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 .
Response to Amendment
This office action is final and in response to the amendment filed on June 25, 2026, which was in response to the non-final office action mailed March 25, 2026.
Claims 1-20 are pending and have been examined. Claims 1-20 are rejected.
Specification
The disclosure is objected to because of the following informalities:
In paragraph [0047], “to serve clients devices” should read “to serve client devices”.
In paragraph [0074], “In more detail, and as shown in Fig. 3, a DNN 600” should read “In more detail, and as shown in Fig. 6, a DNN 600”. The structure “DNN 600” is being referenced in Figure 6 of the disclosure, not Figure 3.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefore, 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 non-statutory subject matter.
According to the USPTO guidelines, a claim is directed to non-statutory subject matter if:
Step 1: The claim does not fall within one of the four statutory categories of invention (process, machine, manufacture, or composition of matter), or,
Step 2: The claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
Step 2A, Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
MPEP 2106.04(a)(2)(I) states: "The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations."
MPEP 2106.04(a)(2)(III) states: "Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgements, and opinions
Further, the MPEP states: "The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g. pen and paper or a slide run) to perform the claim limitation.
Using the two-step inquiry, it is clear that Claims 1-20 are each directed to non-statutory subject matter as shown below:
Please note the following:
The following groups of claims are expressed in different statutory categories:
Claims 1-10 are directed to a method for generating a field service dispatch duration prediction by a machine learning model.
Claims 11-15 are directed to a system comprising of at least one non-transitory machine-readable medium configured to store instructions and a processor configured to carry out a process for generating a field service dispatch duration prediction by a machine learning model when said instructions are executed.
Claims 16-20 are directed to a non-transitory machine-readable medium storing machine-executable instructions which, when executed by a processor, cause the processor to carry out a process.
With respect to Claims 1, 11, and 16, which are independent claims with identical claim limitations:
Step 1: Claim 1 is directed to a method, also known as a process, which is one of the four statutory categories of patentable subject matter. Claim 11 is directed to a system for generating a field service dispatch duration prediction by a machine learning model, corresponding to an article of manufacture, which is one of the four statutory categories of patentable subject matter. Claim 16 is directed to a non-transitory machine-readable medium on which machine-executable instructions are stored, corresponding to an article of manufacture, which is one of the four statutory categories of patentable subject matter.
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
"determining, by the computing device, one or more relevant features from the information regarding the field service dispatch, the one or more relevant features influencing prediction of a dispatch duration, wherein the one or more relevant features include a return-trip feature indicative of whether the field service dispatch is one of a first trip by a field support engineer to a customer location and a return trip by the field support engineer to the customer location to provide product support;"; Determining one or more relevant features from the information regarding the field service dispatch (i.e. whether the trip is a first trip or a return trip to the customer’s location for product support) in order to influence a prediction is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) - See MPEP § 2106.04(a)(2)(III).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application:
“receiving, by a computing device, information regarding a field service dispatch from another computing device;”; Receiving information regarding a field service dispatch is considered insignificant extra-solution activity (mere data gathering) - See MPEP § 2106.05(g).
“by a computing device,”; The utilization of a computing device only amounts to "apply it" and the mere instructions to apply the abstract idea using a generic computer component - see MPEP 2106.05(f)(2).
“generating, by the computing device using a machine learning (ML) model, a prediction of a dispatch duration for the field service dispatch based on the determined one or more relevant features;”; Generating a prediction of a dispatch duration for a field service dispatch using a machine learning model only amounts to "apply it" and the mere instructions to implement an abstract idea on a computer - See MPEP § 2106.05(f)(1).
“and sending, by the computing device, the prediction of the dispatch duration for the field service dispatch to a field service management system for use in scheduling the field service .”; Sending the prediction of the field service dispatch duration is considered insignificant extra-solution activity (mere data gathering) - See MPEP § 2106.05(g). Additionally, a field service management system for use in scheduling the field service generally links the use of the abstract idea to a particular technological environment or field of use – see MPEP § 2106.05(h).
