DETAILED ACTION
Response to Amendment
The amendment filed on 1 April 2026 has been entered.
Claims 1-20 are pending.
Claims 1, 8, 15 are amended.
Applicant’s amendments to the Claims have overcome each and every objection, previously set forth in the Non-Final Office Action mailed 1 December 2025.
Response to Arguments
Applicant’s remarks, regarding the rejections of claims under 35 USC 103, have been fully considered.
Applicant notes Claim 1 recites "identifying, by the document management system, a set of actions that can be taken on the document by the machine learning model based on contents of the document and actions taken on other documents of the document type managed by the document management system".
Applicant submits none of the examples of annotation data comprise "a set of actions that can be taken on the document" as recited in Claim 1. Consequently, the cited references fail to provide an identical disclosure of at least this language of the claimed subject matter. The other cited references fail to remedy the deficiency of Bawa. For example, the Office Action correctly notes that D'Oria fails to teach the identifying step of Claim 1. Further, Dudani fails to mention machine learning in any context. Absence from the cited references of the above-mentioned claim elements negates the rejections.
Examiner respectfully disagrees. Examiner notes claim limitation “identifying, by the document management system, a set of actions that can be taken on the document by the machine learning model based on contents of the document and actions taken on other documents of the document type managed by the document management system” is taught by Bawa (outlined in the Non-Final Office Action mailed 1 December 2025, pg. 7). Bawa teaches an annotator, using trained artificial intelligence and machine learning (cf. Bawa, [0004]), which analyzes the input document, receiving features, and based on the output of the categorizer, generates annotation data corresponding to the input document (cf. Bawa, [0030]). Examiner notes under broadest reasonable interpretation, the “identifying a set of actions that can be taken on the document” of the claimed invention is taught by the various generation of annotation data corresponding to the type of input document, as outlined by Bawa. Each generation of annotation data for each corresponding input document type, is identification of an action that can be taken on the document based on contents of the document and similarly, the various generations of annotation data is identifying a set of actions of the claimed invention.
The rejection of Claim 1, under 35 USC 103, has been maintained. Similarly, the rejections of Claims 8, 15, under 35 USC 103, have been maintained.
Rejections of Claims 3-7, 9-14, 16-20, under 35 USC 103, which depend directly or indirectly from Claims 1, 8, 15, have been maintained.
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 .
Claim Objections
Claim 1 and analogous Claims 8, 15, are objected to because of the following informalities: “the machine learning model” in line 9 should be “the machine-learning model”. Appropriate correction is required.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3-4, 6, 8, 10-11, 13, 15, 17-18, 20 are rejected under 35 U.S.C. 103 as being unpatentable over D'Oria et al. (U.S. Pre-Grant Publication No. 20220012414, hereinafter ‘D'Oria'), in view of Bawa et al. (U.S. Pre-Grant Publication No. 20220237373, hereinafter 'Bawa') and Dudani et al. (U.S. Pre-Grant Publication No. 20190266256, hereinafter 'Dudani').
Regarding claim 1 and analogous claims 8, 15, D'Oria teaches A method comprising: generating, by a document management system, a document at a request of a user ([0028] FIG. 1 is a block diagram of an exemplary system 100 configured with capabilities and functionality for providing an interactive tool for displaying and modifying an automatically generated electronic form. As shown in FIG. 1, system 100 includes server 110, at least at a request of a user one user terminal 160, at least one output terminal 162, at least one data source 170, and network 180. These components, and their individual components, may cooperatively operate to provide functionality in accordance with the discussion herein. For example, in operation according to one or more implementations, data (e.g., corresponding to source documents) may be obtained from data sources 170 and may be provided as input to server 110. The various components of server 110 may cooperatively operate to generating, by a document management system, a document perform generation of an electronic form from the data.);
in response to a selection of one or more of the set of actions via the interface element by the user, performing, by the document management system, the selected action on the document, wherein the selected action modifies content of the document ([0061] At block 216, an interactive tool is provided. The interactive tool may be configured to enable display and modification of the electronic form represented by the intermediate file (e.g., an automatically generated electronic form) in response to a selection of one or more of the set of actions via the interface element by the user based on user input. For example, the interactive tool may display a graphical representation of the electronic form and a user may be able to performing, by the document management system, the selected action on the document edit or modify aspects of the electronic form or the elements within the electronic form, such as by using user terminal 160. For example, the user may be able to wherein the selected action modifies add additional elements to the electronic form or delete one or more elements from the electronic form.; [0042] Based on a determination to perform element detection on the source document, element detector 120 may detect one or more content of the document element of the source document from the data (e.g., the text data, the image data, etc., of the source document).).
D'Oria fails to teach applying, by the document management system, a machine-learning model to a set of features of the document, the machine-learning model trained on a training set of documents each comprising training document features and each tagged with a training document type, the machine-learning model configured to output a document type of the document based on the set of features; identifying, by the document management system, a set of actions that can be taken on the document by the machine learning model based on contents of the document and actions taken on other documents of the document type managed by the document management system; modifying, by the document management system, a document interface presented to the user to include an interface element that identifies the set of actions that can be taken on the document based on contents of the document and actions taken on other documents of the document type; and
Bawa teaches applying, by the document management system, a machine-learning model to a set of features of the document, the machine-learning model trained on a training set of documents each comprising training document features and each tagged with a training document type, the machine-learning model configured to output a document type of the document based on the set of features ([0021] The system may teaches applying, by the document management system, a machine-learning model to a set of features of the document categorize an input document based on similarities between features of the input document and features of documents machine-learning model trained on a training set of documents each comprising training document features and each tagged with a training document type from multiple predefined document categories by utilizing a first set of one or more machine learning (ML) models. After the input document is categorized, the system may annotate the input document by machine-learning model configured to output a document type of the document based on the set of features identifying and tagging specific words or phrases as entity values of one or more category-specific entities (e.g., words or phrases that are highly informative and relevant to summarizing documents of the respective document category).; The various ML models may be trained using training data that is based on labeled and annotated documents of each of the predefined document categories and one or more summary templates corresponding to each of the predefined document templates.);
identifying, by the document management system, a set of actions that can be taken on the document by the machine learning model based on contents of the document and actions taken on other documents of the document type managed by the document management system ([0004] Aspects of the present disclosure provide systems, methods, apparatus, and computer-readable storage media that support type-specific automated document summarization (also referred to as encapsulation). The techniques of the present disclosure may leverage artificial intelligence and machine learning to generate semantically correct document summaries (e.g., encapsulations) for multiple categories of documents using minimal, or no, user input. In some aspects, a system of the present disclosure may categorize a document into one of multiple predefined categories using trained artificial intelligence and machine learning. After the categorization, the system may generate category-specific annotations (e.g., annotation data) based on the document by the machine learning model using trained artificial intelligence and machine learning.; [0030] The annotator 132 is configured to identifying, by the document management system, a set of actions that can be taken on the document by the machine learning model based on contents of the document and actions taken on other documents of the document type managed by the document management system perform category-specific annotation (e.g., to generate annotation data) based on input documents. To illustrate, each document category may have a specific group of entities (e.g., particular names, places, amounts, dates, words, phrases, and the like) that are highly relevant to summarizing documents of the respective document category, and the annotator 132 may be configured to analyze feature data for an unlabeled input document and generate annotation data that includes the names (e.g., identifiers) of the entities and the corresponding entity values within the input document. As an illustrative example, a first document category may include leases, and the respective group of entities corresponding to leases may include a first party, a second party, a duration of the lease, a starting date of the lease, and a payment value from the second party to the first party to pay for the object of the lease.);
D'Oria and Bawa are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of D'Oria, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Bawa to D'Oria before the effective filing date of the claimed invention in order to enable faster summarization of documents using fewer processing resources than conventional summarization systems due to the category-specific document summarization and trained with significantly fewer training documents, enabling fast and accurate document indexing and searching for a variety of different document categories (cf. Bawa, [0006] Also, the systems described herein enable faster summarization of documents using fewer processing resources than conventional summarization systems due to the category-specific document summarization. Further, the systems described herein use ML models that can be trained with significantly fewer training documents as compared to the large volume of reference documents some conventional systems analyze to improve semantic understanding of documents. These improvements to document summarization enable generation of document summaries that enable fast and accurate document indexing and searching for a variety of different document categories.).
