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 . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
DETAILED ACTION
This Final Office Action is in response Applicant communication filled on 05/11/2026.
Status of Claims
Claims 1,3,4,9,11,12 have been amended by Applicant with the 05/11/2026 amendment.
Claims 1, 3-9, 11-16 are currently pending and have been rejected as follows.
Response to amendments / arguments
Applicant’s 05/11/2026 amendment necessitated new grounds of rejection in this action.
1) Response to Applicant’s rebuttal arguments on 112(b) rejection
112(b) rejections in the prior act are withdrawn in view of Applicant’s amendment in the same or similar manner as suggested by Non-Final Act 02/11/2026 at p.14.
2) Response to Applicant’s rebuttal arguments on 101 rejection
101 rejection is maintained with a detailed explanation below.
Step 2A prong one: Remarks 05/11/2026 p.7 last ¶-p.8 ¶1 argues the claims do not merely recite identifying missing information and providing a response but require processing job request data using trained machine learning models to determine required information and to generate replacement or supplemental field values based on learned patterns and relationships derived from historical job request data, which is argued as going beyond mental processes.
Examiner fully considered the Step 2A prong one argument but respectfully disagrees finding it unpersuasive by reincorporating herein similar rationales as in Final Act 07/03/2025 p.2-p4 ¶ 1 and Non-Final Act 02/11/2026 p.2 last ¶ -p.5. Here, the “receiving one or more job requests from the one or more customers or retrieved from a third-party customer system” and “identifying” “one or more insufficiencies” (i.e. “missing required fields or misclassified information”) [akin to risks] “in the one or more job records” for “remedying” [or mitigation] at independent Claims 1,9, remain, along with “initiating creation of a corresponding job record” “based on the one or more job requests” at independent Claims 1,9, examples of fundamental economic or commercial practices or principles (MPEP 2106.04(a)(2) II A), which still fall within the broad abstract grouping of Certain Methods of Organizing Human Activities. Importantly, the term fundamental, was clarified by MPEP 2106.04(a)(2) II A ¶2 as not being used in the sense of being old or well-known but rather as a building blocks of modern economy. It then follows that here, even if “using the one or more trained machine learning models” for “identifying” “one or more insufficiencies” (i.e. “missing required fields or misclassified information”) “in the one or more job records” for “remedying” “by processing the one or more job requests” at independent Claims 1,9, would not be old or well-known, it would sill reflect execution of processes that would otherwise correspond to the abstract fundamental practices, or building blocks of modern economy, which similar to the risk mitigation of MPEP 2106.04(a)(2) II A, would not preclude the claims to recite, describe or set forth the abstract exception. A more granular analysis of the use of the “one or more trained machine learning models” will later follow below.
As per alleged improvement in computer-implemented data processing systems, namely, the automated transformation of incomplete structured job request data into complete, machine-processable records using the one or more trained machine learning models, as argued at Remarks 05/11/2026 p.7 ¶4, the Examiner notes that here the claims themselves provide no transformation but rather a simple “replacement or supplemental” [of] “field values” “based on learned patterns and relationships in the historical job request data”. This finding is important because MPEP 2106.05(c)1 states that such manipulation of basic mathematical constructs [akin here to “field values”] or the paradigmatic ‘abstract idea has not been deemed a transformation capable to render the claims patent eligible. Indeed, as explained by MPEP 2106.04(a)(2) I A iv. organizing information and manipulating information through mathematical correlations by generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form remains abstract.
Here, “generating” information into a new form, is recited as “replacement or supplemental field values” at independent Claims 1,9, in an abstract example of manipulating information through mathematical correlations, represented here by “learned patterns and relationships in the historical job request data”, by generating first and second data by taking existing information, represented here by “historical job request data”, manipulating the data using mathematical functions, represented here by “using the one or more trained machine learning models to determine information required for the one or more job requests”, and organizing this information into a new form, represented here by the “replacement or supplemental field values”.
Next, when tested per MPEP 2106.04(a)(2) II B2, the improvement upon such job management, remains an entrepreneurial or abstract concept for “identifying” and “remedying” “one or more insufficiencies” in the “one or more job requests”, for “generating replacement or supplemental field values for the one or more job requests based on learned patterns and relationships in the historical job request data” not matter of the level of computerization used.
Thus, the argued improvement of Remarks 05/11/2026 p.7 ¶ 4, would at most represent an improved entrepreneurial or abstract concept to an equally entrepreneurial or abstract problem of “customer job requests” in need of “replacement or supplemental” data for “missing” or “misclassified” data. Such abstract or entrepreneurial solution to the equally abstract or entrepreneurial problem, does not render the claims patent eligible. Specifically, the alleged improvement in determining required information and generating replacement or supplemental field values based on learned patterns and relationships derived from historical job request data as argued by Applicant at Remarks 05/11/2026 p.7 ¶4, would at most represent an improvement in the abstract organizing of human activities and/or the cognitive best business practices of one of ordinary skills in the art to visually identify, with his or her own eyes, or even aided by a computer, “one or more insufficiencies” (i.e. “missing required fields or misclassified information”) then cognitively recall, from memory what has previously occurred or has previously worked namely, “historical patterns” and “relationships in the historical job request data”, of “determine information required for the one or more job requests” as “replacement” or “supplemental” for “the one or more job requests”, and thus consistent with what is recited at the current independent Claims 1,9. In such a case, the claimed “machine learning model” used for “identifying” “one or more insufficiencies” (i.e. “missing required fields” or “misclassified information”) in the “job requests”, as broadly recited at independent Claims 1,9, can be argued, given the breadth of said claims, to merely mimic the learning or cognitive capabilities of said one of ordinary skills in the art to: acquire, learn, ingest, assimilate, soak-in, absorb, prior knowledge to perform the best business practice of decision making, or judgement in mitigating, reconciling or remedying missing information by replacing or supplementing with data based on learned patterns and relationships in the historical job request data. It then follows that here, “using one or more trained machine learning models” in “identifying” and “remediating” (i.e. relacing or supplementing) the “one or more insufficiencies” would represent a computer environment or tool, as an equivalent, aid or replacement of the aforementioned cognitive capabilities, upon which the abstract processes of “identifying” and “remediating” (i.e. relacing or supplementing) are being performed. Yet, MPEP 2106.04(a)(2) III C #2, #3 is clear that: #2 performing a mental process in a computer environment, and/or #3 using a computer as a tool to perform a mental process, do not necessarily preclude the claims to recite, describe or set forth the abstract exception.
This finding was further corroborated by the Federal Circuit in Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205, 1212 (Fed. Cir. 2025) and cited by PTAB Appeal 2025-003304: “The requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted based on real time changes do not represent a technological improvement” at least because they are “incident to the very nature of machine learning”. In a similar vein, Brandan Artley, Training a Neural Network by Hand, towardsdatascience webpages, Jun 23, 2022, articulated the capability of training of a neural network by hand to solve a regression problem where the model continually improves its predictions to arrive at a highly accurate model. It then follows that here “using one or more trained machine learning models” for “identifying” “one or more insufficiencies” and “processing the one or more job requests” can be argued part of the very of machine learning, in not altogether as part of the human cognitive processes of observation, evaluation and judgment enumerated at MPEP 2106.04(a)(2) III ¶2.
Also, Examiner points to MPEP 2106.05(a) II ¶2 to again stress that improvement in the abstract exception itself is not improvement in technology. Here, as demonstrated above, the alleged improvement is at best entrepreneurial and abstract for a business practice of managing customer job requests, as summarized at the Title of the Invention. It is not an improvement in actual technology. This finding is especially important since MPEP 2106.04 I ¶3 cited Mayo, 566 U.S. at 79-80, 86-87, 101 USPQ2d at 1968-69, 1971 states that narrow laws that have limited applications were still held ineligible. Specifically in Myriad, 569 US at 591,106 USPQ2d at 1979, the Court found situations where even a groundbreaking, innovative, or even brilliant discovery does not by itself satisfy the §101 inquiry". This finding was corroborated by Versata Dev Grp, Inc v SAP Am, Inc 115 USPQ2d 1681 Fed Cir 2015 again undelaying the difference between improvement to an entrepreneurial goal or objective versus improvement to actual technology, and by SAP Am, Inc v InvestPic, LLC, No 2017-2081, 2018 BL 275354 (Fed. Cir.Aug.02, 2018) which disclosed a comparable solution that “utilizes resampled statistical methods for the analysis of financial data, which do not assume a normal probability distribution”, further narrowed to a cross validation at the dependent claims. Yet, the Court ruled that: “even if one assumes that the techniques claimed are groundbreaking, innovative, or even brilliant, those features are not enough for eligibility because their innovation is innovation in ineligible subject matter”. “An advance of that nature is ineligible for patenting”.
In this instant case, similar to the SAP’s utilization of a resampled statistical model or cross validation for analysis of financial data, the current independent claims 1,9 use similar “one or more” “learning models” for “identifying” and “automatically remedying one or more insufficiencies” (i.e. missing required fields or misclassified information”) in customer job requests, by replac[ing] or supplement[ing] missing data “based on” “learned patterns and historical job request data”. Thus no matter if the data identification, remediation, validation or supplementation is intended for financial purposes as in “SAP” supra, or is intended for managerial purposes of customer job requests, as summarized in the title of the current Application and reflected throughout the current claims, the use of computerized mathematical algorithms and associated processes for implementing and/or aiding such abstract concepts do not render said claims less abstract and eligible, no matter how groundbreaking, innovative, or even brilliant the use of such computerized learning algorithms would be in executing the abstract exception.
It is also clear that here, the Applicant has not been the first to discover “machine learning”, nor is the Applicant alleging as much. Rather, the Applicant preponderantly argues is favor of a mere use of one or more machine learning models to allegedly improve upon the abstract job management using “learned patterns and historical job request data”. Yet use of such “learned patterns and historical job request data” for the subsequent automation, to satisfy a best business practice of replac[ing] or supplement[ing] field values based on learned patterns and relationships in the historical job request data does not immediately render the claims less abstract and eligible. For example MPEP 2106.04(a)(2) II C has consistently shown that concepts such as: considering historical usage information while inputting data3 and automatization by providing information to a person without interfering with the person’s primary activity4 including acquiring content from an information source, controlling the timing of the display of acquired content, displaying the content, and acquiring an updated version of the previously-acquired content when the information source updates its content5 all set forth the abstract Certain Methods of Organizing Human activities grouping. In fact, MPEP 2106.04(a)(2) II ¶6, 4th sentence goes so far to state that certain activity between a person and a computer may still fall within certain methods of organizing human activity. Here, the fact that such computerization, automation or training uses “historical job request data” for “identifying insufficiencies” and “remedying the one or more insufficiencies” by replac[ing] or supplement[ing] fields values” “based on learned patterns and relationships in the historical job request data” (independent Claims 1,9) does not necessarily render the claims eligible because according to BSG Tech LLC v. Buyseasons, Inc., 899 F.3d 1281, 1287-88, 127 USPQ2d 1688, 1693-94 (Fed. Cir. 2018), cited by MPEP 2106.05(a) I, providing historical usage information to users while inputting data, in order to improve the quality and organization of information added to a database, represents improvement to the information stored by a database which, is not equivalent to an improvement in the database’s functionality.