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception. Receiving information regarding a field service dispatch, sending the prediction of the dispatch duration, and or transmitting data over a network or to a service management system are recognized as well‐understood, routine, and conventional when they are claimed in a generic manner." - See MPEP § 2106.05(d)(II). The utilization of a computing device amounts to "apply it" (or an equivalent) and mere instructions to implement an abstract idea on a computer using a generic computer component or merely uses a computer in its ordinary capacity as a tool to perform an existing process. -See MPEP 2106.05(f)(2). Generating a prediction of a dispatch duration for a field service dispatch using a machine learning model only amounts to "apply it" and mere instructions to implement an abstract idea on a computer. The claim limitation fails to recite details of how a solution or outcome to a problem is accomplished because it is unclear how the "AI system" or "machine learning" is used nor does the specification make it clear how these actions are performed - See MPEP § 2106.05(f)(1)). The usage of a field service scheduling management system is generally linked to a particular technological environment or field of use (AI/ML) - see MPEP 2106.05(h).
Therefore, Claims 1, 11, and 16 are directed to non-statutory subject matter and rejected.
With respect to Claims 2, 12, and 17, which have identical claim limitations and are dependent on Claims 1, 11, and 16 respectively:
Step 2A, Prong 1: The claims incorporate the abstract idea of the independent claims.
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application:
“wherein the ML model includes a deep neural network (DNN).”; A ML model including a DNN amounts to "apply it", and the mere instructions to implement an abstract idea on a computer - See MPEP § 2106.05(f)(1) in addition to generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h).
Step 2B: A ML model including a DNN amounts to "apply it" and mere instructions to implement an abstract idea on a computer. The claim fails to recite details of how a solution or outcome to a problem is accomplished because it is unclear how the "AI system" or "machine learning" is used nor does the specification make it clear how these actions are performed - See MPEP § 2106.05(f)(1)). The usage of a DNN as a part of an ML model is generally linked to a particular technological environment or field of use (AI/ML) - See MPEP § 2106.05(h).
Therefore, Claims 2, 12, and 17 are directed to non-statutory subject matter and rejected.
With respect to Claims 3, 13, and 18 which have identical claim limitations and are dependent upon Claims 2, 12, and 17, respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“wherein the DNN predicts a regression response, wherein the regression response is the prediction of the dispatch duration for the field service dispatch.”; A DNN predicting a regression response responsible for predicting a dispatch duration for a field service dispatch is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) - See MPEP § 2106.04(a)(2)(III).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claims 3, 13, and 18 are directed to non-statutory subject matter and rejected.
With respect to Claims 4, 14, and 19 which have identical claim limitations and are dependent upon Claims 1, 11, and 16 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“wherein the ML model is generated using a training dataset generated from a corpus of historical field support data of an organization.”; A machine learning model being generated from a training dataset derived from historical data is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) - See MPEP § 2106.04(a)(2)(III).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claims 4, 14, and 19 are directed to non-statutory subject matter and rejected.
With respect to Claim 5:
Step 2A, Prong 1: A judicial exception is recited in the claim as it recites a mental process which is an abstract idea:
“includes one or more features extracted from the historical field support data, wherein the one or more features includes a feature indicative of a customer associated with the field service dispatch.”; Extracting a specific associative feature(s): (customer) from historical data to compile a dataset is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) - See MPEP § 2106.04(a)(2)(III).
Step 2A, Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claim 5 is directed to non-statutory subject matter and rejected.
With respect to Claim 6:
Step 2A, Prong 1: A judicial exception is recited in the claim as it recites a mental process which is an abstract idea:
“includes one or more features extracted from the historical field support data, wherein the one or more features includes a feature indicative of a type of product associated with the field service dispatch.”; Extracting a specific associative feature(s): (type of product) from historical data to compile a dataset is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) - See MPEP § 2106.04(a)(2)(III).
Step 2A, Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claim 6 is directed to non-statutory subject matter and rejected.
With respect to Claim 7:
Step 2A, Prong 1: A judicial exception is recited in the claim as it recites a mental process which is an abstract idea:
“includes one or more features extracted from the historical field support data, wherein the one or more features includes a feature indicative of a type of support associated with the field service dispatch.”; Extracting a specific associative feature(s): (type of support) from historical data to compile a dataset is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) - See MPEP § 2106.04(a)(2)(III).
