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 .
Status of Claims
This communication is a First Office Action on the merits in reply to application number 18/162,597 filed on 01/31/2023.
Claims 1-20 are currently pending and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 01/31/2023 has been considered.
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.
1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception as further set forth in MPEP 2106.
Step 1: The claimed invention is analyzed to determine if it falls outside one of the four statutory categories of invention. See MPEP 2106.03
Claim(s) 1-8 is/are directed to a method (i.e., Process), claim(s) 9-16 is/are directed to a system (i.e., Machine), and claim(s) 17-20 is/are directed to a computer program product (i.e., Manufacture – as defined by Applicant’s own specification, where in paragraph [0011] the computer program product is defined as: A computer program product includes a computer readable storage medium having stored therein program code for automatically generating business rules to be employed by a business rule management system). Therefore, the claims are directed to patent eligible categories of invention. Accordingly, the claims satisfy Step 1 of the eligibility inquiry.
As drafted, the limitations recited by the claims fall under the “Mental Processes” abstract idea group by setting forth activities that could be performed mentally by a human (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
Independent claims 1/9/17 recite a method, system comprising a hardware processor, and computer program product comprising a computer readable storage medium and program code for automatically generating business rules with the following limitations:
identifying a plurality of external data sources from which to receive data updates; (The step for “identifying a plurality of external data sources” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper.);
obtaining, from at least one of the plurality of external data sources, a data update relevant to a collection of business rules; (The step for “obtaining a data update” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper. Furthermore, even if considered as an additional element, this step amounts to insignificant extra-solution activity as mere data gathering);
performing, using a contextual analysis engine, a contextual analysis of the data update; (But for the additional elements – underlined – recited in this claim limitation, the step for “performing a contextual analysis” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper.);
generating, using a machine learning engine and based upon the contextual analysis of the data update, an update to the collection of business rules to form an updated collection of business rules; (But for the additional elements – underlined – recited in this claim limitation, the step for “generating an update” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper.);
modifying the machine learning engine based upon feedback received on the update to the collection of business rules;
and forwarding the updated collection of business rules to the business rule management system. (This step amounts to insignificant extra-solution activity as both mere data gathering, and insignificant application).
The additional elements beyond the abstract idea for consideration under Step 2A, Prong 2, and Step 2B recited by the independent claims are: using a contextual analysis engine, using a machine learning engine, modifying the machine learning engine, hardware processor, computer readable storage medium, and program code.
Dependent claims 2-8, 10-16, and 18-20 further narrow the abstract idea and introduce the following additional elements for consideration under said steps:
From claims 4/12/18: using a natural language processing engine
From claims 5/13: business process management system
Step 2A, Prong 2: An evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the judicial exception into a practical application of the exception. See MPEP 2106.04(d).
Regarding the computing additional elements, namely hardware processor, and computer readable storage medium, these additional elements have been evaluated but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h). In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
With respect to the limitations for performing, using a contextual analysis engine, a contextual analysis of the data update; generating, using a machine learning engine and based upon the contextual analysis of the data update, an update to the collection of business rules to form an updated collection of business rules; modifying the machine learning engine based upon feedback received on the update to the collection of business rules; the program code, which when executed by a computer hardware system, cause the computer hardware system to perform; the contextual analysis includes performing, using a natural language processing engine, natural language processing on the data update; and the updated collection of business rules are implemented using a business process management system, these limitations fail to integrate the abstract idea into a practical application because the provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
Step 2B: The claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for "inventive concept." See MPEP 2106.05.
Regarding the computing additional elements, namely hardware processor, and computer readable storage medium, these additional element(s) has/have been evaluated, but fail to add significantly more to the claims because they amount to using generic computing elements (computer hardware) or instructions/software (engine) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (network computing environment, the internet, online) and does not amount to significantly more than the abstract idea itself. Applicant’s specification recites the computing additional elements at a high level of generality.
With respect to the limitations for performing, using a contextual analysis engine, a contextual analysis of the data update; generating, using a machine learning engine and based upon the contextual analysis of the data update, an update to the collection of business rules to form an updated collection of business rules; modifying the machine learning engine based upon feedback received on the update to the collection of business rules; the program code, which when executed by a computer hardware system, cause the computer hardware system to perform; the contextual analysis includes performing, using a natural language processing engine, natural language processing on the data update; and the updated collection of business rules are implemented using a business process management system, these limitations fail to add significantly more to the abstract idea because the provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Therefore, the additional elements merely describe generic computing elements or computer-executable instructions (software) merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Furthermore, even if the obtaining a data update, and forwarding the updated collection of business rules steps are interpreted as additional elements, these activities at most amount to insignificant extra-solution activity (i.e., mere data gathering, selecting a particular data source or type of data to be manipulated, insignificant application), which does not add significantly more to the abstract idea, as noted in MPEP 2106.05(g). Additionally, the obtaining a data update, and forwarding the updated collection of business rules extra-solution activity has been recognized as well-understood, routine, and conventional, and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to amount to significantly more than the abstract idea itself. The ordered combination of elements in the claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
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.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 1-5, 7, 9-13, 15, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over De Shetler et al. (US 11645656 B2, hereinafter “De Shetler”), in view of Luo (US 20200293617 A1, hereinafter “Luo”), in further view of Goodman et al. (US 20140122377 A1, hereinafter “Goodman”).
