DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10 June 2026 has been entered.
This action is in reply to the entered RCE.
Claims 1-6, 9, 11-16, 19 and 21 have been amended.
Claims 8 and 18 have been canceled.
Claims 22-23 are added as new.
Claims 1-7, 9-17 and 19-23 are currently pending and have been examined.
Response to Amendment
Applicant’s amendments are sufficient to overcome the 112 and 103 rejections previously raised. Those rejections are respectfully withdrawn.
Applicant’s amendments are insufficient to overcome the 101 rejection. This rejection is respectfully maintained and updated below as necessitated by the amendments to the claims.
Response to Arguments
Applicant’s arguments filed on 10 June 2026 have been fully considered but are not persuasive.
Regarding the 101, applicant’s arguments have been fully considered but are moot in view of the new grounds of rejection necessitated by the amendments to the claims. See below.
Unlike example 47 and McRo, the instant application’s claims do not set forth any details of the training process or a technical solution to a technical problem described in the specification. Instead, the method including training and applying the model are merely performed by executing instructions by a processor and is “based on patterns” and in order “to identify” a particular pattern. The claims only provide results and the machine learning acts as a tool to achieve these results. There is not a specific technical improvement, such as remediating network intrusions, that is solved by performing the specific training functions. Creating more accurate schedules is not a technical solution to a technical problem but instead uses a machine learning model as a tool to address a business problem that exists outside of the a specific technical implementation, i.e. scheduling accuracy for service scheduling and optimizing wait times. The model is merely used to identify patterns and service parameters to offer schedule service session options and the results are analyzed and the model is rerun with different data, no details of how the model learns or is altered in an automated learning fashion is claimed or supported in the specification that would realize a technical improvement to the model or technology, as was the case in Example 47 and McRo. The 101 rejections are updated below as necessitated by the amendments to the claims.
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 1-7, 9-17 and 19-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claims 1, 11 and 21 recite the ability to identify a pattern, conditions, predicting service parameters and combinations of options for parameters to dynamically schedule a service session. As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “certain methods of organizing human activity” (managing relationships between people – including… teaching, and following rules or instructions). Here, the claim has a description of dynamically scheduling services based on predicted wait time and parameters. Accordingly, claim 1 is directed to an abstract idea.
This judicial exception is not integrated into a practical application. The claims recite storing data in memory, executing instructions stored in memory, training a model based on patterns, providing data to the trained model, applying a model to conditions, generating a custom user interface that presents selectable options, querying devices for feedback regarding the accuracy of the predictions and changes to the service, updating the model based on correlations and dynamically generating different custom schedules using the updated combinations of parameters and wait time adjustments. The claims do not establish details of any interaction with interactive selectable features or elements of the interface are claimed. Storing data, generating a UI that presents data, and querying are recited at a high level of generality and amounts to mere data gathering and transmission/outputting, which are forms of insignificant extra solution activity. Describing the training, applying the trained model, identifying, predicting, querying, updating and dynamically generating are done by a processor and are also recited at a high level of generality and merely automate the steps. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component. The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component. The feedback is not an automated learning process that improves the functioning of the model or the model itself but is instead manual feedback regarding the results or outputs of the model, the predicted wait time and type of service. Updating the model is not actually changing the capabilities of the model but is instead a reiteration of the same model with a different set of data, i.e. including the feedback. There are no details regarding how the model itself is changed, instead another or additional set of patterns and parameters are output by the model using the new data. This type of model retraining or reiteration is not changing the parameters and/or variables used in the model, it is merely adapting or applying the model to the current data so that the existing parameters give healthier and more up to date outputs. This does not realize a technical improvement by demonstrating how the model learns or is explicitly trained or changed. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to step 2A Prong 2, the additional elements in the claims amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B and does not provide an inventive concept.
For the storing, generating a UI to present, querying, and generating steps that were considered extra solution activity in step 2A, these have been re-evaluated in step 2B and determined to be well-understood, routine and conventional activity in the field. The specification does not provide any indication that the processor is anything other than a generic, off the shelf computer component, and the Symantec, TLI and OIP Techs court decisions in MPEP 2106.05d indicate that mere collection, receipt and transmission of data by generic devices over a network is a well-understood, routine and conventional function when it is claimed in a merely generic manner, as it is here.
Dependent claims 2-7, 9-17, and 19-23 include all of the limitations of the independent claims and therefore recite the same abstract idea. The claims merely narrow the abstract idea by describing that the wait times are updated based on threshold evaluations and dynamically scheduling including identifying functions. The claims include additional elements that describe the networked environment where data is exchanged through receiving functions, adding data to storing, updating predictions which is just re-executing the instructions, polling a database and describing the monitored conditions to update or reiterate the model are considered extras solution activity since it is mere data gathering, sending which is extra solution activity since it is data transmission. Receiving feedback and updating the model based on the feedback is considered a high level recitation of data gathering and storage, and does not transform the claim into a patent eligible invention. Updating the model is not actually changing the model but is described as reiterating the execution of the model with a different data set and does not detail any changes to the parameters or variables of the model itself. The functions are merely performed in the environment with no details as to what or how the feedback is determined or how the model is updated specifically using or as a result of that feedback. Thus, this limitation does not illustrate a technological improvement to the function of the model itself by demonstrating a specifically recited and supported automated learning process. The additional receiving, adding, generating a notification, storing and deleting functions are also considered extra solution activity since they demonstrate data gathering, receipt and transmission functions. When reconsidered under step 2B they do not transform he claim into a patent eligible invention because they are determined to be well-understood, routine and conventional activity in the field. The specification does not provide any indication that the processor is anything other than a generic, off the shelf computer component, and the Symantec, TLI and OIP Techs court decisions in MPEP 2106.05d indicate that mere collection/deletion, receipt and transmission of data by generic devices over a network is a well-understood, routine and conventional function when it is claimed in a merely generic manner, as it is here.
Claims 1-7, 9-17 and 19-23 are therefore drawn to ineligible subject matter as they are directed to an abstract idea without significantly more.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEPHANIE Z DELICH whose telephone number is (571)270-1288. The examiner can normally be reached on Monday - Friday 7-3:30.
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/STEPHANIE Z DELICH/Primary Examiner, Art Unit 3623
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