Dudani teaches modifying, by the document management system, a document interface presented to the user to include an interface element that identifies the set of actions that can be taken on the document based on contents of the document and actions taken on other documents of the document type ([0045] Turning now to FIG. 4, a screenshot of a modifying, by the document management system, a document interface presented to the user to include an interface element user interface 400 of a document upload page 402 is illustrated. The document upload page 402 may be used by a user and/or a computing device 200 to add a document (e.g., a file) to the document management portal described above (process block 302). The that identifies the set of actions that can be taken on the document document upload page 402 may include one or more of fields, drop downs, attachment selectors, or lists, to receive inputs of selections from a user, the computing device 200, or another computing device. The inputs may relate to a document 404 selected as an attachment for uploading to the user portal and/or may relate to based on contents of the document and actions taken on other documents of the document type metadata 406 (e.g., descriptions and/or tags) to associate with the document 404. The metadata 406 may include information, such as a description 408, a file size 410, a file format 412, a date of creation (e.g., created date) 414, an author (e.g., created by) 416, and/or the like, associated with and/or to associate with the document 404. Further, in an example of the document upload page 402, such as within a human resources application, the metadata 406 may include information associated with an employee (e.g., employee identification 418), a document type 420, a human resources (HR) case 422, one or more tags 424, and/or the like associated with the document 404.); and
D'Oria, Bawa, and Dudani are considered to be analogous to the claimed invention because they are in the same field of document management. In view of the teachings of D'Oria and Bawa, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Dudani to D'Oria before the effective filing date of the claimed invention in order to reduce the amount of time and/or resources involved with receiving and/or managing a document (cf. Dudani, [0020] With the preceding in mind, techniques described below may be implemented via one or more processors of a computing device and/or a computing framework. Accordingly, the one or more processors and/or the computing framework implementing the disclosed techniques may increase the efficiency with which documents are managed. In some embodiments, the techniques may reduce the amount of time and/or resources involved with receiving and/or managing a document. For example, by including suitable metadata related to a document, the one or more processors and/or the computing framework may more rapidly determine a suitable action to perform on the document than if the metadata was associated remotely from the document and/or across several locations. Further, by including configurable metadata, the one or more processors and/or the computing framework may perform operations related to the document with increased granularity. Additionally, the one or more processors and/or the computing system may more reliably purge (e.g., dispose) documents by maintaining metadata specific to purging the document with the document itself.).
Regarding claim 3, D'Oria, as modified by Bawa and Dudani, teaches The method of claim 1.
Bawa teaches wherein the training set of documents comprises documents associated with the user, associated with an entity or other user associated with the user, or associated with users with one or more characteristics in common with the user ([0026] The training engine 126 is configured to generate the training data 110 for training one or more ML models used by the document processing device 102, such as one or more of the first ML models 130, the second ML models 134, or the third ML models 138, as further described below. For example, the training engine 126 may generate the training data 110 based on labeled (e.g., categorized) documents from the databases 142 (or from the user device 140), one or more category-specific summaries or summary templates, other document or feature data, or a combination thereof.; [0035] The user device 140 is configured to provide document creation, management, processing, analysis, and presentation for multiple categories of documents. For example, the user device 140 may be configured to support a cross-domain workbench to enable user interaction with multiple categories of documents. Alternatively, the user device 140 may correspond to multiple user devices that each support a domain-specific workbench enable user interaction with a corresponding category of documents. As an illustrative example, a domain-specific workbench may include a legal document workbench that supports user generation, editing, analyzation, and organizing of legal documents such as leases, contracts, depositions, motions, exhibits, and the like. As another illustrative example, a cross-domain workbench may include an organization-specific workbench that includes supports user access to financial documents (e.g., purchase orders, receipts, electronic fund transfers, budgets, etc.), associated with an entity or other user associated with the user human resource documents (e.g., policies, training manuals, personal reviews, request forms, etc.), users with one or more characteristics in common with the user marketing documents (e.g., press releases, advertisements, marketing plans, news articles, interviews, etc.), and training set of documents comprises documents associated with the user technician documents (e.g., time cards, activity logs, inventory requests, etc.).).
D'Oria, Bawa, and Dudani are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 4, D'Oria, as modified by Bawa and Dudani, teaches The method of claim 1.
Bawa teaches wherein the machine-learning model is trained to identify correlations between document types and document features, and wherein the document type of the document is determined based on the set of features using the identified correlations ([0021 The system may document type of the document is determined based on the set of features using the identified correlations categorize an input document based on similarities between machine-learning model is trained to identify correlations between document types and document features features of the input document and features of documents from multiple predefined document categories by utilizing a first set of one or more machine learning (ML) models. After the input document is categorized, the system may annotate the input document by identifying and tagging specific words or phrases as entity values of one or more category-specific entities (e.g., words or phrases that are highly informative and relevant to summarizing documents of the respective document category). To annotate the input document, the system may utilize a particular set of one or more second ML models that is selected from multiple sets of category-specific second ML models based on the particular set of second ML models corresponding to the determined document category. Each of the multiple sets of category-specific second ML models may be configured to output annotation data for a respective category of documents based on input feature data.).
