DETAILED ACTION
This final Office action is responsive to Applicant’s amendment filed February 16, 2026. Claims 1, 5, 11, 15, and 20 have been amended. Claims 1-20 are presented for examination.
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 .
Response to Arguments
Applicant's arguments filed February 16, 2026 have been fully considered but they are not persuasive.
Preliminarily, it is noted that the previously-pending rejections under 35 U.S.C. § 112(b) are withdrawn in response to Applicant’s amendments to claim 20.
Regarding the rejection under 35 U.S.C. § 101, on page 8 of the response, Applicant argues the following:
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Applicant’s specification states, “The term ‘data object’ refers to a data entity indicative of at least a portion of configuration data for a configuration node associated with a configuration workflow. A data object may represent values for one or more configuration input parameters associated with the configuration node.” (Spec: ¶ 38) In other words, a data object may simply be a collection of data. There is no requirement in the independent claims that specialized computing infrastructure be used to execute the claim limitations. In terms of the additional elements, claim 1 recites a computer-implemented method for intelligent report configuration. Claim 11 recites an apparatus for intelligent report configuration, the apparatus comprising at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus to perform the recited operations. Claim 20 recites a computer program product for intelligent report configuration, the computer program product comprising at least one non-transitory computer-readable storage medium, the at least one non-transitory computer-readable storage medium including instructions that when executed by at least one processor, cause the computer program product to perform the recited operations. Claims 1, 11, and 20 further recite that data objects are displayed on a configuration user interface rendered on a user device. None of the additional elements, neither individually nor in combination, present a specialized computing infrastructure. A human user can generate the one or more data objects comprised of determining at least one data object for a chapter configuration node based at least in part on a report configuration node selected according to the hierarchical arrangement of the report configuration workflow. For example, a human user can map out the hierarchical arrangement of the report configuration and its respective workflow.
On page 9 of Applicant’s response, Applicant argues that any abstract ideas are integrated into a practical application, stating, “For instance, the subject matter of amended independent claim 1 facilitates a practical application of intelligent report configuration technology to automatically determine and generate configuration state data associated with multiple interrelated portions of a report having a hierarchical data structure.” The claims do not present specific details of the technical aspects of intelligent report configuration. At present, the claims generate information to guide a layout of information; however, the claims do not incorporate specific technical details. Generating the one or more data objects comprised of determining at least one data object for a chapter configuration node based at least in part on a report configuration node selected according to the hierarchical arrangement of the report configuration workflow may simply involve defining a template for how to select and/or lay out contents of a book or other document, for example. Causing display of the one or more data objects on a configuration user interface rendered on a user device can present the layout and/or content associated with the data objects. A human user can present such information. At present, the claims are lacking in specificity as it relates to detailed integration of the additional elements and how they perform more than generic processing operations at a high level of generality.
Regarding the rejection under 35 U.S.C. § 102, on page 13 of the response, Applicant argues that Ramanujam does not address the newly amended-in limitation “wherein generating the one or more data objects comprises of determining at least one data object for a chapter configuration node based at least in part on a report configuration node selected according to the hierarchical arrangement of the report configuration workflow.” The Examiner respectfully disagrees. Applicant’s specification states, “The term ‘data object’ refers to a data entity indicative of at least a portion of configuration data for a configuration node associated with a configuration workflow. A data object may represent values for one or more configuration input parameters associated with the configuration node.” (Spec: ¶ 38) In other words, a data object may simply be a collection of data. Figures 10A-10J of Ramanujam present hierarchies of the arrangements of information (such as by section and subsections assigned to certain sections). ¶ 72 of Ramanujam explains that predefined templates may be associated with user-selected sections or sub-sections. The claimed chapters are akin to sections and/or sub-sections, for example. The data objects may simply present guidance regarding report and chapter configuration as well as hierarchical arrangement of the report configuration workflow.
Regarding the aforementioned claim limitation, Applicant further argues, “This introduces a machine-driven configuration dependency mechanism that is not taught or suggested in Ramanujam. This represents a system-level configuration mechanism, not a document authoring or content mapping mechanism. Ramanujam fails to describe any workflow graph in which configuration decisions at one node control or constrain configuration behavior at other nodes.” (Page 13 of Applicant’s response) It is not clear what Applicant means by a “machine-driven configuration dependency mechanism” or one that “represents a system-level configuration mechanism,” especially as it pertains to the particular language explicitly recited in the claims.
The rejections are maintained.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claimed invention is directed to configuring reports (Spec: ¶ 1) without significantly more.
Step
Analysis
1: Statutory Category?
Yes – The claims fall within at least one of the four categories of patent eligible subject matter. Process (claims 1-10), Apparatus (claims 11-19), Article of Manufacture (claim 20)
Independent claims:
Step
Analysis
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claims recite:
[Claims 1, 11, 20] A method for intelligent report configuration, the method comprising:
aggregating historical configuration data based at least in part on a plurality of historical reports;
receiving a configuration request for a report having a hierarchical data structure;
identifying at least one configuration node of a report configuration workflow for configuring the report, the report configuration workflow defined by a hierarchical arrangement of a plurality of configuration nodes corresponding to the hierarchical data structure of the report;
generating, based at least in part on the historical configuration data and using one or more models, one or more data objects for the at least one configuration node, the one or more data objects indicative of at least a portion of the configuration data for one or more portions of the report, wherein generating the one or more data objects comprises of determining at least one data object for a chapter configuration node based at least in part on a report configuration node selected according to the hierarchical arrangement of the report configuration workflow;
causing display of the one or more data objects.
