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 Abstract Idea in regards to claims 18-20, 22-26, 28-37 have been considered, however are not found to be persuasive due to the following reasons. Examiner respectfully disagrees with Applicant’s arguments (arguing that the claims improves a technology or technical field) because when stripped of the technical language, the claims describe the process of reading patent documents, separating the claims from the descriptions, and mathematically comparing them to find language similarities. This is fundamentally a mental process and a method of organizing information, tasks that a human patent examiner or researcher could theoretically do in their mind or with pen and paper. Under patent law, you cannot patent a basic mental concept, a mathematical algorithm, or a routine way of organizing data simply because you are using AI to process the text.
Furthermore, the claims do not integrate this abstract idea into a practical, technical application. To be patentable, a software claim needs to solve a specific technical problem or actually improve how a computer functions. However, these claims use standard computer technology as a tool to execute the text comparison idea. Merely telling a computer to run a generic “machine learning model” on a set of documents doesn’t upgrade the computer’s underlying functionality nor does it transform the abstract idea into a concrete patent-eligible invention. Therefore, the claims stand rejected.
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 18-20, 22-26, 28-37 are rejected under 35 U.S.C. 101.
Claims 18 and 24 are rejected under 35 U.S.C. 101 because they are directed to an abstract idea. When stripped of the technical language, the claims describe the process of reading patent documents, separating the claims from the descriptions, and mathematically comparing them to find language similarities. This is fundamentally a mental process and a method of organizing information, tasks that a human patent examiner or researcher could theoretically do in their mind or with pen and paper. Under patent law, you cannot patent a basic mental concept, a mathematical algorithm, or a routine way of organizing data simply because you are using AI to process the text.
Furthermore, the claims do not integrate this abstract idea into a practical, technical application. To be patentable, a software claim needs to solve a specific technical problem or actually improve how a computer functions. However, these claims use standard computer technology as a tool to execute the text comparison idea. Merely telling a computer to run a generic “machine learning model” on a set of documents doesn’t upgrade the computer’s underlying functionality nor does it transform the abstract idea into a concrete patent-eligible invention.
The claims lack any additional “inventive concept” that would transform it into something significantly more than just the abstract idea itself. The steps listed, gathering document data, providing a generic machine learning model, and training it on a dataset, are highly routine, conventional activities in the field of computer science. Because the claims rely entirely what they are normally programmed to do (processing data and finding patters), it fails patent eligibility test and is rejected.
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 19-20, 22-26 and 28-37 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.
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.
Claims 30 and 34: it merely representing information mathematically, without reciting a specific improvement to computer or machine learning technology.
Claims 31 and 35: an abstract idea performed by a generic computer implementation.
Claims 32 and 36: only a mathematical separation rule for data, not a practical technological improvement.
Claims 33 and 37: does not add significantly more than the abstract idea.
Allowable Subject Matter
Claims 18-20, 22-26 and 28-37 would be allowable if the Applicant can overcome the 101 Abstract Idea.
The following is a statement of reasons for the indication of allowable subject matter:
Knight et al. (US 10,713,443) in view of Lio et al. (US 9,110,971) in view of Huang et al. (“Learning Deep Structured Semantic Models for Web Search using Clickthrough Data”; 2013):
Knight et al. teaches obtaining a plurality of patent documents, each including claims and specification, parsing the claims, aligning claim features to corresponding specification descriptions in the same patent document, and training a machine-learning model on the resulting claim/specification pairs. Knight further teaches that the ML model may include a sequence-to-sequence neural model, recurrent neural network, convolutional neural network, and machine-translation-type model and that objective functions include likelihood maximization.
Liao et al. teaches applying machine learning to patent retrieval and validity/prior-art searching. Liao’s system uses claim text as patent-search query, re-ranks candidate patent documents, uses automatically generated training data, and identifies patent documents as having separately searchable fields including claims and description/specification.
Huang et al. teaches the semantic-search training objective: a DNN maps two natural-language items, such as a query and a document, into a common semantic space, computes relevance by cosine similarity of their semantic vectors, and trains the model by maximizing the likelihood of positive query-document pairs.
The difference between the prior art and the claimed invention is that Knight et al., Liao et al, nor Huang et al. explicitly teach to convert 1) the one or more first natural language units of each patent document into one or more third natural language units, and 2) the one or more second natural language units of each patent document into one or more fourth natural language units.
Therefore, it would not have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teachings of Knight et al., Liao et al and/or Huang et al. to include to convert 1) the one or more first natural language units of each patent document into one or more third natural language units, and 2) the one or more second natural language units of each patent document into one or more fourth natural language units.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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
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