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
The following NON-FINAL Office action is in response to Applicant’s request for continued examination filed on 10/01/2025.
Status of Claims
Claims 1,3,9,11, have been amended and Claims 2,10 have been canceled by Applicant.
Claims 1, 3-9, 11-16 are currently pending and have been rejected as follows.
Continued Examination under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/01/2025 has been entered.
Response to Amendments / Arguments
Applicant’s 09/03/2025 amendment necessitated new grounds of rejection in this action.
1) Response to Applicants’ 112(b) rebuttal arguments
112(b) rejection in the previous act is withdrawn in view of Applicant amending independent Claims 1,9 as proposed by Final Act 07/03/2025 p.7.
2) Response to Applicants’ 101 rebuttal arguments
Step 2A prong 1: Remarks 09/03/2025 p.7 ¶5 argues the claims improve job management of infrastructure service provider using a trained machine learning to automatically identify and remedy insufficiencies in customer job requests, supplement missing data by inference from historical patterns, and initiate creation of job records within enterprise resource planning system.
Examiner fully considered the argument but respectfully disagrees finding it unpersuasive by resubmitting similar rationales as in Final Act 07/03/2025 p.2-p4 ¶ 1. Here, the job management of infrastructure service provider and managing respective interactions within enterprise resource planning, as raised by Remarks 06/24/2025 p.7 ¶5 remain examples of fundamental economic or commercial practices or principles, which still fall within the broad abstract grouping of certain methods of organizing human activities. Importantly, the term fundamental, as in fundamental economic or commercial practices or principles, 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. Also, when tested per MPEP 2106.04(a)(2) II B1, the Examiner finds that improvement upon such job management, as an entrepreneurial or abstract concept by using a trained machine learning model to automatically identify and remedy insufficiencies in customer job requests, to supplement missing data by inference from historical patterns, and initiate creation of job records within an enterprise resource planning (ERP) system as mentioned by Remarks 09/03/2025 p.7 ¶5, would at most represent an improved entrepreneurial or abstract concept to an equally entrepreneurial or abstract problem of customer job requests in need for missing data. Such entrepreneurial or abstract solution to an equal entrepreneurial or abstract problem, does not render the claims eligible. Specifically, the alleged improvement in job management argued as supplementing missing data by inference from historical patterns, and initiating creation of job records within an enterprise resource planning system, would at most represent an improvement in the abstract organizing of human activities and/or a cognitive best business practice of one of ordinary skills in the art to recall, from memory, historical patterns and infer from such patterns to include or supplement missing information, consistent with Claims 1,9. In such a case the claimed “machine learning model” used for “identifying” “insufficiencies” in the “job requests”, as broadly recited at Claims 1,9, would merely mimic the learning or cognitive capabilities of said one of ordinary skills in the art to perform the best business practice of reconciling or remedying missing information by supplementing with inferred data from historical job requests. Then, an “enterprise resource planning (ERP) system” is used as a tool or computer environment to aid “initiating creation of a corresponding job record” as recited at independent Claims 1,9. Yet, MPEP 2106.04(a)(2) III C #2, #3 is clear that: performing a mental process in a computer environment, and/or using a computer as a tool to perform a mental process, do not preclude the claims from reciting, describing or setting forth the abstract exception. Also, MPEP 2106.05(a) II ¶2 is clear that improvement in the abstract idea itself is not improvement in technology. Here, as shown above, the alleged improvement is at best entrepreneurial and abstract for a business practice of managing customer job requests, not a improvement in actual technology. This 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 to state 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 dislosed 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 a similar “model” for “identifying” and “automatically remedying insufficiencies” in customer job requests, supplement missing data by inference from historical patterns, and “initiating creation of job records within an enterprise resource planning (ERP) system”. 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 for implementing and/or aid 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 algorithms would be in executing the abstract processes..
