Notice of Pre-AIA or AIA Status
1. 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
2. Claims 1-20 are presented for examination.
3. This office action is in response to the REM filed 07/21/2026.
4. Claims 1, 8 and 15 are independent claims.
5. The office action is made Final.
Double Patenting Rejection
6. Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1, 3-7, 10, 12-16 and 19-20 of copending Application No. 18/096,994 (U.S Patent No 12222975). A Terminal Disclaimer in compliance with 37 CFR. 1.321(b)(iv) is enclosed herewith to overcome these rejections.
Examiner Note
7. The Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the Applicant(s). Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the Applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
Claim Rejections - 35 USC § 103
8. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
9. 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) A patent may not be obtained through the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
10. Claims 1-20 are rejected under 35 U.S.C.103 as being unpatentable over Sharma et al (US 12051255 B1) hereinafter as Sharma in view of Kadarundalagi Raghura et al (US 20220100772 A1) hereinafter as Raghura.
11. Regarding claim 1, Sharma teaches A method, comprising:
executing a document workflow for a document (Fig 1, para-2, “process input documents and corresponding attachments and to generate a prediction of one or more classifications for each attachment.”, para-27, “a title production system workflow”, para-45, “document workflow steps/stages”), wherein the document represents an interaction between entities ((9), “A title production system is used by title companies to manage workflows and work with various third parties involved in the transaction.”, (14), “the subject line of the message may include a file number. This file number can be linked to data in the title production system including, for example, parties to a real estate transaction in process including lender entities and the geographic location of the parcel of real property, e.g., county and state.”, (37), “These name entity recognition models can be trained to predict the occurrence of different types of named entities based on interpretation of the position of a term in the document relative to other words, capitalization, etc.”) and the document workflow comprises a sequence of steps (Figs 1, 3, and 4 “document analysis/workflow with steps”, see also para-45, “document workflow steps/stages during real estate transaction”, and para-63, “the classification can trigger one or more workflow steps of a title production system, and the attachment can be automatically routed as appropriate for executing the next workflow steps.”);
receiving a status of execution of a step of the document workflow ((45), “the retrieved file information includes a file status, which can also be used as a feature. The file status can indicate the stage of the real estate transaction, which can provide an indication of what types of document attachments are expected at that stage. For example, if closing documents have been signed and the state corresponds to a recording stage, the expected documents would be different than the documents at the beginning stage of a real estate transaction. Therefore, a feature indicating a stage of the real estate transaction can be used by the machine learning model to determine a predicted classification.”, (63), “the classification can trigger one or more workflow steps of a title production system, and the attachment can be automatically routed as appropriate for executing the next workflow steps.”);
determining a triggering criterion for metadata prediction is triggered for the document based on the status, the trigger criterion specifying conditions for triggering execution of machine learning based prediction of metadata attributes for the document (para-48, “the machine learning model is triggered in response to particular rule outcomes from the rule-based model (the trigger criterion specifying conditions for triggering). a rule may determine whether the extracted attachment text is unclean, unfit, or empty.”, also (para-45), “the retrieved file information includes a file status, which can also be used as a feature. The file status can indicate the stage of the real estate transaction, which can provide an indication of what types of document attachments are expected at that stage. For example, if closing documents have been signed and the state corresponds to a recording stage, the expected documents would be different than the documents at the beginning stage of a real estate transaction. Therefore, a feature indicating a stage of the real estate transaction can be used by the machine learning model to determine a predicted classification.”, (63), “the classification can trigger one or more workflow steps of a title production system, and the attachment can be automatically routed as appropriate for executing the next workflow steps.”).
