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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
DETAILED ACTION
The following FINAL Office Action is in response to communication filed on 2/10/2026.
Priority
Examiner has noted Applicant claiming Priority from Provisional Application 63/581,889 filed 09/11/2023.
Status of Claims
Claims 1-20 are currently pending.
Claims 1, 4, 7, 11, 17 are currently amended.
Claims 1-20 are currently under examination and have been rejected as follows.
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Response to Amendment
The previously pending claim objections are withdrawn in view of the amendments.
The previously pending rejections under 35 USC 101 will be maintained. The 101 rejection is updated in view of the amendments.
New grounds for rejection 35 USC 103 are applied as necessitated by the amendments. The 103 rejection is updated in view of the amendments.
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Response to Arguments
Regarding Applicant’s remarks pertaining to 35 USC 101:
Step 2A Prong 1:
Applicant argues on page 9 of remarks 2/10/2026:
“Here, the claims are not directed merely to the abstract idea of ‘mental processes’ or ‘organizing human activity.’ Instead, the claims recite a specific improvement to automated scheduling technician services: the automatic scheduling of a selected resource to undertake service jobs based on a comprehensive value prediction determined by machine learning models and user selection. This improvement can be seen at least in Figs. 3B and 6.
“Furthermore, the method is a technical process that cannot be performed in the human mind or with pen and paper because it requires a computing device capable of processing large, historical data sets, predicting value associated with service jobs, predicting subsequent service jobs to be performed, and updating the schedule. This is a specific improvement to the functioning of the computer itself, and not merely the automation of a business process.”
Examiner respectfully disagrees. Examiner points to MPEP2106.04(a)(2) III C finding that computer aided processes such as: 1. Performing a mental process on a generic computer, 2. Performing a mental process in a computer environment, 3. Using a computer as a tool to perform a mental process can still be considered to recite a mental process. The claims as amended do not appear to present any new additional computer-based elements to the original additional computer-based elements “first trained machine learning model”, “second trained machine learning model”, “processor”, and “memory”. These additional elements perform functions including gathering and organizing data, calculating weighted value predictions, creating a schedule to assign service jobs to technicians, etc. The calculations further detailed in the dependent claims and Application specification ¶ [0046]-[0050] include relationships among quantities such as revenue, number of completed jobs, average ticket values, conversion rates, etc. While the functions performed by the additional elements certainly streamline the process to more efficient execution than if performed mentally and by hand, it is not clear to the Examiner that these functions, however tedious, could not be carried out manually, nor how these functions among the others would improve the functioning of a computer itself.
Step 2A Prong 2:
Applicant argues on page 10 of remarks 2/10/2026:
“…the claims do not merely use a computer as a tool but instead recite a specific way scheduling improved- by automatic generation in response to a user selected weighting factor, and predicted values and service jobs to receive the most value, profit, or revenue generated by the selected technician. Similar to Examples 42 and 47… the present claims recite a specific, technical solution to a computer-centric problem: an automatic scheduling system for technicians taking into account user selections and machine learning models for predicting for evaluating the most profitable technician for a job request. In other words, this situation is analogous to Example 42 as the claimed invention recite automatic updating of information in response to user input. This situation is also analogous to Example 47 as the claimed invention there recite, at a high level of generality, the use of artificial intelligence to detect anomalies and automatically block traffic from a source.”
Examiner respectfully disagrees. Example 42 demonstrates integration into a practical application by “converting updated information that was input by a user in a non-standardized form to a standardized format, automatically generating a message whenever updated information is stored, and transmitting the message to all of the users. The additional elements recite a specific improvement over prior art systems by allowing remote users to share information in real time in a standardized format regardless of the format in which the information was input by the user.” This practical application substantially exceeds mere automatic updating of information in response to user input. Example 47, specifically in claim 3, achieves eligibility despite reciting a judicial exception by demonstrating improvement in network security – in other words, improvement in the technology itself. Examiner again submits, as above, that gathering and organizing data, calculating weighted value predictions, creating a schedule to assign service jobs to technicians, etc. may improve efficiency and accuracy of a scheduling process, but it is difficult to ascertain how the computer technology itself is improved.
Step 2B:
Applicant argues on page 10 of remarks 2/10/2026:
“…The claims here recite significantly more than any alleged abstract idea (Alice, Step 2B) the claims are narrowly tailored to a specific technical solution and do not preempt all ways of scheduling technicians. The claims require a particular manner of automatic technician scheduling by machine learning in response to a user selection. Notably, like Example 42, "the additional elements recite a specific improvement over prior art systems" in that the system predicts different jobs that are necessary and/or recommended to be performed after the original service job is and accounts for this prediction performed (i.e., the second value prediction), the value prediction of the original
service job (i.e., the first value prediction), and a user selection weighting factors in scheduling a technician to perform the original service job. See e.g., Specification, [0050], Fig. 3B. These features are not conventional in the art and thus, amount to significantly more than the exception.”
Examiner respectfully disagrees. Applicant specification ¶s [0045], [0051] delineate several conventional machine learning models potentially applied as the first and second machine learning models in the claims, rendering algorithmic procedures applied to the claimed invention broad. Similarly, the other computer-based additional elements “processor” and “memory” used to calculate quantities, averages, and probabilities are described among conventional arrangements of hardware components in Applicant specification ¶ [0102]-[0104]. A unique combination of these elements demonstrating an improvement over existing technology is unclear to the Examiner. The additional elements recited in the claimed invention individually and in combination fail to integrate a judicial exception into a practical application and for the same reasons they also fail to provide significantly more.
Accordingly, the previously pending rejections under 35 USC 101 will be maintained. The 101 rejection is updated in view of the amendments.