Step 2A, Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claim 7 is directed to non-statutory subject matter and rejected.
With respect to Claim 8:
Step 2A, Prong 1: A judicial exception is recited in the claim as it recites a mental process which is an abstract idea:
“includes one or more features extracted from the historical field support data, wherein the one or more features includes a feature indicative of a support location associated with the field service dispatch.”; Extracting a specific associative feature(s): (support location) from historical data to compile a dataset is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
Step 2A, Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claim 8 is directed to non-statutory subject matter and rejected.
With respect to Claim 9:
Step 2A, Prong 1: A judicial exception is recited in the claim as it recites a mental process which is an abstract idea:
“includes one or more features extracted from the historical field support data, wherein the one or more features includes a feature indicative of a field support engineer associated with the field service dispatch.”; Extracting a specific associative feature(s): (field support engineer) from historical data to compile a dataset is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
Step 2A, Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claim 9 is directed to non-statutory subject matter and rejected.
With respect to Claim 10:
Step 2A, Prong 1: A judicial exception is recited in the claim as it recites a mental process which is an abstract idea:
“includes one or more features extracted from the historical field support data, wherein the one or more features includes a feature indicative of a type of trip associated with the field service dispatch.”; Extracting a specific associative feature(s): (type of trip) from historical data to compile a dataset is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) - See MPEP § 2106.04(a)(2)(III).
Step 2A, Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claim 10 is directed to non-statutory subject matter and rejected.
With respect to Claims 15 and 20, which have identical claim limitations and are dependent upon Claims 14 and 19 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes which are abstract ideas:
“includes one or more features extracted from the historical field support data, wherein the one or more features includes a feature indicative of…”; Extracting a specific associative feature(s) from historical data to compile a dataset is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claims 15 and 20 are directed to non-statutory subject matter and rejected.
Claim Rejections - 35 USC § 103
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.
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 following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 non-obviousness.
Claims 1-4, 11-14, and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Williams II et. Al, (U.S Patent Application Publication No. US20190378397A1 filed on June 10, 2019, hereinafter "Williams"), in view of Idan, (U.S Patent Application Publication No. US20200210931A1 filed on December 23, 2019, hereinafter “Idan”).
With respect to Claims 1, 11, and 16:
Williams teaches:
“A method comprising: receiving, by a computing device, information regarding a field service dispatch from another computing device;” (Paragraph [0083] discloses a SmartAdvisor Commons sub-system that allows the machine learning computer programs (Intelligent Agents) to receive information from the Computer Aided Dispatch (CAD) server sub-system, “The SmartAdvisor Commons sub-system includes software and hardware components that allow Intelligent Agents to be used within the system, including a receiver (REST) API allowing the Intelligent Agents to operate within the system and have access to CAD data and other tools.” Paragraph [0009] clarifies the relevancy of the Intelligent Agents in regards to field service dispatch, “where each Intelligent Agent is configured to perform a distinct dispatch-related analysis of the CAD data and to produce dispatch-related notifications based on such analysis.” Paragraph [0084] mentions another instance of data being transferred between computer devices, “Thus, the AgentHoster may be configured to host on-site agents and interface with remote or cloud-based agents, e.g., passing events and other CAD data to the agents.”)
“determining, by the computing device, one or more relevant features from the information regarding the field service dispatch, the one or more relevant features influencing prediction of a dispatch duration-------------------------;” (Paragraph [0015] teaches the Intelligent Agents’ ability to determine a particular set of variables that can be used to establish the potential predictability of an event such as a dispatch duration, “Additionally or alternatively, the Intelligent Agents may include a correlation agent that produces a notification when a combination of variables is determined to contribute to the occurrence of an event. The correlation agent may include a machine learning correlation detector trained to detect correlations in the CAD data.” Paragraph [0264] discloses how the feature determination occurs, “Then, the Agent extracts from the Event the features for each categorical Variable and sends the feature data to the corresponding Categorial Variable that monitors it, requesting from them a list of possible outliers.” Paragraph [0326] provides a means for gathering the relevant features to influence the prediction of a dispatch duration, “A TimeEstimation Agent can be included to provide time estimates for such things as estimated time of arrival (ETA) and time to complete an event.”)