Additionally or alternatively, as previously demonstrated above, it can also be argued that, given the breadth and the high level of generality of the claims, the use of “one or more trained machine learning models” (independent Claims 1,9), could be argued as an example of computer [modeling] environment [MPEP 2106.04(a)(2) III C #2] or a computer tool [MPEP 2106.04(a)(2) III C #3] upon which to implement the fundamental and/or cognitive or mental processes of observation, evaluation and judgements such as collecting or “receiving” information, analyzing or “identifying” one or more insufficiencies within such collected information it, and displaying certain results of the collection and analysis6 represented here by “replacement or supplemental field values for the one or more job requests” (independent Claims 1,9). This finding and ensuing rationale is corroborated by Brandan Artley, Training a Neural Network by Hand, towardsdatascience webpages, Jun 23, 2022 that showed that the capability of training of a neural network by hand to solve a regression problem where the model continually improves its predictions to arrive at a highly accurate model. Also, in FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 USPQ2d 1293 (Fed. Cir. 2016) as cited by MPEP 2106.04(a)(2) III C #2, the Federal Circuit ruled that even the inability for the human mind to perform each claim step does not alone confer patentability. Specifically in FairWarning supra, the Court found that accessing, compiling and combining of data from disparate information sources that made it possible to generate a full picture of a user's activity, identity, frequency of activity, and the like in a computer environment represents a concept of merely selecting information, by content or source, for collection, analysis, and announcement which does not differentiate from mental processes, whose implicit exclusion from 101 undergirds the information-based category of abstract ideas. It then follows that here, the use of “one or more trained machine learning models” as a computer environment, tool or aid in providing a picture in “identifying” “one or more insufficiencies” (i.e. “missing required fields” or “misclassified information”) “in the one or more job requests” (independent Claims 1,9) for more fully compiling [here “processing the one or more job requests”] by combining [here replac[ing] or supplement[ing] fields values for the one or more job requests based on learned patterns and relationships in the historical request data” (independent Claims 1,9)] would similarly set forth the abstract exception. Step 2A prong one.
Finally, with respect to the alleged large datasets that purportedly cannot practically be performed in the human mind, as raised by Applicant at Remarks 05/11/2026 p.8 ¶1, the Examiner responds that such large datasets do not appear to be reflected in the claims. This finding is important because the “101 inquiry must focus on language of Asserted Claims themselves” as in “Synopsys, Inc. v Mentor Graphics Corp, U.S. Court of Appeals Federal Circuit, No 2015-1599, October 17 2016 2016 BL 344522 839 F3d 1138” citing “Accenture Global Servs., GmbH
PNG
media_image1.png
1
1
media_image1.png
Greyscale
v
PNG
media_image1.png
1
1
media_image1.png
Greyscale
. Guidewire Software, Inc. 728
PNG
media_image1.png
1
1
media_image1.png
Greyscale
F.3d
PNG
media_image1.png
1
1
media_image1.png
Greyscale
1336, 1345 108 USPQ2d 1173 Fed Cir. 2013: admonishing that the important inquiry for a 101 analysis is to look to the claim”, citing “Content Extraction & Transmission LLC
PNG
media_image1.png
1
1
media_image1.png
Greyscale
v.
PNG
media_image1.png
1
1
media_image1.png
Greyscale
Wells Fargo Bank Nat’l Ass’n 776
PNG
media_image1.png
1
1
media_image1.png
Greyscale
F3d
PNG
media_image1.png
1
1
media_image1.png
Greyscale
1343, 1346 113 USPQ2d 1354 (Fed. Cir. 2014): We focus here on whether the claims of the asserted patents fall within the excluded category of abstract ideas”, cert. denied, 136 S Ct 119, 193 L. Ed. 2d 208 2015). This is consistent with MPEP 2103 I.C stating that “claims define the property rights provided by patent, thus require careful scrutiny. The goal of claim analysis is to identify boundaries of protection sought by applicant and to understand how claims relate to and define what applicant indicated is the invention. USPTO personnel must first determine the scope of a claim by thoroughly analyzing the language of claim before determining if claim complies with each statutory requirement for patentability”. Simply said “[T]he name of the game is the claim”. A similar rationale was previously articulated by the Federal Circuit in Planet Bingo LLC v. VKGS LLC U.S. Court of Appeals, Federal Circuit 2013-1663 August 26, 2014, 576 Fed. Appx. 1005, 2014 BL 235907, where Planet Bingo argued that in real world use, literally thousands, if not millions of preselected Bingo numbers are handled by the claimed computer program, making it impossible for the invention to be carried out manually and the Federal Circuit responded that the claimed inventions do not require as much…. We need not, and do not, address whether a claimed invention requiring many transactions might tip the scales of patent eligibility, as the claims fall far short of capturing an invention that necessarily handles "thousands, if not millions" of bingo numbers or players.
Examiner applies a similar rationale and finds that here, there is no requirement for the claims to recite the alleged large datasets Remarks 05/11/2026 p.8 ¶1, nor is there a requirement to address whether requiring large datasets might tip the scales of patent eligibility based on the preponderance of legal evidence above. Instead, the Examiner concludes by stating that, based on the preponderance of legal evidence above, the claims still recite, describe or set forth the abstract exception. Accordingly, the Applicant’s Step 2A prong one argument is unpersuasive.
Step 2A prong two: Remarks 05/11/2026 p.8 ¶2 argues that incomplete job request data is transformed into complete structured records suitable for downstream system processing, including automated creation of job records within the ERP [enterprise resource planning] system. This transformation is argued to enable automated ingestion of data by enterprise systems without manual intervention, improving functioning of the system to reduce reliance on manual data correction and enable consistent, machine-readable structured data. Such improvement is argued as analogous to recognized improvements in computer functionality, such as improved data structures or automated data processing pipelines rather than business practice improvement
Examiner fully considered the argument but respectfully disagrees finding it unpersuasive by reincorporating all the findings and rationales above showing the claims still recite, describe or set forth abstract concepts. Also, with respect to the alleged suitability of the automated data processing pipelines and complete structured records for downstream system processing, as argued at Remarks 05/11/2026 p.8 ¶2, the Examiner finds that such features do not appear to be reflected in the claims. This finding is important because the “101 inquiry must focus on language of Asserted Claims themselves” as in “Synopsys, Inc. v Mentor Graphics Corp, U.S. Court of Appeals Federal Circuit, No 2015-1599, October 17 2016 2016 BL 344522 839 F3d 1138” citing “Accenture Global Servs., GmbH
PNG
media_image1.png
1
1
media_image1.png
Greyscale
v
PNG
media_image1.png
1
1
media_image1.png
Greyscale
. Guidewire Software, Inc. 728
PNG
media_image1.png
1
1
media_image1.png
Greyscale
F.3d
PNG
media_image1.png
1
1
media_image1.png
Greyscale
1336, 1345 108 USPQ2d 1173 Fed Cir. 2013: admonishing that the important inquiry for a 101 analysis is to look to the claim”, citing “Content Extraction & Transmission LLC
PNG
media_image1.png
1
1
media_image1.png
Greyscale
v.
PNG
media_image1.png
1
1
media_image1.png
Greyscale
Wells Fargo Bank Nat’l Ass’n 776
PNG
media_image1.png
1
1
media_image1.png
Greyscale
F3d
PNG
media_image1.png
1
1
media_image1.png
Greyscale
1343, 1346 113 USPQ2d 1354 (Fed. Cir. 2014): We focus here on whether the claims of the asserted patents fall within the excluded category of abstract ideas”, cert. denied, 136 S Ct 119, 193 L. Ed. 2d 208 2015). This is consistent with MPEP 2103 I.C stating that “claims define the property rights provided by patent, thus require careful scrutiny. The goal of claim analysis is to identify boundaries of protection sought by applicant and to understand how claims relate to and define what applicant indicated is the invention. USPTO personnel must first determine the scope of a claim by thoroughly analyzing the language of claim before determining if claim complies with each statutory requirement for patentability”. Simply said “[T]he name of the game is the claim”.
Examiner has established at the previous step that according to MPEP 2106.04(a)(2) II C considering historical usage information while inputting data7 and automatization by providing information to a person without interfering with the person’s primary activity8 including acquiring content from an information source, controlling the timing of the display of acquired content, displaying the content, and acquiring an updated version of the previously-acquired content when the information source updates its content9 all set forth the abstract Certain Methods of Organizing Human activities grouping.
Now, the Examiner further submits that, when more gradually testing the alleged level of computerization at Step 2A prong two of the analysis, that such automation or computerization of “using the one or more machine learning models” for the aforementioned processes, such as “processing” “the one or more job requests” [recited at high level] “to determine” “information required for the one or more job requests based on the historical job request data” “and” “generating replacement or supplemental field values for the one or more job requests based on” [generally recited] “learned patterns and relationships in the historical job request data” does not render the claims eligible because such computerization merely applies the abstract exception, such as: applying a business method [here managing job requests] and its underlining algorithm on computer [MPEP 2106.05(f)(2)(i)], monitoring audit log data [akin here “learned patterns and relationships in the historical job request data”] executed on the computer where the argued increased speed in the process comes from the computer capabilities [MPEP 2106.05(f)(2)(iii)] and requiring use of a computer component to tailor [here replace or supplement] information and provide it to the user on the computer [MPEP 2106.05(f)(2)(v)]. Each and all of such MPEP 2106.05(f)(2) (i),(iii),(v) examples demonstrate that invocation of computer components or machinery in performing the abstract idea or existing processes does not integrate the abstract idea into a practical application. In fact, MPEP 2106.04(f)(2) ¶110 upholds that use of a computer or other machinery to perform economic or other tasks to receive, store, or transmit data, does not integrate the judicial exception into a practical application. Also, given the generality of the application of the judicial exception, expressed at the last limitation of independent Claims 1,9 as: “initiating creation of a corresponding job record in an Enterprise Resource Planning (ERP) system”, the Examiner finds that, as tested per MPEP 2106.05(f)(3), such limitation does not integrate the abstract exception into a practical application and does not provide technological details on what the “initiating” entails.
In a similar vein, MPEP 2106.05(a) I states that mere automation of manual processes11 and accelerating a process of analyzing audit log data when the increased speed comes from the capabilities of a general-purpose computer12 is not an improvement in computer-functionality.
Thus, the Applicant allegation at Remarks 05/11/2026 p.8 ¶2, that the claim actions would somehow reduce reliance on manual data entry would also not render the claims less abstract and eligible. In fact, Examiner submits in the arguendo, that even the inability for the human mind to perform each claim step does not alone confer patentability, by reliance on FairWarning IP,LLC v Iatric Sys., Inc, 839 F.3d 1089,120 USPQ2d 1293 (Fed Cir 2016) cited by MPEP 2106.04 (a)(2) III C #2. Specifically in FairWarning supra, the Court found that accessing, compiling and combining of data from disparate information sources that made it possible to generate a full picture of a user's activity, identity, frequency of activity, and the like in a computer environment represents a concept of merely selecting information, by content or source, for collection, analysis, and announcement which does not differentiate from mental processes, whose implicit exclusion from 101 undergirds the information-based category of abstract ideas. Such picture is described or set of here as: “generating replacement or supplemental field values for the one or more job requests based on learned patterns and relationships in the historical job request data” at independent Claims 1,9, which remains incapable to provide eligibility by at least similar considerations as FairWarning above.
Likewise, MPEP 2106.05(a) I13 states that providing historical usage information (akin here to “learned patterns and relationships in the historical job request data”) while inputting data, to improve the quality and organization of information added to a database, represents an improvement to the information stored by a database which is not equivalent to improvement in the database’s functionality. Equally, MPEP 2106.05(h) iv14 specifies that the abstract monitoring audit log data relates to transactions or activities executed in a computer environment since this requirement merely limits the claims to a computer field or execution on a computer, which represents a narrowing of the abstract exception to a field of use and technological environment, and thus without integrating it into a practical application. Thus here, in addition to the apply it test of MPEP 2106.05(f), the automated ingestion of data by enterprise systems as argued by Applicant at Remarks 05/11/2026 p.8 ¶2 can be also argued, under MPEP 2106.05(h), as an example of a technological environment upon which the abstract exception is being performed, and thus incapable to integrate the abstract exception into a practical application.
In conclusion here, there is a preponderance of legal evidence showing that the level of computerization or automation in the claims, even when considered beyond computer aids, and as additional elements at Step 2A prong two of the analysis, does not integrate the abstract exception into a practical elements, because such argued computerization or automation represent mere example(s) of applying the abstract idea and/or narrowing it to a field of use or technological environment, none of which integrate it into a practical application.