Aside from the additional elements, the aforementioned claim details exemplify the abstract idea(s) of a mental process (since the details include concepts performed in the human mind, including an observation, evaluation, judgment, and/or opinion). As explained in MPEP § 2106(a)(2)(C)(III), “The courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, ‘methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’’ 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)).” The limitations reproduced above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting the additional elements identified in Step 2A – Prong 2 below, nothing in the claim elements precludes the steps from practically being performed in the mind and/or by a human using a pen and paper. For example, but for the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the respectively recited steps/functions of the claims, as drafted and set forth above, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and/or with the use of pen and paper. A human user can perform the operations presented above. For example, a human user can evaluate historical report configuration data and use it to follow a template to create a report of a similar configuration and then display data objects for the report. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with pen and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Additionally, claims 1, 11, and 20 recite identifying at least one configuration node of a report configuration workflow for configuring the report and details thereof. As evidenced by dependent claims 5, 8, 15, and 18 (discussed in greater detail below), workload related to the configuration nodes may include user assignments for a chapter and user workload, thereby implying that (based on a broadest reasonable interpretation) the report configuration workflow may include workflow involving human users. Aside from the additional elements, the aforementioned claim details exemplify a method of organizing human activity (since the details include examples of commercial or legal interactions, including advertising, marketing or sales activities or behaviors, and/or business relations and managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions). More specifically, the evaluated workflow may be interpreted as being related to managing and/or instructing people (i.e., organizing human activity); therefore, aside from the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the limitation regarding identifying at least one configuration node of a report configuration workflow for configuring the report and details thereof encompasses the abstract idea of organizing human activity.
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
Claim 1 recites a computer-implemented method for intelligent report configuration.
Claim 11 recites an apparatus for intelligent report configuration, the apparatus comprising at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus to perform the recited operations.
Claim 20 recites a computer program product for intelligent report configuration, the computer program product comprising at least one non-transitory computer-readable storage medium, the at least one non-transitory computer-readable storage medium including instructions that when executed by at least one processor, cause the computer program product to perform the recited operations.
Claims 1, 11, and 20 further recite that data objects are displayed on a configuration user interface rendered on a user device.
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a computer environment. The claimed processing elements are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea(s). Simply implementing the abstract idea(s) on a general-purpose processor is not a practical application of the abstract idea(s); Applicant’s specification discloses that the invention may be implemented using general-purpose processing elements and other generic components (Spec: ¶¶ 122-124).
The use of a processor/processing elements (e.g., as recited in all of the claims) facilitates generic processor operations. The use of a memory or machine-readable media with executable instructions facilitates generic processor operations.
The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic computer functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic computer components. There is no indication in the Specification that the steps/functions of the claims require any inventive programming or necessitate any specialized or other inventive computer components (i.e., the steps/functions of the claims may be implemented using capabilities of general-purpose computer components). Accordingly, the additional elements do not integrate the abstract ideas into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea(s).
The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
There is no transformation or reduction of a particular article to a different state or thing recited in the claims.
2B: Claim(s) Provide(s) an Inventive Concept?
No – The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s). As discussed above with respect to integration of the abstract idea(s) into a practical application, the use of the additional elements to perform the steps identified in Step 2A – Prong 1 above amounts to no more than mere instructions to apply the exceptions using a generic computer component(s). Mere instructions to apply an exception using a generic computer component(s) cannot provide an inventive concept. The claims are not patent eligible.
Dependent claims:
Step
Analysis
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claims recite:
[Claims 3, 13] wherein the plurality of historical reports are associated with a plurality of entities having at least one characteristic in common.
[Claims 4, 14] wherein the plurality of historical reports are associated with an individual entity.
[Claims 5, 15] wherein the plurality of configuration nodes comprises at least one of the chapter configuration node configured for defining one or more chapters for the report, and one or more user configuration nodes for each of the one or more chapters, wherein each user configuration node for a chapter is configured to enable configuration of one or more user assignments for the chapter.
[Claims 6, 16] wherein the plurality of configuration nodes further comprises one or more chart configuration nodes for each of the one or more chapters, wherein each chart configuration node for a chapter is configured to enable configuration of one or more charts for the chapter.
[Claims 7, 17] wherein at least one model of the one or more models is trained to generate at least one data object for each of the one or more chapters based at least in part on a similarity measure between subsets of the historical configuration data, wherein each subset of the historical configuration data is associated with a particular historical report of the plurality of historical reports.
[Claims 8, 18] wherein at least one model of the one or more models is trained to generate at least one data object for each of the one more chapters based at least in part on the historical configuration data and real-time data indicative of at least a workload for each of one or more candidate users.
[Claim 10] wherein the historical configuration data comprises one or more segments, wherein each segment corresponds to a particular configuration node of the plurality of configuration nodes.
The dependent claims further present details of the abstract ideas identified in regard to the independent claims.
Aside from the additional elements, the aforementioned claim details exemplify the abstract idea(s) of a mental process (since the details include concepts performed in the human mind, including an observation, evaluation, judgment, and/or opinion). As explained in MPEP § 2106(a)(2)(C)(III), “The courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, ‘methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’’ 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)).” The limitations reproduced above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting the additional elements identified in Step 2A – Prong 2 below, nothing in the claim elements precludes the steps from practically being performed in the mind and/or by a human using a pen and paper. For example, but for the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the respectively recited steps/functions of the claims, as drafted and set forth above, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and/or with the use of pen and paper. A human user can perform the operations presented above. For example, a human user can evaluate historical report configuration data and use it to follow a template to create a report of a similar configuration and then display data objects for the report. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with pen and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea. Further noted is that the recitations of the models being trained (in claims 7-8 and 17-18) are not necessarily limited to machine learning. Non-automated models may be trained in the sense of the models being optimized manually by a human mentally or with pen and paper, for example.