Regarding claims 1/9/17: De Shetler teaches a method ([Abstract] one or more embodiments relate to a method including receiving, in a business rules engine, input data from disparate data sources), a computer hardware system comprising a hardware processor ([Column 19, Lines 26 – 27] the computing system (900) may include one or more computer processors (902)), and a computer program product ([Column 1, Lines 65 – 67] One or more embodiments also relate to a non-transitory computer readable medium comprising computer readable program code.) with limitations for:
identifying a plurality of external data sources from which to receive data updates; ([Column 7, Lines 3 – 28] Data repository (302) includes input data (304). The input data (304) may include a wide variety of data. For example, the input data (304) may include a merchant's application to use the electronic payments system (300). The input data (304) may thus described a merchant and an application by the merchant to use the TPEP system. The data repository (302) may access data from other data sources, such as data sources (306) which include data source A (308), data source B (310), and data source C (312). While the data sources (306) are shown outside of the data repository (302), some or all of the other data sources (306) may be part of the data repository (302). More or fewer data sources may be present. The data sources (306) may include data such as, but not limited to, credit reports, merchant account information, merchant revenues and expenses, merchant chargeback history, IOVATION® data, emails, background checks, IDANALYTICS® data, social media ratings of the merchant from a variety of social media platforms, and tax or financial information stored by a financial management application (FMA). The FMA is a software program which the merchant uses to manage taxes and/or finances. The FMA may be operated by the same third party provider that also manages the electronic payments system (300). Note that data is fed into the data repository (302) in parallel while an input vector (described below) is fed directly to the machine learning application model (also described below).);
obtaining, from at least one of the plurality of external data sources, a data update relevant to a collection of business rules; ([[Abstract] receiving, in a business rules engine, input data from disparate data sources.);
performing, using a contextual analysis engine, a contextual analysis of the data update; ([Column 12, Lines 47 – 64] At step 508, the machine learning model predicts a risk score that the merchant will be unable to satisfy an obligation of using the third party electronic payments system. In other words, once the machine readable vector is input into the machine learning model and the machine learning model is executed, then the output of the machine learning model is a risk score. The risk score may be passed back to a business rules layer. At step 510, the business rules, when executed, may limit the merchant's use of the TPEP system. For example, a business rule could be that if the risk score is 0.4, then the merchant is assigned a transaction limit of $100,000 in total payments per 30 day period, but that after 90 days of satisfactory performance of the TPEP system agreement, the transaction limit will be increased to $250,000. A specific example of imposing limits on the merchant's use of the TPEP system is shown with respect to FIG. 6 through FIG. 8.).
De Shetler doesn’t explicitly teach:
generating, using a machine learning engine and based upon the contextual analysis of the data update, an update to the collection of business rules to form an updated collection of business rules;
modifying the machine learning engine based upon feedback received on the update to the collection of business rules;
and forwarding the updated collection of business rules to the business rule management system.
Luo teaches:
generating, using a machine learning engine and based upon the contextual analysis of the data update, an update to the collection of business rules to form an updated collection of business rules; ([0013] Traditionally, organizations utilize natural language mining engines (e.g., business, rule miners, rational asset analyzers, operational decision mangers, etc.) to discover natural language rules (e.g., business rules, business logic, etc.) such as insurance premium calculations or medical record registrations from enterprise legacy software systems (e.g., mainframe system written in Common Business-Oriented Language (COBOL)) and code bases.; [0023] code information may include word embedded vectors that include contextual and relational data (e.g., relating to words that precede and follow a target word).; [0024] Cognitive model 126 utilizes deep learning techniques to generate natural language rules based on code snippets.);
modifying the machine learning engine based upon feedback received on the update to the collection of business rules; ([0013] Traditionally, organizations utilize natural language mining engines (e.g., business, rule miners, rational asset analyzers, operational decision mangers, etc.) to discover natural language rules (e.g., business rules, business logic, etc.) such as insurance premium calculations or medical record registrations from enterprise legacy software systems (e.g., mainframe system written in Common Business-Oriented Language (COBOL)) and code bases.; [0048] Code analysis program 150 generates natural language rules (step 210).; [0049] Code analysis program 150 logs code snippet and the generated rule into code corpus (step 212). In one embodiment, code analysis program 150 logs the snippet and the generated rule into code corpus 124. In another embodiment, code analysis program 150 may receive user feedback through a graphical user interface on client computing device 104 (not depicted). For example, after code analysis program 150 analyzes a snippet, the user can provide feedback for the snippet and the generated rule on a user interface. In various embodiments, feedback may include a simple positive or negative response. In another embodiment, feedback may include a user confirmation or score of the generated rule. For example, if code analysis program 150 generates an incorrect rule, the user can provide negative feedback and provide an accurate rule. In an embodiment, code analysis program 150 adds the user feedback and the corrected rule to code corpus 124, allowing code analysis program 150 to adjust the cognitive model. In another embodiment, code analysis program 150 may use one or more techniques of NLP to determine whether the response of the user is positive or negative.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine De Shetler with Luo’s feature(s) listed above. One would’ve been motivated to do so in order to includes a plurality of connected transferrable neural networks that include networks that are trained utilizing historical snippet and rule pairs, historical snippet and comment pairs, and combinations of snippet, comment, and rule pairs (Luo; [0024]). By incorporating the teachings of Luo, one would’ve been able to generate business rules using machine learning.