Therefore, the Applicant’s Step 2A prong two argument is found unpersuasive.
Step 2B: Remarks 05/11/2026 p.8 ¶3 argues trained machine learning models that generate replacement or supplemental structured data based on learned relationships in historical datasets, as well as automated transformation of job request data into structured records used to initiate ERP system processes represent a specific and unconventional application of machine learning to transform structured data to enables downstream system automation.
Examiner considered the Step 2B argument but respectfully disagrees finding it unpersuasive.
Examiner again finds that some features argued by Applicant such as the intended use or intended result to enable downstream system automation do not appear recited not the claims.
Examiner thus resubmits that the “101 inquiry must focus on language of Asserted Claims themselves” as in “Synopsys, Inc. v Mentor Graphics Corp, U.S. Court of Appeals Federal Circuit, No 2015-1599, October 17 2016 2016 BL 344522 839 F3d 1138” citing “Accenture Global Servs., GmbH
PNG
media_image1.png
1
1
media_image1.png
Greyscale
v
PNG
media_image1.png
1
1
media_image1.png
Greyscale
. Guidewire Software, Inc. 728
PNG
media_image1.png
1
1
media_image1.png
Greyscale
F.3d
PNG
media_image1.png
1
1
media_image1.png
Greyscale
1336, 1345 108 USPQ2d 1173 Fed Cir. 2013: admonishing that the important inquiry for a 101 analysis is to look to the claim”, citing “Content Extraction & Transmission LLC
PNG
media_image1.png
1
1
media_image1.png
Greyscale
v.
PNG
media_image1.png
1
1
media_image1.png
Greyscale
Wells Fargo Bank Nat’l Ass’n 776
PNG
media_image1.png
1
1
media_image1.png
Greyscale
F3d
PNG
media_image1.png
1
1
media_image1.png
Greyscale
1343, 1346 113 USPQ2d 1354 (Fed. Cir. 2014): We focus here on whether the claims of the asserted patents fall within the excluded category of abstract ideas”, cert. denied, 136 S Ct 119, 193 L. Ed. 2d 208 2015). This is consistent with MPEP 2103 I.C stating that “claims define the property rights provided by patent, thus require careful scrutiny. The goal of claim analysis is to identify boundaries of protection sought by applicant and to understand how claims relate to and define what applicant indicated is the invention. USPTO personnel must first determine the scope of a claim by thoroughly analyzing the language of claim before determining if claim complies with each statutory requirement for patentability”. Simply said “[T]he name of the game is the claim”.
With respect to the argued unconventional application of the machine learning, as argued by Applicant at Remarks 05/11/2026 p.8 ¶3, Examiner points to MPEP 2106.05 (d) II ¶4-¶5 and carries over the findings of the MPEP 2106.05 (f) test and/or MPEP 2106.05 (h) test to submit that the additional computer-based elements also do not provide significantly more. Specifically, Examiner asserts that the above tests show using the additional computer-based elements to
apply of the abstract idea, at MPEP 2106.05 (f), and/or narrow it to a field of use or technological environment at MPEP 2106.05 (h), suffice to show that any argued additional, computer-based, elements also do not provide significantly more without having to rely on the conventionality test of MPEP 2106.05(d).
Yet assuming arguendo, that further evidence would be still required to demonstrate the conventionality of the additional, computer-based elements, the Examiner would follow MPEP 2106.05(d) I.2.(a), and point as evidence for the conventionality of the additional elements, as interpreted when read in light of the Applicant’s own Original Disclosure as follows:
* Original Specification ¶ [0004] 3rd sentence reciting at a high level of generality: “The steps can further include opening one or more jobs in an Enterprise Resource Planning (ERP) system associated with the infrastructure service provider based on the one or more job requests”
* Original Specification ¶ [0010] 1st-2nd, 5th sentences: “Fig.1 is a block diagram of a digital device 100 that, in terms of hardware architecture, generally includes a processor 182, input/output (I/O) interfaces 184, wireless interfaces 186, a data store 188, and memory 190. It should be appreciated by those of ordinary skill in the art that FIG. 6 depicts the digital device 100 in an oversimplified manner and a practical embodiment may include additional components and suitably configured processing logic to support known or conventional operating features that are not described in detail herein”. “The local interface 192 can be, for example, but not limited to, one or more buses or other wired or wireless connections, as is known in the art.
* Original Specification ¶ [0011] “The processor 182 is a hardware device for executing software instructions. The processor 182 can be any custom made or commercially available processor, a central processing unit (CPU), an auxiliary processor among several processors associated with the digital device 100, a semiconductor-based microprocessor (in the form of a microchip or chip set), or generally any device for executing software instructions. When the digital device 100 is in operation, the processor 182 is configured to execute software stored within the memory 190, to communicate data to and from the memory 190, and to generally control operations of the digital device 100 pursuant to the software instructions”.
* Original Specification ¶ [0029] last sentence, ¶ [0049] last sentence, ¶ [0051] 6th sentence reciting at high level of generality: “The training can include any of supervised and unsupervised learning for the one or more machine learning models”.
* Original Specification ¶ [0049] 3rd sentence: “The steps can further include opening one or more jobs in an Enterprise Resource Planning (ERP) system associated with the infrastructure service provider based on the one or more job requests”.
* Original Specification ¶ [0052] “It will be appreciated that some embodiments described herein may include or utilize one or more generic or specialized processors (“one or more processors”) such as microprocessors; Central Processing Units (CPUs); Digital Signal Processors (DSPs): customized processors such as Network Processors (NPs) or Network Processing Units (NPUs), Graphics Processing Units (GPUs), or the like; Field-Programmable Gate Arrays (FPGAs); and the like along with unique stored program instructions (including both software and firmware) for control thereof to implement, in conjunction with certain non-processor circuits, some, most, or all of the functions of the methods and/or systems described herein. Alternatively, some or all functions may be implemented by a state machine that has no stored program instructions, or in one or more Application-Specific Integrated Circuits (ASICs), in which each function or some combinations of certain of the functions are implemented as custom logic or circuitry. Of course, a combination of the aforementioned approaches may be used. For some of the embodiments described herein, a corresponding device in hardware and optionally with software, firmware, and a combination thereof can be referred to as “circuitry configured to,” “logic configured to,” etc. perform a set of operations, steps, methods, processes, algorithms, functions, techniques, etc. on digital and/or analog signals as described herein for the various embodiments”.
* Original Specification ¶ [0054] Although the present disclosure has been illustrated and described herein with reference to preferred embodiments and specific examples thereof, it will be readily apparent to those of ordinary skill in the art that other embodiments and examples may perform similar functions and/or achieve like results. All such equivalent embodiments and examples are within the spirit and scope of the present disclosure, are contemplated thereby, and are intended to be covered by the following claims”.
Based on the preponderance of legal and/or factual evidence provided above, it is clear that, no additional computer-based elements are capable to provide significantly more.
Therefore, the Applicant’s Step 2B argument is found unpersuasive.
In conclusion the claims still recite, describe or set forth the abstract exception (Step 2A prong one) with the additional, computer-based elements failing to integrate the abstract exception into a practical application (Step 2A prong two) or providing significantly more (Strep 2B).
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
3) Response to Applicant’s rebuttal arguments on 103 rejection
- independent Claims 1, 9 -
Argument 3i: Remarks 05/11/2026 p.10 ¶1, 2nd sentence argues neither Sethi et al, US 20230229737 A1 nor Stifter et al, US 12243082 B1 teaches or suggest structured data transformation pipeline or the integration with downstream job creation processes.
Examiner fully considered the Applicant’s prior art Argument 3i but respectfully disagrees finding it unpersuasive because the transformation pipeline or the integration with downstream job creation processes as asserted by Applicant above, does not appear to be present in the Original Disclosure much less recited in the current claims. The Examiner reminds the Applicant that although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). This is consistent with MPEP 2103 I.C stating that “claims define the property rights provided by patent, thus require careful scrutiny. The goal of claim analysis is to identify boundaries of protection sought by applicant and to understand how claims relate to and define what applicant indicated is the invention. USPTO personnel must first determine the scope of a claim by thoroughly analyzing the language of claim before determining if claim complies with each statutory requirement for patentability”. Simply said “[T]he name of the game is the claim”. MPEP 2103 I C citing In re Hiniker Co 150 F3d 1362 1369 47 USPQ2d 1523, 1529 Fed Cir 1998. Thus, the Argument 3 i is found unpersuasive.
Argument 3ii: Remarks 05/11/2026 p.9 ¶5-p.10 ¶1, 1st sentence argues neither Sethi et al, US 20230229737 A1 nor Stifter et al, US 12243082 B1 teaches or suggest using the one or more trained machine learning models for generating replacement or supplemental structured data fields for the one or more job requests based on learned relationships in historical job request
Examiner fully considered Argument 3ii but respectfully disagrees finding it unpersuasive.
Stifter et al, US 12243082 B1 teaches or suggests:
- using the one or more trained machine learning models for generating replacement or supplemental structured data fields for the one or more job requests based on learned relationships in historical job request
(Stifter column 3 lines 57-61: the current disclosure is applicable to any situation where invoices need [or are required] to be processed and/or managed (including determining which entity/budget should pay an invoice, to whom and how an invoice should be paid etc.). Thus at
Stifter column 4 lines 8-19: the ERP systems provide access payment records etc. platform 100 use the data accessed to base its determinations and/or predictions, described herein. For example, platform 100 use updated data to train neural network. Specifically,
Stifter column 11 lines 34-38: in situations where information in a field, e.g., field 313, is missing [interpreted as inefficiency] and/or illegible… the system may treat the field as a judgment field, e.g., the value of the field may be determined/inferred via machine learning using historical data, described at column 13 lines 9-30. Indeed, per
Stifter column 17 lines 37-45: mappings between the images and the historical data may be used to build associations [or relationships] between the invoice images and the correct vendor in an ERP or AP workflow system. historical images of invoices may be passed through an OCR extraction process to pull out vendor attributes, e.g., identifiable information, for the associated vendor used to SUPPLEMENT data missing in a corresponding ERP. For instance,
Stifter column 13 lines 9-30: historical data, is exemplified as an enhanced record, regarding past invoices for the vendor used to determine how a client typically codes that vendor's invoices. In embodiments, the historical data may include information corresponding to a vendor and/or a payor of an invoice. In embodiments, the historical data may include previously coded invoices and/or images of previous invoices. OCR extracted data, described herein, may be compared with the historical invoice data to assist in predicting one or more of the following as corresponding to a payor and/or vendor of the invoice: a particular property and/or entity; an expense account; a cash account; an ap account; and/or the like. In certain embodiments, cost allocations may be determined and/or compared to an enhanced record. For example, OCR extracted data may be used to determine how the total amount of an invoice is broken down, e.g., single line item, multiple line items, a table and/or gird of costs. The OCR extracted cost breakdown may then be compared to historical invoice data for the vendor, e.g., an enhanced record, to assist in predicting an appropriate and/or typical level of detail for the cost allocation on the invoice being assessed).
Thus, Stifter teaches or suggests the contested features and argument 3 ii is unpersuasive.
Argument 3 iii: Remarks 05/11/2026 p.10 ¶2-¶3 argues there is no motivation to combine
Sethi and Stifter because neither recognizes the problem addressed by invention; that incomplete or inconsistent structured job request data prevents automated downstream processing and requires transformation into complete structured records. Remarks 05/11/2026 p.10 ¶3-¶4 further argues that the proposed combination would require significant modification of Sethi to incorporate structured data generation functionality, as well as modification of Stifter to operate within a job request processing and ERP integration pipeline going beyond routine combination of known elements and instead constitute reconstructing the Applicant's invention using hindsight.
Examiner fully considered Argument 3iii but respectfully disagrees finding it unpersuasive.