Additionally, claim 5 and 15 recite wherein each user configuration node for a chapter is configured to enable configuration of one or more user assignments for the chapter and claims 8 and 18 recite wherein at least one model of the one or more models is trained to generate at least one data object for each of the one more chapters based at least in part on the historical configuration data and real-time data indicative of at least a workload for each of one or more candidate users. Enabling user assignments for the chapter and indicating a workload for candidate users are examples of managing and/or instructing people. Aside from the additional elements, the aforementioned claim details exemplify a method of organizing human activity (since the details include examples of commercial or legal interactions, including advertising, marketing or sales activities or behaviors, and/or business relations and managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions). More specifically, the evaluated processes (introduced in claims 5, 8, 15, and 18) are related to managing and/or instructing people (i.e., organizing human activity); therefore, aside from the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the limitations identified in the more detailed claim listing above encompass the abstract idea of organizing human activity.
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
The dependent claims include the additional elements of their independent claims.
Claim 1 recites a computer-implemented method for intelligent report configuration.
Claim 11 recites an apparatus for intelligent report configuration, the apparatus comprising at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus to perform the recited operations.
Claim 20 recites a computer program product for intelligent report configuration, the computer program product comprising at least one non-transitory computer-readable storage medium, the at least one non-transitory computer-readable storage medium including instructions that when executed by at least one processor, cause the computer program product to perform the recited operations.
Claims 1, 11, and 20 further recite that data objects are displayed on a configuration user interface rendered on a user device.
Claims 2 and 12 recite that the signal is received from the user device.
Claims 9 and 19 recite wherein the configuration user interface comprises one or more of a chapter configuration user interface, a user configuration user interface, a chart configuration user interface, or a table configuration user interface.
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a computer environment. The claimed processing elements are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea(s). Simply implementing the abstract idea(s) on a general-purpose processor is not a practical application of the abstract idea(s); Applicant’s specification discloses that the invention may be implemented using general-purpose processing elements and other generic components (Spec: ¶¶ 122-124).
The use of a processor/processing elements (e.g., as recited in all of the claims) facilitates generic processor operations. The use of a memory or machine-readable media with executable instructions facilitates generic processor operations.
The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic computer functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic computer components. There is no indication in the Specification that the steps/functions of the claims require any inventive programming or necessitate any specialized or other inventive computer components (i.e., the steps/functions of the claims may be implemented using capabilities of general-purpose computer components). Accordingly, the additional elements do not integrate the abstract ideas into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea(s).
The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
There is no transformation or reduction of a particular article to a different state or thing recited in the claims.
2B: Claim(s) Provide(s) an Inventive Concept?
No – The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s). As discussed above with respect to integration of the abstract idea(s) into a practical application, the use of the additional elements to perform the steps identified in Step 2A – Prong 1 above amounts to no more than mere instructions to apply the exceptions using a generic computer component(s). Mere instructions to apply an exception using a generic computer component(s) cannot provide an inventive concept. The claims are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Ramanujam (US 2024/0086647).
[Claim 1] Ramanujam discloses a computer-implemented method for intelligent report configuration (title, abstract), the method comprising:
aggregating historical configuration data based at least in part on a plurality of historical reports (¶ 28 – “The automated authoring engine receives and stores 102 multiple source documents in a source database. The source documents comprise, for example, a protocol document, a statistical analysis plan (SAP) document, a case report form (CRF), safety narratives, in-text tables, post-text tables, in-text reports, summary reports, tables, listings, and figures (TLFs), etc.”; ¶ 48 – “In an embodiment, the AI-enabled system 200 comprises the source database 802, the section extractor 202, the section mapper 206, and an NLP engine 805 constituting the automated authoring engine 909 exemplarily illustrated in FIG. 9. In an embodiment, the AI-enabled system 200 allows a user 801 to access the automated authoring engine 909 via a graphical user interface (GUI). The user 801 uploads source documents, for example, a protocol document, a statistical analysis plan document, a case report form, safety narratives, in-text tables, post-text tables, summary reports, tables, listings, figures, etc., to the automated authoring engine 909 via the GUI. The automated authoring engine 909 stores the uploaded source documents in a file storage system, for example, the source database 802.”; ¶ 58 – “For section mapping, the section mapper 206 matches the sections of the scientific document template with the sections extracted from the source documents and in an embodiment, if a near match is not found, the section mapper 206 employs the machine learning model 313 trained by the ML module 914 for predicting appropriate sections from among the sections in the scientific document template for rendering the content from the source documents based on the matching. The ML module 914 trains the machine learning model 313 using historical scientific document information acquired from different users involved in preparing scientific documents.”);
receiving a signal indicative of a configuration request for a report having a hierarchical data structure (¶ 57 – “A user 801 logs into the automated authoring engine 909 via a user interface, for example, a graphical user interface (GUI), rendered by the automated authoring engine 909 and accessible on the user device 901, and after authentication by the user authentication module 910, uploads multiple source documents for automatic generation of a scientific document. The data reception module 912 receives the uploaded source documents and stores the source documents in the source database 802. In an embodiment, the data processing module 913 performs file conversion of the source documents to convert the source documents of different formats into a standardized format, for example, a portable document format (PDF). The data processing module 913 stores the converted source documents in the source database 802. In an embodiment, the user 801 configures one or more sub-sections under the fixed sections of the scientific document template via the GUI rendered by the automated authoring engine 909. The ML module 914, in communication with the template configuration module 911, configures the user-configured sub-section(s) as feedback to retrain the machine learning model 313.” The user, via a user device, initiates the process to generate a report.; ¶ 27 – “The predefined clinical study report (CSR) template comprises fixed main sections, for example, a title page, a study synopsis, a table of contents, a list of abbreviations and definition of terms, ethics and regulatory approval, study objectives, etc., and fixed sub-sections, for example, primary objective, secondary objective, etc., that are mandatory. The predefined CSR template further comprises sub-sections that can be added, edited, and deleted based on clinical study requirements. A user, for example, a medical writer, may add a sub-section such as exploratory objective under the main section labeled as “study objectives” in the predefined CSR template. In an embodiment, the automated authoring engine allows dragging and realignment of the sub-sections on a user interface rendered by the automated authoring engine. The automated authoring engine allows user-configurable sub-sections to be realigned based on user preferences.” The existence of sections and sub-sections provides an example of a hierarchical data structure.);
