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 claims filed 01/27/2025.
4. Claims 1, 8 and 15 are independent claims.
5. The office action is made Non-Final.
Double Patenting
6. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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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). Although the claims at issue are not identical, they are not patentably distinct from each other.
In the table below, the left side is parts of claims 1-20 in the current application while the right side is the claims and text that conflict with the parts of claims 1, 3-7, 10, 12-16 and 19-20.
19/037,654 (present application)
18/096,994
Claim 1. A method, comprising:
executing a document workflow for a document, wherein the document represents an interaction between entities and the document workflow comprises a sequence of steps;
receiving a status of execution of a step of the document workflow;
determining a triggering criterion for metadata prediction is triggered for the document based on the status;
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,
wherein the portion of the document comprises a token or a sequence of tokens from the document associated with the metadata attribute;
annotating the document with the metadata attribute; and
causing presentation of the annotated document on a user interface.
Claim 1. (Currently Amended) A computer-implemented method for machine learning based prediction of document metadata, the computer-implemented method comprising: execute, by a document management system, document workflows for one or more documents, wherein each of the one or more documents represents an interaction between a plurality of entities, each document workflow comprising a plurality of steps;
receive one or more trigger criteria, each trigger criterion specifying conditions for triggering execution of machine learning based prediction of metadata attributes for a document; repeatedly execute, for each document of the one or more documents: receiving status of execution of a particular step of document workflow for the document;
evaluating a trigger criterion associated with the document based on the received status of execution of the particular step; responsive to the evaluation of the trigger criterion indicating that metadata prediction should be triggered, send request for prediction of metadata attributes based on the document by
executing one or more machine learning models trained to predict a likelihood that a portion of an input document represents a metadata attribute describing an interaction between a plurality of entities,
wherein the portion of the input document comprises a token or sequence of tokens from the document associated with the metadata attribute;
annotate the document with one or more metadata attributes predicted using the one or more machine learning models; and
cause for display, the annotated document via a user interface
Claim 2. The method of claim 1, wherein the triggering criterion specifies conditions for triggering execution of the machine learning model.
Claim 1… each trigger criterion specifying conditions for triggering execution of machine learning based prediction of metadata attributes for a document
Claims 3-7
Claims 3-7
Same mapping applied to the claims 10-18 and 19-20.
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 Kadarundalagi Raghura et al (US 20220100772 A1) hereinafter as Raghura in view of Roberts et al (US 11314935 B2) hereinafter as Roberts.
11. Regarding claim 1, Raghura teaches A method, comprising:
executing a document workflow for a document (Fig 15 & 16, [0030-0031], “lifecycle management (a document workflow) to perform custom classification on input documents”, [0113], “For example, a custom NLP model 2135 may be used to classify documents in various categories, extract events from documents, extract entities from documents, link entities in documents to database records, and/or other NLP tasks. The lifecycle of a custom NLP model 2135 may include various stages (a document workflow) performed using an NLP model builder component” [0033], Fig 1A “Event extraction service 100 with steps 110 to 170 (a document workflow)”, [0041], “FIG. 1B illustrates an example of a data flow for event extraction from documents”), wherein the document represents an interaction between entities (Fig 2, “detect entities (organization) quoted in a document”, Fig 7, “entity linking”, [0039], “detect a trigger (“elected”) for an “election” event type in a document and to assign entities to election-related roles…entities such as other people who are quoted in the document”, Fig 2, [0053], “ entities present in the document”, [0055], “A trigger may represent a textual reference to a unique event type and a span of tokens within the input document(s). A particular event type may be associated with argument slots that represent relationships (semantic roles) of particular entities to a particular occurrence (an event) of the event type. These relationships may be described by domain-specific taxonomies and may influence the event extraction process once a trigger is detected in a document.”, [0064], “co-reference is an undirected relationship between two entities”, [0077], “An entity linking service 1100 may perform automated analysis of input documents 1015 to link mentions of entities in those documents to records in databases or knowledge bases 1110.” Fig 7, [0095], [0145]) and the document workflow comprises a sequence of steps document (Fig 15 & 16, [0030-0031], “lifecycle management (a document workflow) to perform custom classification on input documents”, [0113], “For example, a custom NLP model 2135 may be used to classify documents in various categories, extract events from documents, extract entities from documents, link entities in documents to database records, and/or other NLP tasks. The lifecycle of a custom NLP model 2135 may include various stages (document workflow comprises a sequence of steps) performed using an NLP model builder component” [0033], Fig 1A “Event extraction service 100 with steps 110 to 170 (a document workflow)”, [0041], “FIG. 1B illustrates an example of a data flow for event extraction from documents”);