“generating, by the computing device using a machine learning (ML) model, a prediction of a dispatch duration for the field service dispatch based on the determined one or more relevant features;” (Paragraph [0326] discloses a type of machine learning Intelligent Agent that specifically generates an estimation/prediction for the duration of an event based on prior pre-determined variables, “A TimeEstimation Agent can be included to provide time estimates for such things as estimated time of arrival (ETA) and time to complete an event.” This prediction based notification further utilizes machine learning to enhance upon its predictability as mentioned in paragraph [0087], “Here, the Intelligent Agents utilize local operational data as well as other data such as pre-defined customer preferences, behaviors, and goals to generate notifications and recommendations which can be filtered through a set of rules (which themselves may be machine learned) to determine which notifications and recommendations ultimately are presented to the dispatcher.”)
“and sending, by the computing device, the prediction of the dispatch duration for the field service dispatch to-------------------.” (Paragraph [0086] discloses an AgentHoster module that takes the dispatch duration prediction-based notification from the Intelligent Agent and sends it to the SmartAdvisor Notification sub-system, “The AgentHoster provides notifications from the Intelligent Agents to the SmartAdvisor Notifications subsystem for possible display on a dispatcher client device.” Paragraph [0089] further discusses the additional transferring of the duration prediction notification, “The SmartAdvisor will communicate with its users by issuing Notifications (represented by the Notifier block in FIG . 2 ). In this context, a Notification is the product or output of an intelligent analysis that communicates relevant information to the user.”)
Williams alone does not appear to explicitly disclose:
“,----------wherein the one or more relevant features include a return-trip feature indicative of whether the field service dispatch is one of a first trip by a field support engineer to a customer location and a return trip by the field support engineer to the customer location to provide product support;”
“-----------a field service management system for use in scheduling the field service dispatch.”
However, Idan teaches:
“,----------wherein the one or more relevant features include a return-trip feature indicative of whether the field service dispatch is one of a first trip by a field support engineer to a customer location and a return trip by the field support engineer to the customer location to provide product support;” (Paragraph [0250] discloses that one of the relevant features pertains to denoting whether the field service dispatch by the field support engineer (field professional) is of a first trip type (single visit/first-time technical service), “…a system that may implement task scheduling for field professionals to complete a service in a single visit (referred to as “first-time technical services") by determining requirements for completing the service based on comparison of attributes of services. The system may receive a request for a service, determine at least one attribute of the requested service, and compare the determined attribute to other services to determine a requirement for completing the service in a single visit.” Paragraph [0415] further discloses that the other claimed relevant feature mentioned pertains to indicating if a return trip to the customer location for additional product support is necessary, “Returning to FIG. 28, at step 2808, an additional customer request for an additional service associated with the same location is received. For example, the additional request may be for a second service, after a first service. The task of providing the second service may be a location-agnostic task associated with the first on-site service.” Paragraph [0417] further emphasizes the presence of the same field service engineer for both the first trip and return trip, “By assigning the same field professional to subsequent tasks that are requested within a predefined period of time, the field professional may provide better, quicker service and the customer have greater satisfaction because there would be continuity of care and familiarity with the user's issues. If, however, the difference is greater than a predefined period, such as a month later, the process proceeds to step 3006, because there may be a reduced benefit of familiarity due to the delay between a first and second service.”)
“-----------a field service management system for use in scheduling the field service dispatch.” (paragraph [0118] discloses the characteristics of the field service management system, “FIG. 4 provides a high-level illustration of system 100, according to embodiments of the disclosure. It illustrates the integrated access, management and analysis to service operations data within one organization, and the interaction with other organizations…System 100 may include an analysis module 441, a forecasting module 442, a planning module 443, a scheduling module 444,) Examiner’s Note: [Fig. 1] further provides a depiction of the field service management system for scheduling field service dispatches.
