DETAILED ACTION
This action is made FINAL in response to the amendments filed on 6/09/2026.
Claim Rejections - 35 USC § 112
The 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 - 20 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 and 11, the limitations “placing, by the workspace provisioning engine, the workspace in the predicted datacenter and/or host” is not understood by the examiner. It is not exactly clear how a workspace is “placed” in a datacenter and/or a host. The applicant needs to go into a lot more detail as to what the process of “placing” is. The examiner will interpret the claims as if “placing” means “storing,” and that the workspace is stored in the predicted datacenter and/or host.
Claims 2 - 10 and 12 - 20 depend on claims 1 and 11, respectively, therefore they 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 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step One
The claims are directed to a method (claims 1 - 10) and a non-transitory storage medium (claims 11 - 20). Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
As to claims 1,
Step 2A, Prong One
The claim recites in part:
predicting, by the workspace size predicting engine, a size of a workspace that corresponds to the workspace provisioning request, wherein the workspace size comprises a predicted number of containers, a predicted composite processing size, and a predicted ephemeral storage size;
For example, a human can make all different types of predictions including (but not limited to) predicting a size of a workspace, predicting the number of containers and the predicting the storage size. Humans have been making predictions before computer were ever invented.
predicting, by the datacenter host prediction engine, a datacenter and/or host that is able to support requirements of the workspace, wherein the datacenter and host comprise target labels predicted by the datacenter host prediction engine based on the predicted number of containers, the predicted composite processing size, and the predicted ephemeral storage size;
For example, a human can label they predictions. Humans have been making and labeling (classifying) predictions before computers where ever invented.
creating, by a workspace provisioning engine, the workspace according to the workspace size and the predicted datacenter and/or host;
For example, a human can create a workspace based on a selected size and human predictions.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
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, by a workspace size predicting engine, a workspace provisioning request regarding a customer machine learning (ML) model;
receiving, by a datacenter host prediction engine from the workspace size predicting engine, the workspace size, wherein the predicted number of containers, the predicted composite processing size, and the predicted ephemeral storage size are received by the datacenter host prediction engine as input variables
placing, by the workspace provisioning engine, the workspace in the predicted datacenter and/or host.
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 a datacenter and host 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)).
The recitation a workspace size predicting engine, workspace provision request, and datacenter host prediction engine 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 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, by a workspace size predicting engine, a workspace provisioning request regarding a customer machine learning (ML) model;
receiving, by a datacenter host prediction engine from the workspace size predicting engine, the workspace size, wherein the predicted number of containers, the predicted composite processing size, and the predicted ephemeral storage size are received by the datacenter host prediction engine as input variables
placing, by the workspace provisioning engine, the workspace in the predicted datacenter and/or host.
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").
The claim further recites a datacenter and host 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)).
The recitation a workspace size predicting engine, workspace provision request, and datacenter host prediction engine 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 claim 2,
Step 2A, Prong One
The claim recites in part:
wherein the workspace size comprises a predicted number of containers, a predicted composite processing size, and a predicted ephemeral storage size, the predicted number of containers, the predicted composite
processing size, and the predicted ephemeral storage size being output values predicted by the workspace size prediction engine
For example, a human can make all different types of predictions including (but not limited to) predicting a size of a workspace, predicting the number of containers and the predicting the storage size. Humans have been making predictions before computer were ever invented.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 3,
Step 2A, Prong One
The claim recites in part:
the workspace size prediction engine provides the workspace size to a workspace provisioning engine that provisions the workspace using the workspace size.
For example, a human can make all different types of predictions including (but not limited to) predicting a size of a workspace, predicting the number of containers and the predicting the storage size. Humans have been making predictions before computer were ever invented.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 4,
Step 2A, Prong One
The claim recites in part:
the workspace size prediction engine comprises a deep neural network (DNN)-based multi-output regressor that uses multi-target regression to predict the size of the workspace.
For example, a human can predict multiple values (multi-output regressor). Humans have been making predictions before computer were ever invented.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 5,
Step 2A, Prong One
The claim recites the abstract idea described above in claim 1, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
the workspace size prediction engine was trained based in part using historical workspace resource metrics data.