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Regarding Applicant’s remarks pertaining to 35 USC 103:
Applicant argues on page 12 of remarks 2/10/2026:
“For example, while Bhatnagar discloses the system may be able to assign a first
technician to complete a third work order for a second customer, and discloses generally a loop related to receiving work orders and assigning technicians to perform installation (Fig. 2, [0056}), Bhatnagar does not disclose in response to a request generating a second value prediction associated with a second service job for each resource in the list of resources, wherein the second service job is predicted to be performed subsequent to the first service job. In other words, the instant invention generates a second value prediction for a second service job predicted to be performed after the first service job based on a single request from a user. For example, the instant invention may predict one or more different jobs that are necessary and/or recommended to be performed after the original service job is performed as the second service job and account for this prediction in scheduling a technician to perform the original service job. Specification, [0050]. Bhatnagar does not predict service jobs and does not account for those predictions when assigning technicians to perform work, but rather may assign a service technician to subsequently received work orders (i.e., subsequent requests). In fact, Bhatnagar uses a feedback survey to determine whether or not additional work may be needed. Bhatnagar, [0042].”
Examiner respectfully disagrees. Under the broadest reasonable interpretation of the claim language “wherein the second service job is predicted to be performed subsequent to the first service job”, the claim limitation reads as predicting the time period in which the second service is to be performed, in this case after the first service job, rather than predicting which job is the second service job. Thus, Bhatnagar reads on the claim as detailed in the 103 rejection section below, with additional support at Bhatnagar ¶ [0003]: “The receiver is configured to receive from a customer mobile device at least one numerical rating of the first technician assigned by a first customer after completion of the first work order…. The processor is also configured to execute instructions stored on the memory to… use the updated technician data base for… assigning the first technician to complete a third work order for a second customer.”
Applicant argues on page 12 of remarks 2/10/2026:
“Further, Bhatnagar only mentions using task ratings or a ‘churn’ factor of technicians for assigning a new task and does not calculate a "comprehensive value prediction" to automatically schedule a technician as claimed based on a first value prediction, a second value prediction, and a user selected weighting based on a conversion rate and an average ticket value of the service jobs to be completed. Bhatnagar [0057], [0062]. In other words, the alleged comprehensive value prediction at most considers ratings and a chum score in assigning a service technician, but does not include calculating the average ticket value (e.g., revenue) or a conversion rate (e.g., probability of a sale or revenue threshold met) received based on the service jobs. Moreover, Bhatnagar does not disclose or suggest such a weighting is user selected.”
Examiner respectfully finds the argument unpersuasive. Additional evidence for the claim limitations as amended regarding weighting based on conversion rates and average ticket values can be found in previously presented reference Merg at least at ¶s [0024], [0028], and [0032], [0092], and Fig 6 and related text. Additional details and citations are included in the 103 rejection section below.
Accordingly, new grounds for rejection 35 USC 103 are applied as necessitated by the amendments. The 103 rejection is updated in view of the amendments.
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Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-10 are directed to a method or process which is a statutory category.
Claims 11-16 are directed to a system or machine which is a statutory category.
Claims 17-20 are directed to a non-transitory computer-readable storage medium or article of manufacture which is a statutory category.
Step 2A Prong One: The claims recite, describe, or set forth a judicial exception of an abstract idea (see MPEP 2106.04(a)). Specifically, the claims recite, describe or set forth observation, judgement, evaluation, opinion, mitigating risk, and business relations including: “determining a list of resources associated with a tenant based at least in part on the resource information, the list of resources comprising one or more resources available for allocation to perform a first service job associated with the request for a customer”, “generating… a first value prediction associated with the first service job for each resource in the list of resources”, “generating… a second value prediction associated with a second service job for each resource in the list of resources, wherein the second service job is predicted to be performed subsequent to the first service job”, “receiving a user selection comprising a weighting based on a… conversion rate associated with the… service job… and a[n]… average ticket value associated with the… service job”, “generating, for each resource in the list of resources, a comprehensive value prediction based on the first value prediction, the second value prediction, and the weighting selection”, and “…scheduling a selected resource of the list of resources to undertake the first service job based on the comprehensive value prediction”. Assessing potential value of service job requests and optimally deploying service technicians to customers requesting the service fall within observation, judgement, evaluation, and opinion under the abstract grouping of Mental Processes (MPEP 2106.04(a)(2) III); as well as mitigating risk as it pertains to fundamental economic principles, and business relations as it pertains to commercial or legal interactions, each under the larger abstract grouping of Certain Methods of Organizing Human Activity (MPEP 2106.04(a)(2) II)1.
Step 2A Prong Two: Independent claims 1, 11, 17 recite the following additional elements: “first trained machine learning model”, “second trained machine learning model”, “processor”, and “memory”. The functions of these additional elements include examples such as “receiving resource information associated with a request”, “determining a list of resources associated with a tenant based at least in part on the resource information”, “generating… a second value prediction associated with a second service job for each resource in the list of resources”, “receiving a user selection comprising a weighting based on a first conversion rate associated with the first service job, a first average ticket value associated with the first service job, a second conversion rate associated with a second resource of the second service job, and a second average ticket value associated with the second service job”, “generating, for each resource in the list of resources, a comprehensive value prediction based on the first value prediction, the second value prediction, and the weighting selection”, and “…scheduling a selected resource of the list of resources to undertake the first service job based on the comprehensive value prediction”. The additional elements are recited at a high level of generality (i.e. as a generic computer performing functions of gathering and organizing data, calculating weighted values, creating a schedule, etc.) such that they amount to no more than mere instructions to apply the exception using generic computer components. Therefore, these functions can be viewed as not meaningfully different than a business method or mathematical algorithm being applied on a general-purpose computer as tested per MPEP 2106.05(f)(2)(i). The claims are directed to an abstract idea and the judicial exception does not integrate the abstract idea into a practical application.
