DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-11 and 13-20 were previously pending, claims 9-10 and 14-20 withdrawn, and claims 1-8, 11, and 13 were subject to a non-final rejection dated December 30, 2025. In the Response, submitted on March 30, 2026, claims 1, 3, and 13 were amended. Therefore, claims 1-11 and 13-20 are currently pending, claims 9-10 and 14-20 withdrawn, and claims 1-8, 11, and 13 are subject to the below final rejection.
Response to Arguments
Applicant’s Remarks on Page 7 of the Response, regarding the previous claim interpretation have been fully considered and are found persuasive in view of the amended claims.
Applicant’s Remarks on Page 8 of the Response, regarding the previous rejection of the claims under 35 U.S.C. 112 have been fully considered and are found persuasive in view of the amended claims.
Applicant’s Remarks on Pages 9-13 of the Response, regarding the previous rejection of the claims under 35 U.S.C. 101 have been fully considered but are not found persuasive.
On Pages 9-10 of the Response, Applicant argues that because the claim “steps are inextricably tied to a computing system and cannot be performed solely by human cognition. Specifically, the method is not directed to a mental act because each step is expressly implemented by one or more processors of a client device coupled to a server…all of which are operations that cannot be performed mentally or by humans without computer assistance…”
Initially, Examiner notes the claims were not categorized as reciting a mental act, and therefore Applicant’s arguments are moot. However, Examiner further notes that “Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, “[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind.” Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer")” See MPEP 2106.04(a)(2)(III).
Additionally, “Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures “can be carried out in existing computers long in use, no new machinery being necessary.” 409 U.S at 67, 175 USPQ at 675.” See MPEP 2106.04(a)(2)(III)(C).
On Page 10 of the Response, Applicant further argues “the claim does not merely recite a business practice of shift handover, rather, it requires a client-server architecture, automated credential comparison by one or more processors, initiation of a machine-tracked HOTO timer, and generation of HOTO summaries containing operational parameters and measured execution time…. Further, the claim recites analyzing collected HOTO summaries using a machine learning model to form data clusters and generate a training dataset, and training that model to classify HOTO and self-takeover activities in accordance with organizational policies to optimize future shifts. This improves the functioning of computer systems that manage operational continuity by
reducing errors, delays, and inefficiencies during shift transitions. Any credential comparison or time analysis is not claimed in the abstract, but is integrated into a concrete technical workflow involving automated authentication, real-time tracking, data aggregation, and ML-based optimization. Accordingly, the claim is directed to a practical application that improves computerized systems for workforce and operations management”.
Examiner respectfully disagrees and notes that “credential comparison… initiation of a…. HOTO timer, and generation of HOTO summaries containing operational parameters and measured execution time…analyzing collected HOTO summaries…to form data clusters and generate a training dataset…to classify HOTO and self-takeover activities in accordance with organizational policies to optimize future shifts…a concrete technical workflow involving…tracking, data aggregation” are limitations that recite the abstract idea (of a certain method of organizing human activity). The “client-server architecture”, “automated” actions, “one or more processors”, a “machine-tracked…timer”, “using a machine learning model”, “training that model”, “real-time” tracking, are additional elements that re recited at a high-level of generality, such that the additional elements amount to no more than mere instructions to apply the judicial exception using generic computer components (See MPEP 2106.05(f)) or do no more than generally link the use of a judicial exception to a particular technological environment or field of use (i.e., machine learning) (See MPEP 2106.05(h)).
Examiner disagrees that any “functioning of computer systems” are improved as alleged. Rather, the reduction of “errors, delays, and inefficiencies” comes “solely from the capabilities of a general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016). (See MPEP 2106.05(a)(1)).
On Page 10 of the Response, Applicant further argues “The clam also recites significantly more than any alleged abstract idea by virtue of its specific combination of client devices, processors, timers, data structures, and trained machine learning models operating together to solve a technical problem in automated shift handover management.”