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 mere use of a machine learning model to allegedly improve upon the abstract job management using “historical job request data”. Yet use of such “historical job request data” for the subsequent automation, to satisfy a best business practice of supplementing missing fields with inferred data determined from historical job requests patterns does not render the claims less abstract and eligible because MPEP 2106.04(a)(2) II C is clear that considering historical usage information while inputting data2 and automatization by providing information to a person without interfering with the person’s primary activity3 both 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. The fact that such computerization, automation or training uses “historical job request data” for “identifying insufficiencies” and “remedying the insufficiencies” of “missing fields” by “supplementing missing fields with inferred data” (independent Claims 1,9) does not 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 be argued that given the breadth and high level of generality in the claims, the use of a trained machine learning model to identify and remedy insufficiencies by supplementing missing fields with inferred data determined from historical job request (independent Claims 1,9), could be argued as a computer modeling environment [MPEP 2106.04(a)(2) III C #2] or tool [MPEP 2106.04(a)(2) III C #3] upon which to implement the fundamental cognitive or mental processes of observation, evaluation and judgements such as collecting or “receiving” information, analyzing or “identifying” insufficiencies within such collected information it, and displaying certain results of the collection and analysis4 represented here by “supplementing missing fields with inferred data determined from historical job request patterns” (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 incorporated herein. 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, where the Federal Circuit ruled that 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 train[ed] machine learning as a computer environment, tool or aid in model[ing] “historical job request data associated with one or more customers” and automating the “job requests from the one or more customers” for providing a picture “identifying one or more insufficiencies in the one or more job requests based on the training” (independent Claims 1,9) for more fully compiling, combining or “supplementing missing fields with inferred data determined from historical job request patterns” (independent Claims 1,9) would similarly set forth the abstract exception. Step 2A prong one. Thus, Examiner resubmits that there is a preponderance of legal evidence showing the claims’ character as a whole is undeniably abstract.
Step 2A prong two: Remarks 09/03/2025 p. 7 ¶6-p.8 ¶1 argues the claims are integrated into a practical application because the machine learning model is trained with historical job request data to detect insufficiencies specific to infrastructure maintenance and audit jobs, initiates ERP job creation, generates links to web storage systems for uploading site-specific data (i.e. photos or videos), and closes jobs upon completion. Thus, it is argued these actions provide improvements to operation of computer systems managing infrastructure job workflows, reducing manual data entry, avoid failed or delayed jobs, and improving billing and reconciliation accuracy.
Examiner reincorporates all the findings and rationales above showing the claims still recite, describe or set forth abstract concepts. Further, here, avoiding failed or delayed jobs, and improving billing and reconciliation accuracy, as argued at Remarks 09/03/2025 p.7 ¶6-p.8 ¶1, would at best represent an improvement in the entrepreneurial or abstract processes identified above, not an improvement in actual technology or an improvement in the computer itself.
Examiner further submits that, even when more gradually tested at step 2A prong two of the analysis, the automation or computerization of such abstract processes, such as “initiating creation of a corresponding job record in an Enterprise Resource Planning (ERP) system” (independent Claims 1,9), “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), as argued at Remarks 09/03/2025 p. 7 ¶6-p.8 ¶1, do not render the claims eligible because such computerization merely applies the abstract exception, such as applying a business method and its underlining algorithm on computer [MPEP 2106.04(f)(2)(i)]. These are examples of invoking computer components or machinery to apply an abstract or existing concept which do not integrate the abstract exception into a practical application. Also, MPEP 2106.04(f)(2) ¶15 states that use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) does not integrate a judicial exception into a practical application. Also MPEP 2106.05(f)(2) (iii) and (v) found that a process for monitoring audit log data executed on a general-purpose computer where the increased speed in the process comes from the capabilities of the general-purpose computer6 and requiring use of software to tailor information and provide it to the user on a generic computer7 are also examples representing mere invocation of computer components or other machinery as tools to apply the abstract exception which does not integrate it into a practical appclaition. It then follows that here, use of computer components to perform economic tasks, as identified above, and other related tasks to receive, monitor and tailor or “supplement missing” information would similarly represent mere invocation of computers components as tools to apply the abstract idea which does not integrate it into a practical appclaition.
Equally important, MPEP 2106.05(a) I states that accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer8 is not an improvement in computer-functionality. Thus, the Applicant allegation at Remarks 09/03/2025 p.8 ¶1 that the claim actions would somehow reduce manual data entry, avoid failed or delayed jobs, and improve billing and reconciliation accuracy would also not render the claims 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 also argued here by Remarks 09/03/2025 p.8 ¶1, as ERP job creation, generation of links to web storage systems for uploading site-specific data (photos, videos), and closes jobs upon completion, which remain incapable to provided eligibility by at least similar considerations to FairWarning above.