Williams and Idan are analogous art and in the same field of invention because both references pertain to utilizing historical data, forecasting conditions, and skill-based routing to achieve optimal resource allocation in relation to emergency dispatching services. While Williams teaches equipping a dispatcher with predictive resources to quickly send the exact type of emergency worker (like a water rescuer or negotiator) needed for a specific crisis using a computer-aided dispatch (CAD) system, Idan teaches utilizing past data stored in a database to schedule and assign the best field professional who has the highest chance of successfully completing a technical service request. It would have been obvious to a person having ordinary skill in the art (PHOSITA) before the effective filing date of the claimed invention to combine the base reference of Williams (training a machine learning model with a dataset of important extracted features) with the teachings of Idan (scheduling technical service workers) in order to enhance response times, improve task completion rates, and optimize technical scheduling. One of ordinary skill in the art would be motivated to do so because by integrating Idan’s framework into the methods of Williams one would be able to recognize that a system as such can benefit, “by tailoring field professional assignments to past customer satisfaction, a company may maintain good customer satisfaction or improve poor customer satisfaction, thereby retaining and adding users {[0403] of Idan}.”
Therefore, Claims 1, 11, and 16 are rejected.
With respect to Claims 2, 12, and 17:
The combination of Williams-Idan teaches:
“The method of claim 1, wherein the ML model includes a deep neural network (DNN).” (Williams [0326] discloses the overall utilization of a DNN to make the field service dispatch duration prediction, “A TimeEstimation Agent can be included to provide time estimates for such things as estimated time of arrival (ETA) and time to complete an event. Time estimation can be based on a specialized regression (e.g., DNN) based on such things as, without limitation, hour of the day, type of event.”)
Therefore, Claims 2, 12, and 17 are rejected.
With respect to Claims 3, 13, and 18:
The combination of Williams-Idan teaches:
“The method of claim 2, wherein the DNN predicts a regression response, wherein the regression response is the prediction of the dispatch duration for the field service dispatch.” (Williams [0051] discloses that the implementation of the DNN also encompasses a regression response that fosters the predictability of the dispatch duration, “FIG. 29 shows a specialized regression graph for a time estimation agent, in accordance with one exemplary embodiment.” This is further conveyed in paragraph [0327], “Historic events can be used to train the regression service, which can be updated constantly to improve time estimation. The dispatcher is notified with a better time estimation.”)
Therefore, Claims 3, 13, and 18 are rejected.
With respect to Claims 4, 14, and 19:
The combination of Williams-Idan teaches:
“The method of claim 1, wherein the ML model is generated using a training dataset generated from a corpus of historical field support data of an organization.” (Williams [0067] mentions a particular instance of a machine learning model (Intelligent Agent) that is generated through the utilization of historical operational data/training data, “and the ML model is trained to perform a given task based on sample data (referred to as “ training data ”) in order to learn how to produce a particular type output based on particular types of inputs. Thus, machine learning is useful for hard-to-solve problems that may have many viable solutions. ML solutions can exhibit non-expected behavior due to the artificial intelligence and can improve over time based on additional training data accumulated over time.” Williams [0084] further exhibits ML model training, “with each Intelligent Agent configured or trained to produce notifications based on a specific type of analysis of the historic operational data (e.g., one Intelligent Agent might detect patterns across events, another Intelligent Agent might detect similarities between events, etc.)”)
Therefore, Claims 4, 14, and 19 are rejected.
Claims 5-10, 15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Williams, in view of Idan, in further view of Gardiner et. Al, (U.S Patent Application Publication No. US 20220252411 A1 filed on May 2, 2022, hereinafter “Gardiner”).
With respect to Claims 5, 15, and 20:
The combination of Williams-Idan teaches:
“The method of claim 4, wherein the training dataset comprises a plurality of training/testing samples, wherein each training/testing sample of the plurality of training/testing samples includes one or more features extracted from the historical field support data,” (Williams [0264] teaches the feature extraction, “Then, the Agent extracts from the Event the features for each categorical Variable and sends the feature data to the corresponding Categorial Variable that monitors it, requesting from them a list of possible outliers.”)
The combination of Williams-Idan does not appear to explicitly disclose:
"wherein the one or more features includes a feature indicative of a customer associated with the field service dispatch."