D'Oria, Bawa, and Dudani are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 6, D'Oria, as modified by Bawa and Dudani, teaches The method of claim 1.
D'Oria teaches wherein the actions taken on other documents of the document type comprise actions taken by the user or actions taken by users with one or more characteristic in common with the user ([0033] Additionally or alternatively, output terminal 162 may include or correspond to (or be replaced with) a network device, such as another server or a database, that is configured to store output files (e.g., electronic forms) for distribution to other terminal devices, such as via a private network, an intranet, the Internet, network 180, or any other type of network connection. In such implementations, output terminal 162 may additionally be configured to store input data received when wherein the actions taken on other documents of the document type comprise actions taken by the user users interact with the electronic form.; [0034] For example, data sources 170 may include an online forms data source, a business data source, a legal compliance data source, a streaming data source, a database, a social media feed, a data room, another data source, the like, or a combination thereof. In some implementations, the data from data source 170 may include or correspond to one or more source documents designed to be at least partially completed by a user, such as a form.).
D'Oria, Bawa, and Dudani are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 10, D'Oria, as modified by Bawa and Dudani, teaches The non-transitory computer-readable storage medium of claim 8.
Bawa teaches wherein the training set of documents comprises documents associated with the user, associated with an entity or other user associated with the user, or associated with users with one or more characteristics in common with the user ([0026] The training engine 126 is configured to generate the training data 110 for training one or more ML models used by the document processing device 102, such as one or more of the first ML models 130, the second ML models 134, or the third ML models 138, as further described below. For example, the training engine 126 may generate the training data 110 based on labeled (e.g., categorized) documents from the databases 142 (or from the user device 140), one or more category-specific summaries or summary templates, other document or feature data, or a combination thereof.; [0035] The user device 140 is configured to provide document creation, management, processing, analysis, and presentation for multiple categories of documents. For example, the user device 140 may be configured to support a cross-domain workbench to enable user interaction with multiple categories of documents. Alternatively, the user device 140 may correspond to multiple user devices that each support a domain-specific workbench enable user interaction with a corresponding category of documents. As an illustrative example, a domain-specific workbench may include a legal document workbench that supports user generation, editing, analyzation, and organizing of legal documents such as leases, contracts, depositions, motions, exhibits, and the like. As another illustrative example, a cross-domain workbench may include an organization-specific workbench that includes supports user access to financial documents (e.g., purchase orders, receipts, electronic fund transfers, budgets, etc.), associated with an entity or other user associated with the user human resource documents (e.g., policies, training manuals, personal reviews, request forms, etc.), users with one or more characteristics in common with the user marketing documents (e.g., press releases, advertisements, marketing plans, news articles, interviews, etc.), and training set of documents comprises documents associated with the user technician documents (e.g., time cards, activity logs, inventory requests, etc.).).
D'Oria, Bawa, and Dudani are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 11, D'Oria, as modified by Bawa and Dudani, teaches The non-transitory computer-readable storage medium of claim 8.
Bawa teaches wherein the machine-learning model is trained to identify correlations between document types and document features, and wherein the document type of the document is determined based on the set of features using the identified correlations ([0021 The system may document type of the document is determined based on the set of features using the identified correlations categorize an input document based on similarities between machine-learning model is trained to identify correlations between document types and document features features of the input document and features of documents from multiple predefined document categories by utilizing a first set of one or more machine learning (ML) models. After the input document is categorized, the system may annotate the input document by identifying and tagging specific words or phrases as entity values of one or more category-specific entities (e.g., words or phrases that are highly informative and relevant to summarizing documents of the respective document category). To annotate the input document, the system may utilize a particular set of one or more second ML models that is selected from multiple sets of category-specific second ML models based on the particular set of second ML models corresponding to the determined document category. Each of the multiple sets of category-specific second ML models may be configured to output annotation data for a respective category of documents based on input feature data.).
D'Oria, Bawa, and Dudani are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 13, D'Oria, as modified by Bawa and Dudani, teaches The non-transitory computer-readable storage medium of claim 8.
D'Oria teaches wherein the actions taken on other documents of the document type comprise actions taken by the user or actions taken by users with one or more characteristic in common with the user ([0033] Additionally or alternatively, output terminal 162 may include or correspond to (or be replaced with) a network device, such as another server or a database, that is configured to store output files (e.g., electronic forms) for distribution to other terminal devices, such as via a private network, an intranet, the Internet, network 180, or any other type of network connection. In such implementations, output terminal 162 may additionally be configured to store input data received when wherein the actions taken on other documents of the document type comprise actions taken by the user users interact with the electronic form.; [0034] For example, data sources 170 may include an online forms data source, a business data source, a legal compliance data source, a streaming data source, a database, a social media feed, a data room, another data source, the like, or a combination thereof. In some implementations, the data from data source 170 may include or correspond to one or more source documents designed to be at least partially completed by a user, such as a form.).
D'Oria, Bawa, and Dudani are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 17, D'Oria, as modified by Bawa and Dudani, teaches The document management system of claim 15.
Bawa teaches wherein the training set of documents comprises documents associated with the user, associated with an entity or other user associated with the user, or associated with users with one or more characteristics in common with the user ([0026] The training engine 126 is configured to generate the training data 110 for training one or more ML models used by the document processing device 102, such as one or more of the first ML models 130, the second ML models 134, or the third ML models 138, as further described below. For example, the training engine 126 may generate the training data 110 based on labeled (e.g., categorized) documents from the databases 142 (or from the user device 140), one or more category-specific summaries or summary templates, other document or feature data, or a combination thereof.; [0035] The user device 140 is configured to provide document creation, management, processing, analysis, and presentation for multiple categories of documents. For example, the user device 140 may be configured to support a cross-domain workbench to enable user interaction with multiple categories of documents. Alternatively, the user device 140 may correspond to multiple user devices that each support a domain-specific workbench enable user interaction with a corresponding category of documents. As an illustrative example, a domain-specific workbench may include a legal document workbench that supports user generation, editing, analyzation, and organizing of legal documents such as leases, contracts, depositions, motions, exhibits, and the like. As another illustrative example, a cross-domain workbench may include an organization-specific workbench that includes supports user access to financial documents (e.g., purchase orders, receipts, electronic fund transfers, budgets, etc.), associated with an entity or other user associated with the user human resource documents (e.g., policies, training manuals, personal reviews, request forms, etc.), users with one or more characteristics in common with the user marketing documents (e.g., press releases, advertisements, marketing plans, news articles, interviews, etc.), and training set of documents comprises documents associated with the user technician documents (e.g., time cards, activity logs, inventory requests, etc.).).
D'Oria, Bawa, and Dudani are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 18, D'Oria, as modified by Bawa and Dudani, teaches The document management system of claim 15.