Luo doesn’t teach:
and forwarding the updated collection of business rules to the business rule management system.
Goodman teaches
and forwarding the updated collection of business rules to the business rule management system. ([0023] In an embodiment, the method displays a hierarchical file tree structure having a plurality of directories for storing and organizing the business rule and stores the business rule within one of the plurality of directories.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified De Shetler with Goodman’s feature(s) listed above. One would’ve been motivated to do so in order to store the business rule within one of the plurality of directories (Goodman; [0023]). By incorporating the teachings of Goodman, one would’ve been able to store the business rules in a business rule management system.
Regarding claims 2/10: De Shetler doesn’t explicitly teach:
the update to the collection of business rules includes a modification to a preexisting business rule.
Goodman teaches:
the update to the collection of business rules includes a modification to a preexisting business rule. ([0059] As generally shown at step 210, a user may define a business rule. As generally shown at step 220, the user may utilize the user terminals 160 to create a new rule within the BR Editor 132. The user may create the new rule by modifying an existing rule and/or creating a new rule.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified De Shetler with Goodman’s additional feature(s) listed above. One would’ve been motivated to do so in order to consider the similarity of the new rule to the existing rule in determining whether to modify the existing rule (Goodman; [0059]). By incorporating the teachings of Goodman, one would’ve been able to modify existing business rules.
Regarding claims 3/11: De Shetler doesn’t explicitly teach:
the update to the collection of business rules includes a creation of a new business rule.
Goodman teaches:
the update to the collection of business rules includes a creation of a new business rule. ([0059] As generally shown at step 210, a user may define a business rule. As generally shown at step 220, the user may utilize the user terminals 160 to create a new rule within the BR Editor 132. The user may create the new rule by modifying an existing rule and/or creating a new rule.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified De Shetler with Goodman’s additional feature(s) listed above. One would’ve been motivated to do so in order to consider the similarity of the new rule to the existing rule in determining whether to modify the existing rule (Goodman; [0059]). By incorporating the teachings of Goodman, one would’ve been able to create new business rules.
Regarding claims 4/12/18: De Shetler doesn’t explicitly teach:
the contextual analysis includes performing, using a natural language processing engine, natural language processing on the data update.
Luo teaches:
the contextual analysis includes performing, using a natural language processing engine, natural language processing on the data update. ([0013] Traditionally, organizations utilize natural language mining engines (e.g., business, rule miners, rational asset analyzers, operational decision mangers, etc.) to discover natural language rules (e.g., business rules, business logic, etc.); [0026] Code analysis program 150 is a program for generating natural language rules (i.e., logic) by detecting, analyzing, extracting, and creating feature vectors from one or more code snippets. In an embodiment, code analysis program 150 may perform preprocessing techniques (e.g., removing reserved words (public, return, void, etc.), extracting contextual words, tokenizing data structures, retrieving and applying programmatic conventions, applying weights, etc.) on entire programs or smaller code snippets.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified De Shetler with Luo’s additional feature(s) listed above. One would’ve been motivated to do so in order to log the code snippet, generated rule, and associated information (e.g., programming language, tokenized vectors, code comments, similar natural language rules, similar business rules, etc.) into code corpus 124 (Luo; [0026]). By incorporating the teachings of Luo, one would’ve been able to use NLP to analyze the data update.
Regarding claims 5/13: De Shetler doesn’t explicitly teach:
the updated collection of business rules are implemented using a business process management system.
