DETAILED ACTION
This action is made FINAL in response to the amendments filed on 2/16/2026.
Claim Rejections - 35 USC § 112
iThe following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 - 7, 9 -17, and 19 - 23 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As to claims 1, 11, and 21, the limitation “adjust, based on the amount” is not understood by the examiner. It is not clear exactly what “the amount” is. The examiner will interpret the claims as if the adjusting is based on the amount of idle computing resources.
The limitations “a third quantity of containers” is not understood by the examiner as there is insufficient antecedent basis for this limitation in the claim. The claims do not recite ‘a first quantity of containers’ or ‘a second quantity of containers’ so it is not understood how it can be a third quantity of containers.
The limitations “running the second quantity in the third quantity to train the initial AI model” is not understood by the examiner. It is not clear what limitation the applicant is referring to when they mention ‘second quantity’ and ‘third quantity.’ Also, how does one run a second quantity in a third quantity, like what process steps would complete this task. The examiner will interpret the claims as if the at least one training task is being run by the containers.
Claims 2 - 7, 9, 10, 22, and 23 depend on claim 1, and are also rejected.
Claims 12 - 17, 19, and 20 depend on claim 11, and are also rejected.
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.
As to claims 1, 11, and 21,
Step 2A, Prong One
The claim recites in part:
generating, based on the first selection, at least one training task;
Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas. Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
receiving a first selection of the user on the training configuration interface;
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The claim further recites:
providing a training configuration interface for a user, wherein the training configuration interface comprises a plurality of training modes for the user to select, and wherein each training mode represents an allocation policy for first computing nodes required for training an initial AI model, wherein the plurality of training modes comprises a first mode, and wherein the
first mode comprises a first quantity of training tasks being automatically adjusted when training the initial AI model;
providing the trained AI model to the user to download or use.
these elements are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
The claim further recites:
performing the at least one training task to train the initial AI model to obtain a trained AI model by;
obtaining an amount of idle computing resources in a computing resource pool when an elastic scaling condition is met;
adjusting, based on the amount, a second quantity of the at least one training task and a third quantity of containers used to run the at least one training task;
running the second quantity in the third quantity to train the initial AI model;
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The claim further recites training configuration interface, computing nodes, computing device, memory and processor which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
In addition, the recitation of training modes, allocation policy, AI model, and training task amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
receiving a first selection of the user on the training configuration interface;
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The limitations:
providing a training configuration interface for a user, wherein the training configuration interface comprises a plurality of training modes for the user to select, and wherein each training mode represents an allocation policy for first computing nodes required for training an initial AI model, wherein the plurality of training modes comprises a first mode, and wherein the
first mode comprises a first quantity of training tasks being automatically adjusted when training the initial AI model;
providing the trained AI model to the user to download or use.
are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
The claim further recites:
performing the at least one training task to train the initial AI model to obtain a trained AI model by;
obtaining an amount of idle computing resources in a computing resource pool when an elastic scaling condition is met;
adjusting, based on the amount, a second quantity of the at least one training task and a third quantity of containers used to run the at least one training task;
running the second quantity in the third quantity to train the initial AI model;
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The computing nodes, training configuration interface, computing device, memory and processor are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The recitation of training modes, allocation policy, AI model, and training task amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 2 and 12, the limitations “wherein the plurality of training modes comprises a second mode, and wherein the second mode comprises indicates that different training tasks sharing share a resource of a same second computing compute node” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
As to claims 3 and 13, the limitations “running the at least one training task in a container; and providing status information o for the user when training the initial AI model, wherein the status information comprises a first quantity of containers for performing the at least one training task” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
The recitation container and status information amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
As to claims 4 and 14, the limitations “receiving a second selection of the user on the training configuration interface, wherein the second selection comprises the first mode and the second mode, and wherein generating the at least one training task comprises generating, based on the first mode and the second mode, the at least one training task” amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
As to claims 5 and 15, the limitations “receiving a second selection of the user on the training configuration interface, wherein the second selection comprises the first mode; and instructing wherein for the user to input or select a second quantity of containers that can run the at least one training task, wherein generating the at least one training task comprises generating, based on the second selection and the second quantity of containers, the at least one training task” amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
As to claims 6 and 16, the limitations “receiving a second selection of the user on the training configuration interface, wherein the second selection comprises the second mode; and wherein instructing the user to input or select resource usage of a container the container that runs the at least one training task, wherein generating the at least one training task comprises generating, based on the second selection and the resource usage, the at least one training task” amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
As to claims 7 and 17, the limitations “wherein the resource usage comprises a graphics processing unit (GPU) resource usage that is less than a single GPU resource usage or video memory usage that is less than a single video memory usage” are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
As to claims 9 and 19, the limitations “adjusting the second quantity and the third quantity the adjusting a quantity comprises: adding partial training tasks of the at least one training task to a target container the at least one training task; running a plurality of training tasks in serial in the target container to obtain values of a model parameter; and using, in a training process, an average value of the values as an update value of the model parameter” are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
As to claims 10 and 20, the limitations “further comprising: receiving a second selection of the user on the training configuration interface, wherein the second selection comprises the second mode; determining, based on resource usage of a container that runs the at least one training task in the second mode, a remaining resource of a third computing node corresponding to the container; and each container; and running one or more other training tasks by using the remaining resource. resource of the compute node corresponding to each container” are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
As to claim 22, the limitations “running the at least one training task in a container; and providing status information for the user when training the initial AI model, wherein the status information comprises a second quantity of second computing nodes for performing the at least one training task” are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
As to claim 23, the limitations “running the at least one training task in a container; and providing status information for the user when training the initial AI model, wherein the status information comprises second resource usage of a third computing node for performing the at least one training task” are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
Response to Arguments
Applicant's arguments filed 2/16/2026 have been fully considered but they are not persuasive.