Bawa teaches wherein the machine-learning model is trained to identify correlations between document types and document features, and wherein the document type of the document is determined based on the set of features using the identified correlations ([0021 The system may document type of the document is determined based on the set of features using the identified correlations categorize an input document based on similarities between machine-learning model is trained to identify correlations between document types and document features features of the input document and features of documents from multiple predefined document categories by utilizing a first set of one or more machine learning (ML) models. After the input document is categorized, the system may annotate the input document by identifying and tagging specific words or phrases as entity values of one or more category-specific entities (e.g., words or phrases that are highly informative and relevant to summarizing documents of the respective document category). To annotate the input document, the system may utilize a particular set of one or more second ML models that is selected from multiple sets of category-specific second ML models based on the particular set of second ML models corresponding to the determined document category. Each of the multiple sets of category-specific second ML models may be configured to output annotation data for a respective category of documents based on input feature data.).
D'Oria, Bawa, and Dudani are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 20, D'Oria, as modified by Bawa and Dudani, teaches The document management system of claim 15.
D'Oria teaches wherein the actions taken on other documents of the document type comprise actions taken by the user or actions taken by users with one or more characteristic in common with the user ([0033] Additionally or alternatively, output terminal 162 may include or correspond to (or be replaced with) a network device, such as another server or a database, that is configured to store output files (e.g., electronic forms) for distribution to other terminal devices, such as via a private network, an intranet, the Internet, network 180, or any other type of network connection. In such implementations, output terminal 162 may additionally be configured to store input data received when wherein the actions taken on other documents of the document type comprise actions taken by the user users interact with the electronic form.; [0034] For example, data sources 170 may include an online forms data source, a business data source, a legal compliance data source, a streaming data source, a database, a social media feed, a data room, another data source, the like, or a combination thereof. In some implementations, the data from data source 170 may include or correspond to one or more source documents designed to be at least partially completed by a user, such as a form.).
D'Oria, Bawa, and Dudani are combinable for the same rationale as set forth above with respect to claim 1.
Claims 2, 9, 16 are rejected under 35 U.S.C. 103 as being unpatentable over D'Oria, in view of Bawa, Dudani, and further in view of Boada et al. (U.S. Pre-Grant Publication No. 20200379755, hereinafter ‘Boada') and Rathje et al. (U.S. Pre-Grant Publication No. 20210026897, hereinafter ‘Rathje').
Regarding claim 2, D'Oria, as modified by Bawa and Dudani, teaches The method of claim 1.
Bawa teaches wherein the set of features of document include one or more of: terms used within the document, clauses used within the document ([0027] The categorizer 128 is configured to categorize input documents into corresponding categories of a group predefined document categories based on similarities between the input documents and documents of the predefined document categories. For example, the categorizer 128 may be configured to analyze the input documents to identify and set of features of document include one or more of extract particular features (also referred to as key performance indicators (KPIs)) that are used to assign the input documents to predefined document categories that include documents having the most similar features.; [0028] To illustrate, the categorizer 128 may be configured to perform vectorization and natural language processing (NLP) on an input document, such as tokenization, lemmatization, clauses used within the document sentencization, part-of-speech tagging, bag of words vectorization, terms used within the document term frequency-inverse document frequency (TF-IDF) vectorization, stop-character parsing, named entity recognition, semantic relation extraction, and the like, to generate word features, such as word identification (e.g., for generating a word corpus), word frequencies, word ratios, and the like, to generate or extract word features from the input document.),
images within the document ([0027] The features may include word features and word layout features, non-word object layout features (referred to herein as “pixel features” that correspond to layout of non-word objects, such as images within the document images, graphics, tables, lines, bullets, designs, colors, headings, sub-headings, and the like), or a combination thereof.),
entities associated with the document ([0030] The annotator 132 is configured to perform category-specific annotation (e.g., to generate annotation data) based on input documents. To illustrate, each document category may have a specific entities associated with the document group of entities (e.g., particular names, places, amounts, dates, words, phrases, and the like) that are highly relevant to summarizing documents of the respective document category, and the annotator 132 may be configured to analyze feature data for an unlabeled input document and generate annotation data that includes the names (e.g., identifiers) of the entities and the corresponding entity values within the input document.),
templates used to generate the document ([0053] Additionally or alternatively, the training data 110 may include a third portion (e.g., third training data) that is used by the training engine 126 to train the third ML models 138. The third training data may be generated based on one or more document templates used to generate the document summary templates (or reference document summaries) for the different predetermined document categories. To illustrate, one or more summary templates for each of the predefined document categories may be generated by one or more document experts, by an automated document management application, or a combination thereof.),
D'Oria, as modified by Bawa and Dudani, fails to teach permissions associated with the document, actions taken on the document, characteristics of the user, or characteristics of entities associated with the documents.
Boada teaches permissions associated with the document, actions taken on the document ([0043] Text editor 412 allows users to view and modify text from documents in the storage database 408 a based on permissions associated with the document user permissions. These changes are stored, tracked, and analyzed as described above. Text editor 412 provides recommendations to the user based on five main cognitive linguistic modules: (readability improvement) engine 410 a, (standardization) engine 410 b, (cross-document consolidation) engine 410 c, (topical coherence) engine 410 d, and (spelling and grammar) engine 410 e. The edit recommendations identified by the engines are each scored to assess the difficulty of the edit task as well the estimate the effort/time required to complete. Features used to generate these estimates include a actions taken on the document type of task (e.g., a consolidation task is more difficult than a single spelling error), a length of text involved in the editing task, and skill set and productivity metrics available in the editor profiles. These estimates, along with the edit recommendations themselves and the editor assignments, are updated based on user activity with hardware controller 14.),
D'Oria, Bawa, Dudani, and Boada are considered to be analogous to the claimed invention because they are in the same field of document management. In view of the teachings of D'Oria, Bawa, and Dudani, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Boada to D'Oria before the effective filing date of the claimed invention in order to advantageously provide a simple method and associated system capable of implementing document action automated systems for efficient software execution (cf. Boada, [0006] The present invention advantageously provides a simple method and associated system capable of implementing document action automated systems for efficient software execution.).
Rathje teaches characteristics of the user, or characteristics of entities associated with the documents ([0019] Topical classifications and/or characteristics of the user user characteristics (e.g., owner, author, and/or editor characteristics) may be associated with the electronic resources as attributes. The attributes may be searchable. In some examples, the resources may be associated/included in a graphical resource matrix comprised of resource nodes and edges. The edges may comprise relationships between the resources in the matrix (e.g., users in common, characteristics of users in common, classification types in common, tasks in common, etc.).; [0020] In additional examples, the resource collaborations service may provide recommendation to unrelated users to collaborate on documents with one another. In some examples, the resource collaboration service may provide recommendations that users collaborate, create new groups and/or join existing groups based on determining that characteristics of entities associated with the documents users share characteristics and/or determining that resources have topical overlap.; [0021] In some examples, the resource collaboration service may provide recommendations that users collaborate on resources, share documents with one another, open, preview and/or incorporate subject matter from documents, and/or recommend that users collaborate and create new groups and/or join existing groups based on clustering documents via application of one or more language processing and/or machine learning models.).