identifying at least one configuration node of a report configuration workflow for configuring the report, the report configuration workflow defined by a hierarchical arrangement of a plurality of configuration nodes corresponding to the hierarchical data structure of the report (¶ 36 – “ In an embodiment, the automated authoring engine allows section-wise editing of the automatically generated scientific document by co-authors. There can be more than one author for the same scientific document. For example, when a particular section such as a safety narrative section for a clinical study needs to be written by another author, then the automated authoring engine allows the primary author to assign that particular section to that co-author. The automated authoring engine allows multiple co-authors to work simultaneously on different sections of the same scientific document as assigned by the primary author. In an embodiment, the automated authoring engine allows the co-author(s) to edit only the section assigned by the primary author and not any other section of the scientific document. In an embodiment, the automated authoring engine provides selective access of an entirety of the automatically generated scientific document to one or more co-authors of the automatically generated scientific document for performing one or more actions on the automatically generated scientific document. In another embodiment, the automated authoring engine provides selective access of one or more sections of the automatically generated scientific document to one or more co-authors of the automatically generated scientific document for performing one or more actions on the automatically generated scientific document. The automated authoring engine transmits the automatically generated scientific document to one or more co-authors electronically, for example, via a hyperlink provided in an electronic mail (email), a short message service (SMS) message, an instant message (IM), a direct message (DM), etc., and allows the co-authors to login and access the entire automatically generated scientific document or one or more sections of the automatically generated scientific document for review, selective editing, commenting, etc. The automated authoring engine then allows the co-authors to save and transmit the updated scientific document to the primary author electronically, for example, via a hyperlink provided in an email, an SMS message, an IM, a DM, etc.”; ¶ 64 – “In an embodiment, the automated authoring engine 909 further comprises a workflow and dashboard module 918 configured in accordance with sponsor requirements. Through the workflow and dashboard module 918, the user, for example, a primary author, sends the finalized scientific document to one or more reviewers for a review process. The workflow and dashboard module 918 allows the primary author to send the scientific document to multiple reviewers. The workflow and dashboard module 918 allows the reviewer(s) to add comments and send the scientific document with the comments back to the primary author. The workflow and dashboard module 918 allows the primary author to view all the comments or changes performed in the scientific document by the reviewer(s), dynamically in real time. In an embodiment, the workflow and dashboard module 918 allows the primary author to view all the comments in a consolidated view on the GUI. The workflow and dashboard module 918 allows execution of multiple iterations between the primary author and the reviewer(s). Once the review of the scientific document is finalized, the workflow and dashboard module 918 allows the primary author to send the reviewed scientific document to an approver for executing an approval process.” There is a hierarchy defining a document configuration, such as how sections may include sub-sections. There is also a hierarchy of nodes in the workflow assignments, such as how a primary author can send a document to reviewers for further review.; figs. 10C, 10U, ¶ 22 – Various sections and sub-sections may be viewed on a GUI for editing, review, etc.);
generating, based at least in part on the historical configuration data and using one or more models, one or more data objects for the at least one configuration node, the one or more data objects indicative of at least a portion of the configuration data for one or more portions of the report (¶ 28 – “The automated authoring engine receives and stores 102 multiple source documents in a source database. The source documents comprise, for example, a protocol document, a statistical analysis plan (SAP) document, a case report form (CRF), safety narratives, in-text tables, post-text tables, in-text reports, summary reports, tables, listings, and figures (TLFs), etc.”; ¶ 48 – “In an embodiment, the AI-enabled system 200 comprises the source database 802, the section extractor 202, the section mapper 206, and an NLP engine 805 constituting the automated authoring engine 909 exemplarily illustrated in FIG. 9. In an embodiment, the AI-enabled system 200 allows a user 801 to access the automated authoring engine 909 via a graphical user interface (GUI). The user 801 uploads source documents, for example, a protocol document, a statistical analysis plan document, a case report form, safety narratives, in-text tables, post-text tables, summary reports, tables, listings, figures, etc., to the automated authoring engine 909 via the GUI. The automated authoring engine 909 stores the uploaded source documents in a file storage system, for example, the source database 802.”; ¶ 58 – “For section mapping, the section mapper 206 matches the sections of the scientific document template with the sections extracted from the source documents and in an embodiment, if a near match is not found, the section mapper 206 employs the machine learning model 313 trained by the ML module 914 for predicting appropriate sections from among the sections in the scientific document template for rendering the content from the source documents based on the matching. The ML module 914 trains the machine learning model 313 using historical scientific document information acquired from different users involved in preparing scientific documents.”; ¶¶ 30, 42, 58 – Machine learning models may be used and trained and retrained.), wherein generating the one or more data objects comprises of determining at least one data object for a chapter configuration node based at least in part on a report configuration node selected according to the hierarchical arrangement of the report configuration workflow (NOTE: Applicant’s specification states, “The term ‘data object’ refers to a data entity indicative of at least a portion of configuration data for a configuration node associated with a configuration workflow. A data object may represent values for one or more configuration input parameters associated with the configuration node.” (Spec: ¶ 38) In other words, a data object may simply be a collection of data.; Figures 10A-10J of Ramanujam present hierarchies of the arrangements of information (such as by section and subsections assigned to certain sections). ¶ 72 of Ramanujam explains that predefined templates may be associated with user-selected sections or sub-sections. The claimed chapters are akin to sections and/or sub-sections, for example. The data objects may simply present guidance regarding report and chapter configuration as well as hierarchical arrangement of the report configuration workflow.);
causing display of the one or more data objects on a configuration user interface rendered on a user device (figs. 10C, 10U, ¶ 22 – Various sections and sub-sections may be viewed on a GUI for editing, review, etc.; ¶ 25 – “FIG. 1 illustrates a flowchart of an embodiment of a method for automatically authoring a scientific document, for example, a clinical study report (CSR), using a machine learning (ML) model and natural language processing (NLP) with minimal user intervention. For purposes of illustration, the disclosure herein refers to a clinical study report being automatically authored using an ML model and NLP; however, the scope of the artificial intelligence (AI)-enabled system and method disclosed herein is not limited to automatically authoring a clinical study report, but extends to include automatic authoring of any lengthy, scientific or other document comprising multiple sections and objects such as tables, listings, figures, etc., that typically requires substantial manual effort and time to be written or typed.”).
