DETAILED ACTION
This office action is in response to communication filed on 22 October 2025.
Claims 1 – 27 are presented for examination.
The following is a FINAL office action upon examination of application number 18/111946. Claims 1 – 27 are pending in the application and have been examined on the merits discussed below.
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
In the response filed 22 October 2025, 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.
Amendments to claim 1, 10, and 19 are sufficient to overcome the 35 USC § 112 rejection. Therefore, the 35 USC § 112 rejection of claims 1 – 27 is withdrawn.
Response to Arguments
Applicant's arguments filed 22 October 2025 have been fully considered but they are not persuasive.
In the remarks regarding independent claims 1, 10, and 19, Applicant argues that Garcia does not disclose the claim limitations. Examiner respectfully disagrees. Garcia retrieves employee data to make decisions on computing resources. Applicant’s argument does not explain how the functionality claimed is not taught by Garcia. The acquisition of computing resources based on historical job information for employees with the use of machine learning. Further, in at least paragraph 17 of Garcia explicitly teaches training of a machine learning algorithm, where Applicant’s claim . “New-employee” is merely a label for data and not functionally different from the disclosure of Garcia. Garcia still discloses acquiring applications, which is equivalent to equipment, satisfying the broadest reasonable interpretation of their claim language. Additionally, Applicant argues that dependent claims 5, 14, and 23 are not taught by Garcia because at least three data types specified are not taught. Garcia teaches in paragraph 16 that the inputs are geographical location and profile information. Job location data is equivalent to geographical location, and profile information can include job type data, title, level, and function.
In the remarks regarding the 35 USC 101 rejection, Applicant argues that claims recite a practical application of an abstract idea. Examiner respectfully disagrees. Database operations and use of them is old and well known technology, and an example of a simple “apply it” to otherwise abstract functionality. Databases can be information written down or provided verbally. Machine learning models are essentially mathematics or algorithms. Use of an algorithm in claims without improving (changing the calculations) the model itself through training or merely applying an algorithm to updated data through training is not a practical application. It is using machine learning as merely a tool to perform otherwise abstract decision making in these claims. There is no specific claim to how the machine learning algorithm works, so the indication that the recent and past Guidance from the Office with respect to Subject Matter Eligibility on this topic is without merit. The technical elements recited are not specific because they are generic. Any database, model, and/or automation system could suffice to have the same end product. Claims remain rejected under 35 USC 101.
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, initiating acquisition of orders for identified computing equipment for the identified new employees. 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, 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 fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Furthermore, these elements have been fully considered, however 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.
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.
Claim Rejections - 35 USC § 102
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1 – 27 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. P.G. Pub. 2020/0151621 (hereinafter, Garcia).
Regarding claim 1, Garcia teaches a method implemented by one or more computing devices, the method comprising:
retrieving new-employee job-related data based on new-employee identification data for each of one or more new employees of an entity from one or more databases (¶ 17, “CDP system 100 may operate in a training mode during which the device preparation manager may log actions of the user and track travel patterns of the user. CDP system 100 may also access and track calendar information of the user, scheduled routines of the user, profile information of the user, and usage history of the predetermined set of computing devices by the user.”) (¶ 38, “The destination of the user and the time of arrival of the user at the predicted destination may be based, in part, on the data received and/or gathered in step S210, known calendar information of the user, known scheduled routines of the user, profile information of the user, computing device (e.g., the predetermined set of computing devices accessible by device preparation manager 132) usage history of the user, or any combination thereof.”);
deploying a first machine learning model to identify which of the one or more new employees require acquisition of the computing equipment based on the retrieved new-employee job-related data for each of one or more new employees, wherein the first machine learning model is trained based on a first training dataset created from at least a portion of: existing-employee job-related data associated with existing employees of the entity collected; and corresponding existing-employee computer procurement data associated with the computing equipment assigned to at least a portion of the existing employees (¶ 13, “The machine learning algorithm may also dynamically predict the one or more computing devices and one or more software applications which may be used by the user at his/her predicted destination based, at least in part, on available information concerning, but not limited to, previous user behavior (e.g., computing device usage, application usage), user profile information, and/or user calendar information.”) (¶ 38, “device preparation manager 132 may utilize the machine learning algorithm of CDP system 100, described above, to predict/determine a destination of the user and a time of arrival of the user at the predicted destination. The destination of the user and the time of arrival of the user at the predicted destination may be based, in part, on the data received and/or gathered in step S210, known calendar information of the user, known scheduled routines of the user, profile information of the user, computing device (e.g., the predetermined set of computing devices accessible by device preparation manager 132) usage history of the user, or any combination thereof.”);