D'Oria, Bawa, Dudani, Boada, and Rathje are considered to be analogous to the claimed invention because they are in the same field of document management. In view of the teachings of D'Oria, Bawa, Dudani, and Boada, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Rathje to D'Oria before the effective filing date of the claimed invention in order to provide technical advantages for surfacing collaborative recommendations in relation to topically classified resources and reducing processing costs (cf. Rathje, [0023] The systems, methods, and devices described herein provide technical advantages for surfacing collaborative recommendations in relation to topically classified resources. Processing costs (i.e., CPU cycles) are reduced via the mechanisms described herein at least in that resources can be automatically topically classified and cross-referenced via application of natural language processing and/or machine learning models and/or execution of search functions in a graphical resource matrix.).
Regarding claim 9, D'Oria, as modified by Bawa and Dudani, teaches The non-transitory computer-readable storage medium of claim 8.
Bawa teaches wherein the set of features of document include one or more of: terms used within the document, clauses used within the document ([0027] The categorizer 128 is configured to categorize input documents into corresponding categories of a group predefined document categories based on similarities between the input documents and documents of the predefined document categories. For example, the categorizer 128 may be configured to analyze the input documents to identify and set of features of document include one or more of extract particular features (also referred to as key performance indicators (KPIs)) that are used to assign the input documents to predefined document categories that include documents having the most similar features.; [0028] To illustrate, the categorizer 128 may be configured to perform vectorization and natural language processing (NLP) on an input document, such as tokenization, lemmatization, clauses used within the document sentencization, part-of-speech tagging, bag of words vectorization, terms used within the document term frequency-inverse document frequency (TF-IDF) vectorization, stop-character parsing, named entity recognition, semantic relation extraction, and the like, to generate word features, such as word identification (e.g., for generating a word corpus), word frequencies, word ratios, and the like, to generate or extract word features from the input document.),
images within the document ([0027] The features may include word features and word layout features, non-word object layout features (referred to herein as “pixel features” that correspond to layout of non-word objects, such as images within the document images, graphics, tables, lines, bullets, designs, colors, headings, sub-headings, and the like), or a combination thereof.),
entities associated with the document ([0030] The annotator 132 is configured to perform category-specific annotation (e.g., to generate annotation data) based on input documents. To illustrate, each document category may have a specific entities associated with the document group of entities (e.g., particular names, places, amounts, dates, words, phrases, and the like) that are highly relevant to summarizing documents of the respective document category, and the annotator 132 may be configured to analyze feature data for an unlabeled input document and generate annotation data that includes the names (e.g., identifiers) of the entities and the corresponding entity values within the input document.),
templates used to generate the document ([0053] Additionally or alternatively, the training data 110 may include a third portion (e.g., third training data) that is used by the training engine 126 to train the third ML models 138. The third training data may be generated based on one or more document templates used to generate the document summary templates (or reference document summaries) for the different predetermined document categories. To illustrate, one or more summary templates for each of the predefined document categories may be generated by one or more document experts, by an automated document management application, or a combination thereof.),
D'Oria, as modified by Bawa and Dudani, fails to teach permissions associated with the document, actions taken on the document, characteristics of the user, or characteristics of entities associated with the documents.
Boada teaches permissions associated with the document, actions taken on the document ([0043] Text editor 412 allows users to view and modify text from documents in the storage database 408 a based on permissions associated with the document user permissions. These changes are stored, tracked, and analyzed as described above. Text editor 412 provides recommendations to the user based on five main cognitive linguistic modules: (readability improvement) engine 410 a, (standardization) engine 410 b, (cross-document consolidation) engine 410 c, (topical coherence) engine 410 d, and (spelling and grammar) engine 410 e. The edit recommendations identified by the engines are each scored to assess the difficulty of the edit task as well the estimate the effort/time required to complete. Features used to generate these estimates include a actions taken on the document type of task (e.g., a consolidation task is more difficult than a single spelling error), a length of text involved in the editing task, and skill set and productivity metrics available in the editor profiles. These estimates, along with the edit recommendations themselves and the editor assignments, are updated based on user activity with hardware controller 14.),
D'Oria, Bawa, Dudani, and Boada are combinable for the same rationale as set forth above with respect to claim 2.
Rathje teaches characteristics of the user, or characteristics of entities associated with the documents ([0019] Topical classifications and/or characteristics of the user user characteristics (e.g., owner, author, and/or editor characteristics) may be associated with the electronic resources as attributes. The attributes may be searchable. In some examples, the resources may be associated/included in a graphical resource matrix comprised of resource nodes and edges. The edges may comprise relationships between the resources in the matrix (e.g., users in common, characteristics of users in common, classification types in common, tasks in common, etc.).; [0020] In additional examples, the resource collaborations service may provide recommendation to unrelated users to collaborate on documents with one another. In some examples, the resource collaboration service may provide recommendations that users collaborate, create new groups and/or join existing groups based on determining that characteristics of entities associated with the documents users share characteristics and/or determining that resources have topical overlap.; [0021] In some examples, the resource collaboration service may provide recommendations that users collaborate on resources, share documents with one another, open, preview and/or incorporate subject matter from documents, and/or recommend that users collaborate and create new groups and/or join existing groups based on clustering documents via application of one or more language processing and/or machine learning models.).
D'Oria, Bawa, Dudani, Boada, and Rathje are combinable for the same rationale as set forth above with respect to claim 2.
Regarding claim 16, D'Oria, as modified by Bawa and Dudani, teaches The document management system of claim 15.