Further, the additional element “machine learning model” language merely requires execution of an algorithm that can be performed by a generic computer component and provides no detail regarding the operation of that algorithm. As such, the claim requirement amounts to mere instructions to implement the abstract idea on a computer, and, therefore, is not sufficient to make the claim patent eligible. See Alice, 573 U.S. at 226 (determining that the claim limitations “data processing system,” “communications controller,” and “data storage unit” were generic computer components that amounted to mere instructions to implement the abstract idea on a computer); October 2019 Guidance Update at 11–12 (recitation of generic computer limitations for implementing the abstract idea “would not be sufficient to demonstrate integration of a judicial exception into a practical application”). Such a generic recitation of “machine learning model” alone is insufficient to show a practical application of the recited abstract idea.
Step 2B: According to MPEP 2106.05(f)(1), considering whether the claim recites only the idea of a solution or outcome i.e., the claims fail to recite the technological details of how the actual technological solution to the actual technological problem is accomplished. The recitation of claim limitations that attempt to cover an entrepreneurial and thus abstract solution to an entrepreneurial problem with no technological details on how the technological result is accomplished and no description of the mechanism for accomplishing the result do not provide significantly more than the judicial exception.
Dependent claims 2, 16 recite the additional element “graphical user interface”. Dependent claim 3 recites the additional element “user device”. The functions of these additional elements include examples such as presenting a job allocation table, receiving schedule inputs, and receiving alerts about service jobs. The additional elements are also recited at a high level of generality (i.e. as a generic computer performing functions of presenting and communicating data, etc.) such that they amount to no more than mere instructions to apply the exception using generic computer components.
Further, dependent claims 3-10, 12-15, 18-20 merely incorporate the additional elements recited in claims 1, 11, 17 along with further narrowing of the abstract idea of claims 1, 11, 17 and their execution of the abstract idea. Specifically, the dependent claims narrow the “first trained machine learning model”, “second trained machine learning model”, “processor”, and “memory” to capabilities such as sending, comprising, updating, determining, and generating various forms of data such as alerts, locations, times, value predictions, thresholds, service job quantities, conversion rates, lead values, service provider information, tenant information, etc. which, when evaluated per MPEP 2106.05(f)(2) represent mere invocation of computers to perform existing processes. Therefore, the additional elements recited in the claimed invention individually and in combination fail to integrate a judicial exception into a practical application (Step 2A prong two) and for the same reasons they also fail to provide significantly more (Step 2B). Thus, claims 1-20 are reasoned to be patent ineligible.
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REJECTIONS BASED ON PRIOR ART
Examiner Note: Some rejections will contain bracketed comments preceded by an “EN” that will denote an examiner note. This will be placed to further explain a rejection.
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Claim Rejections - 35 USC § 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over:
Bhatnagar et al. US 20210326796 A1, hereinafter Bhatnagar, in view of
Gatti et al. US 20130111488 A1, hereinafter Gatti, in further view of
Merg et al. US 20180365621 A1, hereinafter Merg. As per,
Regarding claims 1, 11, 17: Bhatnagar teaches:
(claim 1) A method, comprising:
(claim 11) A system, comprising: a processor; and a memory comprising instructions stored thereon, which when executed by the processor, cause the system to:
(claim 17) A non-transitory computer-readable storage medium having embodied thereon a program executable by a process for implementing a method for dispatching a resource, the method comprising:
receiving (claims 1, 17) / receive (claim 11) resource information associated with a request (Bhatnagar Fig. 6, step 102: Receive Work Order 20 [EN: request]; step 406: Access Technician Database 60 [EN: resource information]; and related text);
determining (claims 1, 17) / determine (claim 11) a list of resources associated with a tenant based at least in part on the resource information, the list of resources comprising one or more resources available for allocation to perform a first service job associated with the request for a customer (Bhatnagar ¶ [0059]: At step 402, the central server 14 receives a work order 20 which has been requested by a customer, for example, in accordance with step 102 above. ¶ [0062]: At step 406, the third processor 46 can review the technician database 60 for the purpose of assigning a specific technician to perform the work order 20. In an embodiment, the third processor 46 can narrow the technician database 60 [EN: to a list or subset of technicians] based on the technicians' work schedules and ability to perform specific tasks specified by the work order 20 [EN: first service job] also, see fig. 5);
generating (claims 1, 17) / generate (claim 11) [..] a first value prediction associated with the first service job for each resource in the list of resources (Bhatnagar ¶ [0059]: Similarly, the third processor 46 can access a technician profile 58 within the third memory 48 and recalculate a technician's individual ratings for individual tasks (e.g., T01, T02, ... T0m) [EN: value prediction] to evaluate the technician's [EN: resource’s] strengths and weaknesses with respect to different tasks [EN: service jobs]. [Also see Figs. 5A, 5B, and related text]);
generating (claims 1, 17) / generate (claim 11) [..] a second value prediction associated with a second service job for each resource in the list of resources wherein the second service job is predicted to be performed subsequent to the first service job (See Bhatnagar Fig. 2 steps 104-118, looping and repeating for multiple service jobs, and related text. ¶ [0003]: The receiver is configured to receive from a customer mobile device at least one numerical rating of the first technician assigned by a first customer after completion of the first work order…. The processor is also configured to execute instructions stored on the memory to:… use the updated technician data base for… assigning the first technician to complete a third work order for a second customer. Mid-¶ [0056]: Referring to FIG. 5A, a technician database 60 can include a technician [EN: resource] identifier 62 and a plurality of numerical ratings 64 [EN: value predictions]. In the illustrated embodiment, the plurality of numerical ratings 64 include overall rating (T0), various individual task ratings (T01, T02, ... T0m), and various individual category ratings (T0f, T0p, T0s, T0q, T0d). Referring to FIG. 5B, which can be included as part of the same technician database 60 as the information from FIG. 5A, a technician database 60 can further include individual category ratings separated for different tasks (e.g., different installation/repair projects organized). End-¶ [0003]: The processor is also configured to execute instructions stored on the memory to: (i) update the technician database using the at least one numerical rating, and (ii) use the updated technician database for at least one of: (a) assigning a second technician to complete a second work order for the first customer; or (b) assigning the first technician to complete a third work order [EN: subsequent to the first work order] for a second customer);
[..]