First, as an issue of claim construction or claim interpretation, the Examiner resubmits that the automated downstream processing does not appear to be present in the Original Disclosure much less recited in the actual claims. The Examiner thus reminds the Applicant that although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). This is consistent with MPEP 2103 I.C stating that “claims define the property rights provided by patent, thus require careful scrutiny. The goal of claim analysis is to identify boundaries of protection sought by applicant and to understand how claims relate to and define what applicant indicated is the invention. USPTO personnel must first determine the scope of a claim by thoroughly analyzing the language of claim before determining if claim complies with each statutory requirement for patentability”. Simply said “[T]he name of the game is the claim”. MPEP 2103 I C citing In re Hiniker Co 150 F3d 1362 1369 47 USPQ2d 1523, 1529 Fed Cir 1998.
Second, the Examiner notes the Applicant appears to only contest the combination rationale (MPEP 2143 A) of the Non-Final Act 02/11/2026 p.25 ¶2 but fails to address the separate modification rationale (MPEP 2143 G) as also utilized by the Non-Final Act 02/11/2026 p.25 ¶1.
Third, with respect to the impermissible hindsight argument, the Examiner follows MPEP 707.07(f) which cites ¶ 7.37.03 to state that, “in response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper”. In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
Here, despite the Applicant’s allegation to the contrary, as made at Remarks 05/11/2026 p.10, the prior Non-Final Act 02/11/2026 found that both Sethi and Stifter recite analogous management of customer requests for an enterprise dealing with missing, incomplete or inconsistent data. Specifically, as articulated by Non-Final Act 02/11/2026 p.23-p.24 ¶1:
Sethi was found to still provide at ¶ [0003] 1st sentence: an example of an issue of missing components and/or mismatch of configuration [or misconfiguration or misclassification] from what has been ordered by a customer. see Sethi ¶ [0032] noting an example where basic order data…include…part(s) which are missing… This is addressed at ¶ [0028] 1st-2nd sentences, where order problem learning and mitigation engine 124 is configured to operate in conjunction with order processing engine 122 and order database 126 to learn of issues associated with orders and to enable actions to be taken to prevent or otherwise mitigate the issue. In one or more illustrative embodiments, order problem learning and mitigation engine 124 utilizes machine learning-based algorithm referred to as reinforcement learning or Q-learning to enable the intelligent learning functionality. Thus at ¶ [0029] the order problem learning and mitigation engine 124 learns from errors of faulty orders and fixes [or corrects] the issues before orders are delivered to customers, i.e. one or more orders from user.
Sethi ¶ [0101] 1st-2nd sentences: In addition to the algorithm, an order object gets populated with the issue and fix details in the respective stage. For example, in the failed stage, the issue details get added to the order object. In the remedy stage, the fix details get added to the order object
Sethi ¶ [0109] goes so far to state: the illustrative embodiments provide a methodology to analyze and formulate faulty orders and speculate the reason for the issues, as well as a method to create the lesson learned from the faulty orders and convert it into a checklist for further orders.
* Analogously *
Stifter column 1 lines 21-23 provides an analogous art of using machine learning to manage large numbers of requests with Stifter column 11 lines 34-38 disclosing that in situations where information in a field, e.g., field 313, is missing [interpreted as inefficiency] and/or illegible … the system may treat the field as a judgment field, e.g., the value of the field may be determined/inferred via machine learning using historical data, described at column 13 lines 9-30. Indeed, per Stifter column 17 lines 37-45: mappings between the images and the historical data may be used to build associations [or relationships] between the invoice images and the correct vendor in an ERP or AP workflow system. historical images of invoices may be passed through an OCR extraction process to pull out vendor attributes, e.g., identifiable information, for the associated vendor used to SUPPLEMENT data missing in a corresponding ERP.
Therefore, consistent with MPEP 2141.01(a) I, each of Sethi and Stifter are analogous art to the claimed invention because they are: (1) from the same field of endeavor of management of customer requests for an enterprise dealing with missing, incomplete or inconsistent data as the claimed invention; or (2) they are reasonably pertinent to the missing, incomplete or inconsistent data problem faced by the inventor (even if, in the arguendo, they would not be in the same field of endeavor as the claimed invention)
Examiner corroborates analogous rationale by relying on MPEP 2141.01(a) IV which cites In re Bigio, 381 F.3d 1320, 1325-26, 72 USPQ2d 1209, 1211-12 (Fed. Cir. 2004) where the patent application claimed a "hair brush" having a specific bristle configuration. The Board affirmed the examiner’s rejection of the claims as being obvious in view of prior art patents disclosing toothbrushes. Id. at 1323, 72 USPQ2d at 1210. The appellant disputed that the patent references constituted analogous art. On appeal, the court upheld the Board’s interpretation of the claim term "hair brush" to encompass any brush that may be used for any bodily hair, including facial hair. Id. at 1323-24, 72 USPQ2d at 1211. With this claim interpretation, the court applied the "field of endeavor test" for analogous art and determined that the references were within the field of the inventor’s endeavor and hence were analogous art because toothbrushes are structurally similar to small brushes for hair, and a toothbrush could be used to brush facial hair. Id. at 1326, 72 USPQ2d at 1212. Since the Court established that use of a toothbrush as hair brush, did contravene the analogous rationale, the Examiner similarly reasons that here, the management of relationships between customer and businesses, to address various risks and issues, within the broad umbrella of organizational planning or management, as taught throughout the prior art references above, would also not render their modification or combination improper in a manner
consistent with the findings of fact established above and not contrary to the Bigio test above.
As per the Applicant’s argument at Remarks 05/11/2026 p.10 ¶2 that the modification of
Sethi and Stifter goes beyond routine combination of known elements and instead constitute reconstruction of Applicant's invention, the Examiner respectfully rebuts by pointing to Non-Final Act 02/11/2026 p.25 ¶1 which relied on Stifter column 14 lines 22-29 and column 15 lines 54-67 to provide an explicit and deliberate citation on the motivation for having modified of Sethi to have included Stifter’s teachings or suggestions, without any mentioning or reconstructing gleaned from the Applicant's own invention. More to the point, the Non-Final Act 02/11/2026 p.25 ¶1 also found that the predictability of such modification would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Sethi ¶ [0116] in view of Stifter column 3 lines 14-17, 44-53, column 30 lines 13-19 which again consolidates the above rationales. Thus, each and all of the modification (MPEP 2143 G) and combination (MPEP 2143 A), rationales have basis in legal and/or factual evidence, and do not reconstruct of the Applicant's invention using hindsight. Thus, Examiner finds that the prior art Argument 3 iii is unpersuasive.
- dependent Claims 3, 11 -
Argument 3 iv: Remarks 05/11/2026 p.10 ¶5 similarly argues that Joseph et al, US 11636381 B1 does not group job requests based on infrastructure-specific attributes using trained machine learning models in the context of transforming job request data for ERP processing.
Examiner fully considered the Applicant’s argument 3 iv but respectfully disagrees finding it unpersuasive because one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
With respect to dependent Claims 3,11, Sethi et al, US 20230229737 A1 further teaches
- “grouping the plurality of job requests using the one or more trained machine learning models based on information sociated with the job requests”.
(Sethi ¶ [0029] order problem learning and mitigation engine 124 learns from errors of faulty orders and fixes the issues before orders are delivered to customers, i.e. one or more orders from user 102. Specifically, ¶ [0030] 2nd sentence: Automated order management process 200 executes 5 main stages respectively depicted as steps 202,204,206,208 and 210. These stages comprise: collating [or grouping] order data (202); analyzing the order data by applying reinforcement learning (Q-learning) algorithm (204) [as an example of trained machine learning model]; generating order weights (206); resolving problem(s) with an order (208); and generating a mitigation plan (210). ¶ [0031] As part of collating order data (202), order problem learning and mitigation engine 124 collates [or groups] the order history which includes customers' accepted orders and faulty orders. ¶ [0109] 2nd sentence: If a portion of an order matches with some of the multiple historical orders, illustrative embodiments perform a fusion (Fig.12) on multiple trees that reflect the historical orders. Thus, ¶ [0111] 1st-3rd sentences: order problem learning and mitigation engine 124 gets trained during training phase and determines the Q matrix. The matrix is used to predict during inference stage. The order data along with the fix data is stored in order database 126 for reference for the steps if a similar order behavior is observed while processing).
Sethi / Stifter however as a combination does not teach:
- “including infrastructure site attributes comprising at least one or geographic region, customer-specific workgroup requirements, or type of maintenance or audit task, and wherein grouped job requests are processed together for creation of corresponding jobs on the ERP system” as claimed.
Joseph et al, US 11636381 B1 nevertheless in analogous machine learning for managing requests teaches or suggests:
- “including infrastructure site attributes comprising at least one or geographic region, customer-specific workgroup requirements, or type of maintenance or audit task, and wherein grouped job requests are processed together for creation of corresponding jobs on the ERP system” (Joseph column 4 lines 50-54: Provided machine learning system for demand forecasting predicting the impact of events on demand forecasting within a geographic area in accordance with the teachings of this disclosure. Specifically, column 5 lines 63-64 recognized: that drivers of demand include past sales, store traffic, seasonality, etc. For example, at column 7 lines 22-29, 48-52: For buildings 221 and 222, at rush hour when employees are coming to work in those buildings the system 100 define two distinct events relating to increased in traffic in Range (1) for buildings 221 and 222. There may be increased demand for products or services provided by the establishment in storefront 220 during those times based on the increase in traffic to buildings 221 and 22. Arena 224 is in same Range (3) as building 226, but the events taking place at arena 224 may cause traffic patterns that are more substantial than the increased traffic at rush hour for building 226 during the time periods associated with those events. This is detailed at
Joseph column 11 lines 40-43: volumes of historical transaction data may therefore be available to those businesses that have archived data produced by various ERP applications. For example, at column 12 line 66-column 13 line 18: process 600 begins at operation 660 by receiving, via the computer networks, demand data for products or services of the organization from a database of demand data and events data comprising event attributes from a database of events data for a plurality of different events located within a distance from the geolocation of the organization. Process 600 continues by receiving geolocation data identifying the geolocation of the organization (operation 662) and generating a range of distances based on segmenting the distance from the geolocation of the organization into a plurality of different distances (operation 664). Event categories for each of the plurality of different events may then be normalized by assigning them to one of a set of predefined event categories at operation 666. The events may then be grouped into different combinations of the events based on event attributes at operation 668. In one embodiment, events in a same predefined event category and within a same range of distances from the geolocation of the organization are grouped together).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have further modified Sethi / Stifter’s “non-transitory medium” / “method” to have further included Joseph’s teachings/suggestions in order to have better predicted demand by grouping similar events into event streams based on event attributes in the events data for helpful workforce management processes (Joseph column 4 lines 26-46 in view of MPEP 2143 G and/or F). The predictability of such modification would have been corroborated by the broad level of skill of one of ordinary skills in the art as further articulated by Sethi ¶ [0116] in view of Stifter column 3 lines 14-17, 44-53, column 30 lines 13-19, and in further view of Joseph column 2 lines 17-20, 56-66, column 16 lines 11-19.
Further, the claimed invention is merely a combination of old elements in a similar field of endeavor dealing with managing requests for an organization. In the combination each element merely would have performed the same analytical and data processing function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements as evidenced by Sethi / Stifter in view of Joseph, the to be combined elements would have fitted together, like puzzle pieces in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A).
Accordingly, the Applicant’s Argument 3 iv is found unpersuasive.
- rejections over Iyer Vaidy US 20080249791 A1 -
Argument 3 v: Remarks 05/11/2026 p.10 ¶5 argues Iyer Vaidy US 20080249791 A1 does not teach machine learning-driven transformation of structured job request data prior to ERP job creation. Examiner fully considered the Applicant’s argument 3 v but respectfully disagrees.