In a similar vein, MPEP 2106.05(a) I9 states that providing historical usage information to users 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) iv10 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, there is a preponderance of legal evidence showing that the level of computerization or automation in the claims, as argued at Remarks 09/03/2025 p. 7 ¶6-p.8 ¶1, even when more granularly considered at step 2A prong two of the analysis, does not raise above use of additional computer-based elements that apply the abstract idea and/or narrows it to a field of use or etchnoglical environment, none of which integrate said abstract idea into a practical application.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Step 2B: Remarks 09/03/2025 p.8 ¶2 argues the claim elements amount to significantly more than abstract idea because the claims recite use of trained machine learning models, ERP system integration, web storage links, and automated job closure based on uploaded data improving the accuracy of job request processing, ensures completeness of data before jobs are accepted, and directly integrates corrected data into ERP systems for job lifecycle management.
Examiner fully considered the Step 2B argument but respectfully disagrees finding it unpersuasive since as shown above, the additional computer-based elements merely apply the already recited abstract idea [MPEP 2106.05(f)] and/or provide a narrowing of the abstract idea to a field of user or technological environment [MPEP 2106.05(h)]. For these reasons, said computer-based additional elements similarly do not provide significantly more than the abstract idea itself in light of MPEP 2106.05(f) and/or (h), tested above, and now asserted by Examiner as sufficient option(s) for evidence without even having to rely of the well understood, routine and conventional test of MPEP 2106.05(d). Yet, assuming in the arguendo, that further evidence would be required to demonstrate conventionality of the additional, computer-based elements, the Examiner would further point to MPEP 2106.05(d) to demonstrate that said additional elements remain well-understood, routine, conventional. In such case, the Examiner would follow MPEP 2106.05(d)(I)(2) guidelines and rely as evidence on the Applicant’s own:
- 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] 2nd-3rd sentences: “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 instruction”
- Original Specification ¶ [0029] last sentence, ¶ [0049] last sentence, ¶ [0051] 6th sentence: “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”.
Thus here, there is a preponderance of legal and/or factual evidence showing the claims recite, describe or set forth the abstract idea, with the additional elements not integrating the abstract idea into a practical appclaition or providing significantly more. The claims are ineligible.
3) Response to prior art Arguments
Prior art argument 1: Remarks 09/03/2025 p.9 last ¶-p.10 ¶1 argues the prior art does not teach or suggest supplementing missing data fields with inferred data determined from historical job request patterns and integration with an ERP system to initiate job creation and lifecycle management, as amended at each of independent Claims 1,9.
Examiner considered prior art argument 1 which is moot in view of new grounds of rejection.
* 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] recites: 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: “wherein the one or more actions include automatically remedying the insufficiencies by supplementing missing fields with inferred data determined from historical job request patterns and initiating creation of a corresponding job record in an Enterprise Resource Planning (ERP) system” as newly amended.
* Nevertheless *
Examiner now relies on Stifter et al, US 12243082 B1, who in analogous art of using machine learning to manage large numbers of service requests teaches or at least suggests:
- “wherein the one or more actions include automatically remedying the insufficiencies by supplementing missing fields with inferred data determined from historical job request patterns and initiating creation of a corresponding job record in an Enterprise Resource Planning (ERP) system” (Stifter column 4 lines 8-19: provide connections to ERP systems to access payment records etc. platform 100 use the data accessed to base its determinations and/or predictions, as described herein. For example, platform 100 may use updated data to train a neural network. Specifically at column 11 lines 34-38: in situations where information in a field, e.g., field 313, is missing 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, as described herein).
Thus, the Examiner finds that the prior art argument 1 is unpersuasive.
Prior art argument 2: Remarks 09/03/2025 p.10 ¶2 argues that the mere fact that both Sethi and Sargent involve some form of notification does not provide a teaching or motivation to combine their features in the manner required by the claims, and thus the Examiner's rationale relies on impermissible hindsight reconstruction of Applicants' invention. Similarly, Remarks 09/03/2025 p.10 ¶3 argues that while Iyer teaches ERP systems for plant malfunctions, its disclosures are directed to manual data entry for malfunction tickets in a manufacturing environment. The present claims, by contrast, require ML-driven validation and automatic correction of insufficiencies in infrastructure job requests, followed by automated ERP job creation and closure. The proposed combination is argued to not yield the claimed invention because lyer's ERP ticketing processes are manual in nature and do not address the ML-based inference, correction, and ERP integration recited in the claims. Finally Remarks 09/03/2025 p.11 ¶1 argues that the rejection combining Sethi with Ethington (claims 7 and 15) is also improper because it is argued that Ethington is directed to repair forecasting in aircraft systems using supervised learning. The cited disclosures relate to predictive modeling of aircraft component failures, which is argued as unrelated to the technical field of infrastructure service provider job request management. Even if Ethington's teachings were considered, they do not suggest or render obvious the use of ML to detect insufficiencies in infrastructure job requests, supplement missing information, and streamline the workflow through ERP systems as claimed. Thus, it is argued that the Examiner's reasoning again depends on hindsight.
