DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This is a final office action in response to the amendment filed 21 October 2025. Claims 1, 13, and 19 have been amended. Claims 1-20 are pending and have been examined.
Response to Amendment
Applicant’s amendment to claims 1, 13, and 19 has been entered.
Applicant’s amendment is insufficient to overcome the pending 35 U.S.C. 112(a) rejection. The rejection is maintained and updated below.
Applicant’s amendment is insufficient to overcome the pending 35 U.S.C. 101 rejection. The rejection remains pending and is updated below, as necessitated by amendment.
Response to Arguments
Applicant’s arguments regarding the 35 U.S.C. 112(a) rejection have been fully considered, but are not persuasive. While the Specification supports iterative analysis on a data model using machine learning (see Spec. at [0012, 0051], iteratively performing data analysis using a model is not a functional equivalent to “... wherein second weights are applied to at least the present infrastructure data and the present employment data for representing a second confidence in the infrastructure projects prediction data based on changes in at least one of the infrastructure data, the employment data, and the demographic data, wherein the second confidence is greater than the first confidence and results in an increase in accuracy of the data used to make predictions; updating, by the computer system, the trend ... corresponding to the second confidence,” as claimed herein. Paragraph [0053] of the Specification discloses “the weighting module 322 is configured to provide a weighting to data, i.e., the infrastructure data and the employment data that indicates a confidence in the infrastructure requirements prediction data. For example, the weighting module 322 may have an increased confidence in population growth due to migration based on indications in payroll data” and [0064] states “a weighting to each of the employment data and the infrastructure data is used to generate weighted infrastructure requirements prediction data. This weighting provides a confidence level in the prediction generated from the machine learning model. The weighting may additionally be modeled with machine learning,” however nothing in the spec expressly or inherently supports applying a second weight to infrastructure data subsequent to performing a task related to the first weight and first confidence, wherein the second confidence is greater than the first confidence, as required by the claims. Per MPEP 2163.07 (a), “Inherency, however, may not be established by probabilities or possibilities. The mere fact that a certain thing may result from a given set of circumstances is not sufficient.” Therefore, the 35U.S.C. 112(a) rejection is proper and maintained.
Applicant’s arguments regarding the 35 U.S.C. 101 rejection have been fully considered, but are not persuasive. Applicant asserts that the amended claims provide a technical solution to a technical problem involving training a neural network predictive model using data with a first confidence, deploying and updating the neural network predictive model using updated data with a second confidence higher than the first confidence to increase the prediction accuracy, and integrates the claimed invention into a practical application by improving the neural network predictive model and by allowing the deployment to the neural network predictive model to networks cloud computing systems in a manner that is not generic, routine, or conventional. Examiner respectfully disagrees.
Determining to retrain a machine learning model using a new training data set when a the reference data is updated, is not itself a technological improvement. Making the determination of whether to re-train a machine learning model is tantamount to applying a mathematical formula and/or performing steps that could be performed in the human mind. The claims and the Specification provide no evidence that the claimed techniques provide a benefit to a computer technology or other technology or technical field. Although a specification need not explicitly set forth the improvement, such improvement must be readily apparent to the ordinarily skilled artisan. MPEP § 2106.05(a). Here, the claims recite automating the process of making a determination regarding infrastructure planning that previously was performed by a user.
The limitation for “uploading … the improved neural network predictive model to the cloud computing device … to cause the cloud computing device to provide access to the improved neural network predictive model to at least the second computer system for deployment” is not a technological improvement or practical application of the abstract idea of analyzing and manipulating data for infrastructure planning. Uploading a model is a data transmission step that is construed as insignificant post-solution activity. And the claimed “operations by the at least second computer system” amounts to results based claiming. The claim limitations do not recite a functional change to the provisioning of cloud resources. The limitations fail to actively claim specific functions performed by the second computer system and as a result the claim language is construed as non-functional descriptive material that does not confer patentable weight. As a result, the 35 U.S.C. 101 rejection is proper and maintained.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claims contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor had possession of the claimed invention.
