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 .
DETAILED ACTION
Response to Arguments
Applicant's arguments with respect to claims 1-19 have been considered but are moot in view of the new ground(s) of rejection. Applicant’s arguments are directed to the amended subject matter; new prior art is provided below.
Note: The claims are not directed towards patent ineligible subject matter under 35 U.S.C. 101
Step 1: IS THE CLAIM DIRECTED TO A PROCESS, MACHINE, MANUFACTURE OR COMPOSITION OF MATTER?
Yes
Step 2A.1: IS THE CLAIM DIRECTED TO A LAW OF NATURE, A NATURAL PHENOMENON (PRODUCT OF NATURE) OR AN ABSTRACT IDEA?
No
Step 2A.2: DOES THE CLAIM RECITE ADDITIONAL ELEMENTS THAT INTEGRATE THE JUDICIAL EXCEPTION INTO A PRACTICAL APPLICATION?
Yes, if the claims are alternatively construed to be abstract in step 2A1. The claims seek to improve document term recognition supported by the specification, and reflected by the claims e.g. in spec: 0025 0048 0053 0072 In other words, the claims enable the invention to improve predictive performance at a faster rate with more accuracy.
Supported by the following:
In Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299, 125 USPQ2d 1282 (Fed. Cir. 2018), the claimed invention was a method of virus scanning that scans an application program, generates a security profile identifying any potentially suspicious code in the program, and links the security profile to the application program. 879 F.3d at 1303-04, 125 USPQ2d at 1285-86. The Federal Circuit noted that the recited virus screening was an abstract idea, and that merely performing virus screening on a computer does not render the claim eligible. 879 F.3d at 1304, 125 USPQ2d at 1286. The court then continued with its analysis under part one of the Alice/Mayo test by reviewing the patent’s specification, which described the claimed security profile as identifying both hostile and potentially hostile operations. The court noted that the security profile thus enables the invention to protect the user against both previously unknown viruses and “obfuscated code,” as compared to traditional virus scanning, which only recognized the presence of previously-identified viruses. The security profile also enables more flexible virus filtering and greater user customization. 879 F.3d at 1304, 125 USPQ2d at 1286. The court identified these benefits as improving computer functionality, and verified that the claims recite additional elements (e.g., specific steps of using the security profile in a particular way) that reflect this improvement. Accordingly, the court held the claims eligible as not being directed to the recited abstract idea. 879 F.3d at 1304-05, 125 USPQ2d at 1286-87. This analysis is equivalent to the Office’s analysis of determining that the additional elements integrate the judicial exception into a practical application at Step 2A Prong Two, and thus that the claims were not directed to the judicial exception (Step 2A: NO).
Examples of claims that improve technology and are not directed to a judicial exception include: Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339, 118 USPQ2d 1684, 1691-92 (Fed. Cir. 2016) (claims to a self-referential table for a computer database were directed to an improvement in computer capabilities and not directed to an abstract idea); McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1315, 120 USPQ2d 1091, 1102-03 (Fed. Cir. 2016) (claims to automatic lip synchronization and facial expression animation were directed to an improvement in computer-related technology and not directed to an abstract idea); Visual Memory LLC v. NVIDIA Corp., 867 F.3d 1253,1259-60, 123 USPQ2d 1712, 1717 (Fed. Cir. 2017) (claims to an enhanced computer memory system were directed to an improvement in computer capabilities and not an abstract idea); Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299, 125 USPQ2d 1282 (Fed. Cir. 2018) (claims to virus scanning were found to be an improvement in computer technology and not directed to an abstract idea); SRI Int’l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1303 (Fed. Cir. 2019) (claims to detecting suspicious activity by using network monitors and analyzing network packets were found to be an improvement in computer network technology and not directed to an abstract idea). Additional examples are provided in MPEP § 2106.05(a).
Regarding the December 5th 2025 Memo in light of September 26, 2025 Appeals Review Panel Decision in Ex parte Desjardins, Appeal 2024-000567 for Application 16/319,040, in deciding if a recited abstract idea does or does not direct the entire claim to an abstract idea, when a claim is considered as a whole:
Paragraph 21 of the Specification, which the Appellant cites, identifies improvements in training the machine learning model itself. Of course, such an assertion in the Specification alone is insufficient to support a patent eligibility determination, absent a subsequent determination that the claim itself reflects the disclosed improvement. See MPEP § 2106.05(a) (citing Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316 (Fed. Cir. 2016)). Here, however, we are persuaded that the claims reflect such an improvement. For example, one improvement identified in the 8 Appeal2024-000567 Application 16/319,040 Specification is to "effectively learn new tasks in succession whilst protecting knowledge about previous tasks." Spec. ,r 21. The Specification also recites that the claimed improvement allows artificial intelligence (AI) systems to "us[e] less of their storage capacity" and enables "reduced system complexity." Id. When evaluating the claim as a whole, we discern at least the following limitation of independent claim 1 that reflects the improvement: "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task." We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation. Under a charitable view, the overbroad reasoning of the original panel below is perhaps understandable given the confusing nature of existing § 101 jurisprudence, but troubling, because this case highlights what is at stake. Categorically excluding AI innovations from patent protection in the United States jeopardizes America's leadership in this critical emerging technology. Yet, under the panel's reasoning, many AI innovations are potentially unpatentable-even if they are adequately described and nonobvious-because the panel essentially equated any machine learning with an unpatentable "algorithm" and the remaining additional elements as "generic computer components," without adequate explanation. Dec. 24. Examiners and panels should not evaluate claims at such a high level of generality.