deploying a second machine learning model to identify the computing equipment to acquire for the identified one or more new employees, wherein the second machine learning model is trained based on a second training dataset created from a part of the first training dataset associated with the identified one or more of the existing employees that required acquisition of the computing equipment (¶ 17, “during the training mode, the machine learning algorithm may analyze the above mentioned inputs and output, via the device preparation manager, suggested device preparation related actions to the user concerning one or more computing devices of the predetermined set of computing devices accessible by CDP system 100. Feedback from the user (e.g., whether or not the user selected the suggested action) during the training mode may be used as an input to the machine learning algorithm in order to facilitate learning/adaptation by the algorithm and to determine a confidence/probability for each suggested action.”); and
initiating acquisition of one or more orders for the identified computing equipment for the identified one or more new employees (¶ 41, “Referring to step S250, device preparation manager 132 may cause the determined computing device to be prepared according to the determined optimal time. In an example embodiment, device preparation module 132 causes the user's workstation (i.e., computing device 121) to power on and to execute both the presentation software application and the conferencing software application five minutes prior to the time of arrival by the user at the user's place of work. Consequently, the user's workstation will be ready for use by the time the user arrives to work.”).
Regarding claim 2, Garcia teaches the method as set forth in claim 1 wherein the second machine learning model is trained to identify specification data for the computing equipment acquired for the identified one or more of the existing employees (¶ 83, “Resource provisioning 81 provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing 82 provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal 83 provides access to the cloud computing environment for consumers and system administrators. Service level management 84 provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment 85 provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA.”).
Regarding claim 3, Garcia teaches the method as set forth in claim 2 wherein the initiating the acquisition of the one or more orders for the identified computer equipment for the identified one or more new employees further comprises: identifying the specification data for the computing equipment to acquire for the identified one or more new employees; and initiating acquisition of the one or more orders based on the identified specification data for the new computer equipment for the identified one or more new employees (¶ 83, “Resource provisioning 81 provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing 82 provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal 83 provides access to the cloud computing environment for consumers and system administrators. Service level management 84 provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment 85 provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA.”) (¶ 41, “Referring to step S250, device preparation manager 132 may cause the determined computing device to be prepared according to the determined optimal time. In an example embodiment, device preparation module 132 causes the user's workstation (i.e., computing device 121) to power on and to execute both the presentation software application and the conferencing software application five minutes prior to the time of arrival by the user at the user's place of work. Consequently, the user's workstation will be ready for use by the time the user arrives to work.”).
Regarding claim 4, Garcia teaches the method as set forth in claim 2 wherein the specification data further comprises model identification data, manufacturer identification data, processor identification data, and memory identification data for the computer equipment (¶ 62, “Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models.”).
Regarding claim 5, Garcia teaches the method as set forth in claim 1 wherein the new-employee job-related data and the existing-employee job-related data each comprise three or more of job location data, job type data, job title, job level data, job function data, job performance data, cost center data, and supporting market data (¶ 16, “The machine learning algorithm may infer decisions of the user, based on dynamic scenarios, not only for one or more computing devices, but also for one or more software applications, or any combination thereof. Inputs to the machine learning algorithm may include previous behaviors of the user (e.g., computing devices usage, travel patterns, daily routines), digital calendar information belonging to the user, profile information belonging to the user, geographical location of the user, travel speed and direction of the user, and computing characteristics (e.g., battery level, processing power, application processing consumption) of the predetermined set of computing devices accessible by CDP system 100. As inputs to the machine learning algorithm may change in response to variations in user activity, the machine learning algorithm may automatically re-adapt itself.”).