Independent claims 1, 13, and 19 include the following limitations that are not supported by the specification: “subsequent to performing the task associated with the selected predicted infrastructure project, layering, by the computer system, present infrastructure data with present employment data and present demographic data, wherein second weights are applied to at least the present infrastructure data and the present employment data for representing a second confidence in the infrastructure projects prediction data based on changes in at least one of the infrastructure data, the employment data, and the demographic data, wherein the second confidence is greater than the first confidence and results in an increase in accuracy of the data used to make predictions; updating, by the computer system, the trend of the historical infrastructure projects according to a layer of the present infrastructure data, the present employment data, and the present demographic data corresponding to the second confidence” is not supported by the written description. While the written description supports iterative analysis and improved predictive models using machine learning (see at least [para. 0051]) and applying weighing to data to provide more accurate predictions (see at least [para. 0053, 0064-0065]), it does not disclose or otherwise describe outputs from the improved neural network predictive model have a higher confidence level or accuracy relative to the neural network before the update. Fig. 4 element 407, and paragraph [0064-0065] of the specification describes “a weighting to each of the employment data and the infrastructure data is used to generate weighted infrastructure requirements prediction data. This weighting provides a confidence level in the prediction generated from the machine learning model. The weighting may additionally be modeled with machine learning. … The weighted future infrastructure needs are used to determine the infrastructure project value by providing more accurate predictions of infrastructure requirements and therefore provide more accurate information used in selecting infrastructure projects for the geographic region; … improved neural network predictive model is subsequently used to repeat the operations of the first iteration to output the data used to make predictions with increased accuracy represented by the second confidence that is greater than the first confidence.” The improvement or update to data input to a machine learning model does not improve or update the model used to process the data. The modified data is used to improve the quality of the data output, not the model itself. Further, as depicted in Fig. 3, the weighting module is an additional element that separate from the prediction module. The weighting module is depicted as a pre-processing module for data that is later used in the prediction module for generating an output for infrastructure project selection and decision making purposes. The claim language is more specific than the written description which does not disclose or otherwise support the claim language. The specification does not disclose comparing confidence levels or accuracy between iterations, as claimed. Therefore, Applicants did not possess priority to the limitation as claimed at the time of filing and the newly added limitation represents "new matter" not previously disclosed. Claims 2-12 depend from claim 1 and inherit all of the deficiencies of claim 1; claims 14-18 depend from claim 13 and inherit all of the deficiencies of claim 13; and claim 20 depends form claim 19 and inherits all of the deficiencies of claim 19.
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. Independent claim 1 recites a process for predicting infrastructure projects, independent claim 13 recites a product for predicting infrastructure projects, and independent claim 19 recites a system for predicting infrastructure projects. The claims are directed to an abstract idea for retrieving infrastructure project related data for analysis and manipulation and input into a predictive model to generate and output used for infrastructure planning decision making (collecting data analyzing it and presenting certain results of the collection and analysis), without significantly more. Claims 1, 13, and 19 recite substantially similar limitations.