generating (claims 1, 17) / generate (claim 11), for each resource in the list of resources, a comprehensive value prediction based on the first value prediction, the second value prediction [..] (Bhatnagar mid-¶ [0056]: Referring to FIG. 5A, a technician database 60 can include a technician [EN: resource] identifier 62 and a plurality of numerical ratings 64 [EN: value predictions]. In the illustrated embodiment, the plurality of numerical ratings 64 include overall rating (T0) [EN: comprehensive value prediction], various individual task ratings (T01, T02, ... T0m), and various individual category ratings (T0f, T0p, T0s, T0q, T0d)); and
automatically scheduling (claims 1, 17) / schedule (claim 11) a selected resource of the list of resources to undertake the first service job based on the comprehensive value prediction (Bhatnagar ¶ [0062]: At step 406, the third processor 46 can review the technician database 60 for the purpose of assigning a specific technician to perform the work order 20. In an embodiment, the third processor 46 can narrow the technician database 60 based on the technicians' work schedules and ability to perform specific tasks specified by the work order 20. ¶ [0065]: In an embodiment, the third processor 46 can use the specific work assigned by the work order 20 to [EN: automatically] assign a technician).
Although Bhatnagar teaches generating value predictions for assigning resources correlating with service jobs requested by customers, Bhatnagar does not specifically teach using trained machine learning models to generate the value predictions, or weighting based on conversion rates and average ticket values.
However, Gatti in analogous art of intelligent allocation of service technicians teaches or suggests:
[..] using a first trained machine learning model [..]; [..] using a second trained machine learning model [..] (Gatti ¶ [0029]: An exemplary embodiment of the present disclosure performs this determination using Machine-Learned Ranking (MLR) (also known as ‘learning to rank’). MLR is a Supervised learning technique for constructing a ranking model from training data, which may be composed of past user choices eliciting their preferences. Mid-¶ [0030]: A Support Vector Machine (SVM) is an implementation of MLR. ¶ [0006]: … a method of ranking workers for an incoming task includes extracting first attributes from a list of tasks that were completed during a pre-determined period, generating a first feature vector for each task and worker from the first extracted attributes, training a Support Vector Machine SVM based on the first feature vector to output a weight vector, extracting second attributes from an incoming task, generating a second feature vector for each worker based on the second extracted attributes, and ranking the workers using the second feature vectors and the weight vector. ¶ [0007]: … a method of training a Support Vector Machine SVM includes recording a list of completed tasks in a computer data structure, extracting attributes from the list for the tasks that were completed during a pre-determined period, generating a feature vector for each task and worker of a plurality of workers from the extracted attributes and a score for each feature vector, and training the SVM based on the feature vectors and their corresponding scores to output a weight vector. The score of the feature vector for a task and a worker that is assigned the task may be higher than the score of the feature vectors for the task for the other workers).
Gatti and Bhatnagar are found as analogous art of intelligent allocation of service technicians. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Bhatnagar’s managing, evaluating and assigning installation and/or repair technicians system and method to have included Gatti’s teachings around using trained machine learning models to generate prediction values. The benefit of these additional features would have improved the efficiency of managing and utilizing resources to complete tasks (Gatti ¶ [0005]). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Bhatnagar in view of Gatti (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of intelligent allocation of service technicians. In such combination each element would have merely performed the same 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 Bhatnagar in view of Gatti above, the to- be combined elements would have fit 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 (see MPEP 2143 A).
Furthermore, Merg in analogous art of intelligent allocation of service technicians teaches or suggests:
receiving a user selection comprising a weighting based on a first conversion rate
associated with the first service job, a first average ticket value associated with the first service job, a second conversion rate associated with a second resource of the second service job, and a second average ticket value associated with the second service job (Merg mid-¶ [0024]: …multiple different types of technician performance data may be combined (e.g., assigned different weights as part of a scoring formula) to generate the scoring metrics. ¶ [0028]: …an expected profitability metric [EN: average ticket value] may be determined based on a ratio of the expected total completion time of all service jobs given a particular set of technician assignments [EN: first resource, second resource] relative to a target total completion time. ¶ [0032]: Upon selection of a particular technician [EN: first or second resource], an expected profitability metric may be updated within the user interface to provide an indication of the effect of a technician assignment or reassignment on overall shop profitability. ¶ [0092]: In FIG. 6, table 602 shows scoring metrics for the three technicians on Job 1 (thermostat replacement). In this example, average completion times may be determined for each technician by averaging the completion times for each past performance of Job 1 by the technician. A scoring metric [EN: first conversion rate] is then generated based on a ratio of the target completion time for Job 1 to a technician's average completion time. Accordingly, a scoring metric above 1.00 may indicate an above average technician (or more efficient than average technician) for thermostat replacement, while a scoring metric below 1.00 may indicate a below average technician. As shown in table 602, Technician 2 [EN: first resource] has the highest scoring metric for Job 1 [EN: first service job] and may be the preferred technician to handle additional performances of Job 1);
generating (claims 1, 17) / generate (claim 11), for each resource in the list of resources, a comprehensive value prediction based on [..] the weighting selection (Merg mid-¶ [0024]: mid-¶ [0024]: …multiple different types of technician performance data may be combined (e.g., assigned different weights as part of a scoring formula) to generate the scoring metrics).
