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 . Claims 1-15 have been reviewed and are under consideration by this office action.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/24/2026 has been entered.
Notice to Applicant
The following is a Non-Final Office action. Applicant, on 02/24/2026, amended claims. Claims 1-15 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are received and acknowledged.
The 102/103 Rejections were overcome and withdrawn in the Final Office action dated 11/24/2025.
Response to Arguments - 35 USC § 101
Applicant’s arguments with respect to the 35 USC 101 rejections have been fully considered, but they are not persuasive.
Applicant contends at Step 2A-P1 that the claims are not directed towards mental processes as the claims recite selecting, by a computing apparatus,… machine learning models; executing simulations by introducing one or more received changes, and generating… insight data, Applicant asserts the limitations are not directed towards the abstract idea categories and cannot be performed in the human mind.
Examiner respectfully disagrees. Selecting a model, receiving data; running simulations; and outputting data are all concepts capable of being performed in the human mind (i.e. via pen and paper) but are being applied using a general purpose computer. The additional elements are separated from the abstract idea as seen below and analyzed both individually as well as in combination. Further the claims are directed towards Certain methods of organizing human activity as the claims are directed towards business relations (i.e. modeling business scenarios) and further following rules or instructions such as selecting a model retrieving data, simulating data, etc.
Applicant contends claims are similar to Examples 39, 47(c3), and 48 (c2) and that the cited limitations cannot be practically performed in the mind. Applicant further asserts that much like the examples that the claims require processing of datasets similar to the data transformations of Example 39 and further addresses a technical problem.
Examiner respectfully disagrees. The claims are not analogous to the cited example. The example requires applying transformation to digital images, creating training sets of images, and multiple training steps, whereas the present limitations merely require machine learning models and training each recited at a high level recited at a high level of generality. Similarly, Example 47includes the additional elements of training an ANN, detecting anomalies in network traffic, determining malicious packets, detecting a source address in real time, dropping the packets, and blocking future traffic while Example 48 recites the additional elements of receiving mixed speech signals from different sources, using a DNN to convert signals into embeddings in a feature space, applying binary masks, converting masked clusters into a time domain, and extracting special features from a target source and generating a sequence of words to produce a transcript, which does not match the fact patterns of the present application which merely requires selecting machine learning models, retrieving subsets of data, executing simulations, wherein the ML is trained using correlated subsets, and generating predictions and insight data. Further the cited limitations in the arguments contain various additional elements which are each addressed in the 101 rejection below.
Applicant contends that the claims are not directed towards an abstract idea pointing to MPEP 2106.04(d)(1) and limitations in the claims such as selecting machine learning models; retrieving …data… from a server; executing simulations by introducing one or more received changes… using a trained machine learning model, generating… insight data, etc. Applicant asserts the limitations are not directed to well-understood, routine, or conventional steps
Examiner respectfully disagrees. The recited limitations merely recite an abstract idea and recites a machine learning model, a trained ML model (i.e. at a high level of generality, further noting the claims do not recite training but merely a trained model with no details of training/retraining), and various other additional elements ( each addressed below). The claims are analyzed below with the additional elements each bolded and addressed in Step 2A-Prong 2 of the analysis and are determined to be performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Applicant contends with respect to Desjardins, that the claims recite a specific technical training methodology that reflects improvements such as using correlated datasets and is not merely applying “machine learning.” Applicant contends that the claims are not “apply it” as the claims recite identifying workplace environments that share characteristics and leveraging historic data.
Examiner respectfully disagrees. The selection of data used to train the machine learning model does not constitute an additional element and is mental process wherein a user could identify workplaces with similar characteristics, execute simulations (via pen and paper) and further selecting correlated datasets to be used in training the ML model.
Applicant contends at Step 2B that the claims recite an inventive concept that is significantly more than the judicial exception. Applicant further states the claims are adding specific limitations other than what is well-understood, routine, conventional activity in the field.
Examiner respectfully disagrees. The additional elements of the present claims are analyzed below both individually as well as in combination and are determined they do not integrate the judicial exception into practical application nor do the amount significantly more. The Examiner further notes that the claims are not addressed as being rejected MPEP 2106.05(d) just 2106.05 (f and h) and as such do not require Berkheimer test.
The 101 Rejection is updated below.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step One - First, pursuant to step 1, the claim(s) 1-15 is/are directed to statutory categories.