Regarding claim 6, Garcia teaches the method as set forth in claim 1 the initiating the acquisition of the one or more orders for the identified computer equipment for the identified one or more new employees further comprises: automating the one or more orders for the identified computer equipment for each of the identified one or more of the new employees (¶ 64, “On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service's provider.”) (¶ 68, “Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service.”).
Regarding claim 7, Garcia teaches the method as set forth in claim 1 wherein the initiating the acquisition of the one or more orders for the identified computing equipment for the identified one or more new employees further comprises: identifying parts of the one or more orders for the identified computer equipment for each of the identified one or more of the new employees currently available at the entity (¶ 47, “The media used by persistent storage 908 may also be removable. For example, a removable hard drive may be used for persistent storage 908. Other examples include optical and magnetic disks, thumb drives, and smart cards that are inserted into a drive for transfer onto another computer-readable storage medium that is also part of persistent storage 908.”).
Regarding claim 8, Garcia teaches the method as set forth in claim 7 wherein the initiating the acquisition of the one or more orders for the identified computer equipment for the identified one or more new employees further comprises: identifying any alternative computer equipment available at the entity and within computing performance tolerances for the identified computer equipment for each of the identified one or more of the new employees (¶ 42, “if device preparation manager 132 determines that the remaining battery power is insufficient to power to the computing device during the user's remaining travel time to the determined destination and power the computing device for use by the user upon arrival at the determined destination, device preparation manager 132 may cause an alternate computing device, of the predetermined set of computing devices accessible by device preparation manager 132, to be prepared for use by the user upon arrival at the determined destination.”).
Regarding claim 9, Garcia teaches the method as set forth in claim 7 wherein the initiating the acquisition of the one or more orders for the identified computer equipment for the identified one or more new employees further comprises: automating the one or more orders for other parts of the one or more orders for the identified computer equipment for each of the identified one or more of the new employees currently unavailable at the entity (¶ 68, “Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service.”).
Regarding claims 10 and 19, the claims recite substantially similar limitations to claim 1. Therefore, claims 10 and 19 are similarly rejected for the reasons set forth above with respect to claim 1.
Regarding claims 11 and 20, the claims recite substantially similar limitations to claim 2. Therefore, claims 11 and 20 are similarly rejected for the reasons set forth above with respect to claim 2.
Regarding claims 12 and 21, the claims recite substantially similar limitations to claim 3. Therefore, claims 12 and 21 are similarly rejected for the reasons set forth above with respect to claim 3.
Regarding claims 13 and 22, the claims recite substantially similar limitations to claim 4. Therefore, claims 13 and 22 are similarly rejected for the reasons set forth above with respect to claim 4.
Regarding claims 14 and 23, the claims recite substantially similar limitations to claim 5. Therefore, claims 14 and 23 are similarly rejected for the reasons set forth above with respect to claim 5.
Regarding claims 15 and 24, the claims recite substantially similar limitations to claim 6. Therefore, claims 15 and 24 are similarly rejected for the reasons set forth above with respect to claim 6.
Regarding claims 16 and 25, the claims recite substantially similar limitations to claim 7. Therefore, claims 16 and 25 are similarly rejected for the reasons set forth above with respect to claim 7.
Regarding claims 17 and 26, the claims recite substantially similar limitations to claim 8. Therefore, claims 17 and 26 are similarly rejected for the reasons set forth above with respect to claim 8.
Regarding claims 18 and 27, the claims recite substantially similar limitations to claim 9. Therefore, claims 18 and 27 are similarly rejected for the reasons set forth above with respect to claim 9.
Conclusion
THIS ACTION IS MADE FINAL. 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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/AMANDA GURSKI/Primary Examiner, Art Unit 3625