Bawa teaches wherein the set of features of document include one or more of: terms used within the document, clauses used within the document ([0027] The categorizer 128 is configured to categorize input documents into corresponding categories of a group predefined document categories based on similarities between the input documents and documents of the predefined document categories. For example, the categorizer 128 may be configured to analyze the input documents to identify and set of features of document include one or more of extract particular features (also referred to as key performance indicators (KPIs)) that are used to assign the input documents to predefined document categories that include documents having the most similar features.; [0028] To illustrate, the categorizer 128 may be configured to perform vectorization and natural language processing (NLP) on an input document, such as tokenization, lemmatization, clauses used within the document sentencization, part-of-speech tagging, bag of words vectorization, terms used within the document term frequency-inverse document frequency (TF-IDF) vectorization, stop-character parsing, named entity recognition, semantic relation extraction, and the like, to generate word features, such as word identification (e.g., for generating a word corpus), word frequencies, word ratios, and the like, to generate or extract word features from the input document.),
images within the document ([0027] The features may include word features and word layout features, non-word object layout features (referred to herein as “pixel features” that correspond to layout of non-word objects, such as images within the document images, graphics, tables, lines, bullets, designs, colors, headings, sub-headings, and the like), or a combination thereof.),
entities associated with the document ([0030] The annotator 132 is configured to perform category-specific annotation (e.g., to generate annotation data) based on input documents. To illustrate, each document category may have a specific entities associated with the document group of entities (e.g., particular names, places, amounts, dates, words, phrases, and the like) that are highly relevant to summarizing documents of the respective document category, and the annotator 132 may be configured to analyze feature data for an unlabeled input document and generate annotation data that includes the names (e.g., identifiers) of the entities and the corresponding entity values within the input document.),
templates used to generate the document ([0053] Additionally or alternatively, the training data 110 may include a third portion (e.g., third training data) that is used by the training engine 126 to train the third ML models 138. The third training data may be generated based on one or more document templates used to generate the document summary templates (or reference document summaries) for the different predetermined document categories. To illustrate, one or more summary templates for each of the predefined document categories may be generated by one or more document experts, by an automated document management application, or a combination thereof.),
D'Oria, as modified by Bawa and Dudani, fails to teach permissions associated with the document, actions taken on the document, characteristics of the user, or characteristics of entities associated with the documents.
Boada teaches permissions associated with the document, actions taken on the document ([0043] Text editor 412 allows users to view and modify text from documents in the storage database 408 a based on permissions associated with the document user permissions. These changes are stored, tracked, and analyzed as described above. Text editor 412 provides recommendations to the user based on five main cognitive linguistic modules: (readability improvement) engine 410 a, (standardization) engine 410 b, (cross-document consolidation) engine 410 c, (topical coherence) engine 410 d, and (spelling and grammar) engine 410 e. The edit recommendations identified by the engines are each scored to assess the difficulty of the edit task as well the estimate the effort/time required to complete. Features used to generate these estimates include a actions taken on the document type of task (e.g., a consolidation task is more difficult than a single spelling error), a length of text involved in the editing task, and skill set and productivity metrics available in the editor profiles. These estimates, along with the edit recommendations themselves and the editor assignments, are updated based on user activity with hardware controller 14.),
D'Oria, Bawa, Dudani, and Boada are combinable for the same rationale as set forth above with respect to claim 2.
Rathje teaches characteristics of the user, or characteristics of entities associated with the documents ([0019] Topical classifications and/or characteristics of the user user characteristics (e.g., owner, author, and/or editor characteristics) may be associated with the electronic resources as attributes. The attributes may be searchable. In some examples, the resources may be associated/included in a graphical resource matrix comprised of resource nodes and edges. The edges may comprise relationships between the resources in the matrix (e.g., users in common, characteristics of users in common, classification types in common, tasks in common, etc.).; [0020] In additional examples, the resource collaborations service may provide recommendation to unrelated users to collaborate on documents with one another. In some examples, the resource collaboration service may provide recommendations that users collaborate, create new groups and/or join existing groups based on determining that characteristics of entities associated with the documents users share characteristics and/or determining that resources have topical overlap.; [0021] In some examples, the resource collaboration service may provide recommendations that users collaborate on resources, share documents with one another, open, preview and/or incorporate subject matter from documents, and/or recommend that users collaborate and create new groups and/or join existing groups based on clustering documents via application of one or more language processing and/or machine learning models.).
D'Oria, Bawa, Dudani, Boada, and Rathje are combinable for the same rationale as set forth above with respect to claim 2.
Claims 5, 12, 19 are rejected under 35 U.S.C. 103 as being unpatentable over D'Oria, in view of Bawa, Dudani, and further in view of Coquard et al. (U.S. Pre-Grant Publication No. 20200327172, hereinafter ‘Coquard') and Lillemo et al. (U.S. Pre-Grant Publication No. 20220180051, hereinafter ‘Lillemo').
Regarding claim 5, D'Oria, as modified by Bawa and Dudani, teaches The method of claim 1.
D'Oria teaches wherein the set of actions can include one or more of: replacing text with fields ([0048] As another example, the user may highlight text within a particular element and may type in new or modified text to replace the highlighted text.),
adding signature fields ([0042] The detectable elements may include any type of element or component of a document, such as one or more text blocks, one or more labels, one or more fields (e.g., text input fields), one or more tables, one or more buttons, one or more check boxes, one or more particularly formatted fields (e.g., date fields, time fields, currency fields, signature fields, etc.), other elements, or a combination thereof.; [0091] For example, a producer signature field 514 may be linked to a file location that stores a digital signature for the user. In such an example, the digital signature may be auto-populated in producer signature field 514 when the user opens the electronic form.),
populating fields with data from external data sources ([0048] For example, the user may link an input field to a file or directory path, a database, or another data source, and, when a final version of the electronic form is accessed for user completion, the input field may be auto-populated with information located at the file or directory path, the database, or the other data source.), and
providing the document for review or signature to an entity associated with the document ([0091] Additionally or alternatively, one or more input fields may be linked to data sources to cause auto-population of the input fields. For example, a producer signature field 514 may be linked to a file location that stores a digital signature for the user. In such an example, the digital signature may be auto-populated in producer signature field 514 when the user opens the electronic form.).
Bawa teaches synchronizing the document with an external document system ([0036] Documents generated by the user device 140 and the document processing device 102 may be synchronizing the document with an external document system provided to the databases 142 for storage, and documents retrieved from the databases 142 may be provided to the document processing device 102 or the databases 142. Although illustrated in FIG. 1 as a single database, in some other implementations, the databases 142 include multiple communicatively coupled databases.),
D'Oria, as modified by Bawa and Dudani, fails to teach replacing text with pre-approved versions of clauses, change a tense within the document,
Coquard teaches replacing text with pre-approved versions of clauses ([0068]
In an embodiment, the output data comprises a second contract document including at least one new clause replacing at least one clause of the plurality of clauses.; [0118] In some embodiments, a clause title in a contract document may be replaced with a predetermined clause title associated with other clauses that are clustered with and/or within a threshold distance of the clause corresponding to the title.),
D'Oria, Bawa, Dudani, and Coquard are considered to be analogous to the claimed invention because they are in the same field of document management. In view of the teachings of D'Oria, Bawa, and Dudani, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Coquard to D'Oria before the effective filing date of the claimed invention in order to generate contract summaries and annotated contract documents, extraction of parameters, real-time management of metadata, and other like outputs (cf. Coquard, [0090] The unique arrangement and configuration of some embodiments allow for numerous beneficial results, including the generation of contract summaries and annotated contract documents, extraction of parameters, real-time management of metadata, and other like outputs. Further, some embodiments allow for the processing of contract documents that are in various formats, with or without fields, and in different languages. Additional technical benefits are provided as explained herein. Embodiments may be utilized in any domain and for any type of contract document, such as a contract for the sale of goods, a license, a service level agreement, and/or the like.).