Specifically, Ex Parte Desjardins explained the following:
Enfish ranks among the Federal Circuit's leading cases on the eligibility of technological improvements. In particular, Enfish recognized that “[m]uch of the advancement made in computer technology consists of improvements to software that, by their very nature, may not be defined by particular physical features but rather by logical structures and processes.” 822 F.3d at 1339. Moreover, because “[s]oftware can make non-abstract improvements to computer technology, just as hardware improvements can,” the Federal Circuit held that the eligibility determinations should turn on whether “the claims are directed to an improvement to computer functionality versus being directed to an abstract idea.” Id. at 1336. (Desjardins, page 8).
Further in Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), the claimed invention was a method of training a machine learning model on a series of tasks. The Appeals Review Panel (ARP) overall credited benefits including reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks as technological improvements that were disclosed in the patent application specification. Specifically, the ARP upheld the Step 2A Prong One finding that the claims recited an abstract idea (i.e., mathematical concept). In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” reflected the improvement disclosed in the specification. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were
The claim itself does not need to explicitly recite the improvement described in the specification (e.g., “thereby increasing the bandwidth of the channel”). See, e.g., Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), in which the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. Indeed, enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220327173 A1 RAJPARA; Vishalkumar (hereinafter RAJPARA) in view of US 20240135740 A1 Nakshathri; Srirama R (hereinafter Nakshathri) and further in view of US 20220083733 A1 ZHANG; Zhenzhong (hereinafter ZHANG)
Re claim 1, RAJPARA teaches
1. A method for building predictive machine learning models, comprising: (model is generated/updated based on inputs using document tagging and review of documents thereof 0074-0077)
parsing text of a document review protocol in order to extract at least one description of at least one concept to be tagged; (parsing text to identify a concept/term for tagging that is case or genre specific such as name of person or company fig. 5 with 0038… model is generated/updated based on inputs using document tagging and review of documents thereof 0074-0077)
constructing at least one classifier machine learning model based on the extracted at least one description and the knowledge base, as extracted; and (a data repository as a knowledge base per se 0058 classification with a model is a classifier per se 0081, training using tags, user interaction queries model rules and outputs tags and settings thereof 0058 with fig. 4, 61, and 6b, utilizing parsing text to identify a concept/term for tagging that is case or genre specific such as name of person or company fig. 5 with 0038… model is generated/updated based on inputs using document tagging and review of documents thereof 0074-0077)
training the at least one classifier machine learning model using a training set, wherein the training set includes the plurality of tagged documents. (classification with a model is a classifier per se 0081, training using tags, user interaction queries model rules and outputs tags and settings thereof 0058 with fig. 4, 61, and 6b, utilizing parsing text to identify a concept/term for tagging that is case or genre specific such as name of person or company fig. 5 with 0038… model is generated/updated based on inputs using document tagging and review of documents thereof 0074-0077)
However, while RAJPARA teaches learning models and classification, tagging, reviewing docs, and grouping documents thereof, it fails to teach:
tagging at least a portion of a plurality of documents in order to create a plurality of tagged documents by applying a language model to the plurality of documents, wherein tagging the at least a portion of the plurality of documents further comprises querying the language model using at least one query generated based on the extracted at least one description; (Nakshathri a learning model queries a language model with document tagging and review 0081, with a language model 0090 vectorized 0099)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of RAJPARA to incorporate the above claim limitations as taught by Nakshathri to allow for combining prior art elements according to known methods to yield predicate results, such as a known use of a language model + learning model of Nakshathri with the existing learning model combined to cause interaction/retrieval/querying from learning model to user and the language model as a medium for linguistic purposes, analogous with the meaning extraction in RAJPARA, to at least produce improved context recognition and better grouping of words for descriptions of documents or their respective text.