Raghura implicitly teaches receiving a status of execution of a step of the document workflow (“FIG. 1B illustrates an example of a data flow for event extraction from documents with co-reference”, [0041], “As also shown in FIG. 1B, role assignment 140 may be performed intermediate output of trigger detection 120 and entity detection 130. As further shown in FIG. 1B, trigger co-reference 160 may be performed intermediate output of trigger detection 120 and role assignment 140.”); and determining a triggering criterion for metadata prediction is triggered for the document based on the status ([0027], “identify important events in such documents. For example, a consumer of public health data may seek to discover events in announcements regarding public health concerns, announcements regarding progress towards treatments, and so on, such that the consumer can plan a course of action. As another example, a consumer that builds predictive models about private-sector organizations may seek to discover relevant events such as mergers, acquisitions, initial public offerings, product announcements, leadership changes, and so on.”, [0028], “For a given document, the event extraction service may identify words that represent triggers for occurrences of events, identify words (mentions) that represent entities (e.g., real-world objects such as persons, organizations, places, dates, and so on), and assign entities to semantic roles for the triggers (e.g., who, where, when, etc.).”, [0029], “using automated techniques for trigger detection, event detection, role assignment, trigger co-reference, and entity co-reference; and so on.”, [0037-0040], [0116]);
Further Raghura explicitly teaches 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 ([0028-0029], [0031], “An NLP model may include a machine learning model that can analyze natural language input (e.g., in the form of documents) and output predictions based (at least in part) on the contents of the input.”, [0072], “The machine learning techniques may be used to perform tasks such as trigger detection 120, event detection 130, role assignment 140, trigger co-reference 150, and/or entity co-reference 160.”),
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”);
annotating the document with the metadata attribute ([0072], “system environment for event extraction from documents with co-reference, including machine learning and annotation of document”, [0074], “The annotated documents may include a plurality of labeled triggers, a plurality of labeled entities, a plurality of labeled argument slots, and/or a plurality of co-reference groups.”, [0075-0076], “one or more machine learning models may be used to label triggers, entities, argument slots, and/or co-reference groups. Annotation may be performed using partially automated processes such as self-training, deep supervision, bootstrapping, and so on.”, [0128], “The data annotation 2220 may assign labels to portions of previously collected documents that are intended to be used for training and evaluation.”); and
causing presentation of the annotated document on a user interface ([0075], “annotation that includes labeling the document, wherein one or more annotation vendors receive documents and a custom annotation interface”).
However, Roberts explicitly teaches receiving a status of execution of a step of the document (col 4, lines 23-36, "In one example for a contract electronic document, multiple contract participants may each have a "copy" of the document on the same management platform. These copies may then receive status updates from an inbound interaction schema associated with external API (e.g. a web-trading platform).", col 17, lines 5-12, "a multiplicity of data visualization tools may be used to provide analytics and other data to users on the status of tasks. For example, data exposed in contract event objects may be used to show changes to the status of contract obligations over time, time elapsed since obligations became effective, overdue obligations, obligations that need performing on an external resource, and/or other exemplary changes to the status."); and determining a triggering criterion for metadata prediction is triggered for the document based on the status (col 4, lines 23-36, "In one example for a contract electronic document, multiple contract participants may each have a "copy" of the document on the same management platform. These copies may then receive status updates from an inbound interaction schema associated with external API (e.g. a web-trading platform).", col 17, lines 5-12, "a multiplicity of data visualization tools may be used to provide analytics and other data to users on the status of tasks. For example, data exposed in contract event objects may be used to show changes to the status of contract obligations over time, time elapsed since obligations became effective, overdue obligations, obligations that need performing on an external resource, and/or other exemplary changes to the status.").
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 Roberts’s system into Raghura’s and by incorporating Roberts into Raghura because both systems are related to the field of electronic document processing would create a new and useful system and method for configuring the formation, execution, and management of executable documents, for efficient implementation of interactions with external resources (Roberts).