Lillemo teaches change a tense within the document ([0027] In some implementations, patent analysis system 102 may generate a set of claims based on the document model. For example, based on a user selection and the document model including claims of a first statutory class (e.g., a method class), patent analysis system 102 may generate claims of a second statutory class (e.g., an apparatus class) by change a tense within the document altering a verb tense, changing wordings, or identifying claim dependencies, among other examples.),
D'Oria, Bawa, Dudani, Coquard, and Lillemo are considered to be analogous to the claimed invention because they are in the same field of document management. In view of the teachings of D'Oria, Bawa, Dudani, and Coquard, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Lillemo to D'Oria before the effective filing date of the claimed invention in order to enable preparation of highly accurate output text in a patent application by using one or more natural language models coupled with a programmable templating language (cf. Lillemo, [0013] Some aspects described herein enable preparation of highly accurate output text in a patent application by using one or more natural language models coupled with a programmable templating language. For example, a system may perform natural language processing (NLP) using a plurality of component processes, such as a first natural language understanding (NLU) process and a second natural language generation (NLG) process. As a specific example, for NLU, a system may use an NLP platform, such as a spaCy modularizable platform. The system may use a templating language (e.g., used for templating websites), such as Handlebars, and may embed logic in a generated template. Templating languages may be used for generating HTML, but may not be well suited to generating formatted patent document text. Accordingly, the system may deploy a set of helper functions and logic to translate the template text to a format useable in a word processing environment, such as in Microsoft Word. The system may extend a functionality of an underlying engine of the templating language with functions that manipulate the text based on a data model presented by the NLP platform. In this way, the system may generate precise text (e.g., more precisely output based on a given input text than is achieved with, for example, autoregressive language models, such as GPT-3 style free-form) that is more multi-dimensional than a template type output (e.g., in which words are filled into a static template.)).
Regarding claim 12, D'Oria, as modified by Bawa and Dudani, teaches The non-transitory computer-readable storage medium of claim 8.
D'Oria teaches wherein the set of actions can include one or more of: replacing text with fields ([0048] As another example, the user may highlight text within a particular element and may type in new or modified text to replace the highlighted text.),
adding signature fields ([0042] The detectable elements may include any type of element or component of a document, such as one or more text blocks, one or more labels, one or more fields (e.g., text input fields), one or more tables, one or more buttons, one or more check boxes, one or more particularly formatted fields (e.g., date fields, time fields, currency fields, signature fields, etc.), other elements, or a combination thereof.; [0091] For example, a producer signature field 514 may be linked to a file location that stores a digital signature for the user. In such an example, the digital signature may be auto-populated in producer signature field 514 when the user opens the electronic form.),
populating fields with data from external data sources ([0048] For example, the user may link an input field to a file or directory path, a database, or another data source, and, when a final version of the electronic form is accessed for user completion, the input field may be auto-populated with information located at the file or directory path, the database, or the other data source.), and
providing the document for review or signature to an entity associated with the document ([0091] Additionally or alternatively, one or more input fields may be linked to data sources to cause auto-population of the input fields. For example, a producer signature field 514 may be linked to a file location that stores a digital signature for the user. In such an example, the digital signature may be auto-populated in producer signature field 514 when the user opens the electronic form.).
Bawa teaches synchronizing the document with an external document system ([0036] Documents generated by the user device 140 and the document processing device 102 may be synchronizing the document with an external document system provided to the databases 142 for storage, and documents retrieved from the databases 142 may be provided to the document processing device 102 or the databases 142. Although illustrated in FIG. 1 as a single database, in some other implementations, the databases 142 include multiple communicatively coupled databases.),
D'Oria, as modified by Bawa and Dudani, fails to teach replacing text with pre-approved versions of clauses, change a tense within the document,
Coquard teaches replacing text with pre-approved versions of clauses ([0068]
In an embodiment, the output data comprises a second contract document including at least one new clause replacing at least one clause of the plurality of clauses.; [0118] In some embodiments, a clause title in a contract document may be replaced with a predetermined clause title associated with other clauses that are clustered with and/or within a threshold distance of the clause corresponding to the title.),
D'Oria, Bawa, Dudani, and Coquard are combinable for the same rationale as set forth above with respect to claim 5.
Lillemo teaches change a tense within the document ([0027] In some implementations, patent analysis system 102 may generate a set of claims based on the document model. For example, based on a user selection and the document model including claims of a first statutory class (e.g., a method class), patent analysis system 102 may generate claims of a second statutory class (e.g., an apparatus class) by change a tense within the document altering a verb tense, changing wordings, or identifying claim dependencies, among other examples.),
D'Oria, Bawa, Dudani, Coquard, and Lillemo are combinable for the same rationale as set forth above with respect to claim 5.
Regarding claim 19, D'Oria, as modified by Bawa and Dudani, teaches The document management system of claim 15.
D'Oria teaches wherein the set of actions can include one or more of: replacing text with fields ([0048] As another example, the user may highlight text within a particular element and may type in new or modified text to replace the highlighted text.),
adding signature fields ([0042] The detectable elements may include any type of element or component of a document, such as one or more text blocks, one or more labels, one or more fields (e.g., text input fields), one or more tables, one or more buttons, one or more check boxes, one or more particularly formatted fields (e.g., date fields, time fields, currency fields, signature fields, etc.), other elements, or a combination thereof.; [0091] For example, a producer signature field 514 may be linked to a file location that stores a digital signature for the user. In such an example, the digital signature may be auto-populated in producer signature field 514 when the user opens the electronic form.),
populating fields with data from external data sources ([0048] For example, the user may link an input field to a file or directory path, a database, or another data source, and, when a final version of the electronic form is accessed for user completion, the input field may be auto-populated with information located at the file or directory path, the database, or the other data source.), and
providing the document for review or signature to an entity associated with the document ([0091] Additionally or alternatively, one or more input fields may be linked to data sources to cause auto-population of the input fields. For example, a producer signature field 514 may be linked to a file location that stores a digital signature for the user. In such an example, the digital signature may be auto-populated in producer signature field 514 when the user opens the electronic form.).