Examiner fully considered the Applicant’s argument but respectfully disagrees finding it unpersuasive. First, Examiner notes the Applicant contests only the combination rationale (MPEP 2143 A) as utilized at the Final Act 07/03/2025, but fails to address the separate and independent modification rationale (MPEP 2143 G) as also utilized by the Final Act 07/03/2025.
As per 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 09/03/2025 p.10 ¶, the Final Act 07/03/2025 p.15 ¶2 has found that both Sethi and Sargent recite an analogous management of relationships between customer and businesses, with the Final Act 07/03/2025 p.15 ¶3 having relied on Sargent ¶ [0038] to provide a deliberate teaching or motivation for the modification of Sethi to include the Sargent’s teachings.
Similarly, despite the Applicant’s allegation to the contrary as made at Remarks 09/03/2025 p.10 ¶3, the Final Act 07/03/2025 p.15-p.16 ¶2 has found that both Sethi and Iyer deal with management of relationships between customer and businesses, with Final Act 07/03/2025 p.16 ¶3 having relied on Iyer ¶ [0003]-¶ [0004] to provide a deliberate teaching or motivation for the modification of Sethi to include the Iyer’s teachings.
Last but not least, despite the Applicant’s allegation to the contrary as made at Remarks 09/03/2025 p.11 ¶ 1, the Final Act 07/03/2025 p.17-p.18 has found that both Sethi and Ethington deal with analogous management of relationships between customer and businesses,
with Final Act 07/03/2025 p.18 having relied on Ethington ¶ [0003] to provide a deliberate teaching or motivation for the modification of Sethi to include the Ethington teachings.
Finally, Examiner points to MPEP 2141.01(a) IV citing In re Bigio, 381 F.3d 1320, 1325-26, 72 USPQ2d 1209, 1211-12 (Fed. Cir. 2004). 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.
Thus, the Examiner finds that the prior art argument 2 is unpersuasive.
Prior art argument 3: Remarks 09/03/2025 p11 ¶2 argues while Sethi describes collating historical product order data as part of OEM order fulfillment process, he does not disclose or suggest “wherein the grouping is performed according to 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” as amended at each of independent Claims 1,9.
Examiner considered prior art argument 3 which is moot in view of new grounds of rejection.
Examiner now relies on Joseph et al, US 11636381 to teach or suggests:
- “wherein the grouping is performed according to 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”
(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: In the illustrated embodiment, 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).
Accordingly, the Applicant’s prior art argument 3 is found 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 1,3-9,11-16 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.
Claims 1,9 are independent and have been amended to reach recite, among others:
- “identifying one or more insufficiencies in the one or more job requests based on the training, wherein the insufficiencies comprise missing required fields or misclassified information
- “performing one or more actions based on the identifying, wherein the one or more actions include automatically remedying the insufficiencies by supplementing missing fields with inferred data determined from historical job request patterns and initiating creation of a corresponding job record in an Enterprise Resource Planning (ERP) system” [emphasis added]
Claims 1,9 are rendered vague and indefinite because there is insufficient antecedent basis for “the insufficiencies” [plural] as subsequently recited at last performing limitation, when the claims would only cover one insufficiency [singular] as introduced by expression “one or more insufficiencies” recited at the prior “identifying” limitation.
Claims 1,9 are recommended to be amended to each recite, among others, as example only:
- “identifying one or more insufficiencies in the one or more job requests based on the training, wherein the insufficiencies comprise missing required fields or misclassified information
- “performing one or more actions based on the identifying, wherein the one or more actions include automatically remedying the one or more insufficiencies by supplementing missing fields with inferred data determined from historical job request patterns and initiating creation of a corresponding job record in an Enterprise Resource Planning (ERP) system”
Claims 3-8, 11-16 are dependent and rejected based on rejected parent Claims 1,9.