Examiner respectfully disagrees and notes nothing in the cited portions of the specification disclose a “technical problem” in an automated task as alleged. Examiner urges Applicant to provide the specific language in the specification describing a technical solution to a technical problem. As discussed above, the additional elements are recited in a high-level of generality, and amount to no more than mere instructions to apply the judicial exception using generic computer components (See MPEP 2106.05(f)) or do no more than generally link the use of a judicial exception to a particular technological environment or field of use (i.e., machine learning) (See MPEP 2106.05(h)). As will be discussed further below in detail, when viewed as an ordered combination the additional elements do not integrate the abstract idea into a practical application or provide an inventive concept.
On Pages 10-11 of the Response, Applicant further argues that the claim is “integrated into a practical application because it is implemented within a specific client-server computing environment that manages real-time Handover-Takeover (HOTO) operations in an enterprise system, as described in the specification at paragraphs [0031]-[0042]. Each claimed step contributes to a concrete technological process. The client device receives and verifies user credentials using stored authentication parameters, initiates a processor-controlled HOTO timer to track the duration of the shift handover, generates HOTO summaries containing operational data, and transmits that data to the server for analysis and machine-learning-based optimization of future HOTO activities. The trained model's output is then used to improve subsequent system operations by identifying efficient or inefficient handovers. These operations transform input user credentials and shift data into actionable system configurations that enhance authentication accuracy, timing precision, and process efficiency. The method thus provides a specific improvement in how the computing system performs authenticated shift transitions and data-driven process optimization, not merely a generic application of an abstract idea.”
Examiner disagrees, and notes the test for subject matter eligibility is not whether a claim recites or “contributes to a concrete technological process”. Rather, “manag[ing]…Handover-Takeover (HOTO) operations in an enterprise system, as …receives and verifies user credentials using stored authentication parameters, initiates a…HOTO timer to track the duration of the shift handover, generates HOTO summaries containing operational data, and transmits that data … for analysis and…. optimization of future HOTO activities….transform input user credentials and shift data into actionable system configurations …performs authenticated shift transitions and data-driven process optimization” are limitations that recite the abstract idea.
Similar to Trading Technologies, it appears Applicant is arguing a business process improvement (“improve subsequent system operations by identifying efficient or inefficient handovers”, “configurations that enhance authentication accuracy, timing precision, and process efficiency”, rather than a “specific improvement in how the computing system performs” (i.e., any of the claimed additional elements). See Trading Technologies v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), where the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology. Thus, Applicant’s arguments are not found persuasive.
On Pages 11-12, in arguing that the claims “are integrated into a practical implementation”, Applicant states “The subject matter of amended independent claim 1 can be practically realized in domain of say, industrial operations such as, for example, manufacturing plants, oil refineries, power generation facilities, and hospitals, where continuous operation is critical and shift transitions must be handled with precision and accountability. In practice, a client device, such as a workstation, handheld terminal, or control console used by a shift operator, is coupled to a central server over a communication network. When a shift is about to end, the client device of the first user receives an initiation request containing that user's credentials. The same client device then receives credentials from a second user who will take over the operation. The one or more processors of the client device automatically compare authentication parameters or identifiers of both users stored in device memory or a connected credential database to determine whether to perform a handover (HOTO) or a self-takeover operation. Once the appropriate operation is identified, the system initiates a HOTO timer to electronically record the duration of the shift handover. The client and server then cooperate to generate and store HOTO summaries comprising operational parameters and time data, which are analyzed by the server to form a training dataset. A machine learning model is trained using that dataset to classify subsequent handovers or self-takeovers according to organizational policies. The trained model's output can be used by the system to automatically refine future HOTO activities, such as optimizing timer thresholds or workflow settings. These operations are realized entirely through computing devices communicating over a network and manipulating stored electronic data producing tangible improvements in shift authentication accuracy, timing precision, and process efficiency. Accordingly, the claimed method is implemented as a practical, real-world computing solution and not as a mere abstract or conceptual idea.”