[Claim 2] Ramanujam discloses wherein the signal is received from the user device (¶ 57 – “A user 801 logs into the automated authoring engine 909 via a user interface, for example, a graphical user interface (GUI), rendered by the automated authoring engine 909 and accessible on the user device 901, and after authentication by the user authentication module 910, uploads multiple source documents for automatic generation of a scientific document. The data reception module 912 receives the uploaded source documents and stores the source documents in the source database 802. In an embodiment, the data processing module 913 performs file conversion of the source documents to convert the source documents of different formats into a standardized format, for example, a portable document format (PDF). The data processing module 913 stores the converted source documents in the source database 802. In an embodiment, the user 801 configures one or more sub-sections under the fixed sections of the scientific document template via the GUI rendered by the automated authoring engine 909. The ML module 914, in communication with the template configuration module 911, configures the user-configured sub-section(s) as feedback to retrain the machine learning model 313.” The user, via a user device, initiates the process to generate a report.).
[Claim 3] Ramanujam discloses wherein the plurality of historical reports are associated with a plurality of entities having at least one characteristic in common (¶ 43 – “Consider an example where a source document 401, for example, a protocol document, is uploaded by a user and saved in a portable document format (PDF) in a common repository, for example, the source database. The section extractor 202 of the automated authoring engine exemplarily illustrated in FIG. 2, extracts 402 and passes sections listed in the Table of Contents (TOC) of the protocol document to a multilayer perceptron (MLP) model to obtain page numbers and section numbers of a “Title” section and a “Synopsis” section of the protocol document. The MLP model finds a best match with an accuracy of, for example, greater than about 80%. Based on the page numbers of the “Title” section and the “Synopsis” section, the section extractor 202 reads 403 title page content, for example, using a Camelot library. The Camelot library is a Python library that extracts content from a PDF file. In another example, the section extractor 202 reads 403 the title page content using a custom entity recognition model. The custom entity recognition model allows identification of different entity types and extraction of entities from the PDF file.” Each user may upload source documents for future use, thereby making the source documents relatively “historical” when used in the future. The documents are thus, associated with each individual (i.e., the user who uploaded the respective document) and, collectively, associated with a plurality of entities (i.e., the collective group of users who uploaded all documents).; ¶ 30 – “The mapping further comprises predicting 104b appropriate sections from among the sections in the scientific document template for rendering the content from the source documents based on the matching using the machine learning model and historical scientific document information acquired from users.”; ¶ 49 – “The section mapper 206 matches the sections of the scientific document template with sections 202a extracted from the source documents; and predicts appropriate sections from among the sections in the scientific document template for rendering the content from the source documents based on the matching using the machine learning model 313 and historical scientific document information acquired from users.” A match implies a commonality in at least one characteristic.).
[Claim 4] Ramanujam discloses wherein the plurality of historical reports are associated with an individual entity (¶ 43 – “Consider an example where a source document 401, for example, a protocol document, is uploaded by a user and saved in a portable document format (PDF) in a common repository, for example, the source database. The section extractor 202 of the automated authoring engine exemplarily illustrated in FIG. 2, extracts 402 and passes sections listed in the Table of Contents (TOC) of the protocol document to a multilayer perceptron (MLP) model to obtain page numbers and section numbers of a “Title” section and a “Synopsis” section of the protocol document. The MLP model finds a best match with an accuracy of, for example, greater than about 80%. Based on the page numbers of the “Title” section and the “Synopsis” section, the section extractor 202 reads 403 title page content, for example, using a Camelot library. The Camelot library is a Python library that extracts content from a PDF file. In another example, the section extractor 202 reads 403 the title page content using a custom entity recognition model. The custom entity recognition model allows identification of different entity types and extraction of entities from the PDF file.” Each user may upload source documents for future use, thereby making the source documents relatively “historical” when used in the future. The documents are thus, associated with each individual (i.e., the user who uploaded the respective document) and, collectively, associated with a plurality of entities (i.e., the collective group of users who uploaded all documents).; ¶ 30 – “The mapping further comprises predicting 104b appropriate sections from among the sections in the scientific document template for rendering the content from the source documents based on the matching using the machine learning model and historical scientific document information acquired from users.”; ¶ 49 – “The section mapper 206 matches the sections of the scientific document template with sections 202a extracted from the source documents; and predicts appropriate sections from among the sections in the scientific document template for rendering the content from the source documents based on the matching using the machine learning model 313 and historical scientific document information acquired from users.”).
