Two) and Step 2B because it is not meaningfully integrated into a practical application and lacks an inventive concept. The additional elements—“computer-implemented,” providing patent documents, identifying blocks of text, providing a “machine learning model,” and training on paired data—amount to generic data preparation and generic computer/ML implementation steps that do not impose a meaningful limit on the abstract mathematical optimization itself. The Supreme Court has held that “merely requiring generic computer implementation fails to transform” an abstract idea into a patent-eligible invention. Similarly, limiting the abstraction to a particular field of use (here, “patent search or novelty evaluation”) is not enough, because such narrowing does not add an inventive concept to the abstract idea. And Federal Circuit precedent recognizes that claims can remain ineligible where the “innovation” lies in mathematical processing of selected information without a non-abstract technological improvement—i.e., “nothing but a series of mathematical calculations … and the presentation of the results,” with “nothing ‘inventive’” in the non-abstract application realm. Therefore, still rejected.
Applicant's filling of a Terminal Disclaimer with respect to Double Patenting of claims 18-29 have been considered and found persuasive, and the rejection has been withdrawn.
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 18-29 are rejected under 35 U.S.C. 101:
Claims 18 and 24 are rejected because the claimed invention is directed to an abstract idea because it recites mathematical concepts and data manipulation steps: converting text blocks (claims/specifications) into vectors and training the model to minimize vector angles between paired vectors from the same document. These recited operations are fundamentally a mathematical relationship/calculation (e.g., optimizing similarity or angle between vectors) applied to information (text).
The claim are not found integrated into a practical application, because the additional elements are recited at a high level of generality and primarily amount to “apply the math to patent text” for training a model. The steps of “providing a plurality of patent documents,” “providing a machine learning model configured to convert claims and specifications into vectors,” and “training…using a training data set” would be characterized as generic ML training workflow steps that do not recite a specific technological implementation or a specific improvement to computer functionality beyond the abstract mathematical optimization itself.
The claim would be found to lack an inventive concept because the additional limitations (collecting patents, separating into blocks, embedding to vectors, and optimizing similarity/angle) would be treated as routine and conventional data processing / ML training operations performed on a computer, without additional claim elements that add “significantly more” than the abstract idea itself.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims are (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. There is further no improvement to the computing device.
Dependent claims 19-23 and 25-29 are further recite an abstract idea performable by a human and do not amount to significantly more than the abstract idea as they do not provide steps other than what is conventionally known in document analysis.
Claims 19 and 25: directed to an abstract idea because it merely expands the training data to include text pairs linked by a “database reference,” which is still just organizing/associating information and performing mathematical similarity training on vectors.
Claims 20 and 26: directed to the mathematical concept of optimizing vector relationships because it adds a training objective to maximize vector angles / enforce non-zero angles for unrelated cross-document pairs (i.e., a negative-pair separation objective).
Claim 21 and 27: it recites an additional training target to enforce non-zero vector angles between unrelated claim/specification blocks from different documents—again a mathematical constraint on embeddings.
Claims 22 and 28: it merely narrows the input “claim block” to an independent claim, which is a data selection/partitioning rule applied to text before embedding.
Claims 23 and 29: it selects the “claim block” as an independent claim plus a dependent claim, which is again just choosing/arranging information for the same embedding-and-angle-optimization training.
Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter (Application needs to overcome the 101): Khamis et al. (US 10,073,890) in view of Gu et al. (“Deep Coe Search”; ICSE May 27-June 3, 2018, Gothenbury, Sweden) teach:
Khamis is relevant because it teaches patents as structured documents with identifiable sections (e.g., “Summary,” “Detailed Description,” “Claims,” etc.) and performs semantic comparison across those sections (i.e., it discloses processing patent content in section/block form and comparing (at least) “claims” and “description.”). For example, Khamis explains comparing an input to a reference using “the same sections of a patent or patent application (Summary, Figures, Detailed Description of the Figures, Claims, etc.)” and iterating through those sections to find differences, and separately notes that differences may be determined “between the claims and description of two references following a ‘patent’ format.” Khamis further describes conducting an optional literature search using “concepts found in the input patent,” loading other patents in the same category, and displaying comparison results including sub-section differences (e.g., “comparison of the abstract” and “comparison of the claims”).
Gu is relevant because it provides a specific training model for learning joint embeddings: its model training goal is that if two items “have similar semantics, their embedded vectors should be close to each other,” and it trains using paired inputs (code snippet + correct description + an incorrect description) while optimizing a loss that drives higher cosine similarity for the correct pair than for the incorrect pair. This “make matched-pairs close in vector space by cosine similarity” teaching is the most direct analogue to Claim 18’s “learning target…to minimize vector angles between [paired] vectors.”
The difference between the prior art and the claimed invention is that Khamis nor Gu explicitly teach Applicant’s claims requirements: (i) patent documents having a “computer-identifiable claim block” and “computer-identifiable specification block” (including at least part of the patent description), (ii) a machine-learning model that converts claims and specifications into vectors, and (iii) training the model using pairs of claim blocks and specification blocks from the same patent document as training cases, with the learning target being to “minimize vector angles between claim and specification vectors of the same patent document.”
Khamis supports the patent-search/novelty-evaluation setting and teaches section-based processing/comparison of patents including “Claims” and “Detailed Description,” but it does not expressly teach training a vector-embedding model for claim/specification alignment or minimizing vector angles as a training target.
Gu teaches vector embedding + cosine similarity and training to make matched pairs close in vector space, but Gu’s disclosure is in the code-search domain (code vs. natural-language descriptions) and does not expressly describe (a) patent documents, (b) claim blocks and specification blocks, or (c) using same-patent claim/specification pairs as the training cases.
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.
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SHREYANS A. PATEL
Primary Examiner
Art Unit 2653
/SHREYANS A PATEL/Examiner, Art Unit 2659