Step 2A, Prong One – The claims are found to recite limitations that set forth the abstract idea(s), namely in independent claims 1, 6, and 11 recite a series of steps for assessing an impact of a change in a scenario of a workplace:
Regarding Claims 1, 6, and 11; (additional elements bolded)
A method comprising:/ A non-transitory computer readable medium having stored thereon instructions comprising executable code, which when executed by at least one processor, cause the processor to:/ A computing apparatus comprising: a processor; and a memory coupled to the processor which is configured to be capable of executing programmed instructions comprising and stored in the memory to:
receiving, by a computing apparatus, an identification of a workplace environment and a selection of one of a plurality of types of scenarios from one of a plurality of client devices;
selecting, by the computing apparatus, one of a plurality of types of workplace management machine learning models associated with the selection of the one of the plurality of types of scenarios by using a table, wherein the table maps the workplace management machine learning models comprising at least one of: a voluntary attrition machine learning model, a retirement machine learning model, an involuntary machine learning model, a job change machine learning model, or a level change machine learning model for a workplace environment to types of scenarios;
retrieving, by the computing apparatus, a subset of stored workplace environment data from a server based on the identification of the workplace environment and one or more inputs for the one of workplace management machine learning models associated with the selected one of the scenarios;
executing, by the computing apparatus, one or more simulations by introducing one or more received changes in the retrieved subset of workplace environment data in the selected one of the types of workplace management machine learning models, wherein the workplace management machine learning models are trained using correlated subsets of the stored workplace environment data from a plurality of other workplace environments having one or more matching characteristics comprising similar size and geographic footprint within set ranges;
generating, by the computing apparatus, one or more predictions of the future based on an average of the one or more simulations, wherein the one or more predictions comprise scenarios with the one or more received changes in the workplace environment and scenarios without changes in the workplace environment;
generating, by the computing apparatus, a set of insight data based on the one or more simulations and the one or more predictions; and
outputting, by the computing apparatus, the generated set of insight data for the workplace environment to the one of the client devices.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea groupings of “Mental processes—concepts performed in the human mind” (observation, evaluation, judgment, opinion) as the claims are directed towards receiving an indication of a workplace environment, executing simulations based on received changes, and outputting a generated set of insight data all of which are concepts capable of being performed in the human mind (i.e. via pen and paper).
Further the claims are directed towards “Certain methods of organizing human activity” — commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and/or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) as the claims are directed towards assessing impact of change in a scenario on workplace management.
Step 2A, Prong Two - This judicial exception is not integrated into a practical application. The independent claims utilize at least A non-transitory computer readable medium having stored thereon instructions comprising executable code, which when executed by at least one processor, cause the processor to:/ A computing apparatus comprising: a processor; and a memory coupled to the processor which is configured to be capable of executing programmed instructions comprising and stored in the memory to; receiving, by a computing apparatus… from one of a plurality of client devices; computing apparatus; machine learning models (recited at a high level of generality); workplace management machine learning models comprising at least one of: a voluntary attrition machine learning model, a retirement machine learning model, an involuntary machine learning model, a job change machine learning model, or a level change machine learning model; retrieving, by the computing apparatus… from a server; executing, by the computing apparatus, one or more simulations; machine learning models are trained (i.e. recited at a high level of generality and Examiner further notes even if not recited generally, the training is not actively recited just merely that the model was trained) and outputting, by the computing apparatus… to the one of the client devices. The additional elements are performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Step 2B - The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are just “apply it” on a computer. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h). Further the additional element of receiving, by a computing apparatus…. from one of a plurality of client devices is an activity that has been recognized by the courts as well-understood, routine, and conventional activity (See MPEP 2106.05(d) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362)
Regarding Claim(s) 3, 5, 8, 10, 13, and 15, the claim further narrows the abstract idea or recite additional elements previously rejected in the independent claims.
Regarding Claim(s) 2, 7, and 12, the claims further recite the additional element(s) of training, by the computing apparatus, each of the workplace management machine learning models (recited at a high level of generality). This element(s) is performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) in Steps 2A-Prong 2 and 2B.
Regarding Claim(s) 4, 9, and 14, the claims further recite the additional element(s) of processing, by the computing apparatus, the retrieved subset of workplace environment data which is in two or more obtained formats into a standard format (recited at a high level of generality). This element(s) is performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) in Steps 2A-Prong 2 and 2B.
Accordingly, the claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Conclusion
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/JEREMY L GUNN/Examiner, Art Unit 3624