Merg, Gatti and Bhatnagar are found as analogous art of intelligent allocation of service technicians. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Bhatnagar / Gatti’s managing, evaluating and assigning installation and/or repair technicians system and method to have included Merg’s teachings around weighting based on conversion rates and average ticket values. The benefit of these additional features would have improved efficiency in assigning service jobs to technicians (Merg ¶ [0021]). The predictability of such modifications and/or variations would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Bhatnagar in view of Gatti and Merg (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of intelligent allocation of service technicians. In such combination each element would have merely performed the same 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 Bhatnagar in view of Gatti and Merg above, the to- be combined elements would have fit 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 (see MPEP 2143 A).
Regarding claims 2, 16: Bhatnagar / Gatti / Merg teaches all the limitations of claims 1, 11 above.
Bhatnagar further teaches:
generating [..] a job allocation table comprising the comprehensive value prediction associated with each resource [..] (See Bhatnagar Fig. 5A: task [EN: job] table with technician [EN: resource] ID and Overall Rating [EN: comprehensive value prediction] and related text).
Although Bhatnagar teaches generating a job allocation table with resources and value predictions, Bhatnagar does not specifically teach the presentation of the table on a graphical user interface, including time windows for each resource, or receiving input to schedule the resources.
However, Merg in analogous art of intelligent allocation of service technicians teaches or suggests:
generating, on a graphical user interface [..] available time windows for each resource, wherein the graphical user interface is configured to receive input to schedule the selected resource for a time window (Merg ¶ [0101]: FIG. 7 illustrates a portion of an example technician assignment interface. More specifically, display 700 may show an assignment window 702 that shows suggested technician assignments within a given time window for a repair shop. As illustrated, a suggested set of technician assignments may include assignment of Job 1 to Technician 2, Job 2 to Technician 1, and Job 3 to Technician 3. The assignments may be determined to assign the highest scoring technician on each service job to perform the service job).
Merg, Gatti and Bhatnagar are found as analogous art of intelligent allocation of service technicians. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Bhatnagar / Gatti’s managing, evaluating and assigning installation and/or repair technicians system and method to have included Merg’s teachings around the presentation of a table on a graphical user interface, including time windows for each resource, and receiving input to schedule the resources. The benefit of these additional features would have improved efficiency in assigning service jobs to technicians (Merg ¶ [0021]). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Bhatnagar in view of Gatti and Merg (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of intelligent allocation of service technicians. In such combination each element would have merely performed the same 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 Bhatnagar in view of Gatti and Merg above, the to- be combined elements would have fit 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 (see MPEP 2143 A).
Regarding claim 3: Bhatnagar / Gatti / Merg teaches all the limitations of claim 1 above.
Bhatnagar further teaches:
further comprising:
sending an alert to a user device associated with the selected resource, wherein the alert comprises information associated with the first service job, including job location information and job time information (Bhatnagar ¶ [0025]: The central server 14 then sends a data packet 16 including the work order 20 to the technician mobile device 12b of the assigned technician, for example, by transmitting the data packet 16 from the transmitter 44a of the third data transmission device 44 to the receiver 37b of the second data transmission device 34b. The data packet 16 sent to the technician mobile device 12b can also include, for example, the customer's location, the type of work to perform, and/or updated processing instructions 18 to use by the technician mobile device 12b during completion of the work order 20. Bhatnagar implies that job time information is also included, as the customer can provide feedback as described in ¶ [0036]: For example, the ratings for various technician categories 54 can include the following: … (ii) a punctuality rating, RP, e.g. relating to the technician arriving onsite at the time provided during or after the work order 20 was placed).
Regarding claim 4: Bhatnagar / Gatti / Merg teaches all the limitations of claim 1 above.
Bhatnagar further teaches:
further comprising:
updating, for a first resource, the first value prediction with the first conversion rate (Bhatnagar ¶ [0069]: Based on customers remaining or leaving, each technician profile 58 within the technician database 60 can include a churn score (CS), for example, wherein a higher churn score indicates a high number of customers remaining with the company [EN: conversion rate] and/or increasing business with the company after visits from the technician, and wherein a lower churn score indicates a high number of customers leaving within a certain amount of time after visits from the technician).
Regarding claim 5: Bhatnagar / Gatti / Merg teaches all the limitations of claim 1 above.
Bhatnagar further teaches:
wherein the first conversion rate is determined by at least:
[..]
[..] generating [..] a single service job conversation rate associated with the first resource (Bhatnagar ¶ [0069]: Based on customers remaining or leaving, each technician profile 58 within the technician database 60 can include a churn score (CS), for example, wherein a higher churn score indicates a high number of customers remaining with the company [EN: conversion rate] and/or increasing business with the company after visits from the technician, and wherein a lower churn score indicates a high number of customers leaving within a certain amount of time after visits from the technician).
Although Bhatnagar teaches generating conversion rates for resources, Bhatnagar does not specifically teach determining the conversion rates by a ratio of the quantity of jobs completed with a value above a threshold to the total quantity of jobs completed by the resource.
However, Gatti in analogous art of intelligent allocation of service technicians teaches or suggests:
determining a quantity of service jobs completed by the first resource with a value above a threshold (Gatti ¶ [0025]: SLAS are elements of IT infrastructure services that permit contracts formally detailing a promised service level, clearly delineating quality requirements including the maximum time [EN: threshold] in which service requests should be handled);
determining a total quantity of service jobs completed by the first resource; and generating, based on the quantity of service jobs and the total quantity of service jobs, a single service job conversation rate associated with the first resource (Gatti ¶ [0025]: In another example, a feature could be generated that indicates the percentage of tasks assigned to a given worker that caused an SLA breach. For example, the feature could indicate that 80% of the tasks assigned to a particular worker in the last day caused a breach [EN: 20% conversion rate of tasks which did not breach out of total tasks]).