[Claim 5] Ramanujam discloses wherein the plurality of configuration nodes comprises at least one of the chapter configuration node configured for defining one or more chapters for the report, and one or more user configuration nodes for each of the one or more chapters, wherein each user configuration node for a chapter is configured to enable configuration of one or more user assignments for the chapter (¶ 36 – “ In an embodiment, the automated authoring engine allows section-wise editing of the automatically generated scientific document by co-authors. There can be more than one author for the same scientific document. For example, when a particular section such as a safety narrative section for a clinical study needs to be written by another author, then the automated authoring engine allows the primary author to assign that particular section to that co-author. The automated authoring engine allows multiple co-authors to work simultaneously on different sections of the same scientific document as assigned by the primary author. In an embodiment, the automated authoring engine allows the co-author(s) to edit only the section assigned by the primary author and not any other section of the scientific document. In an embodiment, the automated authoring engine provides selective access of an entirety of the automatically generated scientific document to one or more co-authors of the automatically generated scientific document for performing one or more actions on the automatically generated scientific document. In another embodiment, the automated authoring engine provides selective access of one or more sections of the automatically generated scientific document to one or more co-authors of the automatically generated scientific document for performing one or more actions on the automatically generated scientific document. The automated authoring engine transmits the automatically generated scientific document to one or more co-authors electronically, for example, via a hyperlink provided in an electronic mail (email), a short message service (SMS) message, an instant message (IM), a direct message (DM), etc., and allows the co-authors to login and access the entire automatically generated scientific document or one or more sections of the automatically generated scientific document for review, selective editing, commenting, etc. The automated authoring engine then allows the co-authors to save and transmit the updated scientific document to the primary author electronically, for example, via a hyperlink provided in an email, an SMS message, an IM, a DM, etc.”; ¶ 64 – “In an embodiment, the automated authoring engine 909 further comprises a workflow and dashboard module 918 configured in accordance with sponsor requirements. Through the workflow and dashboard module 918, the user, for example, a primary author, sends the finalized scientific document to one or more reviewers for a review process. The workflow and dashboard module 918 allows the primary author to send the scientific document to multiple reviewers. The workflow and dashboard module 918 allows the reviewer(s) to add comments and send the scientific document with the comments back to the primary author. The workflow and dashboard module 918 allows the primary author to view all the comments or changes performed in the scientific document by the reviewer(s), dynamically in real time. In an embodiment, the workflow and dashboard module 918 allows the primary author to view all the comments in a consolidated view on the GUI. The workflow and dashboard module 918 allows execution of multiple iterations between the primary author and the reviewer(s). Once the review of the scientific document is finalized, the workflow and dashboard module 918 allows the primary author to send the reviewed scientific document to an approver for executing an approval process.” There is a hierarchy defining a document configuration, such as how sections may include sub-sections. There is also a hierarchy of nodes in the workflow assignments, such as how a primary author can send a document to reviewers for further review.; figs. 10C, 10U, ¶ 22 – Various sections and sub-sections may be viewed on a GUI for editing, review, etc.).
[Claim 6] Ramanujam discloses wherein the plurality of configuration nodes further comprises one or more chart configuration nodes for each of the one or more chapters, wherein each chart configuration node for a chapter is configured to enable configuration of one or more charts for the chapter (figs. 10C, 10U, ¶ 22 – Various sections and sub-sections may be viewed on a GUI for editing, review, etc. The sections and sub-sections and other specified portions of the document seen in the GUI are examples of chapters.; ¶ 25 – “FIG. 1 illustrates a flowchart of an embodiment of a method for automatically authoring a scientific document, for example, a clinical study report (CSR), using a machine learning (ML) model and natural language processing (NLP) with minimal user intervention. For purposes of illustration, the disclosure herein refers to a clinical study report being automatically authored using an ML model and NLP; however, the scope of the artificial intelligence (AI)-enabled system and method disclosed herein is not limited to automatically authoring a clinical study report, but extends to include automatic authoring of any lengthy, scientific or other document comprising multiple sections and objects such as tables, listings, figures, etc., that typically requires substantial manual effort and time to be written or typed.”). It is noted that, based on Applicant’s description of a “chart”, a chart may be interpreted as possibly including a table format. For example, ¶ 105 of Applicant’s Specification references fig. 4D as showing a chart configuration. Fig. 4D of Applicant’s disclosure presents information in columns and rows, which is indicative of a table.).
[Claim 7] Ramanujam discloses wherein at least one model of the one or more models is trained to generate at least one data object for each of the one or more chapters based at least in part on a similarity measure between subsets of the historical configuration data, wherein each subset of the historical configuration data is associated with a particular historical report of the plurality of historical reports (figs. 10C, 10U, ¶ 22 – Various sections and sub-sections may be viewed on a GUI for editing, review, etc. The sections and sub-sections and other specified portions of the document seen in the GUI are examples of chapters.; ¶ 25 – “FIG. 1 illustrates a flowchart of an embodiment of a method for automatically authoring a scientific document, for example, a clinical study report (CSR), using a machine learning (ML) model and natural language processing (NLP) with minimal user intervention. For purposes of illustration, the disclosure herein refers to a clinical study report being automatically authored using an ML model and NLP; however, the scope of the artificial intelligence (AI)-enabled system and method disclosed herein is not limited to automatically authoring a clinical study report, but extends to include automatic authoring of any lengthy, scientific or other document comprising multiple sections and objects such as tables, listings, figures, etc., that typically requires substantial manual effort and time to be written or typed.”). It is noted that, based on Applicant’s description of a “chart”, a chart may be interpreted as possibly including a table format. For example, ¶ 105 of Applicant’s Specification references fig. 4D as showing a chart configuration. Fig. 4D of Applicant’s disclosure presents information in columns and rows, which is indicative of a table.).; ¶ 30 – “The mapping further comprises predicting 104b appropriate sections from among the sections in the scientific document template for rendering the content from the source documents based on the matching using the machine learning model and historical scientific document information acquired from users.”; ¶ 49 – “The section mapper 206 matches the sections of the scientific document template with sections 202a extracted from the source documents; and predicts appropriate sections from among the sections in the scientific document template for rendering the content from the source documents based on the matching using the machine learning model 313 and historical scientific document information acquired from users.”; abstract – “A system and a method for automatically authoring a scientific document using a machine learning model and natural