Claim Rejections - 35 USC § 101
The 101 Rejection still has not been overcome. The claims are abstract and the steps in the claims can be completed with a mental process and/or generic computer components. Additionally, the steps in the claims do not describe an improvement of technology in any way
The applicant argues:
Claims 1-21 stand rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter. Claims 8 and 18 have been cancelled. Claims 2-7 and 9-10 depend from independent claim 1, and claims 12-17 and 19-20 depend from independent claim 11. Therefore, claims 1-7, 9-17, and 19-21 are allowable if independent claims 1, 11, and 21 are allowable. As amended, independent claims 1, 11, and 21 recite "obtaining an amount of idle computing resources in a computing resource pool when an elastic scaling condition is met," "adjusting, based on the amount, a second quantity of the at least one training task and a third quantity of containers used to run the at least one training task," and "running the second quantity in the third quantity to train the initial AI model." The Applicant respectfully submits that the process of adjusting, based on the amount of idle computing resources, the second quantity of the at least one training task and the third quantity of containers used to run the at least one training task cannot be performed as a mental process by humans.
The examiner disagrees. The claimed “adjusting” based on idle resources merely involves evaluating available capacity and reallocating tasks and containers accordingly, which is a process that can be performed mentally or with generic computer components. The claim recites only the result of such an adjustment without a specific technological improvement.
The applicant argues:
First, training the initial AI model, and adjusting the second quantity of the at least one training task and the third quantity of containers alters the efficiency of the computing resources in training the initial Al model and has nothing to do with the human mind. For instance, paragraph 20 of the specification states:
In the solution shown in this disclosure, when the first mode is selected, in a process in which the Al platform performs the at least one training task to train the initial AI model, the AI platform may detect whether the at least one training task meets the elastic scaling condition. When it is detected that the elastic scaling condition is met, the AI platform may obtain the amount of idle computing resources in the computing resource pool. Then, the AI platform adjusts, by using an idle amount of idle computing resources, the quantity of at least one training task and the quantity of containers that run the training task. Then, the AI platform may run the adjusted quantity of training tasks in the adjusted quantity of containers to train the initial AI model. In this way, because elastic scaling can be performed, a training speed can be accelerated.
Specification, 1 20 (emphasis added). As shown above, the specification states that when it is detected that an elastic scaling condition is met, an AI platform may obtain the amount of idle computing resources in a computing resource pool. Then, the AI platform adjusts, by using an idle amount of idle computing resources, the quantity of at least one training task and the quantity of containers that run the training task. The claims require obtaining idle computing resources when an elastic scaling condition is met, and then adjusting training task and container quantities based on that idle resource amount. This targeted adjustment imposes meaningful limits, linking configuration directly to available computer resources.
Finally, running the adjusted tasks in the adjusted containers to train the AI model is no extra solution activity. These limitations are not routine. Routine AI training does not adjust tasks and container quantities based on real-time idle resources upon elastic scaling.
The examiner disagrees. The claimed steps of detecting an elastic scaling condition, obtaining an amount of idle computing resources, and adjusting quantities of training tasks and containers based on that amount merely involve evaluating system conditions and reallocating workloads accordingly which are mental processes that could be performed by a human. The so called efficiency improvement (i.e. accelerated training speed) is a result of this adjustment, not a specific technological improvement. The claims fail to recite how the adjustment is technological implemented or any improvement to computer functionality. Instead the claims recite generic AI platforms, containers, and resource pools performing their conventional functions. Accordingly, the limitations do not impose meaningful limits and are directed to an abstract idea.
Claiming the improved speed or efficiency inherent with applying the abstract idea on a computer does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018))
It is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology (MPEP 2106.05(a)(II).
Claim Rejections - 35 USC § 102
The newly added amendments overcome the 102 Rejection and the 102 Rejection has been withdrawn.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON S COLE whose telephone number is (571)270-5075. The examiner can normally be reached Mon - Fri 7:30pm - 5pm EST (Alternate Friday's Off).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez can be reached at 571-272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRANDON S COLE/ Primary Examiner, Art Unit 2128
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