Examiner respectfully disagrees and again notes, the test for subject matter eligibility is not whether the “claimed method is implemented as a practical, real-world computing solution”. Rather, applicant is describing a business process (“When a shift is about to end, … the first user [provides] an initiation request containing that user's credentials…receives credentials from a second user who will take over the operation…compare authentication parameters or identifiers of both users stored…to determine whether to perform a handover (HOTO) or a self-takeover operation. Once the appropriate operation is identified… initiates a HOTO timer to …record the duration of the shift handover… generate and store HOTO summaries comprising operational parameters and time data, which are analyzed by the server to form a training dataset. …using that dataset to classify subsequent handovers or self-takeovers according to organizational policies…refine future HOTO activities, such as optimizing timer thresholds or workflow settings” using additional elements (“a client device, such as a workstation, handheld terminal, or control console”, “a central server over a communication network”, “The one or more processors of the client device automatically” perform actions, “in device memory or a connected… database”, “electronically record”, “A machine learning model is trained using that dataset”, that as discussed above amount to no more than mere instructions to apply the judicial exception using generic computer components (See MPEP 2106.05(f)) or do no more than generally link the use of a judicial exception to a particular technological environment or field of use (i.e., machine learning) (See MPEP 2106.05(h)). Thus, Applicant’s arguments are not found persuasive.
On Pages 12-13, in arguing that “claim 1 provide[s] an inventive concept and amounts to significantly more than the exception itself”, Applicant “asserts that traditional approaches end up in unreliable and inefficient execution of shift transitions in operational environments, which can compromise safety, accountability, and continuity of operations. Specifically, manual or checklist-based handovers are prone to human error, lack real-time validation, and often fail to capture critical operational context, leading to miscommunication and operational delays. These issues are particularly problematic in high-stakes environments such as industrial plants, refineries, and hospitals, where even minor lapses in shift transitions can result in safety incidents, equipment damage, or service disruptions. Traditional systems also lack mechanisms to verify whether a shift is being handed over to a different user or continued by the same user, making it difficult to enforce compliance and traceability…In view of the above challenges, the subject matter of amended independent claim 1 provides a method for automatically managing and optimizing operational shift handovers by defining a specific sequence of processor-executed steps that operate on authenticated data and execution metrics. The method receives a HOTO initiation request and user credentials, automatically comparing the credentials to deterministically select between a handover-takeover activity and a self-takeover activity, thereby eliminating subjective or manual decision-making. The method further includes initiating a processor-controlled HOTO timer that tracks the time consumed during execution of the handover, collecting structured HOTO information corresponding to the operation shift, and generating HOTO summaries containing both operational parameters and measured execution time. The method then analyzes the HOTO summaries to generate a training dataset by classifying handover activities into data clusters using a machine learning model based on execution time and operational parameters, and trains the machine learning model to classify future HOTO or self-takeover activities according to organizational policies. This constitutes a concrete technical procedure for executing, measuring, and evaluating handover events using automated processing and machine learning, rather than a mental process or an abstract organizational rule.”
Examiner respectfully disagrees and notes that “[m]ere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential)”. That is, “automatically managing and optimizing operational shift handovers”, “automatically comparing”, and “thereby eliminating subjective or manual decision-making”, are “[m]ere automation of manual processes” and do not show an improvement in computer functionality. See MPEP 2106.05(a)(I).
Furthermore, as re-iterated above “initiating a….HOTO timer that tracks the time consumed during execution of the handover, collecting structured HOTO information corresponding to the operation shift, and generating HOTO summaries containing both operational parameters and measured execution time…then analyzes the HOTO summaries to generate a training dataset by classifying handover activities into data clusters….based on execution time and operational parameters…to classify future HOTO or self-takeover activities according to organizational policies. This constitutes a concrete technical procedure for executing, measuring, and evaluating handover events…” recite the abstract idea of a certain method of organizing human activity. Additionally, “a processor”, “using a machine learning model”, “trains the machine learning model” and “using automated processing and machine learning” amount to no more than mere instructions to apply the judicial exception using generic computer components (See MPEP 2106.05(f)) or do no more than generally link the use of a judicial exception to a particular technological environment or field of use (i.e., machine learning) (See MPEP 2106.05(h)). Thus, Applicant’s arguments are not found persuasive.