executing a machine learning model trained to predict a likelihood that a portion of the document represents a metadata attribute describing an interaction between the entities (para-48, “the machine learning model is triggered in response to particular rule outcomes from the rule-based model (the trigger criterion specifying conditions for triggering). a rule may determine whether the extracted attachment text is unclean, unfit, or empty.”, also (para-45), “the retrieved file information includes a file status, which can also be used as a feature. The file status can indicate the stage of the real estate transaction, which can provide an indication of what types of document attachments are expected at that stage. For example, if closing documents have been signed and the state corresponds to a recording stage, the expected documents would be different than the documents at the beginning stage of a real estate transaction. Therefore, a feature indicating a stage of the real estate transaction can be used by the machine learning model to determine a predicted classification.”, (63), “the classification can trigger one or more workflow steps of a title production system, and the attachment can be automatically routed as appropriate for executing the next workflow steps, wherein the portion of the document comprises a token or a sequence of tokens from the document associated with the metadata attribute (para-17, “only a portion of the attachment document (tokens) is processed. For example, a specified number of pages can be defined for processing, e.g., a first-k number of pages. The attachment features are extracted only from content of those pages of the document. This can speed up processing by limiting the size of an attachment portion used to make a classification decision.”);
annotating the document with the metadata attribute (para-1, “The machine learning model can be trained such that the model correctly labels the input training data. New data can then be input into the machine learning model to determine a corresponding label for the new data.”, para-24, “Each of the attachments can be labeled with a type corresponding to a known classification of the type of attachment document.”, para-27, “the training data is based on a set of messages associated with a real estate transaction and include labeled attachments corresponding to various forms exchanged as part of a title production system workflow.”); and
causing presentation of the annotated document on a user interface (Fig 3, step 318, para-49, “a highest scoring classification is output as the predicted classification 318 of the attachment.”, Fig 4, step 416, para-62, “The system generates an output based on the model prediction (step 416).”).
Sharma implicitly teaches wherein the portion of the document comprises a token or a sequence of tokens from the document associated with the metadata attribute (para-17, “only a portion of the attachment document (tokens) is processed. For example, a specified number of pages can be defined for processing, e.g., a first-k number of pages. The attachment features are extracted only from content of those pages of the document. This can speed up processing by limiting the size of an attachment portion used to make a classification decision.”).
However, Raghura explicitly teaches wherein the portion of the document comprises a token or a sequence of tokens from the document associated with the metadata attribute ([0037], “A trigger may represent a textual reference to a unique event type and a span of tokens within the input document.”, [0054], “The document(s) may be pre-processed to generate a sequence of tokens representing words and numbers.”, [0055], “A trigger may represent a textual reference to a unique event type and a span of tokens within the input document(s).”, [0084], “A recognized mention may be encoded such that it captures an appropriate amount of context. The context may include other tokens or spans of characters from the document. I”).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to incorporate the concept of teachings suggested in Raghura’s system into Sharma’s and by incorporating Roberts into Sharma because both systems are related to the field of document management context-sensitive linking of entities to private databases (Raghura).
12. Regarding claim 2, Sharma and Raghura teach the invention as claimed in claim 1 above and Sharma further teaches wherein the triggering criterion specifies conditions for triggering execution of the machine learning model (para-48, “the machine learning model is triggered in response to particular rule outcomes from the rule-based model (the trigger criterion specifying conditions for triggering). a rule may determine whether the extracted attachment text is unclean, unfit, or empty.”, also (para-45), “the retrieved file information includes a file status, which can also be used as a feature. The file status can indicate the stage of the real estate transaction, which can provide an indication of what types of document attachments are expected at that stage. For example, if closing documents have been signed and the state corresponds to a recording stage, the expected documents would be different than the documents at the beginning stage of a real estate transaction. Therefore, a feature indicating a stage of the real estate transaction can be used by the machine learning model to determine a predicted classification.”, (63), “the classification can trigger one or more workflow steps of a title production system, and the attachment can be automatically routed as appropriate for executing the next workflow steps.”).
13. Regarding claim 3, Sharma and Raghura teach the invention as claimed in claim 1 above and Raghura further teaches wherein the sequence of steps of the document workflow comprise a document upload step (Fig 1A, input documents), a document update step (Fig 9, [0012], “the updating of entity representations and entity linking when corresponding records are changed in private databases”, [0136], “an updated and fine-tuned model 2136 may be deployed to the production environment for model inference 2260.”), a document signing step, an identity verification step ([0136], “the event extraction service 100 may use an access credential associated with the client (e.g., an account name and password or an identity and access management role) to read input documents 50 from the storage location.”), or a step for configuring and presenting a form for receiving information ([0100], “extract-transform-load (ETL)”).
Also, Sharma teaches wherein the sequence of steps of the document workflow comprise a document signing step (para-45, “if closing documents have been signed and the state corresponds to a recording stage”).