Taking independent claim 1 as representative, independent claim 1 recites the following limitations:
aggregating, by a computer system, employment data;
determining, by the computer system using machine learning for at least a first iteration of a plurality of iterations, a trend of historical infrastructure projects based on at least the employment data, infrastructure data, and demographic data, wherein first weights are applied to at least the employment data and the infrastructure data for representing a first confidence in data used to make predictions;
training, by the computer system using the machine learning in at least the first iteration of the plurality of iterations, a neural network predictive model using the trend of the historical infrastructure projects as input by layering the infrastructure data with the employment data and the demographic data;
deploying, by the computer system subsequent to the training, the neural network predictive model configured to execute, in the first iteration of the plurality of iterations, operations of the neural network predictive model comprising:(i) obtaining, by the computer system using the deployed neural network predictive model, predicted values as part of the data used to make predictions associated with a plurality of predicted infrastructure projects for a set of geographical regions; (ii) converting, by the computer system, the predicted values that were obtained using the deployed neural network predictive model into corresponding observed values of a total observed value associated with the plurality of predicted infrastructure projects for the set of geographical regions within a predetermined time period, wherein each of the observed values is indicative of a corresponding one of the plurality of predicted infrastructure projects satisfying at least one condition in a corresponding one of the set of geographical regions; (iii) generating, by the computer system, a plurality of indices based on the observed values, each of the plurality of indices associated with a corresponding one of the plurality of predicted infrastructure projects and a corresponding one of the set of geographical regions; and (iv) ranking, by the computer system, the set of geographical regions according to the plurality of indices of the plurality of predicted infrastructure projects;
generating, by the computer system via a user interface, a display of at least a subset of the plurality of predicted infrastructure projects based on the ranked set of geographical regions output from the neural network subsequent to executing the operations;
detecting, by the computer system via the user interface, an interaction with the user interface, indicative of a selection of a predicted infrastructure project of at least the subset of the plurality of predicted infrastructure projects displayed, wherein the predicted infrastructure project is associated with an observed value that is greater than observed values of other predicted infrastructure projects of the subset of the plurality of predicted infrastructure projects;
performing, by the computer system responsive to the detecting, a task on one or more cloud resources related to the predicted infrastructure project selected from the subset of the plurality of predicted infrastructure projects associated with the ranked set of geographical regions output from the neural network predictive model subsequent to executing the operations, the task comprising:
accessing the one or more cloud resources stored on a cloud computing device, the one or more cloud resources comprising resource allocation for infrastructure projections across the geographical regions, and
adjusting the resource allocation across the geographical regions to one or more of (i) configure the one or more cloud resources to provide at least one of a service model or computing resources for at least one client device according to the selected predicted infrastructure project, (ii) configure the one or more cloud resources to support at least one client mode according to the selected predicted infrastructure project, or (iii) provide at least a portion of the one or more cloud resources associated with the selected predicted infrastructure project to the at least one client device via the user interface;
subsequent to performing the task, associated with the selected predicted infrastructure project, layering, by the computer system, present infrastructure data with present employment data and present demographic data, wherein second weights are applied to at least the present infrastructure data and the present employment data for representing a second confidence in the infrastructure projects prediction data based on changes in at least one of the infrastructure data, the employment data, and the demographic data, wherein the second confidence is greater than the first confidence and results in an increase in accuracy of the data used to make predictions;
updating, by the computer system, the trend of the historical infrastructure projects according to a layer of the present infrastructure data, the present employment data, and the present demographic data corresponding to the second confidence;
updating, by the computer system using the machine learning in at least a second iteration of the plurality of iterations, the neural network predictive model by training the neural network predictive model using the updated trend of the historical infrastructure projects associated with the second weight as input, wherein the neural network predictive model is deployed, in the at least the second iteration, as an improved neural network predictive model after the update, wherein: the training in the at least the second iteration improves the neural network predictive model by using the trend updated with the layer of the present infrastructure data, the present employment data, and the present demographic data associated with the second weight representing the second confidence, resulting from performing the task on the one or more cloud resources according to the predicted infrastructure project, in relation to the selected predicted infrastructure project, and
the improved neural network predictive model is subsequently used to repeat the operations of the first iteration to output the data used to make predictions with increased accuracy represented by the second confidence that is greater than the first confidence, and
uploading, by the computer system, the improved neural network predictive model to the cloud computing device communicatively coupled to at least a second computer system, to cause the cloud computing device to provide access to the improved neural network predictive model to at least the second computer system for deployment, and wherein the deployment of the improved neural network predictive model comprises execution of the operations by at least the second computer system.
Independent claim 13 recites the following limitations in addition to those substantially similar to the steps of claim 1: obtain payroll data; obtain infrastructure data.
Independent claim 19 recites the following limitations in addition to those substantially similar to the steps of claim 1: collect employment data; collect infrastructure data.
Under Step 1 independent claims 1, 13, and 19 recite at least one step or act, including determining a trend of historical infrastructure projects.