Claims 3,4,11,12 are dependent and recite or have been amended to each recite:
“an ERP system” rendering said claims vague and indefinite because it is unclear if
“an ERP system” as recited in said dependent Claims 3,11 relates back to
“an Enterprise Resource Planning (ERP) system” as now amended at each of parent Claims 1,9.
Claims 3,4,11,12 are recommended to be further amended to each recite, among others: [[an]] the ERP system. Claims 5,6,13,14 are rejected based on rejected parent Claims 4,12.
Clarifications and/or corrections 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 and 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 in the one or more job requests; and performing one or more actions based on the identifying” (independent Claims 1,9). For example, MPEP 2106.04(a)(2) II B ii finds that using an algorithm for determining the optimal number of visits by a business representative to a client11 is an example of commercial interactions of certain methods of organizing human activities, with MPEP 2106.04(a)(2) II A further explaining that activities that mitigate or minimize of risks12 are example of fundamental economic practices or principles of the same certain methods of organizing human activities grouping. It would then follow that here “receiving one or more job requests from the one or more customers”; “identifying one or more insufficiencies in the one or more job requests based on the training”; “and” “performing one or more actions based on the identifying” (independent Claims 1,9) 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. Also, per MPEP 2106.04(a)(2) II A ¶2, the term fundamental is not used in the sense of necessarily being old or well-known. In fact, MPEP 2106.04(a)(2) II ¶6, 4th sentence states that activity that involves multiple people, as well as certain activity between a person and a computer may still fall within the certain methods of organizing human activity grouping. Here, such activity with the computer is evidenced by general recitations of: “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, “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, “closing one or more jobs based on the job data uploaded to the web storage system” at dependent Claims 6,14.
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. This finding is further corroborated by MPEP 2106.04(a)(2) II C ii. by stating that considering historical usage information while inputting data13 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 “wherein the data includes historical job request data” at dependent Claims 8,16 would similarly describe or set forth the abstract certain methods of organizing human activity grouping. MPEP 2106.04(a)(2) II C is clear that considering historical usage information while inputting data and automatization of providing information to a person without interfering with the person’s primary activity still set forth the abstract Certain Methods of Organizing Human Activities. In fact, MPEP 2106.04(a)(2) II ¶6, 4th sentence is clear that certain activity between a person and a computer may still fall within certain methods of organizing human activity.
Further when tested per MPEP 2106.04(a) ¶3, 3), and MPEP 2106.04(a)(2) III C, the Examiner submits that here, the “Certain Methods of Organizing Human Activities” as recited, described or set forth above, could be argued as implementable14 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 the abstract mental processes.
- Here, such computer-aided collection can be argued as set forth by the aid of “one or more processors” at independent Claim 1, “user interface” and “third party system” in “receiving one or more job requests from the one or more customers” 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 set forth by the aid of “one or more processors” (independent Claim 1) in “identifying one or more insufficiencies in the one or more job requests” (independent Claims 1,9); and “wherein the grouping is performed according to 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 insufficiencies by supplementing missing fields with inferred data determined from historical job request patterns and initiating creation of a corresponding job record in an Enterprise Resource Planning (ERP) system” (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, Examiner will more granularly test such computerization below, be it hardware and/or software. For now, given the preponderance of legal evidence as shown above, the character as a whole of the claims remains undeniably abstract.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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, 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 be treated as additional computer-based elements, they would still merely apply the aforementioned abstract exception, such as by applying the aforementioned business method and/or its underlining machine learning or mathematical algorithm on a computer, as tested per MPEP 2106.05(f)(2)(i)15. Further requirement for the “one or more processors” of independent Claim 1 for “identifying one or more insufficiencies in the one or more job requests based on the training” (independent Claim 1), could also be argued as a computerized attempt to monitor audit log data executed on a general-purpose computer, as tested per MPEP 2106.05(f)(2) iii16. Also, the capabilities of the “one or more processors” in “performing one or more actions” independent Claim 1 narrowed as “wherein the one or more actions include automatically remedying the insufficiencies by supplementing missing fields with inferred data determined from historical job request patterns and initiating creation of a corresponding job record in an Enterprise Resource Planning (ERP) system”