Merg, Gatti and Bhatnagar are found as analogous art of intelligent allocation of service technicians. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Bhatnagar / Gatti’s managing, evaluating and assigning installation and/or repair technicians system and method to have included Gatti’s teachings around determining conversion rates by a ratio of the quantity of jobs completed with a value above a threshold to the total quantity of jobs completed by a resource. The benefit of these additional features would have improved the efficiency of managing and utilizing resources to complete tasks (Gatti ¶ [0005]). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Bhatnagar in view of Gatti and Merg (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of intelligent allocation of service technicians. In such combination each element would have merely performed the same 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 Bhatnagar in view of Gatti and Merg above, the to- be combined elements would have fit 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 (see MPEP 2143 A).
Regarding claim 6: Bhatnagar / Gatti / Merg teaches all the limitations of claim 1 above.
Bhatnagar further teaches:
wherein the first conversion rate is determined by at least:
[..]
[..] generating [..] a lead conversation rate associated with the first resource (Bhatnagar ¶ [0069]: Based on customers remaining or leaving, each technician profile 58 within the technician database 60 can include a churn score (CS), for example, wherein a higher churn score indicates a high number of customers remaining with the company and/or increasing business with the company [EN: lead conversion rate] after visits from the technician, and wherein a lower churn score indicates a high number of customers leaving within a certain amount of time after visits from the technician).
Although Bhatnagar teaches generating lead conversion rates for resources, Bhatnagar does not specifically teach determining the lead conversion rates by a ratio of the quantity of jobs completed with a value above a threshold to the total quantity of jobs completed by the resource.
However, Gatti in analogous art of intelligent allocation of service technicians teaches or suggests:
determining a quantity of service jobs that have been completed by the first resource, that have generated a lead, and that are associated with a value above a threshold (Gatti ¶ [0025]: SLAS are elements of IT infrastructure services that permit contracts formally detailing a promised service level, clearly delineating quality requirements including the maximum time [EN: threshold] in which service requests should be handled);
determining a total quantity of service jobs completed by the first resource; and generating, based on the quantity of service jobs and the total quantity of service jobs completed by the first resource, a lead conversation rate associated with the first resource (Gatti ¶ [0025]: In another example, a feature could be generated that indicates the percentage of tasks assigned to a given worker that caused an SLA breach. For example, the feature could indicate that 80% of the tasks assigned to a particular worker in the last day caused a breach [EN: 20% conversion rate of tasks which did not breach out of total tasks]).
Rationales to have combined / modified Bhatnagar / Gatti / Merg are above and reincorporated.
Regarding claim 7: Bhatnagar / Gatti / Merg teaches all the limitations of claim 1 above.
Bhatnagar further teaches:
further comprising:
updating, for a first resource, the second value prediction, based on the second conversion rate associated with the first resource performing the second service job (Bhatnagar ¶ [0033]: At step 118, the individual technician profile 58 can be used to update a larger technician database 60 stored by the third memory 48. In an embodiment, the technician database 60 can include a plurality of technician profiles 58 which include, for example, overall ratings (T0), overall task-specific ratings (e.g., T01, T02, ... T0m), overall categorical rankings (e.g., T0f, T0p, T0s, T0q, T0d), a churn score (CS) [EN: value prediction based on conversion rate] and/or any other ratings for each technician employed and/or used to perform various work orders 20 [EN: first, second, etc. service jobs]. Once the technician profile 58 has been updated within the technician database 60 of the third memory 48, the updated technician database 60 can be used by the third processor 46 to assign a technician the next time a work order 20 is received by central database at step 102).
Regarding claim 8: Bhatnagar / Gatti / Merg teaches all the limitations of claim 1 above.
Bhatnagar further teaches:
wherein [..] to generate the first value prediction associated with the first service job using at least one of: historical data associated with a type of the first service job, historical data associated with a job service provider, and historical data associated with a location of the first service job (Bhatnagar end-¶ [0056]: Referring to FIG. 5B, which can be included as part of the same technician database 60 as the information from FIG. 5A, a technician database 60 can further include individual category ratings separated for different tasks (e.g., different installation/repair projects organized by type, or different subparts of installation/repair projects)).
Although Bhatnagar teaches generating value predictions for assigning resources using historical data, Bhatnagar does not specifically teach training a machine learning model with the historical data to generate the value predictions.
However, Gatti in analogous art of intelligent allocation of service technicians teaches or suggests:
the first trained machine learning model is trained (Gatti ¶ [0006]: … a method of ranking workers for an incoming task includes extracting first attributes from a list of tasks that were completed during a pre-determined period, generating a first feature vector for each task and worker from the first extracted attributes, training a Support Vector Machine SVM based on the first feature vector to output a weight vector, extracting second attributes from an incoming task, generating a second feature vector for each worker based on the second extracted attributes, and ranking the workers using the second feature vectors and the weight vector).
Rationales to have combined / modified Bhatnagar / Gatti / Merg are above and reincorporated.
Regarding claim 9: Bhatnagar / Gatti / Merg teaches all the limitations of claim 1 above.
Bhatnagar further teaches:
wherein [..] to generate the second value prediction associated with the second service job using at least one of: historical data associated with a type of the second service job, historical data associated with one or more resources of the tenant, historical data associated with a job service provider, and historical data associated with a location of the first service job (Bhatnagar end-¶ [0032]: At step 116, a technician [EN: tenant resource] profile 58 stored within the third memory 48 of the central server 14 can be updated based on the processed customer feedback survey 52, and optionally the additional feedback from the technician. The technician profile 58 can be updated, for example, by using the overall rating and/or one or more task or categorical ratings determined at step 112).
Although Bhatnagar teaches generating value predictions for assigning resources using historical data, Bhatnagar does not specifically teach training a machine learning model with the historical data to generate the value predictions.