12. Regarding claim 2, Raghura and Roberts teach the invention as claimed in claim 1 above and Raghura further teaches wherein the triggering criterion specifies conditions for triggering execution of the machine learning model (Fig 1B, [0041], “As also shown in FIG. 1B, role assignment 140 may be performed intermediate output of trigger detection 120 and entity detection 130. As further shown in FIG. 1B, trigger co-reference 160 may be performed intermediate output of trigger detection 120 and role assignment 140.”, [0187], “one or more triggers are identified in the document (the triggering criterion specifies conditions), the one or more entity groups are identified in the document, and the one or more of the entity groups are assigned to the one or more of the semantic roles using one or more machine learning models (triggering execution of the machine learning model)”).
13. Regarding claim 3, Raghura and Roberts 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, Roberts teaches wherein the sequence of steps of the document workflow comprise a document upload step (col 14, lines 65-67 & col 15, lines 1-9, “The document editor may provide a GUI for user interaction. The document editor may enable importing saved documents for editing, or importing document templates for creating new documents.”), a document update step (col 2, lines 50-61, “the system and method enables the electronic document to perform actions on or within resources external to the document (e.g. record/update copies of the document, process calculations external to the document, or perform transactions).”), a document signing step (col 12, lines 3-23, Block S212, “an inbound interaction schema from an e-signature external resource to a contract document object may initiate the execution of the contract document object (e.g. when all contract signatories have signed).”, “In a general contract example for a contract document object, detected confirmation of signing of a contract can be used initiating various operations managed through the executable contract.”), an identity verification step (col 12, lines 3-23, Block S212, “In a fourth example, an inbound interaction schema from an identity or credential verification service containing identity verification data (e.g. an access token or other data) may trigger executable logic within the document object”), or a step for configuring and presenting a form for receiving information (col 16, lines 60-67, “The task management component preferably exposes contract events and external actions to the user through the management system. In a preferred embodiment, the task management component aggregates and collates events scoped by document for display to the end user.”).
14. Regarding claim 4, Raghura and Roberts 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”).
Also, Roberts teaches executing the document workflow on a cloud platform according to a workflow specification comprising the sequence of steps associated with the document (col 4, lines 4-15, “As a method for executable electronic document management, the method may be implemented in numerous preferred variations, wherein the method may comprise just a single or multiple aforementioned steps or sub-steps, multiple iterations of any steps or sub-steps, and/or in conjunction with additional desired steps.”), the document workflow executed on a cloud platform (col 3, lines 1-3, “This service may be as part of a regularly updated software, cloud service, or similar implementation”. Col 5, lines 14-19, Block S110, “stores the document object (e.g. on a cloud storage serve, mainframe, database, execution platform, etc.) such that the executable document object will or can be executed.”, col 8, lines 6-12, Block S120, “An interaction schema preferably configures and manages integration through an API such as an API for services such as, but not limited to, cloud services (e.g. Google Cloud), a payment gateway (e.g. Stripe), a secure private server, a blockchain or distributed ledger system (e.g. an Ethereum-based blockchain), a network-connected device (e.g. via an IoT platform API) and/or other APIs.”, col 13, lines 46-53, “the system may be implemented as a service wherein users may access the system remotely (e.g. cloud service).”, col 16, lines 4-15, “The repository system 120 may be a multi-tenant cloud-based system”).
15. Regarding claim 5, Raghura and Roberts 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, Roberts teaches receiving user feedback via the user interface, the user feedback comprising a correction of the metadata attribute or an approval of the metadata attribute (col 14, lines 23-31, “The document management system 110 functions to enable users to edit, form, manage, and analyze electronic documents. In contract specialized variations the document management system 110 may further enable user interactions (e.g. negotiation, decision making, contract signing).”, col 17, lines 13-28, “The task management component preferably enables users to interact with contract events and other forms of contract action. For example, a configured action such as a payment may be ‘queued’ as a task requiring user initiation (e.g. a signatory to a contract or a user within the signatory's organization scope), such as approval, before outbound interaction schema steps are triggered to execute a payment transaction on an external resource such as a payment gateway.”).
16. Regarding claim 6, Raghura and Roberts 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, Raghura and Roberts 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 performs the method of claims 1-7 respectively and are rejected under the same rationale.
CONCLUSION
20. The prior art made of record and not relied upon is considered pertinent to applicant s disclosure.
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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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