However, while the combination teaches synonyms of terms on a case-specific or context-specific basis, which may read upon the claims, in lieu of official notice, the combination fails to teach normalization per se:
extracting a knowledge base of facts from case data, wherein the knowledge base includes case-specific entities and relationships, and wherein extracting the knowledge base includes normalizing and linking different names to a same individual; (ZHANG normalize multiple expressions corresponding to the same entity 0060 and 0044)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of RAJPARA in view of Nakshathri to incorporate the above claim limitations as taught by ZHANG to allow for combining prior art elements according to known methods to yield predicate results, such as a known use of a language model with normalized named entity recognition such that various cases e.g. in the medical field, where disease, drug, and treatment can utilize terms that could be missed if the classification differs but the term is important applied to a persons name (entity) as in RAJPARA such as title and company can be relative, now normalized in sub-contexts by ZHANG,
Re claim 10, this claim has been rejected for teaching a broader, or narrower claim based on general inclusion of hardware alone (e.g. processor, memory, instructions), representation of claim 1 omitting/including hardware for instance, otherwise amounting to a virtually identical scope.
Re claim 11, this claim has been rejected for teaching a broader, or narrower claim based on general inclusion of hardware alone (e.g. processor, memory, instructions), representation of claim 1 omitting/including hardware for instance, otherwise amounting to a virtually identical scope.
See fig. 1a of RAJPARA
Re claims 2 and 12, RAJPARA teaches
2. The method of claim 1, wherein parsing the document review protocol further comprises:
determining a structure of the document review protocol; (assigning rules such as in fig. 6a and 6b)
identifying the at least one concept to be tagged based on the structure of the document review protocol; and (use the structure rules in fig. 6a and 6b, training using tags, user interaction queries model rules and outputs tags and settings thereof 0058 with fig. 4, 6a, and 6b, utilizing parsing text to identify a concept/term for tagging that is case or genre specific such as name of person or company fig. 5 with 0038… model is generated/updated based on inputs using document tagging and review of documents thereof 0074-0077)
parsing out text indicating at least one rule for identifying the at least one concept to be tagged within the plurality of documents based on the structure of the document review protocol and the identified at least one concept to be tagged; and (utilizing parsing text to identify a concept/term for tagging that is case or genre specific such as name of person or company fig. 5 with 0038…user interaction queries model rules and outputs tags and settings thereof 0058 with fig. 4, 6a, and 6b, model is generated/updated based on inputs using document tagging and review of documents thereof 0074-0077)
extracting the parsed out text. (following completion of search, results shown as in fig. 7, utilizing parsing text to identify a concept/term for tagging that is case or genre specific such as name of person or company fig. 5 with 0038…user interaction queries model rules and outputs tags and settings thereof 0058 with fig. 4, 6a, and 6b, model is generated/updated based on inputs using document tagging and review of documents thereof 0074-0077)
Re claims 3 and 13, RAJPARA teaches
3. The method of claim 1, wherein the at least one concept to be tagged includes at least one case-specific concept indicated in the document review protocol. (identify a concept/term for tagging that is case or genre specific such as name of person or company fig. 5 with 0038… model is generated/updated based on inputs using document tagging and review of documents thereof 0074-0077 supplemented with fig. 6a-6b)
Re claims 4 and 14, RAJPARA teaches
4. The method of claim 1, further comprising: determining a score for each document of the plurality of documents based on the extracted at least one description of the at least one concept to be tagged, wherein the determined score for each document represents a likelihood that the document includes text indicating at a portion of the at least one concept to be tagged. (scoring as in probabilistic weight/scores 0005… classification with a model is a classifier per se 0081, training using tags, user interaction queries model rules and outputs tags and settings thereof 0058 with fig. 4, 6a, and 6b, utilizing parsing text to identify a concept/term for tagging that is case or genre specific such as name of person or company fig. 5 with 0038… model is generated/updated based on inputs using document tagging and review of documents thereof 0074-0077)
Re claims 5 and 15, RAJPARA teaches
5. The method of claim 1, further comprising: updating the at least one classifier machine learning model based on feedback data until each of at least one performance metric for the at least one classifier machine learning model meets a respective performance threshold. (using a threshold for a review total for instance as a performance metric as user provides feedback for tagging and review 0078… classification with a model is a classifier per se 0081, training using tags, user interaction queries model rules and outputs tags and settings thereof 0058 with fig. 4, 6a, and 6b, utilizing parsing text to identify a concept/term for tagging that is case or genre specific such as name of person or company fig. 5 with 0038… model is generated/updated based on inputs using document tagging and review of documents thereof 0074-0077)