First, as an issue of claim construction or claim interpretation, the Examiner resubmits that the machine learning-driven transformation of structured job request data prior to ERP job creation does not appear to be recited in the claims 4-6, 12-14 taught by Iyer. The Examiner reminds that although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). This is consistent with MPEP 2103 I.C stating that “claims define the property rights provided by patent, thus require careful scrutiny. The goal of claim analysis is to identify boundaries of protection sought by applicant and to understand how claims relate to and define what applicant indicated is the invention. USPTO personnel must first determine the scope of a claim by thoroughly analyzing the language of claim before determining if claim complies with each statutory requirement for patentability”. Simply said “[T]he name of the game is the claim”. MPEP 2103 I C citing In re Hiniker Co 150 F3d 1362 1369 47 USPQ2d 1523, 1529 Fed Cir 1998.
Examiner reincorporates all findings and rationales of Non-Final Act 02/11/2026 p.25 last ¶ -p.30 ¶1 and finds the Applicant’s argument 3v unpersuasive.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 8,16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 8,16 are dependent on parent independent claims 1,19 respectively, and further recite “wherein the data includes historical job request data”.
Claims 1,9 have now been amended to remove the term “inferred data”, and thus it is now unclear if “the data includes historical job request data” as recited at each of dependent Claims 8,16, refers to “historical job request data” of Claims 1,9.
Clarification and correction is required of whether the “the data includes historical job request data” in child claims 8,16, is same “historical job request data” of parent independent claims 1,9 or if it refers to another yet to be recited, inferred data as previously recited.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 8,16 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 8,16 depends upon parent claims 1,9 respectively, and further recite “the data includes historical job request data”. Yet, parent claims 1,9 already recite “historical job request data”. Unless “the data” in expression “the data includes historical job request data” [emphasis on dual recitation of the term data] refers to some other data, each of Claims 8,16 fails to further limit the subject matter of the respective claims 1,9 upon which they each depend. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements. Clarification and correction are required.
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 1,3-9,11-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea, here abstract idea) without significantly more. The claim(s) recite(s) set forth or describe the abstract grouping of Certain Methods of Organizing Human Activities (MPEP 2106.04(a)(2) II), namely commercial or fundamental economic practices, namely “receiving one or more job requests from the one or more customers”; “identifying” “one or more insufficiencies”
(i.e. “missing required fields” or “misclassified information”) “in the one or more job requests”; “generating replacement or supplemental field values for the one or more job requests based on learned patterns and relationships in the historical job request data” and “initiating creation of a corresponding job record” (independent Claims 1,9). For example, MPEP 2106.04(a)(2) II A explains that activities that mitigate or minimize of risks15 are example of fundamental economic practices or principles of the same certain methods of organizing human activities grouping. It would then follow that here “generating replacement or supplemental field values for the one or more job requests based on learned patterns and relationships in the historical job request data”
for “remedying” [akin to mitigating] “one or more” [risks or] insufficiencies” (i.e. “missing required fields” or “misclassified information”) “in the one or more job requests” would also represent fundamental economic practices or principles of the abstract organizing human activities.
It can also be argued that the “replacement or supplemental” [of] “field values” “based on learned patterns and relationships in the historical job request data” is not meaningfully different than a mere manipulation of basic mathematical constructs [here “field values”] or the paradigmatic ‘abstract idea which, as explained by MPEP 2106.04(a)(2) I A iv., represents organizing of information and manipulating information through mathematical correlations by generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form remains abstract. Here, the “generating” information or new form, recited as “replacement or supplemental field values” at independent Claims 1,9 is such an example of the abstract manipulating information through mathematical correlations, the latter represented here by “learned patterns and relationships in the historical job request data”, by generating first and second data by taking existing information, represented here as “historical job request data”, manipulating the data using mathematical functions, represented here by “using the one or more trained machine learning models to determine information required for the one or more job requests”, and organizing this information into a new form represented here by the “replacement or supplemental field values” at claims 1,9.
It is also clear that use of such “learned patterns and historical job request data” for the subsequent automation, to satisfy a best business practice of replac[ing] or supplement[ing] field values based on learned patterns and relationships in the historical job request data does not render the claims less abstract and eligible, because MPEP 2106.04(a)(2) II C found that considering historical usage information while inputting data16 and automatization by providing information to a person without interfering with the person’s primary activity17 including acquiring content from an information source, controlling the timing of the display of acquired content, displaying the content, and acquiring an updated version of the previously-acquired content when the information source updates its content18 all set forth the abstract grouping of Certain Methods of Organizing Human activities. In fact, MPEP 2106.04(a)(2) II ¶6, 4th sentence goes so far to state that certain activity between a person and a computer may still fall within certain methods of organizing human activity. It then follows that here, activity with a “user interface”, “ERP system”, “link” “to a web storage system”, “adapted to allow uploading” as well as “closing data uploaded to the web storage system” as recited at the following limitations: “wherein the one or more job requests are submitted via a user interface or retrieved from a third-party customer system”; and “initiating creation of a corresponding job record in an Enterprise Resource Planning (ERP) system” at independent Claims 1,9, “opening one or more jobs in the Enterprise Resource Planning (ERP) system associated with the infrastructure service provider based on the one or more job requests” at dependent Claims 4,12, “providing a link associated with each of the one or more job requests to a web storage system, wherein the link is adapted to allow uploading of job data associated with each of the one or more job requests” at dependent Claims 5,13, and “closing one or more jobs based on job data uploaded to the web storage system” at dependent Claim 6,14 would similarly fall within abstract grouping of certain methods of organizing human activities, as not meaningfully different than the commercial interactions and risk mitigations of the respective MPEP 2106.04(a)(2) II B and A.
As such, when tested per MPEP 2106.04(a)(2) II ¶6, 4th sentence, the current claims still fall within the abstract certain methods of organizing human activity grouping, despite activity with a computer with MPEP 2106.04(a)(2) II A ¶2 further clarifying that the term fundamental is not used in the sense of necessarily being old or well-known but rather as a building block of modern economy, and with MPEP 2106.04(a)(2) II C ii stating that considering historical usage information while inputting data19 represents an example of certain methods of organizing human activity. It then follows that here, the comparable recitations of: “allow uploading of job data associated with each of the one or more job requests” at dependent Claims 5, 13 and considerations for “historical job request data” at independent Claims 1,9 and dependent Claims 8,16 would similarly describe or set forth the abstract certain methods of organizing human activity grouping.
Also, MPEP 2106.04(a)(2) II C is clear that considering historical usage information while inputting data20 and automatization by providing information to a person without interfering with the person’s primary activity21 including acquiring content from an information source, controlling the timing of the display of acquired content, displaying the content, and acquiring an updated version of the previously-acquired content when the information source updates its content22 set forth the abstract grouping of Certain Methods of Organizing Human activities.
Here, acquiring content from an information source is set forth by “receiving one or more job requests from the one or more customers, wherein the one or more job requests are submitted via a user interface or retrieved from a third-party customer system” Claims 1,9, while the controlling the timing and updated version of previously-acquired content when the information source updates its content is set forth by “processing the one or more lob requests” “to determine information required for the one or more job requests based on the historical job request data, and generating replacement or supplemental field values for the one or more job requests based on learned patterns and relationships in the historical job request data” at Claims 1,9.
Further when tested per MPEP 2106.04(a) ¶3, 3) and MPEP 2106.04(a)(2) III C, Examiner submits that here, the Certain Methods of Organizing Human Activities as recited, described or set forth above, could be argued as implementable23 through computer-aided mental processes, such as by equally abstract computer-aided evaluation, judgement and observation. For example, MPEP 2106.04(a)(2) III cites Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739,1741-42 Fed Cir 2016 to state that combination of collecting information, analyzing it, and displaying certain results of the collection and analysis, recite abstract mental processes.
- Here, such computer-aided collection can be argued as “receiving one or more job requests from the one or more customers, wherein the one or more job requests are submitted via a user interface or retrieved from a third-party customer system” at independent Claims 1,9, and by “opening one or more jobs in an Enterprise Resource Planning (ERP) system associated with the infrastructure service provider based on the one or more job requests” at dependent Claims 4,12, and “providing a link associated with each of the one or more job requests to a web storage system, wherein the link is adapted to allow uploading of job data associated with each of the one or more job requests” at dependent Claims 5,13.
- Here, such evaluation or analysis and/or computer-aided evaluation or analysis can be argued as “identifying” “one or more insufficiencies in the one or more job requests wherein the one or more insufficiencies comprise missing required fields or misclassified information” (independent Claims 1,9); and “grouping the plurality of job requests” “based on information associated with the job requests including infrastructure site attributes including at least one or geographic region, customer-specific workgroup requirements, or type of maintenance or audit task, and wherein grouped job requests are processed together for creation of corresponding jobs on an ERP system” (dependent Claims 3,11).
- Here such computer-aided judgment can be argued as set forth by “remedying the one or more insufficiencies by processing the one or more job requests” “to determine information required for the one or more job requests based on the historical job request data, and generating replacement or supplemental field values for the one or more job requests based on learned patterns and relationships in the historical job request data” (independent Claims 1,9)
- Here such observation and displaying of the collection and analysis, as well as any computer-aided observation and displaying can be argued as set forth by: “wherein the job requests are submitted via a user interface or retrieved from a third-party customer system” (Claims 1,9), “opening one or more jobs in an Enterprise Resource Planning (ERP) system associated with the infrastructure service provider based on the one or more job requests” (Claims 4,12); “providing a link associated with each of the one or more job requests to a web storage system, wherein the link is adapted to allow uploading of job data associated with each of the one or more job requests” (Claims 5,13).
In an abundance of caution, the Examiner will more granularly test such computerization below, be it hardware and/or software below. For now, given the preponderance of legal evidence as shown above, the claims still recite, describe or set forth the abstract exception.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
This judicial exception is not integrated into a practical application because per Step 2A prong two, the individual or combination of the computer elements identified above appear to represent mere physical aids to implement the aforementioned abstract exception, as tested at the prior step. Even when construed, in the arguendo, as additional, computer-based elements, the aforementioned “one or more processors” (independent Claim 1) and general recitations of “user interface” or “third-party customer system” (independent Claims 1,9) and “trained machine learning model” (independent Claims 1,9, dependent Claims 3,7,11,15), further narrowed as “supervised and unsupervised learning” (dependent Claims 7,15), “Enterprise Resource Planning (ERP) system”, (dependent Claims 4,12), “web storage system” (dependent Claims 5,6,13,14), along with the autom[ation] as in “automatically remedying” (independent Claims 1,9) would still merely apply the aforementioned abstract exception, such as economic or other tasks to receive, or transmit data, of MPEP 2106.05(f)(2) ¶124 and/or the applying of the aforementioned business method and/or its underlining machine learning or mathematical algorithm on a computer of MPEP 2106.05(f)(2)(i)25, which do not integrate the abstract exception into a practical application.
Further the requirement for the “one or more processors” of independent Claim 1 and “one or more trained machine learning models” of independent Claims 1,9 for “identifying one or more insufficiencies in the one or more job requests based on the training” (independent Claims 1,9) could also be argued as a computerized attempt to monitor audit log data executed on a general-purpose computer, which, as tested per MPEP 2106.05(f)(2) iii26 does not integrate the abstract exception into a practical application.
Also, the capabilities of the “one or more processors” (independent Claim 1) and “the one or more trained machine learning models” (independent Claims 1,9) “to determine information required for the one or more job requests based on the historical job request data, and generating replacement or supplemental field values for the one or more job requests based on learned patterns and relationships in the historical job request data” (independent Claims 1,9), would correspond, along with the “opening one or more jobs in an Enterprise Resource Planning (ERP) system associated with the infrastructure service provider based on the one or more job requests” (dependent Claims 4,12), the “providing a link associated with each of the one or more job requests to a web storage system, wherein the link is adapted to allow uploading of job data associated with each of the one or more job requests”(dependent Claims 5,13) and “closing one or more jobs based on the job data uploaded to the web storage system” (dependent Claims 6,14), and “wherein the job requests are submitted via a user interface or retrieved from a third-party customer system” (independent Claims 1,9) to mere use of software to tailor information and provide it to user on generic computer27 as tested per MPEP 2106.05(f)(2) v and/or generating second menu from first menu and sending the second menu to other location as performed by generic computer components28, tested per MPEP 2106.05(f)(2) ii. None of these examples, as articulated by MPEP 2106.05(f)(2), integrate the abstract idea into a practical application because, they merely invoke computers or other machinery as a mere tool to perform an existing, abstract process. Such limitations at Claims 4-6, 12-14, along with “initiating creation of a corresponding job record in an Enterprise Resource Planning (ERP) system based on the one or more job requests with the generated replacement or supplemental field values” could also be argued as
examples of generality of the application of the judicial exception, which according to MPEP 2106.05(f)(3) also does not integrate the abstract exception into a practical application, without providing the requisite degree of a technological solution, as required by MPEP 2106.05(f)(1).