language processing (NLP) with minimal user intervention are provided. The system configures a scientific document template including multiple sections based on scientific document requirements. The system maps the sections in the scientific document template with content from the source documents by executing a section mapping algorithm and automatically generates the scientific document. The mapping includes matching the sections of the scientific document template with sections extracted from the source documents, and predicting appropriate sections in the scientific document template for rendering the content from the source documents based on the matching using the machine learning model and historical scientific document information. The system executes one or more content editing functions, for example, tense conversion, additional information fetch and display, post-text to in-text conversion, etc., on the scientific document using NLP.”; ¶ 45 – “ Based on the selected file source, the section mapper 206 of the automated authoring engine exemplarily illustrated in FIG. 2, performs a section name match 504, for example, using Word Mover's Distance (WMD), to identify a close match between section names of the corresponding file source and section names of the predefined clinical study report (CSR) template. WMD measures a semantic distance between two documents, for example, the uploaded source document and the predefined CSR template. The section mapper 206 determines whether the nearest match is found 505. If WMD is unable to find the nearest match, the section mapper 206 passes the section name inputs to a custom trained multilayer perceptron (MLP) model 506. The MLP model 506 operates to find 507 a matching section from the uploaded source document. On finding the nearest match, the section mapper 206 maps 508 the content of the uploaded source document to a relevant section of the predefined CSR template and outputs the mapped section 509. For user-defined sections or sub-sections, the section extractor 202 performs pre-processing steps comprising, for example, removing stop words, numbers, punctuations, etc., post which, the section mapper 206 determines the best match for the user-defined sections or sub-sections, for example, using FuzzyWuzzy ratio, SequenceMatcher semantic similarity, etc., and returns the best match along with the mapping for the sections in the predefined CSR template. In an embodiment, the automated authoring engine improves section mapping accuracy and retrains the MLP model 506 in case of failed predictions. If the section mapping custom trained MLP model 506 fails to generate section predictions, the automated authoring engine captures and stores keywords that the user entered for section mapping, for example, in a keyword repository. The automated authoring engine retrains the MLP model 506 in periodic process batches, for example, nightly process batches, which helps in improving the section mapping and failed predictions from a previous run of the MLP model 506.”).
[Claim 8] Ramanujam discloses wherein at least one model of the one or more models is trained to generate at least one data object for each of the one more chapters based at least in part on the historical configuration data and real-time data indicative of at least a workload for each of one or more candidate users (figs. 10C, 10U, ¶ 22 – Various sections and sub-sections may be viewed on a GUI for editing, review, etc. The sections and sub-sections and other specified portions of the document seen in the GUI are examples of chapters.; ¶ 42 – “FIG. 3 exemplarily illustrates a flowchart of an embodiment of a method of applying artificial intelligence (AI) for automatically authoring a scientific document, for example, a clinical study report (CSR), with minimal user intervention. In an embodiment, the automated authoring engine comprises a machine learning (ML) module configured to generate and train a machine learning model. The ML module provides a machine learning algorithm with training data, for example, historical section mapping data, to learn from. The machine learning algorithm identifies patterns in the historical section mapping data that map input data attributes to target data attributes. The machine learning algorithm outputs the machine learning model that captures the identified patterns. As exemplarily illustrated in FIG. 3, the automated authoring engine receives historical mapping data 301 and performs cleansing and pre-processing 302 of the historical mapping data 301. By cleansing and pre-processing 302, the automated authoring engine transforms the historical mapping data 301 into an understandable format. The ML module receives the transformed historical mapping data 301 and proceeds to train 303 the machine learning model and generate a trained model 313. In an embodiment, the ML module trains 303 the machine learning model in communication with an AI engine 309 comprising a natural language processing (NLP) component, a natural language generation (NLG) component, and a natural language understanding (NLU) component. The AI engine 309 facilitates execution of multiple functions of the AI-enabled system 200 exemplarily illustrated in FIG. 2 and FIGS. 8-9, and the method disclosed herein. For example, the AI engine 309 facilitates the execution of AI-enabled functions 304 comprising section mapping 305, in-text mapping 306, tense conversion 307, and in-text table interpretation and summary generation 308. The ML module loads 310 the trained machine learning model 313 for generating a CSR draft. The section mapper 206 of the automated authoring engine exemplarily illustrated in FIG. 2, executes the section mapping algorithm for mapping the sections configured in the predefined CSR template with the content from the source documents as disclosed in the description of FIG. 5. In the execution of the section mapping algorithm, the section mapper 206 matches the sections defined in the predefined CSR template with the sections in the source documents uploaded by a user and utilizes the machine learning model 313 to obtain predictions on new data, for example, the content from the uploaded source documents. The document generator of the automated authoring engine generates and outputs the CSR draft with the mapped sections on a user interface and allows the user to add 311 new sections or sub-sections. The ML module utilizes the newly added sections to retrain 312 the machine learning model 313. On receiving the newly added sections or sub-sections, the document generator generates 314 the final CSR.”;¶ 58 -- “The section extractor 202 automatically extracts and pre-processes content from the source documents using NLP as disclosed in the description of FIG. 4. In an embodiment, the data processing module 913, in communication with the section extractor 202, pre-processes the content from the source documents using NLP. The section extractor 202 extracts the sections configured in the scientific document template and sections from the source documents and stores the extracted sections in the section repository 205. The section mapper 206, in communication with the section repository 205 and the metadata database 204, maps the sections configured in the scientific document template with the content from the source documents by executing the section mapping algorithm disclosed in the description of FIG. 5. For section mapping, the section mapper 206 matches the sections of the scientific document template with the sections extracted from the source documents and in an embodiment, if a near match is not found, the section mapper 206 employs the machine learning model 313 trained by the ML module 914 for predicting appropriate sections from among the sections in the scientific document template for rendering the content from the source documents based on the matching. The ML module 914 trains the machine learning model 313 using historical scientific document information acquired from different users involved in preparing scientific documents. In an embodiment, the NLP engine 805 employs the trained machine learning model 313 for performing various functions of the method disclosed herein.”; ¶ 36, 64 -- There is a hierarchy defining a document configuration, such as how sections may include sub-sections. There is also a hierarchy of nodes in the workflow assignments, such as how a primary author can send a document to reviewers for further review. The sections and subsections to which certain users are given access are examples of workload.).
