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 .
This office action is in response to the claimed amendment filed on June 11, 2026, in which claim 1 is canceled and claims 2-21 are presented for further examination.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on June 11, 2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to claims 2-21 have been considered but are moot in view of a new ground of rejection necessitated by amendment.
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 2-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract without significantly more.
Step 1, Statutory Category:
Claims 2-11 are directed to a method
Claims 12-21 are directed to a computer system.
Therefore, claims 2-21 fall into at least one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
Step 2A, Prong One (Judicial exception recited)
The limitation “generating, utilizing the first classification model, first data identifying a first subset of sample documents determined to be in class associated with an identified technology, wherein generating the first data comprises generating document embeddings as vectors of floating point numbers representing text contents of the sample documents in a coordinate system, and determining whether vector representations of the sample documents are closer to in-class vectors than to out-of- class vectors in the coordinate system, wherein the first classification model is associated with a first confidence threshold score indicating a first degree of confidence for predicting a given document as in class, wherein the first classification model utilizes computer-centric transfer learning to generate the first data” in claims 2 and 12, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. One can manually generate a first data identifying a first subset of sample documents determined to be in class associated with an identified technology.
The limitation “generating a second classification model configured to identify the in-class document subsets, wherein the second classification model is built utilizing information obtained while generating the first data” in claims 2 and 12, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. One can manually generate a second classification model configured to identify the in-class document subsets.
The limitation “generating, utilizing the second classification model, second data identifying a second subset of sample documents determined to be in class, wherein the second classification model utilizes computer-centric transfer learning to generate the second data” in claims 2 and 12, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. One can manually generate a second data identifying a second subset of sample documents determined to be in class.
The limitation “generating third data indicating a third subset of sample documents that are in the first subset and the second subset” in claims 2 and 12, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. One can manually generate a second data identifying a third data indicating a third subset of sample documents.
The limitation “updating the user interface upon receiving user input data indicating a second confidence threshold score to apply to at least the first classification model, wherein the user input data further indicates that a keyword is to be added to or removed from the included keyword window or the excluded keyword window, and wherein the first classification model is retrained based at least in part on the user input data, the user input data in response to the keywords as displayed via the user interface.” in claims 2 and 12, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation, judgement or/and mathematical formula, but for the recitation of generic computer components. One can manually update the user interface upon receiving user input data indicating a second confidence threshold score to apply to at least the first classification model.
At Step 2A, Prong Two:
The claim recites the following additional elements:
That the method is "implemented by a computing system" is a high-level recitation of a generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
The limitation “displaying, to a user, a user interface comprising an included keyword window and an excluded keyword window, wherein the included keyword window is configured to display keywords from documents predicted as in class by at least the first classification model utilizing the first confidence threshold score, and the excluded keyword window is configured to display keywords from documents predicted as out of class” recites insignificant extra-solution activity such as mere outputting of the result. The mere outputting of data does not meaningfully limit the abstract idea. Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application. (See MPEP 2106.05 (g)).
The limitation “one or more processors and non-transitory computer-readable media” are recited at a high level of generality such that they amount to on more than mere instructions to apply the exception using a generic component. (see MPEP 2106.05(f)). These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer (see MPEP 2106.05(h)). Note, the mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application.
Step 2B (claim provides an inventive concept):
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
With respect to the "for display …." identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional in displaying information as evidenced by the court cases in MPEP 2106.05(d)(II), " iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93" and "i. … transmitting data over a network, …Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … 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)".
With respect to the “one or more processors and non-transitory computer-readable media” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
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.
Looking at the claim as a whole does not change this conclusion and the claim appears to be ineligible.
Accordingly, claim 2 is directed to an abstract idea. The remaining independent claim 12 falls short the 35 USC 101 requirement under the same rationale.
The dependent claims 3-11 and 13-21 when analyzed and each taken as a whole are held to be patent ineligible under 35 USC 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea.
Claim 3 recites “generating a third classification model configured to identify documents that are relevant to a subcategory associated with the identified technology; generating, utilizing the third classification model, fourth data identifying a fourth subset of the sample documents determined to be in class; and wherein the third subset includes the sample documents that are in the first subset, the second subset, and the fourth subset”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception. The same analysis applies to claim 13.
Claim 4 recites “determining, for individual ones of the sample documents, a claim score for claims of the individual ones of the sample documents; and determining a fourth subset of the sample documents that are in the third subset and have a claim score that satisfies a threshold claim score.”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception. The same analysis applies to claim 14.
Claim 5 recites “applying the second confidence threshold to the first classification model instead of the first confidence threshold score”. This additional element is recited at a high level of generality and would function in its ordinary capacity for applying the second confidence threshold to the first classification model instead of the first confidence threshold score, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more. The same analysis applies to claim 15.
