DETAILED ACTION
This office action is in response to communication filed on 27 April 2026.
Claims 1 – 27 are presented for examination.
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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 27 April 2026 has been entered.
Response to Amendment
In the response filed 27 April 2026, Applicant amended claims 1, 10, and 19.
Amendments to claims 1, 10, and 19 are insufficient to overcome the 35 USC § 101 rejection. Therefore, the 35 USC § 101 rejection of claims 1 – 27 are maintained.
Response to Arguments
Applicant's arguments filed 27 April 2026 have been fully considered but they are not persuasive.
In the remarks regarding the 35 USC 101 rejection, Applicant argues that claims Models used in sequence are the same as utilizing mathematics in sequence when the model functionality itself or the “how” of the machine learning is not claimed. It is not an accurate to say that models cannot be mental processes, especially when the models are merely described with the label “machine learning” and no indication of training other than updating database relationships. Acquisition of computing equipment is an abstract task that is arguably improved by these claims, but that does not result an improvement to a technology or technical field as Applicant is arguing in their response. While Applicant may further feel that pending claims “recite a specific ordered combination of technical operations,” the facts are not supportive. Creating a training database by merging data, deploying (or implementing) a machine learning model (labeled algorithm), filtering data based on one model to use as training data for a second model, and deploying (or implementing) a second model are representative of fully manual task that could be performed by a person. These are not operations that are specific or required to be technical in nature. Examiner disagrees that the Example 47 of the Office’s Subject Matter Eligibility Examples is relevant here, because the additional steps in Applicant’s claims herein do not describe improvement in technology, nor do the machine learning claims require technology. Claims remain rejected under 35 USC 101.
In the remarks regarding independent claims 1, 10, and 19, Applicant argues that the prior art does not disclose the newly amended claims. Examiner agrees. The prior art rejection is withdrawn.
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 – 27 are rejected under 35 U.S.C. 101 because the claimed invention is directed to the judicial exception of abstract ideas without significantly more. The claims recite retrieving new employee job related data based on new employee identification data for each new employee of an entity from databases, deploying a first model to identify which new employees require acquisition of computing equipment based on retrieved new employee job related data for each new employee wherein the first model is trained based on a first training dataset created from at least a portion of existing employee job related data and corresponding existing employee computer procurement data associated with the computing equipment assigned to at least a portion of the existing employees, deploying a second model to identify computing equipment to acquire for the identified new employees wherein the model is trained based on a second training dataset created from a part of the first training dataset with the identified existing employees that required acquisition of the computing equipment, and initiating acquisition of orders for identified computing equipment for the identified new employees, wherein the second training dataset is created by filtering the first training dataset to include only records associated with the existing employees for which the first model identified a requirement for acquisition of computing equipment. This judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance section 2106 of the MPEP (hereinafter, MPEP 2106).
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is noted that the method, the device, and the non-transitory computer readable medium are directed to an eligible categories of subject matter. Step 1 is satisfied.
With respect to Step 2A prong 1 of MPEP 2106, it is next noted that the claims recite an abstract idea by reciting concepts of modeling data to determine if equipment should be purchased for employees, which falls into the “certain methods of organizing human activity” group within the enumerated groupings of abstract ideas set forth in the MPEP 2106. The claimed invention also recites an abstract idea that falls within the mental processes grouping, as claims describe retrieving data. The limitations reciting the abstract idea in independent claims are retrieving new employee job related data based on new employee identification data for each new employee of an entity from databases, deploying a first model to identify which new employees require acquisition of computing equipment based on retrieved new employee job related data for each new employee wherein the first model is trained based on a first training dataset created from at least a portion of existing employee job related data and corresponding existing employee computer procurement data associated with the computing equipment assigned to at least a portion of the existing employees, deploying a second model to identify computing equipment to acquire for the identified new employees wherein the model is trained based on a second training dataset created from a part of the first training dataset with the identified existing employees that required acquisition of the computing equipment, and initiating acquisition of orders for identified computing equipment for the identified new employees.
With respect to Step 2A Prong Two of the MPEP 2106, the judicial exception is not integrated into a practical application. The additional elements are directed to computing devices, machine learning, memory, processors, and a non-transitory computer readable medium, to implement the abstract idea. However, these elements fail to integrate the abstract idea into a practical application because they are directed to the use of generic computing elements to perform the abstract idea, which is not sufficient to amount to a practical application (as noted in the MPEP 2106) and is tantamount to simply saying “apply it” using a general purpose computer, which merely serves to tie the abstract idea to a particular technological environment by using the computer as a tool to perform the abstract idea, which is not sufficient to amount to particular application. Applicant’s independent claims now recite that the machine learning models reduce computational processing requirements and maintain prediction accuracy, which is not an improvement to technology. Using less technology reduces requirements, but does not change the technology. Prediction accuracy is related to the abstract idea, not to any technology.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are directed to: computing devices, machine learning, memory, processors, and a non-transitory computer readable medium. These elements have been considered, but merely serve to tie the invention to a particular operating environment, though at a very high level of generality and without imposing meaningful limitation on the scope of the claim. This does not amount to significantly more than the abstract idea, and it is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo.
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
The dependent claims have been fully considered as well, however, similar to the finding for claims above, these claims are similarly directed to the abstract idea of concepts of identifying types of data, identifying alternative equipment based on performance tolerances, and identifying specification data for equipment, by way of example, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMANDA GURSKI whose telephone number is (571)270-5961. The examiner can normally be reached Monday to Thursday 7am to 5pm EST.
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/AMANDA GURSKI/Primary Examiner, Art Unit 3625
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