Bawa teaches synchronizing the document with an external document system ([0036] Documents generated by the user device 140 and the document processing device 102 may be synchronizing the document with an external document system provided to the databases 142 for storage, and documents retrieved from the databases 142 may be provided to the document processing device 102 or the databases 142. Although illustrated in FIG. 1 as a single database, in some other implementations, the databases 142 include multiple communicatively coupled databases.),
D'Oria, as modified by Bawa and Dudani, fails to teach replacing text with pre-approved versions of clauses, change a tense within the document,
Coquard teaches replacing text with pre-approved versions of clauses ([0068]
In an embodiment, the output data comprises a second contract document including at least one new clause replacing at least one clause of the plurality of clauses.; [0118] In some embodiments, a clause title in a contract document may be replaced with a predetermined clause title associated with other clauses that are clustered with and/or within a threshold distance of the clause corresponding to the title.),
D'Oria, Bawa, Dudani, and Coquard are combinable for the same rationale as set forth above with respect to claim 5.
Lillemo teaches change a tense within the document ([0027] In some implementations, patent analysis system 102 may generate a set of claims based on the document model. For example, based on a user selection and the document model including claims of a first statutory class (e.g., a method class), patent analysis system 102 may generate claims of a second statutory class (e.g., an apparatus class) by change a tense within the document altering a verb tense, changing wordings, or identifying claim dependencies, among other examples.),
D'Oria, Bawa, Dudani, Coquard, and Lillemo are combinable for the same rationale as set forth above with respect to claim 5.
Claims 7, 14 are rejected under 35 U.S.C. 103 as being unpatentable over D'Oria, in view of Bawa, Dudani, and further in view of Sahgal et al. (U.S. Pre-Grant Publication No. 20210326482, hereinafter ‘Sahgal').
Regarding claim 7, D'Oria, as modified by Bawa and Dudani, teaches The method of claim 1.
D'Oria teaches wherein the set of actions is identified to include actions taken by a threshold number of users or actions a threshold number of times on other documents of the document type ([0111] For example, configuration options 1204 may include an option for specifying the one or more data structures to be created, an option for naming the one or more data structures, an option for selecting a theme associated with the electronic form, actions a threshold number of times on other documents of the document type options for controlling a number of views associated with the electronic form, and options for controlling a source view or template for the electronic form, as non-limiting examples.),
D'Oria, as modified by Bawa and Dudani, fails to teach wherein the set of actions is identified to include actions taken by a threshold number of users or actions a threshold number of times on other documents of the document type.
Sahgal teaches wherein the set of actions is identified to include actions taken by a threshold number of users or actions a threshold number of times on other documents of the document type ([0077] FIG. 8 is an interaction diagram illustrating a process for e-signing a secondary document set during an online sharing session according to one embodiment. In the example shown in FIG. 8, a secondary document set is shared between a representative of the enterprise using enterprise device 107 and a non-representative of the enterprise using client device 109. However, in other embodiments a secondary document set set of actions is identified to include actions taken by a threshold number of users may be shared with any type of participant and any number of participants. Furthermore, the process for e-signing a secondary document set may include other steps than shown in FIG. 8.; [0080] The document management system 103 creates 809 an online sharing session that includes the secondary document set for e-signing. In the embodiment shown in FIG. 8A, only the client device 109 participants in the online sharing session of the secondary document set. During the online sharing session, the enterprise device 107 displays 811 a list of the secondary document set 811. The list indicates the secondary documents that require e-signing during the online sharing session. The client device 109 iteratively selects each secondary document from the list for review and adds 813 the participant's electronic signature to each secondary document upon approval of each secondary document.).
D'Oria, Bawa, Dudani, and Sahgal are considered to be analogous to the claimed invention because they are in the same field of document management. In view of the teachings of D'Oria, Bawa, and Dudani, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Sahgal to D'Oria before the effective filing date of the claimed invention in order to perform the techniques described in order to improve efficiency of the document management system and reduce computation time when sharing documents for e-signing (cf. Sahgal, [0093] In one implementation, the document management system 103/1003, enterprise devices 107, client devices 109, and third-party document source each include processing resources 1101, main memory 1103, read only memory (ROM) 1105, storage device 1107, and a communication interface 1109. The document management system 103/1003, enterprise devices 107, client devices 109, and third-party document source 111 each include at least one processor 1101 for processing information and a main memory 1103, such as a random access memory (RAM) or other dynamic storage device, for storing information and instructions to be executed by the processor 1001. In one embodiment, multiple processors are employed by the document management system 103/1003 to perform the techniques described above in order to improve efficiency of the document management system 103/1003 and reduce computation time when sharing documents for e-signing.).
Regarding claim 14, D'Oria, as modified by Bawa and Dudani, teaches The non-transitory computer-readable storage medium of claim 8.
D'Oria teaches wherein the set of actions is identified to include actions taken by a threshold number of users or actions a threshold number of times on other documents of the document type ([0111] For example, configuration options 1204 may include an option for specifying the one or more data structures to be created, an option for naming the one or more data structures, an option for selecting a theme associated with the electronic form, actions a threshold number of times on other documents of the document type options for controlling a number of views associated with the electronic form, and options for controlling a source view or template for the electronic form, as non-limiting examples.),
D'Oria, as modified by Bawa and Dudani, fails to teach wherein the set of actions is identified to include actions taken by a threshold number of users or actions a threshold number of times on other documents of the document type.
Sahgal teaches wherein the set of actions is identified to include actions taken by a threshold number of users or actions a threshold number of times on other documents of the document type ([0077] FIG. 8 is an interaction diagram illustrating a process for e-signing a secondary document set during an online sharing session according to one embodiment. In the example shown in FIG. 8, a secondary document set is shared between a representative of the enterprise using enterprise device 107 and a non-representative of the enterprise using client device 109. However, in other embodiments a secondary document set set of actions is identified to include actions taken by a threshold number of users may be shared with any type of participant and any number of participants. Furthermore, the process for e-signing a secondary document set may include other steps than shown in FIG. 8.; [0080] The document management system 103 creates 809 an online sharing session that includes the secondary document set for e-signing. In the embodiment shown in FIG. 8A, only the client device 109 participants in the online sharing session of the secondary document set. During the online sharing session, the enterprise device 107 displays 811 a list of the secondary document set 811. The list indicates the secondary documents that require e-signing during the online sharing session. The client device 109 iteratively selects each secondary document from the list for review and adds 813 the participant's electronic signature to each secondary document upon approval of each secondary document.).
D'Oria, Bawa, Dudani, and Sahgal are combinable for the same rationale as set forth above with respect to claim 7.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAGGIE MAIDO whose telephone number is (703) 756-1953. The examiner can normally be reached M-Th: 6am - 4pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael Huntley can be reached on (303) 297-4307. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MM/Examiner, Art Unit 2129
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129
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