Also, MPEP 2106.05(h)29 states that narrowing a combination of collecting information, analyzing, and displaying certain results of the collection and analysis to a particular field of use or technological environment does not integrate the abstract idea into a practical application. It follows that here, narrowing the collecting, analyzing, and displaying of certain results of the collection and analysis to a field of use or a particular technological environment characterized by “machine learning” (Claims 1,3,7,9,11,15) and “Enterprise Resource Planning (ERP) system” (Claims 1,3-6,9,12-14), would similarly not integrate the abstract idea into a practical application.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as shown above, the additional computer-based elements merely apply the already recited abstract idea [MPEP 2106.05(f)] and/or narrow it to a field of use or technological environment [MPEP 2106.05(h)]. Examiner follows MPEP 2106.05 (d) II and carries over the findings at MPEP 2106.05 (f) and (h) as a sufficient option for evidence that the additional computer-based elements also do not provide significantly more, without having to rely on conventionality test of MPEP 2106.05(d). Yet, even assuming arguendo, that further evidence would still be required to demonstrate conventionality of the additional elements, the Examiner would further point to MPEP 2106.05(d) to demonstrate the conventionality of the computer components performing: electronic recordkeeping30 / gathering statistics31, arranging hierarchy of groups and sorting information32, performing repetitive calculations33. Specifically, here the electronic recordkeeping, gathering statistics, arranging hierarchy of groups and sorting information, are reflected in the capabilities of the “one or more processors” of independent Claim 1 and “one or more trained machine learning models”
of independent Claims 1,9 in “receiving one or more job requests from the one or more customers”; “identifying” “insufficiencies in the one or more job requests” at independent Claim 1, then possibly the “grouping the plurality of job requests” “based on information associated with the job requests” of dependent Claim 3, and “the link is adapted to allow uploading of job data associated with each of the one or more job requests” at dependent Claims 5,13.
Also here, the repetitive calculations are reflected in the capabilities of the “one or more processors” of independent Claim 1 in “training one or more a machine learning models with data associated with one or more customers of an infrastructure service provider” as generally recited at independent Claim 1 and “grouping the plurality of job requests based on the training” as generally recited at dependent Claim 3. Further the capabilities of “opening one or more jobs in an Enterprise Resource Planning (ERP) system” at dependent Claim 4 and “closing one or more jobs based on the job data uploaded to the web storage system” at dependent Claim 6 are not meaningfully different than the conventional capabilities of a web browser’s back and forward button functionality34 as cited by MPEP 2106.05(d)II vi at MPEP 2106.05(d) II ¶8.
If necessary, the Examiner would also follow MPEP 2106.05(d) I.2.(a), and point as evidence for the conventionality of the additional elements, as interpreted when read in light of:
* Original Specification ¶ [0004] 3rd sentence reciting at a high level of generality: “The steps can further include opening one or more jobs in an Enterprise Resource Planning (ERP) system associated with the infrastructure service provider based on the one or more job requests”
* Original Specification ¶ [0010] 1st-2nd, 5th sentences: “Fig.1 is a block diagram of a digital device 100 that, in terms of hardware architecture, generally includes a processor 182, input/output (I/O) interfaces 184, wireless interfaces 186, a data store 188, and memory 190. It should be appreciated by those of ordinary skill in the art that FIG. 6 depicts the digital device 100 in an oversimplified manner and a practical embodiment may include additional components and suitably configured processing logic to support known or conventional operating features that are not described in detail herein”. “The local interface 192 can be, for example, but not limited to, one or more buses or other wired or wireless connections, as is known in the art.
* Original Specification ¶ [0011] “The processor 182 is a hardware device for executing software instructions. The processor 182 can be any custom made or commercially available processor, a central processing unit (CPU), an auxiliary processor among several processors associated with the digital device 100, a semiconductor-based microprocessor (in the form of a microchip or chip set), or generally any device for executing software instructions. When the digital device 100 is in operation, the processor 182 is configured to execute software stored within the memory 190, to communicate data to and from the memory 190, and to generally control operations of the digital device 100 pursuant to the software instructions”.
* Original Specification ¶ [0029] last sentence, ¶ [0049] last sentence, ¶ [0051] 6th sentence reciting at high level of generality: “The training can include any of supervised and unsupervised learning for the one or more machine learning models”.
* Original Specification ¶ [0049] 3rd sentence: “The steps can further include opening one or more jobs in an Enterprise Resource Planning (ERP) system associated with the infrastructure service provider based on the one or more job requests”.
* Original Specification ¶ [0052] “It will be appreciated that some embodiments described herein may include or utilize one or more generic or specialized processors (“one or more processors”) such as microprocessors; Central Processing Units (CPUs); Digital Signal Processors (DSPs): customized processors such as Network Processors (NPs) or Network Processing Units (NPUs), Graphics Processing Units (GPUs), or the like; Field-Programmable Gate Arrays (FPGAs); and the like along with unique stored program instructions (including both software and firmware) for control thereof to implement, in conjunction with certain non-processor circuits, some, most, or all of the functions of the methods and/or systems described herein. Alternatively, some or all functions may be implemented by a state machine that has no stored program instructions, or in one or more Application-Specific Integrated Circuits (ASICs), in which each function or some combinations of certain of the functions are implemented as custom logic or circuitry. Of course, a combination of the aforementioned approaches may be used. For some of the embodiments described herein, a corresponding device in hardware and optionally with software, firmware, and a combination thereof can be referred to as “circuitry configured to,” “logic configured to,” etc. perform a set of operations, steps, methods, processes, algorithms, functions, techniques, etc. on digital and/or analog signals as described herein for the various embodiments”.
* Original Specification ¶ [0054] Although the present disclosure has been illustrated and described herein with reference to preferred embodiments and specific examples thereof, it will be readily apparent to those of ordinary skill in the art that other embodiments and examples may perform similar functions and/or achieve like results. All such equivalent embodiments and examples are within the spirit and scope of the present disclosure, are contemplated thereby, and are intended to be covered by the following claims”.
In conclusion, Claims 1,3-9,11-16 although directed to statutory categories (“non-transitory computer readable medium” or article of manufacture at Claims 1, 3-8 and method or process at Claims 9, 11-16, they still recite, describe or set forth the abstract exception (Step 2A prong one), with no additional, computer-based elements, capable to integrate, either alone or in combination the abstract idea into a practical application (Step 2A prong two) or providing significantly more than the abstract idea itself (Step 2B).
Therefore, Claims 1,3-9,11-16 are believed to be ineligible.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Rejections under 35 § U.S.C. 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1,8,9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over:
Sethi et al, US 20230229737 A1 hereinafter Sethi, in view of
Stifter et al, US 12243082 B1 hereinafter Stifter. As per,
Claims 1,9 Sethi teaches: “A non-transitory computer-readable medium comprising instructions that, when executed, cause one or more processors to perform steps of: / A method comprising steps of” (Sethi ¶ [0114]-¶ [0116]):
- “training one or more machine learning models with data including input characteristics
associated with historical job request data associated with one or more customers of an infrastructure service provider”; (Sethi ¶ [0002] Currently, when a customer places an order, there are several stages and checks involved in taking the order to the shipping stage at an original equipment manufacturer (OEM) facility. Online first article (OFA) is one of the checks which gives the customer the ability to review the first item in the placed order before the OEM proceeds with the rest of the order with a similar configuration. However, OFA is platform-specific and there is significant manual effort involved to coordinate this review by the customer. To address this
Sethi Fig.2, ¶ [0030] discloses automated order management process 200 which is implemented by order problem learning and mitigation engine 124 according to an illustrative embodiment. Specifically, at ¶ [0031] 1st sentence: As part of the collate and order data stage, the order problem learning and mitigation engine 124 collates the order history which includes customers' accepted orders and faulty orders [as input characteristics examples]. Advantageously at ¶ [0109] the embodiments provide a methodology to analyze and formulate faulty orders and speculate the reason for the issues, as well as a method to create the lesson learned from the faulty orders and convert it into a checklist for further orders. Further, if a portion of an order matches with some of the multiple historical orders, illustrative embodiments perform a fusion (e.g. Fig.12) on multiple trees that reflect the historical orders. ¶ [0111] Thus, order problem learning and mitigation engine 124 gets trained during the training phase and determines the Q matrix. The matrix is used to predict during the inference stage. The order data along with the fix data is stored in order database 126 for reference for the steps if a similar order behavior is observed while processing. Gamma parameter is set as 0.8 (closer to 1) so that order problem learning and mitigation engine 124 will explore more than exploit (i.e. explore the environment rather than sticking to the known paths)
- “receiving one or more job requests from the one or more customers, wherein the one or more job requests are submitted via a user interface or retrieved from a third-party customer system”; (Sethi ¶ [0026] 2nd sentence: When user 102 places order via online mode, user 102 accesses automated order processing system 120 via a communication network such as Internet using a uniform resource locator URL controlled by OEM 110)
- “identifying, using the one or more trained machine learning models, one or more insufficiencies in the one or more job requests, wherein the one or more insufficiencies comprise missing required fields or misclassified information” (Sethi ¶ [0003] 1st sentence:… orders are still getting returned due to missing components and/or mismatch of configuration [or misconfiguration or misclassification] from what has been ordered by a customer. See ¶ [0032] noting an example where basic order data… include but …part(s) which are missing…); “and”
* While *
Sethi still provides at ¶ [0003] 1st sentence: an example of an issue of missing components and/or mismatch of configuration [or misconfiguration or misclassification] from what has been ordered by a customer. see Sethi ¶ [0032] noting an example where basic order data…include…part(s) which are missing… This is addressed at ¶ [0028] 1st-2nd sentences, where order problem learning and mitigation engine 124 is configured to operate in conjunction with order processing engine 122 and order database 126 to learn of issues associated with orders and to enable actions to be taken to prevent or otherwise mitigate the issue. In one or more illustrative embodiments, order problem learning and mitigation engine 124 utilizes machine learning-based algorithm referred to as reinforcement learning or Q-learning to enable the intelligent learning functionality. Then
Sethi ¶ [0029] Thus order problem learning and mitigation engine 124 learns from errors of faulty orders and fixes [or corrects] the issues before orders are delivered to customers, i.e. one or more orders from user.
Sethi ¶ [0101] 1st-2nd sentences: In addition to the algorithm, an order object gets populated with the issue and fix details in the respective stage. For example, in the failed stage, the issue details get added to the order object. In the remedy stage, the fix details get added to the order object
Sethi ¶ [0109] goes so far to state: the illustrative embodiments provide a methodology to analyze and formulate faulty orders and speculate the reason for the issues, as well as a method to create the lesson learned from the faulty orders and convert it into a checklist for further orders.