However, Gardiner teaches:
“wherein the one or more features includes a feature indicative of a customer associated with the field service dispatch." (Paragraph [0095] references the customer feature associated with the field service dispatch, “k. Driver's age group/gender/location.” Paragraph [0455] details why indicating specific features is integral to the system, “The Service Scheduling and Dispatch Device Prediction Al module automatically selects those independent variables or predictors that have greatest predictive power.”)
Williams-Idan-Gardiner are analogous art and in the same field of invention because all three references pertain to incorporating machine learning predictive accuracy into the scheduling of field service operations to optimize resource allocation, reduce operational costs, and improve customer satisfaction. While Williams teaches developing a machine learning model that focuses on real-time crisis responding, prioritizing life-safety variables, matching specialized extreme skill sets (hostage, water rescue), and assisting human dispatchers under high-stress, urgent conditions, Idan teaches utilizing predictive analytics and stored historical records of past technical service completions to statistically calculate and determine the likelihood of future task success before assigning a professional. Similarly, Gardiner teaches automating predictive scheduling, customer segmentation, and direct data transmission, enabling proactive service visits without manual input. It would have been obvious to a person having ordinary skill in the art (PHOSITA) before the effective filing date of the claimed invention to combine the base reference of Williams (emergency CAD systems) with the teachings of Idan (data-driven field scheduling), further with the teachings of Gardiner (autonomous diagnostic assessment within SSDD) in order to process incoming demands and make optimal routing, interpretability, and scheduling choices. One of ordinary skill in the art would be motivated to do so because by integrating Idan’s and Gardiner’s frameworks into the methods of Williams one would be able to recognize that a system as such can, “provide service organizations with the capabilities to address both current and future aspects of care, maintenance, predictive needs, and potential upgrades not previously available {[0003] of Gardiner}.”
Therefore, Claims 5, 15, and 20 are rejected.
With respect to Claims 6, 15, and 20:
The combination of Williams-Idan teaches:
“The method of claim 4, wherein the training dataset comprises a plurality of training/testing samples, wherein each training/testing sample of the plurality of training/testing samples includes one or more features extracted from the historical field support data,” (Williams [0264] teaches the feature extraction, “Then, the Agent extracts from the Event the features for each categorical Variable and sends the feature data to the corresponding Categorial Variable that monitors it, requesting from them a list of possible outliers.”)
The combination of Williams-Idan does not appear to explicitly disclose:
"wherein the one or more features includes a feature indicative of a type of product associated with the field service dispatch."
However, Gardiner teaches:
“wherein the one or more features includes a feature indicative of a type of product associated with the field service dispatch." (Paragraph [0081] references the type of product associated with the field service, “iii. Vehicle brand (make) and model.” Paragraph [0455] details why indicating specific features is integral to the system, “The Service Scheduling and Dispatch Device Prediction Al module automatically selects those independent variables or predictors that have greatest predictive power.”)
Therefore, Claims 6, 15, and 20 are rejected.
With respect to Claims 7, 15, and 20:
The combination of Williams-Idan teaches:
“The method of claim 4, wherein the training dataset comprises a plurality of training/testing samples, wherein each training/testing sample of the plurality of training/testing samples includes one or more features extracted from the historical field support data,” (Williams [0264] teaches the feature extraction, “Then, the Agent extracts from the Event the features for each categorical Variable and sends the feature data to the corresponding Categorial Variable that monitors it, requesting from them a list of possible outliers.”)
The combination of Williams-Idan does not appear to explicitly disclose:
"wherein the one or more features includes a feature indicative of a type of support associated with the field service dispatch."
However, Gardiner teaches:
“wherein the one or more features includes a feature indicative of a type of support associated with the field service dispatch." (Paragraph [0078] references the type of support associated with the field service, “a. Historical repair order information (booked service items, recommended items, sold items” Paragraph [0455] details why indicating specific features is integral to the system, “The Service Scheduling and Dispatch Device Prediction Al module automatically selects those independent variables or predictors that have greatest predictive power.”)
Therefore, Claims 7, 15, and 20 are rejected.