However, Gatti in analogous art of intelligent allocation of service technicians teaches or suggests:
the second trained machine learning model is trained (Gatti ¶ [0006]: … a method of ranking workers for an incoming task includes extracting first attributes from a list of tasks that were completed during a pre-determined period, generating a first feature vector for each task and worker from the first extracted attributes, training a Support Vector Machine SVM based on the first feature vector to output a weight vector, extracting second attributes from an incoming task, generating a second feature vector for each worker based on the second extracted attributes, and ranking the workers using the second feature vectors and the weight vector).
Rationales to have combined / modified Bhatnagar / Gatti / Merg are above and reincorporated.
Regarding claims 10, 20: Bhatnagar / Gatti / Merg teaches all the limitations of claims 1, 17 above.
Although Bhatnagar teaches generating value predictions for assigning resources correlating with service jobs requested by customers, Bhatnagar does not specifically teach retraining machine learning models on a regular basis.
However, Gatti in analogous art of intelligent allocation of service technicians teaches or suggests:
further comprising retraining at least one of the first trained machine learning model and the second trained machine learning model on a regular scheduled basis (Gatti mid-¶ [0070]: Further, optionally, the SVM may be retrained periodically to revise the weight vector (S306). For example, the SVM can be re-trained every minute, hour, day, week, month, etc. to generate a new weight vector. Although not shown in FIG. 3A, the training period may be adjusted to a different value prior to the re-training).
Rationales to have combined / modified Bhatnagar / Gatti / Merg are above and reincorporated.
Regarding claim 12: Bhatnagar / Gatti / Merg teaches all the limitations of claim 11 above.
Bhatnagar further teaches:
wherein [..] to generate the first value prediction associated with the first service job using historical service job data associated with at least one different customer (Bhatnagar end-¶ [0056]: Referring to FIG. 5B, which can be included as part of the same technician database 60 as the information from FIG. 5A, a technician database 60 can further include individual category ratings separated for different tasks (e.g., different installation/repair projects organized by type, or different subparts of installation/repair projects. End-¶ [0059]: The third processor 46 can then access a customer database 66 stored within the third memory 48 to determine whether the customer is a repeat customer or a new customer)), and
wherein [..] to generate the comprehensive value prediction associated with the second service job using historical service job information associated with a job service provider (Bhatnagar end-¶ [0032]: At step 116, a technician [EN: tenant resource] profile 58 stored within the third memory 48 of the central server 14 can be updated based on the processed customer feedback survey 52, and optionally the additional feedback from the technician. The technician profile 58 can be updated, for example, by using the overall rating [EN: comprehensive value prediction] and/or one or more task or categorical ratings determined at step 112).
Although Bhatnagar teaches generating value predictions for assigning resources correlating with service jobs requested by customers, Bhatnagar does not specifically teach using trained machine learning models to generate the value predictions.
However, Gatti in analogous art of intelligent allocation of service technicians teaches or suggests:
[..] the first trained machine learning model is trained; [..] the second trained machine learning model is trained (Gatti ¶ [0006]: … a method of ranking workers for an incoming task includes extracting first attributes from a list of tasks that were completed during a pre-determined period, generating a first feature vector for each task and worker from the first extracted attributes, training a Support Vector Machine SVM based on the first feature vector to output a weight vector, extracting second attributes from an incoming task, generating a second feature vector for each worker based on the second extracted attributes, and ranking the workers using the second feature vectors and the weight vector).
Rationales to have combined / modified Bhatnagar / Gatti / Merg are above and reincorporated.
Regarding claim 14: Bhatnagar / Gatti / Merg teaches all the limitations of claim 11 above.
Bhatnagar further teaches:
wherein [..] to generate the first value prediction associated with the first service job using at least one of: historical data associated with a type of the first service job, historical data associated with a job service provider, and historical data associated with a location of the first service job (Bhatnagar end-¶ [0056]: Referring to FIG. 5B, which can be included as part of the same technician database 60 as the information from FIG. 5A, a technician database 60 can further include individual category ratings separated for different tasks (e.g., different installation/repair projects organized by type, or different subparts of installation/repair projects)), and
wherein [..] to generate the second value prediction associated with the second service job using at least one of: historical data associated with a type of the second service job, historical data associated with the at least one resource, historical data associated with a job service provider, and historical data associated with a location of the first service job (Bhatnagar end-¶ [0032]: At step 116, a technician [EN: tenant resource] profile 58 stored within the third memory 48 of the central server 14 can be updated based on the processed customer feedback survey 52, and optionally the additional feedback from the technician. The technician profile 58 can be updated, for example, by using the overall rating and/or one or more task or categorical ratings determined at step 112).
Although Bhatnagar teaches generating value predictions for assigning resources using historical data, Bhatnagar does not specifically teach training machine learning models with the historical data to generate the value predictions.
However, Gatti in analogous art of intelligent allocation of service technicians teaches or suggests:
[..] the first trained machine learning model is trained [..] and the second trained machine learning model is trained (Gatti ¶ [0006]: … a method of ranking workers for an incoming task includes extracting first attributes from a list of tasks that were completed during a pre-determined period, generating a first feature vector for each task and worker from the first extracted attributes, training a Support Vector Machine SVM based on the first feature vector to output a weight vector, extracting second attributes from an incoming task, generating a second feature vector for each worker based on the second extracted attributes, and ranking the workers using the second feature vectors and the weight vector).
Rationales to have combined / modified Bhatnagar / Gatti / Merg are above and reincorporated.
Regarding claim 15: Bhatnagar / Gatti / Merg teaches all the limitations of claim 11 above.
Although Bhatnagar teaches generating value predictions for assigning resources correlating with service jobs requested by customers, Bhatnagar does not specifically teach retraining a machine learning model based on information from the service job.