On Page 13 of the Response, Applicant argues “This architecture provides technical advantages by improving how computerized operations-management platforms execute and evaluate shift transitions over time, rather than merely organizing human behavior. By automatically comparing credentials and selecting the appropriate takeover activity, the method reduces authentication errors and delays that arise from manual handover processes. The initiation of a processor-tracked HOTO timer and the generation of structured HOTO summaries enable objective, repeatable measurement of handover execution, producing machine-readable data that supports further computational analysis. Additionally, by analyzing historical HOTO summaries and training a machine learning model to classify handover activities, the method enables data-driven optimization of future operational shifts, such as identifying inefficient handovers or policy violations. This advantage arises from the method's specific processing steps and automated analysis workflow, which improve the technical functioning of computer-implemented operations-management methods by enabling accurate execution tracking, classification, and predictive optimization, rather than from abstract planning, scheduling, or human decision-making…”
Examiner respectfully disagrees and notes it is unclear where the claim “improv[es] how computerized operations-management platforms execute and evaluate shift transitions over time” beyond merely arguing that manual tasks have been automated. Therefore, it is unclear who mere automation of manual tasks is an improvement to” computerized operations-management platforms” themselves.
As discussed above “[m]ere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential)”. That is, “automatically comparing…[to] reduce[] authentication errors and delays that arise from manual handover processes” does not show an improvement in computer functionality. See MPEP 2106.05(a)(I).
Furthermore, as re-iterated above “evaluate shift transitions over time”, “comparing credentials and selecting the appropriate takeover activity”, “initiation of a…tracked HOTO timer and the generation of structured HOTO summaries enable objective, repeatable measurement of handover execution, producing…data that supports further …analysis. Additionally, by analyzing historical HOTO summaries and training a….model to classify handover activities, the method enables data-driven optimization of future operational shifts, such as identifying inefficient handovers or policy violations”, “processing steps…analysis workflow…enabling accurate execution tracking, classification, and predictive optimization” recite the abstract idea of a certain method of organizing human activity. Additionally, “computerized operations-management platforms”, “a processor, “computational analysis, “training a machine learning model” and “computer-implemented operations” amount to no more than mere instructions to apply the judicial exception using generic computer components (See MPEP 2106.05(f)) or do no more than generally link the use of a judicial exception to a particular technological environment or field of use (i.e., machine learning) (See MPEP 2106.05(h)). Thus, Applicant’s arguments are not found persuasive.
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 1-8, 11 and 13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-8, 11 and 13 recite a method (i.e., process), and therefore the claims all fall within one of the four statutory categories of invention.
Step 2A, Prong One
Claim 1 recites a series of steps of: receiving an initiation request to initiate a Handover-Takeover (HOTO) activity corresponding to an operation shift, the initiation request being received from a first user initiating handover of the operation shift, and the initiation request comprising credentials of the first user; receiving credentials of a second user taking over the operation shift from the first user; comparing the credentials of the second user with the credentials of the first user; performing one of the HOTO activity and a self-takeover activity based on the comparison of the credentials of the second user with the credentials of the first user, wherein the HOTO activity is performed when credentials of the second user are different from the credentials of the first user; initiating a HOTO timer to track the time consumed in performing the HOTO activity, wherein the HOTO activity is for the handover of the operation shift from the first user to the second user, receiving, from the first user, HOTO information corresponding to the operation shift from the first user: generating HOTO summaries corresponding to the operation shift based on the HOTO information, wherein the HOTO summaries comprise one or more operational parameters and the time consumed in performing the HOTO activity; analyzing HOTO summaries for generating a training dataset, wherein the analyzing comprises classifying the HOTO activity corresponding to the HOTO summaries to form data clusters based on the time consumed and the one or more operational parameters; and classifying HOTO activities or self-takeover activity based on organizational policies to optimize future operational shifts.