With respect to Claims 8, 15, and 20:
The combination of Williams-Idan teaches:
“The method of claim 4, wherein the training dataset comprises a plurality of training/testing samples, wherein each training/testing sample of the plurality of training/testing samples includes one or more features extracted from the historical field support data,” (Williams [0264] teaches the feature extraction, “Then, the Agent extracts from the Event the features for each categorical Variable and sends the feature data to the corresponding Categorial Variable that monitors it, requesting from them a list of possible outliers.”)
The combination of Williams-Idan does not appear to explicitly disclose:
"wherein the one or more features includes a feature indicative of a support location associated with the field service dispatch."
However, Gardiner teaches:
“wherein the one or more features includes a feature indicative of a support location associated with the field service dispatch." (Paragraph [0095] discloses the support location feature associated with the field service, “k. Driver's age group/gender/location.” Paragraph [0455] details why indicating specific features is integral to the system, “The Service Scheduling and Dispatch Device Prediction Al module automatically selects those independent variables or predictors that have greatest predictive power.”)
Therefore, Claims 8, 15, and 20 are rejected.
With respect to Claims 9, 15, and 20:
The combination of Williams-Idan teaches:
“The method of claim 4, wherein the training dataset comprises a plurality of training/testing samples, wherein each training/testing sample of the plurality of training/testing samples includes one or more features extracted from the historical field support data,” (Williams [0264] teaches the feature extraction, “Then, the Agent extracts from the Event the features for each categorical Variable and sends the feature data to the corresponding Categorial Variable that monitors it, requesting from them a list of possible outliers.”)
The combination of Williams-Idan does not appear to explicitly disclose:
"wherein the one or more features includes a feature indicative of a field support engineer associated with the field service dispatch."
However, Gardiner teaches:
“wherein the one or more features includes a feature indicative of a field support engineer associated with the field service dispatch." (Paragraph [0088] discloses the field support engineer feature associated with the field service, “d. Technician's number and type of recommendations.” Paragraph [0455] details why indicating specific features is integral to the system, “The Service Scheduling and Dispatch Device Prediction Al module automatically selects those independent variables or predictors that have greatest predictive power.”)
Therefore, Claims 9, 15, and 20 are rejected.
With respect to Claims 10, 15, and 20:
The combination of Williams-Idan teaches:
“The method of claim 4, wherein the training dataset comprises a plurality of training/testing samples, wherein each training/testing sample of the plurality of training/testing samples includes one or more features extracted from the historical field support data,” (Williams [0264] teaches the feature extraction, “Then, the Agent extracts from the Event the features for each categorical Variable and sends the feature data to the corresponding Categorial Variable that monitors it, requesting from them a list of possible outliers.”)
The combination of Williams-Idan does not appear to explicitly disclose:
"wherein the one or more features includes a feature indicative of a type of trip associated with the field service dispatch."
However, Gardiner teaches:
“wherein the one or more features includes a feature indicative of a type of trip associated with the field service dispatch." (Paragraph [0086] discloses the type of trip feature associated with the field service, “iv. Service visit frequency.” The service visit frequency data feature is influenced by whether or not a service session can be completed in one visit or over consecutive days which is further disclosed in paragraph [0359], “In FIG. 2F, a third decision tree is provided [254] for the selected services that also requires a Yes [232] / No [234] answer to an additional question , “Is the service time block longer than eight (8) hours?”. When the answer is “yes” [232], the Carmen SSDD [100] determines looks up consecutive dates when the time blocks could be split and accommodated via a consecutive date determinator [256].” Paragraph [0455] details why indicating specific features is integral to the system, “The Service Scheduling and Dispatch Device Prediction Al module automatically selects those independent variables or predictors that have greatest predictive power.”) Examiner’s Note: The present disclosure’s specification indicates “type of trip” to mean “return trip” to showcase whether or not a field service engineer needs to make a return trip to the customer’s location for more servicing. In the case of Gardiner, the “service visit frequency” feature encompasses data that covers whether or not the driver (customer) returns to the servicing location for another servicing session. Gardiner teaches selecting/indicating certain independent variables/predictors from historical data that have the greatest predictive power in regards to vehicular servicing, therefore, “service visit frequency” can take the place of a “type of trip” in regards to this disclosure.