Claim 6 recites “generating first vectors representing the sample documents associated with the third subset in a coordinate system; determining an area of the coordinate system associated with the first vectors; and identifying additional documents represented by second vectors in the coordinate system that are within the area”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception. The same analysis applies to claim 16.
Claim 7 recites “generating a third classification model configured to identify the sample documents that are relevant to a subcategory associated with the identified technology; generating, utilizing the third classification model, fourth data identifying a fourth subset of the sample documents determined to be in class; and wherein the third subset includes the sample documents that are in at least one of: the first subset and the second subset; the second subset and the fourth subset; or the first subset and the fourth subset”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception. The same analysis applies to claim 17.
Claim 8 recites “storing a model hierarchy of models including the first classification model and the second classification model, the model hierarchy indicating relationships between the first and second classification models; and generating an indicator that in-class prediction of documents for the identified technology is performed utilizing the first classification model and the second classification model”. This additional element is recited at a high level of generality and would function in its ordinary capacity for storing a model hierarchy of models including the first classification model and the second classification model, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more. The same analysis applies to claim 18.
Claim 9 recites “receiving a search query for a model to utilize from the model hierarchy; determining that the search query corresponds to the identified technology; and providing response data to the search query representing the indicator instead of the first classification model and the second classification model”. This additional element is recited at a high level of generality and would function in its ordinary capacity for receiving a search query for a model to utilize from the model hierarchy, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more. . The same analysis applies to claim 19.
Claim 10 recites “generating the first classification model trained utilizing transfer learning techniques and configured to identify documents that are relevant to a first subcategory associated with the identified technology; and generating the second classification model trained utilizing transfer learning techniques and configured to identify the documents that are relevant to a second subcategory associated with the identified technology”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception. . The same analysis applies to claim 20.
Claim 11 recites “generating a node for a model taxonomy of classification models, wherein the node corresponds to the third subset of sample documents; and associating a location of the node within the model taxonomy such that the node is indicated as being related to the first classification model and the second classification model”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception. The same analysis applies to claim 21.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2018/0300315 (involved in receiving a set of training documents. The set of training documents is parsed to generate a parsed set of training documents. A semantic word model for the parsed set of training documents is generated. A semantic topic model for the parsed set of training documents is generated. A statistical classification model is created using the semantic word model and the semantic topic model. A set of related expressions is retrieved. An n-gram statistical model is created based on the related expressions, the semantic word model and the semantic topic model. A target document is received. A set of suggested tags for the target document is generated based on the statistical classification model and the n-gram statistical model.)
US 20070294232 (involved modeling of various indicators of the legal, commercial and technological quality of a patent document as would be useful and meaningful to, inter alia, patent attorneys, intellectual property managers, inventors, financial professionals and professionals who use patent analysis in support of business, financial and legal decisions).
US 10586178 B1 ( involves in selecting a batch of documents from the set of documents that have not been reviewed. The batch of documents are classified by using multiple models, and each model trained to calculate a relevance of the document to a categories. Multiple classified documents are selected from the classified batch of documents for review of the selected document. A review of the selected documents are received from the document reviewer. The reviewed document and metadata about the review of the reviewed document are stored in a reviewed document repository. The errors are correlated with the time of the review by the document reviewer to determine the document reviewer to performs better reviews according to time.)
US 11281858 B1 (involves in document classification includes receiving a text document. A first classification is generated for the document, and a text corpus is searched for one or more terms from the document. Searched terms having an incidence in the text corpus lower than a threshold incidence are flagged, and at least one classification is generated after removing at least one flagged term from the document. An output is generated if the further classification is different from the first classification.)
US20220398857A1 (involves in generating a classification model based on user input data corresponding to a user input, where the classification model is trained utilizing a portion of the documents indicated to be in class by the data. A user interface is caused to display an indication of the first unit of documents marked as in class in response to the user input. A second unit is marked as out of class, and a third unit is in class utilizing the model. A fourth unit is determined to be out of the class, where a confidence value associated with the results of the model is determined.)
US 10963503 (involves in a document classifier that is a data model that is used to evaluate documents and assign the documents to one or more categories. The document classifier may be binary, in which case the document classifier assigns each document to one of two categories, generally of the form “A” and “not A.” For example, a binary document classifier may assign documents to categories such as “fiction” or “non-fiction.” Alternatively, a document classifier may assign documents to one or more of a larger number of categories. For example, a document classifier may sort documents into subject matter categories, such as “biography,” “mystery,” “geology,” “religion,” and so forth.)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEAN M CORRIELUS whose telephone number is (571)272-4032. The examiner can normally be reached Monday-Friday 6:30a-10p(Midflex).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ann J Lo can be reached at (571)272-9767. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JEAN M CORRIELUS/Primary Examiner, Art Unit 2159 June 24, 2026
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