* However *
Sethi does not explicitly recite to clearly anticipate:
- “automatically remedying the one or more insufficiencies by processing the one or more lob requests using the one or more trained machine learning models to determine information required for the one or more job requests based on the historical job request data, and generating replacement or supplemental field values for the one or more job requests based on learned patterns and relationships in the historical job request data; and”
- “initiating creation of a corresponding job record in an Enterprise Resource Planning (ERP) system based on the one or more job requests with the generated replacement or supplemental field values” as explicitly claimed
* Nevertheless *
Stifter in analogous art of using machine learning to manage large numbers of requests (Stifter column 1 lines 21-23, column 1 line 64-column 3 line 10) teaches or at least suggests:
- “automatically remedying the one or more insufficiencies by processing the one or more job requests using the one or more trained machine learning models to determine information required for the one or more job requests based on the historical job request data, and generating replacement or supplemental field values for the one or more job requests based on learned patterns and relationships in the historical job request data”;
(Stifter column 3 lines 57-61: current disclosure may be applicable to any situation where invoices need [or are required] to be processed and/or managed (including determining which entity/budget should pay an invoice, to whom and how an invoice should be paid etc.). Thus at
Stifter column 4 lines 8-19: the ERP systems provide access payment records etc. platform 100 use the data accessed to base its determinations and/or predictions, described herein. For example, platform 100 use updated data to train neural network. Specifically,
Stifter column 11 lines 34-38: in situations where information in a field, e.g., field 313, is missing [interpreted as inefficiency] and/or illegible [also interpreted as inefficiency… the system may treat the field as a judgment field, e.g., the value of the field may be determined/inferred via machine learning using historical data, described at column 13 lines 9-30. Indeed, per
Stifter column 17 lines 37-45: mappings between the images and the historical data may be used to build associations [or relationships] between the invoice images and the correct vendor in an ERP or AP workflow system. historical images of invoices may be passed through an OCR extraction process to pull out vendor attributes, e.g., identifiable information, for the associated vendor used to SUPPLEMENT data missing in a corresponding ERP. For instance,
Stifter column 13 lines 9-30: historical data, is exemplified as an enhanced record, regarding past invoices for the vendor used to determine how a client typically codes that vendor's invoices. In embodiments, the historical data may include information corresponding to a vendor and/or a payor of an invoice. In embodiments, the historical data may include previously coded invoices and/or images of previous invoices. OCR extracted data, described herein, may be compared with the historical invoice data to assist in predicting one or more of the following as corresponding to a payor and/or vendor of the invoice: a particular property and/or entity; an expense account; a cash account; an ap account; and/or the like. In certain embodiments, cost allocations may be determined and/or compared to an enhanced record. For example, OCR extracted data may be used to determine how the total amount of an invoice is broken down, e.g., single line item, multiple line items, a table and/or gird of costs. The OCR extracted cost breakdown may then be compared to historical invoice data for the vendor, e.g., an enhanced record, to assist in predicting an appropriate and/or typical level of detail for the cost allocation on the invoice being assessed)
- “initiating creation of a corresponding job record in an Enterprise Resource Planning (ERP) system based on the one or more job requests with the generated replacement or supplemental field values” (Stifter column 13 lines 55-63: the system capture and/or index data 410, e.g., common fields such as currency type, total amount, date of invoice, invoice number, due date, etc., contained within invoice 412, and combine data 410 with historical data to automatically, i.e, without direct human input, enter/fill in one or more fields 414, e.g., judgment fields such as expense type, payment method, cash accounts, accounts payable, etc., which may be blank or illegible via OCR. This is achieved at column 17 lines 40- 45: by supplementing data missing in the corresponding ERP using the vendor attributes, e.g., identifiable information, for the associated vendor pulled out from the historical images of invoices. column 21 lines 1-6: automatically processes the invoices as described herein such that they get automatically coded, and then provided for approval and/or editing of the automated coding 838. For example, at Fig. 6, step 620 and Fig. 7 step 720).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention to have modified Sethi’s “non-transitory medium” / “method” to have included
Stifter’s teachings in order to have allowed for machine learning to have improved its ability to accurately predict codes via learning from downstream corrections previously processed by embodiments of the platform of the current disclosure, (Stifter column 14 lines 22-29 in view of MPEP 2143 G) while, at the same time, providing the benefit of removing junk (Stifter column 15 lines 54-67 in view of MPEP 2143 G). For example, Stifter would have provided for automated full coding of invoices and/or the transmission thereof, to reduce the amount of manual data entry, which is typically a time-consuming and monotonous task and/or also reduce the need to hire, train, and/or retain staff, e.g., accounts payable staff to focus on higher value work rather than coding of invoices (Stifter column 20 lines 41-51 in view of MPEP 2143 G). The predictability of such modification would have been corroborated by the broad level of skills of one of ordinary skills in the art as articulated by Sethi ¶ [0116] in view of Stifter column 3 lines 14-17, 44-53, column 30 lines 13-19, with Sethi having allowed for many variations to be made in the particular arrangements shown and applicable to a wide variety of other types of data processing systems, processing devices and distributed virtual infrastructure arrangements. Thus, Sethi, as the primary, base reference would have been primed for modification given the incentives provided by Stifter above and the broad knowledge of one of ordinary skills in the art.
Further, the claimed invention could have also been viewed as a mere combination of old entrepreneurial and data processing elements in a similar field of endeavor dealing with request management for an enterprise. In such combination each element merely would have performed the same analytical and managerial function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements as evidenced by Sethi in view of Stifter, the to be combined elements would have fitted together, like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A).
Claims 8,16 Sethi / Stifter teaches all the limitations in claims 1,9 above. Further,
Sethi teaches or suggests: “wherein the data includes historical job request data”
(Sethi ¶ [0005] 1st sentence: obtaining historical order data associated with an order processing system. ¶ [0031] 1st sentence: As part of the collate and order data stage, the order problem learning and mitigation engine 124 collates the order history which includes customers' accepted orders. at ¶ [0109] 2nd sentence: if a portion of an order matches with some of the multiple historical orders, illustrative embodiments perform a fusion (Fig.12) on multiple trees that reflect the historical orders).
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Claims 3,11 are rejected under 35 U.S.C. 103 as being unpatentable over:
Sethi / Stifter as applied to parent independent Claims 1,9, and in further view of
Joseph et al, US 11636381 B1 hereinafter Joseph. As per,
Claims 3,11 Sethi / Stifter teaches all the limitations in claims 1,9 above. Further,
Sethi further teaches
- “grouping the plurality of job requests using the one or more trained machine learning models based on information sociated with the job requests”.
(Sethi ¶ [0029] order problem learning and mitigation engine 124 learns from errors of faulty orders and fixes the issues before orders are delivered to customers, i.e., one or more orders from user 102. Specifically,
Sethi ¶ [0030] 2nd sentence: Automated order management process 200 executes 5 main stages respectively depicted as steps 202,204,206,208 and 210. These stages comprise: collating [or grouping] order data (202); analyzing the order data by applying reinforcement learning (Q-learning) algorithm (204); generating order weights (206); resolving problem(s) with an order (208); and generating a mitigation plan (210).
Sethi ¶ [0031] As part of collating order data (202), order problem learning and mitigation engine 124 collates [or groups] the order history which includes customers' accepted orders and faulty orders.
Sethi ¶ [0109] 2nd sentence: If a portion of an order matches with some of the multiple historical orders, illustrative embodiments perform a fusion (Fig.12) on multiple trees that reflect the historical orders. Thus,
Sethi ¶ [0111] 1st-3rd sentences: order problem learning and mitigation engine 124 gets trained during the training phase and determines the Q matrix. The matrix is used to predict during the inference stage. The order data along with the fix data is stored in order database 126 for reference for the steps if a similar order behavior is observed while processing).
* However *
Sethi / Stifter as a combination does not teach:
- “including infrastructure site attributes comprising at least one or geographic region, customer-specific workgroup requirements, or type of maintenance or audit task, and wherein grouped job requests are processed together for creation of corresponding jobs on the ERP system” as claimed.
* Nevertheless *
Joseph in analogous machine learning for managing requests teaches/suggests:
- “including infrastructure site attributes comprising at least one or geographic region, customer-specific workgroup requirements, or type of maintenance or audit task, and wherein grouped job requests are processed together for creation of corresponding jobs on the ERP system” (Joseph column 4 lines 50-54: Provided machine learning system for demand forecasting predicting the impact of events on demand forecasting within a geographic area in accordance with the teachings of this disclosure. Specifically, column 5 lines 63-64 recognized: that drivers of demand include past sales, store traffic, seasonality, etc. For example, at column 7 lines 22-29, 48-52: For buildings 221 and 222, at rush hour when employees are coming to work in those buildings the system 100 define two distinct events relating to increased in traffic in Range (1) for buildings 221 and 222. There may be increased demand for products or services provided by the establishment in storefront 220 during those times based on the increase in traffic to buildings 221 and 22. Arena 224 is in same Range (3) as building 226, but the events taking place at arena 224 may cause traffic patterns that are more substantial than the increased traffic at rush hour for building 226 during the time periods associated with those events. This is detailed at
Joseph column 11 lines 40-43: volumes of historical transaction data may therefore be available to those businesses that have archived data produced by various ERP applications. For example, at column 12 line 66-column 13 line 18: process 600 begins at operation 660 by receiving, via the computer networks, demand data for products or services of the organization from a database of demand data and events data comprising event attributes from a database of events data for a plurality of different events located within a distance from the geolocation of the organization. Process 600 continues by receiving geolocation data identifying the geolocation of the organization (operation 662) and generating a range of distances based on segmenting the distance from the geolocation of the organization into a plurality of different distances (operation 664). Event categories for each of the plurality of different events may then be normalized by assigning them to one of a set of predefined event categories at operation 666. The events may then be grouped into different combinations of the events based on event attributes at operation 668. In one embodiment, events in a same predefined event category and within a same range of distances from the geolocation of the organization are grouped together)
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have further modified Sethi / Stifter’s “non-transitory medium” / “method” to have further included Joseph’s teachings/suggestions in order to have better predicted demand by grouping similar events into event streams based on event attributes in the events data for helpful workforce management processes (Joseph column 4 lines 26-46 in view of MPEP 2143 G and/or F). The predictability of such modification would have been corroborated by the broad level of skill of one of ordinary skills in the art as further articulated by Sethi ¶ [0116] in view of Stifter column 3 lines 14-17, 44-53, column 30 lines 13-19, in further view of Joseph column 2 lines 17-20, 56-66, column 16 lines 11-19.
Further, the claimed invention is merely a combination of old elements in a similar field of endeavor dealing with managing requests for an organization. In the combination each element merely would have performed the same analytical and data processing function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements as evidenced by Sethi / Stifter in view of Joseph, the to be combined elements would have fitted together, like puzzle pieces in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A).
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Claims 4-6 and 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over:
Sethi / Stifter as applied to claims 1,9, and in further view of
Iyer; Vaidy US 20080249791 A1 hereinafter Iyer. As per,
Claims 4,12. Sethi teaches all the limitations in claims 1,9 above.
Sethi ¶ [0002] 2nd sentence recites an online first article (OFA) as one of the checks which gives the customer ability to review the first item in the placed order before the OEM proceeds with the rest of the order with a similar configuration.
Sethi / Stifter as a combination does not explicitly recite to clearly anticipate:
- “opening one or more jobs in the Enterprise Resource Planning (ERP) system associated with the infrastructure service provider based on the one or more job requests” as claimed.
Iyer however in analogous of recording occurrences such as malfunctions, inspection results, or requests for repair, teaches or suggests:
- “opening one or more jobs in the Enterprise Resource Planning (ERP) system associated with the infrastructure service provider based on the one or more job requests”
(Iyer ¶ [0022] 1st sentence: Enterprise resource planning (ERP) applications are applications that integrate data and processes of an organization into a unified system. ¶ [0025] The plant worker may have visually inspected a malfunction with a piece of equipment in the plant. The plant worker can bring up the piece of equipment on his tablet pc (by clicking the appropriate selections) and indicate (e.g. by clicking an appropriate box) that the piece of equipment has malfunctioned. The plant worker can select a particular type of form to fill out (e.g. maintenance request, inspection, etc. ¶ [0026] 1st sentence: the form can automatically be opened and relevant data from the equipment that the worker has identified (e.g. machine number, name, location, etc.) can automatically be filled into the form. Similarly, ¶ [0042] 1st sentence: From operation 304, the method can proceed to operation 306, which opens the form on the tablet pc. Specifically, ¶ [0058] 2nd sentence: the list of open tickets can be maintained in ERP system and as employees resolve each ticket, a status of each ticket can be changed from open to closed. For additional details of subsequent steps of transmittal and document generation see ¶ [0047], ¶ [0049]).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have modified Sethi/Stifter’s “non-transitory computer-readable medium” / “method” to have included Iyer’s teachings to have provided an improved way to document occurrences of malfunctions and requests for repair, and to have further improved the way of notifying additional appropriate personnel of any such activities (Iyer ¶ [0003]-¶ [0004] in view of MPEP 2143 G and/or F). The predictability of such modification would have been corroborated by the broad level of skills of one of ordinary skills in the art articulated by Sethi ¶ [0116] in view of Stifter column 3 lines 14-17, 44-53, column 30 lines 13-19, and in further view of Iyer ¶ [0080].