The claim as a whole recites a certain method of organizing human activity. The limitations recited above, under broadest reasonable interpretation, recite the abstract idea of a certain method of organizing human activity, e.g., managing personal behavior or relationships or interactions between people, and commercial interactions. Therefore, the claim recites an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. Claim 1 as a whole amounts to: (i) merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract, or “apply it” or (ii) generally links the use of a judicial exception to a particular technological environment or field of use (i.e., machine learning).
The additional elements include: (i) a client device (coupled to a server); (ii) a server; (iii) one or more processors (automatically performing functions); and (iv) training a machine learning model based on training data set, and using a machine learning model.
The additional elements (i) –(iii) are recited at a high-level of generality, such that when viewed as a whole/ordered combination, they amount to no more than mere instructions to apply the judicial exception using generic computer components (See MPEP 2106.05(f)).
The additional element (iv) is recited at a high-level of generality, such that when viewed as a whole/ordered combination, it does no more than generally link the use of a judicial exception to a particular technological environment or field of use (i.e., machine learning) (See MPEP 2106.05(h)). Thus, the claim is directed to an abstract idea.
Accordingly, the additional elements (i)-(iv), when viewed as a whole/ordered combination do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements amount to no more than: (i) merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract, or "apply it" or (ii) generally link the use of a judicial exception to a particular technological environment or field of use (i.e., machine learning), and are not a practical application of the abstract idea. The same analysis applies here in Step 2B, i.e., merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract, or "apply it" (See MPEP 2106.05(f)) or generally link the use of a judicial exception to a particular technological environment or field of use (i.e., machine learning ) (See MPEP 2106.05(h)), does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B.
Therefore, the additional elements do not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination (See Figs. 1 and 2), nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. Thus, the claim is ineligible.
Dependent claims 2-4, 11 and 13 further recite details which merely narrow the previously recited abstract idea limitiaitions of claim 1. For these reasons, as described above with respect to claim 1 these judicial exceptions are not meaningfully integrated into a practical application or significantly more than the abstract idea. Thus, claims 2-4, 11 and 13 are also ineligible.
Step 2A, Prong One
Claim 5 further narrows the abstract idea of claims 1 and 3, by reciting transmitting an operation shift report corresponding to the operation shift, wherein the operation shift report is prepared by the first user, and wherein the operation shift report is indicative of operation details associated with the operation shift.
Claim 6 further narrows the abstract idea of claims 1 and 3, by reciting transmitting the HOTO summary.
Claim 7 further narrows the abstract idea of claims 1 and 3, by reciting transmitting the HOTO summary along with an operation shift report corresponding to the operation shift to, wherein the operation shift report is prepared by the first user, and wherein the operation shift report is indicative of operation details associated with the operation shift.
Claim 8 further narrows the abstract idea of claims 1 and 3, by reciting determining the HOTO summary to be in a submitted state; and transmitting an indication for locking the HOTO summary for preventing editing of the handover report.
Step 2A, Prong Two
Claims 5-8 recite the additional element of an operations management server, which is recited at a high-level of generality (See Para. 28 of Applicant’s Publication disclosing the operations management server as either a standalone computer or a combination of multiple computing devices operating together in a distributed computing environment. Examples of the operations management server may include, but are not limited to, laptops, desktops, tower servers, rack servers, blade servers, and mainframes), such that, when viewed as whole/ordered combination, it amounts to no more than mere instructions to apply the judicial exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, the additional element, when viewed as a whole/ordered combination (e.g., Figs. 1 and 2) does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the claims are directed to an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional element amounts to no more than mere instructions to apply the judicial exception using generic computer components, and is not a practical application of the abstract idea. The same analysis applies here in Step 2B, i.e., mere instructions to apply the judicial exception using generic computer components (See MPEP 2106.05(f)), does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B.