6. The method of claim 5, wherein the feedback data includes at least one feedback tag for the plurality of documents. (user provides feedback for tagging and review 0078… training using tags, user interaction queries model rules and outputs tags and settings thereof 0058 with fig. 4, 6a, and 6b, utilizing parsing text to identify a concept/term for tagging that is case or genre specific such as name of person or company fig. 5 with 0038… model is generated/updated based on inputs using document tagging and review of documents thereof 0074-0077)
Re claims 7 and 17, RAJPARA teaches
7. The method of claim 5, wherein the feedback data includes at least one feedback modification to the document review protocol. (modifying parameters as in in fig. 5 with 0085 and 0074, as user changes tags, new sets are presented, e.g. fig. 14 new batches come based on tag changes such as subsets of documents e.g. 0009 and 0030… and user provides feedback for tagging and review 0078… training using tags, user interaction queries model rules and outputs tags and settings thereof 0058 with fig. 4, 6a, and 6b, utilizing parsing text to identify a concept/term for tagging that is case or genre specific such as name of person or company fig. 5 with 0038… model is generated/updated based on inputs using document tagging and review of documents thereof 0074-0077)
Re claims 8 and 18, RAJPARA teaches
8. The method of claim 7, wherein each document of the at least a portion of the plurality of documents is tagged with a respective first tag, further comprising: (Nth number of different parameters selected as in in fig. 5 with 0085 and 0074, as user changes tags, new sets are presented, e.g. fig. 14 new batches come based on tag changes such as subsets of documents e.g. 0009 and 0030… and user provides feedback for tagging and review 0078… training using tags, user interaction queries model rules and outputs tags and settings thereof 0058 with fig. 4, 6a, and 6b)
determining a second tag for each document of the at least a portion of the plurality of documents based on the at least one feedback modification to the document review protocol; (Nth number of different parameters selected as in in fig. 5 with 0085 and 0074, as user changes tags, new sets are presented, e.g. fig. 14 new batches come based on tag changes such as subsets of documents e.g. 0009 and 0030… and user provides feedback for tagging and review 0078… training using tags, user interaction queries model rules and outputs tags and settings thereof 0058 with fig. 4, 6a, and 6b)
identifying at least one first document to be reviewed from among the plurality of documents, wherein the second tag each first document is different from the first tag for the first document; (Nth number of different parameters selected as in in fig. 5 with 0085 and 0074, as user changes tags, new sets are presented, e.g. fig. 14 new batches come based on tag changes such as subsets of documents e.g. 0009 and 0030… and user provides feedback for tagging and review 0078… training using tags, user interaction queries model rules and outputs tags and settings thereof 0058 with fig. 4, 6a, and 6b)
presenting the at least one first document to a user for review; and (fig. 7 presenting results base on modification in fig. 5-6b)
re-tagging the plurality of documents based on the review. (modifying parameters as in in fig. 5 with 0085 and 0074, as user changes tags, new sets are presented, e.g. fig. 14 new batches come based on tag changes such as subsets of documents e.g. 0009 and 0030… and user provides feedback for tagging and review 0078… training using tags, user interaction queries model rules and outputs tags and settings thereof 0058 with fig. 4, 6a, and 6b, utilizing parsing text to identify a concept/term for tagging that is case or genre specific such as name of person or company fig. 5 with 0038… model is generated/updated based on inputs using document tagging and review of documents thereof 0074-0077)
Re claims 9 and 19, RAJPARA teaches
9. The method of claim 1, further comprising:
iteratively determining a subset of the plurality of documents to be labeled based on user inputs and querying a user based on the subset of documents determined at each iteration, wherein the user provides the user inputs indicating labels based on the subset of documents queried at each iteration; and (modifying parameters as in in fig. 5 with 0085 and 0074, as user changes tags, new sets or subsets per se are presented e.g. a narrower set, e.g. fig. 14 new batches come based on tag changes such as subsets of documents e.g. 0009 and 0030… and user provides feedback for tagging and review 0078… training using tags, user interaction queries model rules and outputs tags and settings thereof 0058 with fig. 4, 6a, and 6b, utilizing parsing text to identify a concept/term for tagging that is case or genre specific such as name of person or company fig. 5 with 0038… model is generated/updated based on inputs using document tagging and review of documents thereof 0074-0077)
tagging the determined subset of documents based on the labels indicated in the user inputs. (labeling e.g. 0029… modifying parameters as in in fig. 5 with 0085 and 0074, as user changes tags, new sets or subsets per se are presented e.g. a narrower set, e.g. fig. 14 new batches come based on tag changes such as subsets of documents e.g. 0009 and 0030… and user provides feedback for tagging and review 0078… training using tags, user interaction queries model rules and outputs tags and settings thereof 0058 with fig. 4, 6a, and 6b, utilizing parsing text to identify a concept/term for tagging that is case or genre specific such as name of person or company fig. 5 with 0038… model is generated/updated based on inputs using document tagging and review of documents thereof 0074-0077)
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 extension fee 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 date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 8560378 B1 Kibbe; Laura M.
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