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))
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:
the workspace size prediction engine was trained based in part using historical workspace resource metrics data.
which amounts to extra-solution activity of gathering data for use in the claimed 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))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 6,
Step 2A, Prong One
The claim recites the abstract idea described above in claim 1, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein during training of the datacenter host prediction engine, the predicted number of containers, the predicted composite processing size, and the predicted ephemeral storage size comprise independent variables and datacenter identifiers and host identifiers comprise target labels
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))
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:
wherein during training of the datacenter host prediction engine, the predicted number of containers, the predicted composite processing size, and the predicted ephemeral storage size comprise independent variables and datacenter identifiers and host identifiers comprise target labels
which amounts to extra-solution activity of gathering data for use in the claimed 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))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 7,
Step 2A, Prong One
The claim recites the abstract idea described above in claim 1, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
the host prediction engine was trained based in part using historical workspace creation data.
which amounts to extra-solution activity of gathering data for use in the claimed 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))
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:
the host prediction engine was trained based in part using historical workspace creation data.
which amounts to extra-solution activity of gathering data for use in the claimed 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))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 8,
Step 2A, Prong One
The claim recites in part:
wherein the datacenter host prediction engine comprises a DNN-based multi-label classifier configured to predict one or more hosts and a datacenter comprising the one or more hosts host
For example, a human can predict and classify those predictions. Humans have been making predictions and classifying before computers were ever invented.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 9,
Step 2A, Prong One
The claim recites in part:
wherein the workspace is provisioned, based on the workspace size, in a shared hybrid cloud platform.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, A human can mentally estimate a workspace size and the provision resources using generic computer components.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 10,
Step 2A, Prong One
The claim recites in part:
wherein the workspace is placed in the predicted host and/or datacenter.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, a human can mentally predict which host and/or datacenter has sufficient capacity for a workspace and then place the workspace in that predicted host or data center using a generic computer system.
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
Claim 11 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
The claim further recites a non-transitory storage medium, one or more processors, and a datacenter 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)).
Claim 12 has similar limitations as claim 2. Therefore, the claim is rejected for the same reasons as above.
Claim 13 has similar limitations as claim 3. Therefore, the claim is rejected for the same reasons as above.
Claim 14 has similar limitations as claim 4. Therefore, the claim is rejected for the same reasons as above.
Claim 15 has similar limitations as claim 5. Therefore, the claim is rejected for the same reasons as above.
Claim 16 has similar limitations as claim 6. Therefore, the claim is rejected for the same reasons as above.
Claim 17 has similar limitations as claim 7. Therefore, the claim is rejected for the same reasons as above.
Claim 18 has similar limitations as claim 8. Therefore, the claim is rejected for the same reasons as above.
Claim 19 has similar limitations as claim 9. Therefore, the claim is rejected for the same reasons as above.
Claim 20 has similar limitations as claim 10. Therefore, the claim is rejected for the same reasons as above.
Response to Arguments
Applicant's arguments filed 6/09/2026 have been fully considered but they are not persuasive.
Claim Rejections - 35 USC § 102/103
The newly added claims overcome both the 102 and 103 Rejections and both rejections have been withdrawn.
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:
With respect to Step 2A, Prong One, the Office Action characterizes the claims as reciting mental processes involving prediction of workspace requirements and prediction of infrastructure placement. Applicant respectfully disagrees. The amended claims do not merely recite a generalized prediction. Rather, the claims recite a specific machine-learning architecture comprising a workspace size prediction engine and a datacenter host prediction engine that cooperate in a defined manner. In particular, the workspace size prediction engine generates specific predicted workspace metrics including a predicted number of containers, a predicted composite processing size, and a predicted ephemeral storage size. Those predicted metrics are then received by the datacenter host prediction engine as input variables, and the datacenter host prediction engine predicts datacenter and host target labels based on those inputs. These limitations define a particular computer-implemented predictive workflow that is not reasonably performable in the human mind and that is directed to management of computing infrastructure. Even assuming, arguendo, that some aspect of the prediction process could be viewed as reciting a judicial exception, the claims must still be evaluated under Step 2A, Prong Two.
Examiner’s Note. The claims still recite predicting workspace requirements and infrastructure placement, which are evaluations that can be performed mentally or with pen and paper. Merely labeling the claimed components as a “workspace size prediction engine” and a “datacenter host prediction engine” does not define a specific machine-learning architecture or algorithm.
The claims do not recite any particular model architecture, training technique, neural network structure, feature extraction, or any other technological implementation that would separate the prediction engines from generic computer components performing predictions. Instead, the predicting engines are described as being abstract, to receive inputs and generic computer components and do not recite a technological improvement or a specific implementation.
The applicant argues:
Under Step 2A, Prong Two, the amended claims integrate any alleged judicial exception into a practical application. Critically, the claims no longer stop at generating predictions. Instead, the claims require creation of a workspace according to the predicted workspace size and predicted datacenter and/or host, followed by placement of the workspace in the predicted datacenter and/or host. Thus, the predictions are not ends in themselves. Rather, the predictions are used to control specific operations within a computing environment, namely creation and deployment of machine-learning workspaces in computing infrastructure. The claims therefore recite a complete technical workflow in which machine-learning predictions are used to govern downstream infrastructure-management operations. This is not a situation in which information is merely generated, displayed, or reported. Instead, the generated information is applied to effect concrete changes within a computing environment by creating and deploying workspaces according to the predicted infrastructure requirements.