(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 computer17 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 components18, 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. In fact, MPEP 2106.05(f)(2) ¶119 is clear that use of a computer for economic or other tasks such as to receive, or transmit data, does not integrate the abstract idea into practical application. Also, when tested per MPEP 2106.05(f)(1),(3) “automatically remedying the insufficiencies by supplementing missing fields with inferred data determined from historical job request patterns and initiating creation of a corresponding job record in an Enterprise Resource Planning (ERP) system” (independent Claims 1,9), would correspond to generality of application of judicial exception, revealed by MPEP 2106.05(f)(3), without providing the requisite degree of a technological solution, as needed per MPEP 2106.05(f)(1). Similarly, MPEP 2106.05(h)20 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 then 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 exception 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 relying on conventionality test of MPEP 2106.05(d). 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 recordkeeping21 / gathering statistics22, arranging hierarchy of groups and sorting information23, performing repetitive calculations24. 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 in “receiving one or more job requests from the one or more customers”; “identifying one or more insufficiencies in the one or more job requests based on the training” at independent Claim 1 and then possibly the “grouping the plurality of job requests based on the training” 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. Here the repetitive calculations are reflected in the capabilities of the “one or more processors” of independent Claim 1 in “training a machine learning model 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 functionality25 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 a machine learning model with data 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 and ¶ [0030] discloses an 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. Advantageously at ¶ [0109] 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. 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. The 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 one or more insufficiencies in the one or more job requests based on the training, wherein the 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”
- “performing one or more actions based on the identifying” (Sethi ¶ [0003] 1st sentence: noting 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 ¶ [0032] noting an example where basic order data… include but …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. ¶ [0029] Accordingly, 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 102. Another example at ¶ [0108])
* 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:
- “wherein the one or more actions include automatically remedying the insufficiencies by supplementing missing fields with inferred data determined from historical job request patterns and initiating creation of a corresponding job record in an Enterprise Resource Planning (ERP) system” as 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:
- “wherein the one or more actions include automatically remedying the insufficiencies by supplementing missing fields with inferred data determined from historical job request patterns and initiating creation of a corresponding job record in an Enterprise Resource Planning (ERP) system” (Stifter column 4 lines 8-19: provide connections to ERP systems to access payment records etc. platform 100 use the data accessed to base its determinations and/or predictions, as described herein. For example, platform 100 may use updated data to train a neural network. Specifically at column 11 lines 34-38: in situations where information in a field, e.g., field 313, is missing 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, as described herein)
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). 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.
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 based on the training”.
(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:
- “wherein the grouping is performed according to 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” as claimed.
* Nevertheless *
Joseph in analogous machine learning for managing requests teaches/suggests:
- “wherein the grouping is performed according to 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”
(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: In the illustrated embodiment, 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, 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.
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 an 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 an 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 be 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).
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
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.
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
February 8th, 2026
1 In re Maucorps, 609 F.2d 481, 485, 203 USPQ 812, 816 (CCPA 1979)
2 BSG Tech. LLC v. Buyseasons, Inc., 899 F.3d 1281, 1286, 127 USPQ2d 1688, 1691 (Fed. Cir. 2018);
3 Interval Licensing LLC, v. AOL, Inc., 896 F.3d 1335, 127 USPQ2d 1553 (Fed. Cir. 2018).
4 Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)
5 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)
6 FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)
7 Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015)
8 FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016);
9 BSG Tech LLC v. Buyseasons, Inc., 899 F.3d 1281, 1287-88, 127 USPQ2d 1688, 1693-94 (Fed. Cir. 2018),
10 FairWarning v. Iatric Sys., 839 F.3d 1089, 1094-95, 120 USPQ2d 1293, 1295 (Fed. Cir. 2016)
11 In re Maucorps, 609 F.2d 481, 485, 203 USPQ 812, 816 (CCPA 1979);
12 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)
13 BSG Tech. LLC v. Buyseasons, Inc., 899 F.3d 1281, 1286, 127 USPQ2d 1688, 1691 (Fed. Cir. 2018)
14 Per MPEP 2106.04(a): “…examiners should identify at least one abstract idea grouping, but preferably identify all groupings to the extent possible…”.
15 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);
16 FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)
17 Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015)
18 Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
19 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)
20 Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)
21 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);
22 OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93;
23 Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015).
24 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)
25 Internet Patent Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015)