[Claim 9] Ramanujam discloses wherein the configuration user interface comprises one or more of a chapter configuration user interface, a user configuration user interface, a chart configuration user interface, or a table configuration user interface (figs. 10C, 10U, ¶ 22 – Various sections and sub-sections may be viewed on a GUI for editing, review, etc. The sections and sub-sections and other specified portions of the document seen in the GUI are examples of chapters.; ¶ 25 – “FIG. 1 illustrates a flowchart of an embodiment of a method for automatically authoring a scientific document, for example, a clinical study report (CSR), using a machine learning (ML) model and natural language processing (NLP) with minimal user intervention. For purposes of illustration, the disclosure herein refers to a clinical study report being automatically authored using an ML model and NLP; however, the scope of the artificial intelligence (AI)-enabled system and method disclosed herein is not limited to automatically authoring a clinical study report, but extends to include automatic authoring of any lengthy, scientific or other document comprising multiple sections and objects such as tables, listings, figures, etc., that typically requires substantial manual effort and time to be written or typed.”). It is noted that, based on Applicant’s description of a “chart”, a chart may be interpreted as possibly including a table format. For example, ¶ 105 of Applicant’s Specification references fig. 4D as showing a chart configuration. Fig. 4D of Applicant’s disclosure presents information in columns and rows, which is indicative of a table.).
[Claim 10] Ramanujam discloses wherein the historical configuration data comprises one or more segments, wherein each segment corresponds to a particular configuration node of the plurality of configuration nodes (¶ 36 – “ In an embodiment, the automated authoring engine allows section-wise editing of the automatically generated scientific document by co-authors. There can be more than one author for the same scientific document. For example, when a particular section such as a safety narrative section for a clinical study needs to be written by another author, then the automated authoring engine allows the primary author to assign that particular section to that co-author. The automated authoring engine allows multiple co-authors to work simultaneously on different sections of the same scientific document as assigned by the primary author. In an embodiment, the automated authoring engine allows the co-author(s) to edit only the section assigned by the primary author and not any other section of the scientific document. In an embodiment, the automated authoring engine provides selective access of an entirety of the automatically generated scientific document to one or more co-authors of the automatically generated scientific document for performing one or more actions on the automatically generated scientific document. In another embodiment, the automated authoring engine provides selective access of one or more sections of the automatically generated scientific document to one or more co-authors of the automatically generated scientific document for performing one or more actions on the automatically generated scientific document. The automated authoring engine transmits the automatically generated scientific document to one or more co-authors electronically, for example, via a hyperlink provided in an electronic mail (email), a short message service (SMS) message, an instant message (IM), a direct message (DM), etc., and allows the co-authors to login and access the entire automatically generated scientific document or one or more sections of the automatically generated scientific document for review, selective editing, commenting, etc. The automated authoring engine then allows the co-authors to save and transmit the updated scientific document to the primary author electronically, for example, via a hyperlink provided in an email, an SMS message, an IM, a DM, etc.”; ¶ 64 – “In an embodiment, the automated authoring engine 909 further comprises a workflow and dashboard module 918 configured in accordance with sponsor requirements. Through the workflow and dashboard module 918, the user, for example, a primary author, sends the finalized scientific document to one or more reviewers for a review process. The workflow and dashboard module 918 allows the primary author to send the scientific document to multiple reviewers. The workflow and dashboard module 918 allows the reviewer(s) to add comments and send the scientific document with the comments back to the primary author. The workflow and dashboard module 918 allows the primary author to view all the comments or changes performed in the scientific document by the reviewer(s), dynamically in real time. In an embodiment, the workflow and dashboard module 918 allows the primary author to view all the comments in a consolidated view on the GUI. The workflow and dashboard module 918 allows execution of multiple iterations between the primary author and the reviewer(s). Once the review of the scientific document is finalized, the workflow and dashboard module 918 allows the primary author to send the reviewed scientific document to an approver for executing an approval process.” There is a hierarchy defining a document configuration, such as how sections may include sub-sections. There is also a hierarchy of nodes in the workflow assignments, such as how a primary author can send a document to reviewers for further review.; figs. 10C, 10U, ¶ 22 – Various sections and sub-sections may be viewed on a GUI for editing, review, etc.).
[Claims 11-19] Claims 11-19 recite limitations already addressed by the rejections of claims 1-9 above; therefore, the same rejections apply.
Furthermore, Ramanujam discloses an apparatus for intelligent report configuration, the apparatus comprising at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus to perform the disclosed operations (¶¶ 66-70).
[Claim 20] Claim 20 recites limitations already addressed by the rejection of claim 1 above; therefore, the same rejection applies.
Furthermore, Ramanujam discloses a computer program product for intelligent report configuration, the computer program product comprising at least one non-transitory computer-readable storage medium, the at least one non-transitory computer-readable storage medium including instructions that when executed by at least one processor, cause the computer program product to perform the disclosed operations (¶¶ 66-70).
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 SUSANNA M DIAZ whose telephone number is (571)272-6733. The examiner can normally be reached M-F, 8 am-4:30 pm.
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/SUSANNA M. DIAZ/
Primary Examiner
Art Unit 3625A
Prosecution Insights