Goodman teaches:
the updated collection of business rules are implemented using a business process management system. ([0037] A still further advantage of the present invention is to provide a system and a method for the user to publish a business rule into an environment and define when the business rule will execute.; [0076] In an alternative embodiment, the BRMS 110 may allow the user to specify when the new rule may execute. The user may designate the occurrence of an event or specified conditions after which the business rule may execute. The user may specify that the new rule may execute only after the satisfaction of a user specified precondition.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified De Shetler with Goodman’s feature(s) listed above. One would’ve been motivated to do so in order to specify that the new rule may only execute after the submission of a medical insurance claim for a patient having a preexisting medical condition (Goodman; [0076]). By incorporating the teachings of Goodman, one would’ve been able to implement the business rules using a business process management system.
Regarding claims 7/15: De Shetler teaches:
the obtaining the data update includes receiving an electronic document from at least one of the plurality of external data sources. ([Column 7, Lines 15 – 22] The data sources (306) may include data such as, but not limited to, credit reports, merchant account information, merchant revenues and expenses, merchant chargeback history, IOVATION® data, emails, background checks, IDANALYTICS® data, social media ratings of the merchant from a variety of social media platforms, and tax or financial information stored by a financial management application (FMA).).
Claims 6/14/19 are rejected under 35 U.S.C. 103 as being unpatentable over De Shetler et al. (US 11645656 B2, hereinafter “De Shetler”), in view of Luo (US 20200293617 A1, hereinafter “Luo”), in further view of Goodman et al. (US 20140122377 A1, hereinafter “Goodman”), as applied to claims 1/9/17 above, in further view of Stevenson et al. (US 20210065305 A1, hereinafter “Stevenson”).
Regarding claims 6/14/19: De Shetler teaches:
the plurality of external data sources includes a website, ([Column 7, Lines 8 – 20] The data repository (302) may access data from other data sources, such as data sources (306) which include data source A (308), data source B (310), and data source C (312). While the data sources (306) are shown outside of the data repository (302), some or all of the other data sources (306) may be part of the data repository (302). More or fewer data sources may be present. The data sources (306) may include data such as, but not limited to, credit reports, merchant account information, merchant revenues and expenses, merchant chargeback history, IOVATION® data, emails, background checks, IDANALYTICS® data, social media ratings of the merchant from a variety of social media platforms).
De Shetler doesn’t explicitly teach:
and the obtaining the data update includes crawling the website for the data update.
Stevenson teaches:
and the obtaining the data update includes crawling the website for the data update. ([0048] the data collection module 201 may use a web-crawling component to access various websites and databases on the internet).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified De Shetler with Stevenson’s feature(s) listed above. One would’ve been motivated to do so in order to collect data associated with the users (Stevenson; [0048]). By incorporating the teachings of Stevenson, one would’ve been able to obtain data by crawling a website.
Claims 8/16/20 are rejected under 35 U.S.C. 103 as being unpatentable over De Shetler et al. (US 11645656 B2, hereinafter “De Shetler”), in view of Luo (US 20200293617 A1, hereinafter “Luo”), in further view of Goodman et al. (US 20140122377 A1, hereinafter “Goodman”), as applied to claims 1/9/17 above, in further view of Fisher et al. (US 11361268 B1, hereinafter “Fisher”).
Regarding claims 8/16/20: De Shetler doesn’t explicitly teach:
the feedback includes an indication as to whether the update to the collection of business rules is approved.
Fisher teaches:
the feedback includes an indication as to whether the update to the collection of business rules is approved. ([Column 5, Lines 42 – 48] The user interface circuit 112 is also configured to provide an approval manager tool to the user system 106 using information received from the approval circuit 118. As shown in FIG. 9, the approval manager tool provides a workflow display to the user, allowing the user to manage business rules and/or feedback messages that are awaiting approval.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified De Shetler with Fisher’s feature(s) listed above. One would’ve been motivated to do so in order to determine if any approval requirements are associated with the business rule (Fisher; [Column 11, Lines 38 – 39]). By incorporating the teachings of Fisher, one would’ve been able to determine if business rules are approved.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Hunn et al. (WO 2017173399 A1), which discloses a system and method that includes providing a contract management platform.
Jober et al. (WO 2020043510 A1), which discloses methods and a computer system arrangement for minimizing communication and integration complexity between a plurality of external systems and/or software applications having each an individual data model defining an individual set of application parameters.
E. Iftikhar, A. Iftikhar and M. K. Mehmood, "Identification of textual entailments in business rules," 2016 Sixth International Conference on Innovative Computing Technology (INTECH), Dublin, Ireland, 2016, pp. 706-711.
P. B. F. Njonko and W. El Abed, "From natural language business requirements to executable models via SBVR," 2012 International Conference on Systems and Informatics (ICSAI2012), Yantai, China, 2012, pp. 2453-2457.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GABRIEL J TORRES CHANZA whose telephone number is (571)272-3701. The examiner can normally be reached Monday thru Friday 8am - 5pm ET.
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/G.J.T./Examiner, Art Unit 3625
/BRIAN M EPSTEIN/Supervisory Patent Examiner, Art Unit 3625