14. Regarding claim 4, Sharma and Raghura teach the invention as claimed in claim 1 above and Raghura further teaches comprising executing the document workflow on a cloud platform according to a workflow specification comprising the sequence of steps associated with the document ([0024], “The entity linking service may be hosted in the cloud using a provider network that offers numerous services to a distributed set of clients”, [0122], “a cloud-based service may build, train, and evaluate a model”).
15. Regarding claim 5, Sharma and Raghura teach the invention as claimed in claim 1 above and Raghura further teaches comprising receiving user feedback via the user interface, the user feedback comprising a correction of the metadata attribute or an approval of the metadata attribute ([0132], “the inference data 2280 may comprise explicit feedback, e.g., feedback generated based (at least in part) on user input about model accuracy. In some embodiments, the inference data 2280 may comprise implicit feedback, e.g., feedback generated in an automated manner. For example, implicit feedback may be generated if a user clicks on a disambiguated mention of an entity in a GUI.”).
Also, Sharma teaches receiving user feedback via the user interface (para-74, “feedback”).
16. Regarding claim 6, Sharma and Raghura teach the invention as claimed in claim 5 above and Raghura further teaches evaluating the machine learning model based on the user feedback ([0132], “the inference data 2280 may comprise explicit feedback, e.g., feedback generated based (at least in part) on user input about model accuracy (evaluation). In some embodiments, the inference data 2280 may comprise implicit feedback, e.g., feedback generated in an automated manner. For example, implicit feedback may be generated if a user clicks on a disambiguated mention of an entity in a GUI.”, [0134], “a feedback loop for NLP model retraining”).
17. Regarding claim 7, Sharma and Raghura teach the invention as claimed in claim 5 above and Raghura further teaches generating training data for retraining the machine learning model based on the user feedback ([0132], “explicit feedback, e.g., feedback generated based (at least in part) on user input about model accuracy (evaluation)”, [0134], “a feedback loop for NLP model retraining”).
18. Regarding claims 8-14, those claims recite a non-transitory computer readable storage medium storing instruction performs the method of claims 1-7 respectively and are rejected under the same rationale.
19. Regarding claims 15-20, those claims recite a system that performs the method of claims 1-7 respectively and is rejected under the same rationale.
Respond to Amendments and Arguments
19. In the remarks received 07/21/2026, applicant amended claim 1 to recite new features and argued that the combination of Raghura et al and Roberts et al does not teach the invention recited in Claim 1, for several reasons, including but not limited to, the following.
The event extraction service of Raghura does not identify an event to trigger execution of a machine learning model. By way of contrast, claim 1 recites "determining a triggering criterion for metadata prediction is triggered for the document based on the status, the trigger criterion specifying conditions for triggering execution of machine learning based prediction of metadata attributes for the document." Consequently, at least this language of claim 1 represents patentable subject matter.
Absence from the cited references of the above-mentioned claim elements negates the rejections. Accordingly, Applicant respectfully requests removal of the rejections with respect to the independent claims. Furthermore, Applicant respectfully requests withdrawal of the rejections with respect to the dependent claims, which depend on at least one of the independent claims, and therefore contain additional features that further distinguish these claims from the cited references.
Examiner presents the following responses to Applicant’s arguments:
Applicants’ arguments see REM, filed 12/01/2014, with respect to the rejection(s) of claim(s) under 35 USC § 103 have been fully considered and are persuasive. However, upon further consideration, a new ground(s) of rejection is made by KELLEY et al (US 20140052663 A1) in view of Procopio et al. (US 8,756,236 B1).
CONCLUSION
20. The prior art made of record and not relied upon is considered pertinent to applicant s disclosure.
- Manasse et al (US 20210383008 A1)
The Applicant’s amendment necessitated a new ground of rejection. Therefore, THIS ACTION IS MADE FINAL. Applicants are reminded of the extension of time policy as set forth in 37 C.F.R. § 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 HICHAM SKHOUN whose telephone number is (571)272-9466. The examiner can normally be reached Normal schedule: Mon-Fri 10am-6:30pm.
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/HICHAM SKHOUN/Primary Examiner, Art Unit 2164
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