However, Gatti in analogous art of intelligent allocation of service technicians teaches or suggests:
further comprising retraining at least one of the first trained machine learning model and the second trained machine learning model based on information associated with the first service job (Gatti mid-¶ [0070]: Features vectors that correspond to a task [EN: first service job] and to a worker that was actually assigned the task may be given a higher score (e.g. 1) than feature vectors that correspond to a task and a worker that was not originally assigned the task (e.g., 0). Further, optionally, the SVM may be retrained periodically to revise the weight vector (S306). For example, the SVM can be re-trained every minute, hour, day, week, month, etc. to generate a new weight vector. Although not shown in FIG. 3A, the training period may be adjusted to a different value prior to the re-training).
Rationales to have combined / modified Bhatnagar / Gatti / Merg are above and reincorporated.
Regarding claim 13: Bhatnagar / Gatti / Merg teaches all the limitations of claim 11 above.
Although Bhatnagar teaches generating value predictions for resources and service jobs using historical data, Bhatnagar does not specifically teach using historical data which includes one or more of service job value total, service job revenue, or service job profits.
However, Merg in analogous art of intelligent allocation of service technicians teaches or suggests:
wherein historical service job data comprises one or more of a service job value total, service job revenue, or profits for the service job performed by the job service provider or one or more other job service providers over a period of time (Merg ¶ [0028]: In some examples, suggested technician assignments may be generated to maximize overall shop efficiency and/or profitability. For instance, an expected profitability metric may be determined based on a ratio of the expected total completion time of all service jobs given a particular set of technician assignments relative to a target total completion time. The target total completion time may be a sum of target completion times for each of the service jobs. The target completion time for an individual service job may be based on a standard flat rate charge for the service job. In other examples, the target completion time may be based on an accumulation (e.g., an average) of past completion times of the service job (e.g., a national average, an average for a particular region, or an average for a particular shop. ¶ [0104]: In further examples, the effects of manual overrides on profitability may be tracked and later indicated on a technician assignment interface. In particular, the actual shop profitability after a manual override may be determined and compared to the expected profitability if the suggested technician assignments had been followed. A representation of this comparison may be displayed to provide a service writer with an indication of how past manual overrides have affected profitability. In some examples, the comparison may be determined and displayed for a particular type of technician assignment when a manual override is attempted).
Merg, Gatti and Bhatnagar are found as analogous art of intelligent allocation of service technicians. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Bhatnagar / Gatti’s managing, evaluating and assigning installation and/or repair technicians system and method to have included Merg’s teachings around using historical data which includes service job profits. The benefit of these additional features would have improved efficiency in assigning service jobs to technicians (Merg ¶ [0021]). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Bhatnagar in view of Gatti and Merg (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of intelligent allocation of service technicians. In such combination each element would have merely performed the same 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 Bhatnagar in view of Gatti and Merg above, the to- be combined elements would have fit 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 (see MPEP 2143 A).
Regarding claim 18: Bhatnagar / Gatti / Merg teaches all the limitations of claim 17 above.
Bhatnagar further teaches:
wherein before generating the first value prediction, determining that the first service job is not associated with a value of zero, wherein determining that the first service job is not associated with a value of zero comprises one or more of: determining that the first service job is not a recall service job, determining that the first service job is not a warranty service job, and determining that the first service job is not a recurring service included in a subscription (Bhatnagar ¶ [0064]: At step 410, a new technician can be assigned to perform the work order 20, for example, in cases of: (i) a new customer; (ii) a returning customer who rated a previous technician below a minimum score or previously indicated that the same technician should not return; or (iii) a previous technician unavailable or unable to perform the new work order 20. When selecting a new technician, the third processor 46 can review the technician database 60 to determine the best technician to assign based on the customer and/or the work order).
Regarding claim 19: Bhatnagar / Gatti / Merg teaches all the limitations of claim 17 above.
Bhatnagar further teaches:
wherein receiving the information associated with the first service job comprises receiving the data, in real-time and from a job service provider, in response to the job service provider receiving a request to perform the first service job for the customer (Bhatnagar end-¶ [0024]: In an alternative embodiment, the central server 14 first sends a message to the technician mobile device 12b to confirm the assigned technician's availability and/or competency to perform the installation and/or repair requested by the work order 20, and then the central server 14 sends the data packet 16 including the work order 20 upon confirmation from the technician mobile device 12b that the assigned technician has accepted the assignment).
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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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The following art is made of record and considered pertinent to Applicant’s disclosure:
Ramer; Jorey E. US 20230186251 A1, System and method for covering cost of delivering repair and maintenance services to premises of subscribers including predictive service.
Gordenker; Miles et al. US 20210019690 A1, Technician dispatching method and system.
Thompson; Stuart US 20170004420 A1, Worker venturing coefficient for workforce routing.
Meng; Xiaoqiao et al. US 20130104140 A1, Resource aware scheduling in a distributed computing environment.
Jaggers; Adam et al. US 20210398054 A1, Automated generation of condition state information for one or more machines, devices, or systems associated with service or repair events.
Shah; Akash Gaurav et al. US 20220164747 A1, Operations task creation, prioritization, and assignment.
Qiu; Tianyu et al. US 20200334616 A1, Job value model generation method and system.
Yeung; Man N. et al. US 20200402013 A1, Predicting successful outcomes.
Xiong Guanglei et al. AU 2018201691 A1, Job allocation.
Pekel, Engin. "Deep Learning Approach to Technician Routing and Scheduling Problem." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 11.2 (2022): 191.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to REED M. BOND whose telephone number is (571) 270-0585. The examiner can normally be reached Monday - Friday 8:00 am - 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patricia Munson can be reached at (571) 270-5396. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/REED M. BOND/Examiner, Art Unit 3624
April 17, 2026
/HAMZEH OBAID/Primary Examiner, Art Unit 3624 April 20, 2026
1 MPEP 2106.04(a): “examiners should identify at least one abstract idea grouping, but preferably identify all groupings to the extent possible”.
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