Further, the claimed invention could have been viewed as mere combination of old elements in a similar ERP based field of endeavor. In such combination each element merely would have merely performed the same analytical and managerial function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Sethi / Stifter in further view of Iyer, the to be combined elements would have fitted together, like puzzle pieces in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A).
Claims 5,13 Sethi/Stifter/Iyer teaches all the limitations in claims 4,12 above.
Sethi/Stifter does not explicitly recite as claimed:
- “providing a link associated with each of the one or more job requests to a web storage system, wherein the link is adapted to allow uploading of job data associated with each of the one or more job requests”.
Iyer however in analogous of recording occurrences such as malfunctions, inspection results, or requests for repair, teaches or suggests:
- “providing a link associated with each of the one or more job requests to a web storage system, wherein the link is adapted to allow uploading of job data associated with each of the one or more job requests”
(Iyer ¶ [0047] From operation 310, the method can proceed to operation 312, which transmits [or uploads] ticket information from ERP system to server 100 or 200. The server 100 or 200 then generate a web page (or other database entry) containing the ticket info. The web page link can then be transmitted back to the plant worker, so that the plant worker can visit the web page on his or her tablet pc in order to see a confirmation of his or her ticket request. Similarly ¶ [0059] After the plant worker completes the form and the form is transmitted to the ERP application, the tablet pc can then be directed to a particular web page. The particular web page can be served or associated with the ERP system and can display the ticket that the plant worker just generated including a ticket number. Similarly, ¶ [0074] 4th sentence: The plant worker periodically visit this web page in order to check on the status of the ticket, to see where it is in queue, whether it has been resolved, etc. Similarly, Iyer claim 12 wherein ticket data is uploaded to a web page which servers the ticket data to requesters. Iyer claim 13 wherein when a status of the ticket changes, the web page is automatically updated).
Rationales to have modified/combined Sethi/Stifter/Iyer are above and reincorporated.
Claims 6,14 Sethi/Stifter/Iyer teaches all the limitations in claims 5,13 above.
Sethi/Stifter does not recite as explicitly claimed:
- “closing one or more jobs based on the job data uploaded to the web storage system”
Iyer however in analogous of recording occurrences such as malfunctions, inspection results, or requests for repair, teaches or suggests:
- “closing one or more jobs based on the job data uploaded to the web storage system”
(Iyer ¶ [0074] 4th sentence: The plant worker can periodically visit this web page in order to check on the status of the ticket, for example to see where it is in the queue, whether it has been resolved, etc. per ¶ [0074] last sentence: Once a ticket has been addressed, the ticket's status can change from open to closed. Specifically, per ¶ [0058] 2nd sentence: A list of open tickets can be maintained in the ERP system and as employees resolve each ticket, a status of each ticket can be changed from open to closed).
Rationales to have modified/combined Sethi / Stifter / Iyer are above and reincorporated.
Claims 7,15 are rejected under 35 U.S.C. 103 as being unpatentable over:
Sethi/Stifter as applied to claims 1,9, and in view of
Ethington et al, US 20190156298 A1 hereinafter Ethington. As per,
Claims 7,15. Sethi/Stifter teaches all the limitations in claims 1,9 above.
Sethi/Stifter does not explicitly recite:
- “wherein the training includes any of supervised and unsupervised learning” as claimed.
Ethington however in analogous identifying needs teaches or suggests:
- “wherein the training includes any of supervised and unsupervised learning”.
(Ethington teaches several examples as follows: ¶ [0030] supervised learning algorithms may be used to train a model on training data including K Nearest Neighbor, Support Vector Machine, Naïve Bayes, Neural Networks etc. In some examples, ensemble method may be used. Once trained on a training data subset, the model may be tested on corresponding validation data subset. ¶ [0063] Referring to Fig.2, training module 116 train a machine learning model according to a supervised or guided learning algorithm. In order to select an algorithm best suited to the dataset and minimize error such as overfitting, training module 116 may 1st train and evaluate a model according to each of a plurality of algorithms. ¶ [0084] Step 318 includes performing supervised machine learning using the predictor variables and demand labels. The predictor variable values and cluster assignments for each aircraft may be organized as inputs and known outputs in a training dataset. One or more demand forecasting models may be trained on the training dataset, each according to a supervised machine learning method or algorithm. The algorithms may be preselected, or in some examples may be received from an outside source. In some examples, step 318 may also include testing or validation of the models. For example, a subset of the training dataset may be reserved as a validation dataset. Techniques such as cross-validation may also be used. ¶ [0115] generate a system repair forecasting model, using selected predictor variables, repair forecast labels, and historical dataset to train the system repair forecasting model according to supervised machine learning method; ¶ [0130] generate a plurality of system repair forecasting models, using the one or more selected predictor variables and the historical dataset to train each system repair forecasting model according to a supervised leave-one-out cross validation machine learning method; and ¶ [0135] generating a system repair forecasting model, using one or more selected predictor variables, the repair forecast labels, and the historical dataset to train the system repair forecasting model according to a supervised machine learning method; ¶ [0150] generating a plurality of system repair forecasting models, using the one or more selected predictor variables and the historical dataset to train each system repair forecasting model according to a supervised leave-one-out cross validation machine learning method; and ¶ [0156] at least one instruction to generate a system repair forecasting model, using one or more selected predictor variables, the repair forecast labels, and the historical dataset to train the system repair forecasting model according to a supervised machine learning method; ¶ [0171] at least one instruction to generate a plurality of system repair forecasting models, using the one or more selected predictor variables and the historical dataset to train each system repair forecasting model according to a supervised leave-one-out cross validation machine learning method);
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have modified Sethi/Stifter’s “non-transitory medium” / “method” to have further included Ethington’s teachings in order to have provided more accurate forecasting while, at the same time, having allowed for more efficient inventory to have been selected (Ethington ¶ [0003] in view of MPEP 2143 G and/or F). The predictability of such modification would have been further corroborated by Sethi ¶ [0116] in view of the flexibility of forecasting provided by Ethington with improved modeling capabilities (Ethington ¶ [0036] in view of MPEP 2143 G and/or F), and the benefits of dimensionality reduction (Ethington ¶ [0083] in view of MPEP 2143 G).
Further, the claimed invention could have been viewed as a mere combination of old elements in a similar field of endeavor identifying needed repairs. In such combination each element merely would have merely performed the same analytical and managerial function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements as evidenced by Sethi/Stifter in view of Ethington the to be combined elements would have fitted together, like puzzle pieces, in logical, complementary, technologically feasible and/or econocmailly desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A).
Conclusion
The following art is made of record and considered pertinent to Applicant's disclosure:
- Zweben et al, Scheduling and Rescheduling with Iterative Repair, IIE Transactions on Systems, V23, N6, December 1993
- WO 2019191329 A1 teaching Property investigation system and method
- US 20190304026 A1 ¶ [0125] 3rd sentence: if the detection is roof damage and more than 80% of the time the recommended action of the entity is to immediately repair/replace the roof, then a decision can be made whether this recommendation should be automatically generated by the system as a default. ¶ [0256] The method of Example 9, wherein the roof score is calibrated, based on historical data for comparable properties, to be predictive of a priority for performing at least one action selected from the group consisting of monitoring a roof status, performing a roof repair on a non-urgent basis, and performing a roof repair on an urgent basis
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 OCTAVIAN ROTARU whose telephone number is (571)270-7950. The examiner can normally be reached on 571.270.7950 from 9AM to 6PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PATRICIA H MUNSON, can be reached at telephone number (571)270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/Octavian Rotaru/
Primary Examiner, Art Unit 3624 A
June 11th, 2026
1 CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2, 99 USPQ2d 1690, 1695 n.2 (Fed. Cir. 2011) (quoting In re Warmerdam, 33 F.3d 1354, 1355, 1360, 31 USPQ2d 1754, 1755, 1759 (Fed. Cir. 1994)
2 In re Maucorps, 609 F.2d 481, 485, 203 USPQ 812, 816 (CCPA 1979)
3 BSG Tech. LLC v. Buyseasons, Inc., 899 F.3d 1281, 1286, 127 USPQ2d 1688, 1691 (Fed. Cir. 2018);
4 Interval Licensing LLC, v. AOL, Inc., 896 F.3d 1335, 127 USPQ2d 1553 (Fed. Cir. 2018).
5 Licensing LLC, v. AOL, Inc., 896 F.3d 1335, 127 USPQ2d 1553,1555 (Fed. Cir. 2018).
6 Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)
7 BSG Tech. LLC v. Buyseasons, Inc., 899 F.3d 1281, 1286, 127 USPQ2d 1688, 1691 (Fed. Cir. 2018);
8 Interval Licensing LLC, v. AOL, Inc., 896 F.3d 1335, 127 USPQ2d 1553 (Fed. Cir. 2018).
9 Licensing LLC, v. AOL, Inc., 896 F.3d 1335, 127 USPQ2d 1553,1555 (Fed. Cir. 2018).
10 Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016);
TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016)
11 Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017), LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016)
12 FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016);
13 BSG Tech LLC v. Buyseasons, Inc., 899 F.3d 1281, 1287-88, 127 USPQ2d 1688, 1693-94 (Fed. Cir. 2018),
14 FairWarning v. Iatric Sys., 839 F.3d 1089, 1094-95, 120 USPQ2d 1293, 1295 (Fed. Cir. 2016)
15 Alice Corp. v. CLS Bank,573 U.S. 208, 218, 110 USPQ2d 1976, 1982 (2014);
Bilski v. Kappos, 561 U.S. 593, 611, 95 USPQ2d 1001, 1010 (2010)
16 BSG Tech. LLC v. Buyseasons, Inc., 899 F.3d 1281, 1286, 127 USPQ2d 1688, 1691 (Fed. Cir. 2018);
17 Interval Licensing LLC, v. AOL, Inc., 896 F.3d 1335, 127 USPQ2d 1553 (Fed. Cir. 2018).
18 Licensing LLC, v. AOL, Inc., 896 F.3d 1335, 127 USPQ2d 1553,1555 (Fed. Cir. 2018).
19 BSG Tech. LLC v. Buyseasons, Inc., 899 F.3d 1281, 1286, 127 USPQ2d 1688, 1691 (Fed. Cir. 2018)
20 BSG Tech. LLC v. Buyseasons, Inc., 899 F.3d 1281, 1286, 127 USPQ2d 1688, 1691 (Fed. Cir. 2018);
21 Interval Licensing LLC, v. AOL, Inc., 896 F.3d 1335, 127 USPQ2d 1553 (Fed. Cir. 2018).
22Interval Licensing LLC, v. AOL, Inc., 896 F.3d 1335, 127 USPQ2d 1553,1555 (Fed. Cir. 2018).
23 Per MPEP 2106.04(a): “…examiners should identify at least one abstract idea grouping, but preferably identify all groupings to the extent possible…”.
24 Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit)
25 Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
26 FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)
27 Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015)
28 Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
29 Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)
30 Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts");
Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log);
31 OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93;
32 Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015).
33 Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values);
Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012)
34 Internet Patent Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015)
Prosecution Insights