Therefore, Claims 10, 15, and 20 are rejected.
Response to Arguments
Applicants’ arguments filed on June 25, 2026, have been fully considered but are not deemed to be fully persuasive as per Examiner’s discretion.
Claim Rejections - 35 USC § 101
A. Applicant argues that independent claims 1, 11, and 16 do not fall under the mental process category for Step 2A, Prong 1 and rather provide practical application for the alleged abstract ideas.
Examiner respectfully disagrees. None of the recited benefits as per Applicant Remarks [pg. 8] (avoidance of scheduling conflicts, avoidance of cancelled appointments, reduction of wasted engineer time, reduction of unnecessary return trips) are themselves claimed method steps. The claims do not schedule, reschedule, cancel, confirm, or reassign anything; they instead perform a single output action, namely “sending…the prediction of the dispatch duration…to a field service management system for use in scheduling the field service dispatch.” Under MPEP 2106.05(g), transmitting a data value to an unclaimed downstream system for that system’s optional unspecified use is insignificant extra-solution activity and does not integrate an abstract idea into a practical application, because the claim itself does not itself perform or require the alleged improvement.
B. Applicant argues that amended independent Claims 1, 11, and 16 reflect a technical improvement for Step 2A, Prong 2, particularly a practical application improving computerized field service management through a predictive workflow that schedules field service dispatches. Applicant further relies on the USPTO’s August 4, 2025, Director’s memorandum, arguing that the claim recites “a particular solution to a technological problem” consistent with the memorandum’s guidance against oversimplifying claim features.
Examiner respectfully disagrees. The memorandum does not alter the governing Alice/Mayo framework, and it does not lower the threshold for eligibility where, as here, the claims lack a particular technical solution. The memorandum’s examples of eligible claims involve a specifically recited technical mechanism (e.g., a particular manner of processing data using defined rules or a defined architecture) that itself affects the improvement. Claims 1, 11, and 16 recite no comparable technical mechanism: they do not specify how the return-trip feature is technically derived (e.g., from what specific stored data structure, by what comparison or lookup operation), nor do they specify any technical detail of the “ML model” beyond its generic label. Therefore, the claims are a generalized representation of instructions to apply machine learning to categorized dispatch data to communicate the results which the cited memorandum itself deems ineligible.
Therefore, the rejection of claims 1-20 under 35 U.S.C. § 101 is maintained.
Claim Rejections - 35 USC § 102
A. Applicant argues that Williams does not disclose determining whether a field support engineer is returning on another trip to the same customer location to provide additional product support, much less using that determination as a feature for predicting the dispatch duration.
Examiner agrees. The rejection of Claims 1-4, 11-14, and 16-19 under 35 U.S.C. § 102(a)(2) as anticipated by Williams (US20190378391A1) is withdrawn in light of Applicant’s amendment to Independent Claims 1, 11, and 16. This deficiency has been addressed by the new grounds of rejection under 35 U.S.C. § 103 which address the return-trip/additional visit/single-visit-completion classification. This modification is necessitated by the Applicant’s amendment of independent claims 1, 11, and 16 to add the return-trip feature limitation and is a proper basis for a final rejection under MPEP § 706.07(a).
Claim Rejections - 35 USC § 103
A. Applicant argues that dependent claims 2-4, 12-14, and 17-19 depend on patentable independent claims 1, 11, and 16, respectively, and therefore are patentable for at least the same reason. Applicant also separately argues that dependent claims 2-10, 12-15, and 17-20 by at least their dependence on patentable independent claims are also patentable.
Examiner respectfully disagrees. The independent claims remain rejected. Applicant has not presented any argument addressing the patentability of the dependent claims’ additional limitations on their own merits, independent of the dependency argument, which in itself does not hold due to the new grounds of rejection under 35 U.S.C. §103.
Therefore, the rejection of claims 1-20 under 35 U.S.C. § 103 is maintained
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/N.F.C./Examiner, Art Unit 2142
/HAIMEI JIANG/Primary Examiner, Art Unit 2142
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