Therefore, the additional element, does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. Thus, the claims are ineligible.
Allowable over the Prior Art
Claims 1-8, 11, and 13 are allowable over the prior art because none of the prior art references teach or suggest all the limitations of claim 1 in its entirety. However, the claims are subject to the rejection under 35 U.S.C.101.
The closest prior art for the claims include:
CN111176630A to Yongfeng et al. (hereinafter “Yongfeng”). Yongfeng discloses a method for changing the identity information of a duty officer by calling a camera to obtain the image of the on-duty personnel who are taking over the shift. The acquired image of the on-duty personnel taking over is used as an input parameter of the application programming interface of the face recognition service application, and the face recognition service application determines whether to change the identity information of the on-duty personnel in the management system.
U.S. Application Publication No. 2014/0172484 to Liu et al. (hereinafter “Liu”). Liu discloses a first worker and a second worker are assigned to be responsible for a work detail. The work detail completed is transferred to the second worker from the first worker. The first worker uses the electronic device to complete transferred contents of the work detail before passing the electronic device to the second worker. Receiving module 100 receives information in relation to a first worker and a second worker that are assigned to be responsible for a work detail from the electronic devices. The detection module 101 accesses the cloud server 3 and detects whether the obtained information from the second worker accords with the working schedule. When the person who is using the electronic device is the second worker, the execution module 103 accesses the cloud server 3 and receives job contents selected by the first worker and the second worker.
U.S. Patent Application Publication No. 2022/0058589 to Qian et al. (hereinafter “Qian”). Qian discloses a work shift handover report includes (or summarizes) data that is relevant for operational continuity and which needs to be made available to a team of operators who will be operating the plant in the subsequent/incoming work shift. Generated work shift handover report(s) 308 may be generated based on selection of specific data records/data logs.
CN 10009260 to Wang (hereinafter “Wang”). Wang discloses monitoring shift changeover times.
U.S. Patent Application No. 2015/0066552 to Shami (hereinafter “Shami”). Shami discloses a system that may include an event type generator configured to cause the at least one processor to provide a training set for training a supervised machine learning algorithm to classify documents with respect to an event type. A set of documents from the corpus 122, and this set of documents may be used as a training set in training a supervised machine learning algorithm of the event classifier 114 to classify received documents with respect to the new, created event type. In this regard, it will be appreciated that the selected subset defined as the training dataset may be considered to have particular predictive value with respect to correctly classifying subsequently-received input. During the evaluation, it may occur that a false negative occurs in which a training document or training match that should be classified in a certain manner, is in fact failed to be classified as such a determination as to whether the evaluation is satisfactory (512) may generally depend on a number of situation-specific factors. If the evaluated training set is considered not to be satisfactory (512), then any of the previous operations 502-510 may be revisited and adjusted. For example, different features and corresponding values may be selected (e.g., different training documents may be selected), and a modified training set may be selected therefrom. Of course, in separate iterations, different algorithms may be selected. Further, in the evaluations thereof, various parameters (e.g., a tolerance for false positives and/or false negatives, as referenced above) may be specified.
U.S. Patent Application Publication No. 2010/0191568 to Kashyap et al. (hereinafter “Kashyap”). Kashyap discloses a server for automatically obtaining and storing information from a current work shift of the plant from the control system; and at least one client connected to the server and comprising a logbook application module for manipulating the obtained information. Preferably, the server is configured to automatically transmit manipulated information to identified users of a subsequent work shift.
However, Yongfen, Wang, Liu, Qian, Shami nor Kashyap teach the combination of the claim limitations of claim 1 in its entirety.
Prior Art
The following is prior art not cited but considered relevant:
U.S. Patent Application Publication No. 2017/0085603 to Qian et al. (hereinafter “Qian II”). Qian II discloses a shift handover system that includes a shift hand over report.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/RUPANGINI SINGH/Primary Examiner, Art Unit 3628
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