The practical application recited by the claims is further reflected in the specification. The specification explains that the workspace size prediction engine generates workspace sizing metrics, that those metrics are supplied to the datacenter host prediction engine as inputs, and that the resulting predictions are used to create and place machine-learning workspaces within selected infrastructure resources. The claims therefore apply any alleged abstract idea through a specific technological architecture that manages allocation and deployment of computing resources. Under MPEP $2106.05(a), the claims improve the operation of computer infrastructure management by using a defined multi-stage prediction workflow to determine workspace resource requirements and deploy workspaces accordingly. At a minimum, the claims integrate any alleged exception into a practical application and therefore are not directed to a judicial exception under Step 2A.
Examiner’s Note. The newly added limitations merely apply the results of generic predictions to routine computer operations, such as creating and placing a workspace, and therefore do not integrate the judicial exception into a practical application. Further, the claimed workspace size prediction engine and datacenter host prediction engine are recited
The “the claimed workspace size prediction engine and datacenter host prediction engine” are recited at a high-level of generality 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 applicant argues:
Even if the Office were to maintain that the claims recite a judicial exception and do not integrate that exception into a practical application, the claims remain patent eligible under Step 2B. The amended claims recite significantly more than any alleged abstract idea. The claims require a specific arrangement of prediction engines having a defined relationship in which outputs of a workspace size prediction engine become input variables of a datacenter host prediction engine. The claims further require prediction of specific workspace metrics, prediction of datacenter and host target labels, creation of a workspace according to those predictions, and placement of the workspace in the predicted infrastructure. These limitations are not generic instructions to apply a prediction concept on a computer. Rather, they specify how the claimed functionality is achieved through a particular machine-learning architecture and a particular infrastructure-deployment workflow. Considered individually and as an ordered combination, the claim limitations amount to significantly more than any alleged abstract idea and therefore satisfy Step 2B as well.
As an independent basis for withdrawal of the §101 rejection, the amended claims are analogous to USPTO Subject Matter Eligibility Example 48 (Speech Separation), which the USPTO determined to be patent eligible. In Example 48, eligibility was found because the claim did not merely recite mathematical processing. Instead, the claim recited specific technical operations that transformed the results of the mathematical processing into a new technical output. The USPTO explained that the eligible claim recited limitations that "integrate the abstract idea into a practical application" because the claim was directed to "creating a new speech signal that no longer contains extraneous speech signals from unwanted sources." The USPTO further explained that "the claim integrates the abstract idea into a practical application" because the claimed operations "reflect the improvement described in the disclosure."
The same eligibility rationale applies here. The present claims do not merely recite predicting workspace characteristics or predicting infrastructure placement. Rather, the claims recite a specific technical mechanism by which workspace size metrics are generated, supplied as inputs to a second predictive model, used to generate infrastructure-placement predictions, and then used to create and place a machine-learning workspace within the predicted infrastructure. Just as the eligible claim in Example 48 transformed mathematical processing into a concrete technical result by generating a new speech signal, the present claims transform predictive processing into a concrete technical result by creating and deploying a machine- learning workspace within selected computing infrastructure. The predictions are therefore used to control operation of a computing environment rather than merely generating information.
The analogy extends to the nature of the technical improvement. Example 48 was found eligible because the claim recited a particular mechanism for improving computer processing of speech signals and specified how the improvement was achieved. Likewise, the present claims recite a particular mechanism for managing deployment of machine-learning workspaces. The claims specify how workspace requirements are predicted, how those predictions are used by a second prediction engine, and how the resulting infrastructure predictions are used to create and place a workspace. The claimed invention therefore improves infrastructure-management operations through a defined technological workflow rather than through a generalized result- oriented objective. Under the same reasoning applied by the USPTO in Example 48, the presently amended claims are patent eligible because they recite a specific technical mechanism that uses machine-learning predictions to control creation and deployment of computing resources, thereby integrating any alleged abstract idea into a practical application.
Examiner’s Note. The newly added limitations do not amount to significantly more than the recited judicial exception. While the applicant characterizes the claims as reciting a specific machine-learning architecture, the claims merely recite generic prediction engines that perform prediction functions without specifying any particular machine-learning model, architecture, training technique, or technological improvement. The claimed relationship between the prediction engines simply describes the use of one prediction as an input to another prediction, which is itself an abstract idea (processing concept).
Further, the claimed creation and placement of a workspace based on the prediction results merely use the predicted information as part of a generic computer implementation and do not improve the functioning of a computer or any other technology. The claims do not recite a specific technological. The claims do not recite a specific technological mechanism comparable to USPTO Example 48, where the claimed operation generated an improved speech signal. Instead, the present claims merely generate predictions and use those prediction’s as inputs to subsequently processing, resulting in no technological improvement beyond the abstract idea itself. Accordingly, the claims do not integrate the judicial exception into a practical application.
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).
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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