Under Step 2A Prong 1, the limitations of independent claim 1 for aggregating employment data; determining a trend; training a neural network predictive model; deploying the neural network predictive model comprising obtaining predicted values, converting the predicted values, generating a plurality of indices, and ranking a set of geographical regions; generating a display of a subset of projects; detecting an interaction indicative of a selection of a project of the subset of projects; performing a task comprising adjusting resource allocation; accessing one or more cloud resources; adjusting resource allocation across geographic regions; layering infrastructure project data with updated data values, updating the trend of historical infrastructure projects, wherein weights are applied to the data; and updating the neural network predictive model by training the neural network predictive model using the updated trend as input, repeating the operations of the first iteration to output data; and uploading the improved neural network predictive model to the networked cloud computing device, including the additional limitations of independent claim 13 for obtaining payroll data and obtaining infrastructure data, and the limitations of independent claim 19 for collecting employment data and collecting infrastructure data, as drafted, illustrate a process that, under its broadest reasonable interpretation falls within the fundamental economic principles or practices grouping of abstract ideas because the data collection and analysis steps are implemented to “prioritize which infrastructure needs are to be completed, and prioritize these projects based on, for example, population migrations, employee needs, etc.,” per the specification at paragraph [0022] (See also, spec. at [para. 0012, 0038]). Building or updating roads, telecommunication networks, and waste management capacity are all matters related to the economy and commerce. Accessing and evaluating economic activity data to generate and report an index for predicting and recommending infrastructure projects and project values, is a fundamental economic practice, and falls into the certain methods of organizing human activity category of abstract ideas. (See MPEP 2106.04(a)(2)(II)).
Further, the analysis of known data to make a prediction and determine a need for future infrastructure projects reasonably falls within the mental processes grouping of abstract ideas. (See MPEP 2106.04(a)(2)(III)). Additionally, the limitations for obtaining, collecting, and uploading data are data gathering steps that constitute insignificant extra-solution activity. See at least MPEP 2106.05(g). Further, the step for performing a task on one or more cloud resources, when viewed in light of the specification involves a human operator performing the task, which reasonably falls within the certain methods of organizing human activities grouping of abstract concepts. See at least paragraph [0027] of the specification, stating: “In embodiments, an operator may interact with computing device 105 via the one or more input devices 130 and/or the one or more output devices 135 to facilitate performance of the tasks and/or realize the end results of such tasks in accordance with aspects of the present disclosure.”
Regarding Step 2A Prong Two, claim 1 recites a computer system, storage device, graphical user interface, and neural network predictive model for performing the recited steps. These elements are recited at a high level of generality (i.e., as a generic processor performing a generic computer function) and amount to no more than mere instructions to apply the exception using generic computer components or computing technology. See MPEP 2106.05(f). For example, Applicant’s specification at paragraphs [0019-0021] states: “The processor 115 may be one or more processors or microprocessors that include any processing circuitry operative to interpret and execute computer readable program instructions, such as program instructions for controlling the operation and performance of one or more of the various other components of computing device 105. In embodiments, processor 115 interprets and executes the processes, steps, functions, and/or operations of the present disclosure, which may be operatively implemented by the computer readable program instructions.” Adding generic computer components to perform generic functions, such as data gathering, performing calculations, and outputting a result would not transform the claim into eligible subject matter. See MPEP 2106.05(d).
When broadly and generically claimed, training a learning model constitutes a mathematical concept, such as the concept of using known data to set and adjust coefficients and mathematical relationships of variables that represent some modeled characteristic or phenomenon. The MPEP expressly recognizes mathematical concepts including mathematical relationships as constituting an abstract idea. MPEP § 2106.04(a). As claimed, the training step is merely the addition or update of data to be analyzed by the claimed machine learning model to update the output, the model itself is not iteratively improved to increase the accuracy of the algorithm used – the data set is merely updated and the algorithm is re-run to generate a new output. Determining to retrain a machine learning model using a new training data set when a the input data is updated, is not itself a technological improvement. Making the determination of whether to re-train a machine learning model is tantamount to applying a mathematical formula and/or performing steps that could be performed in the human mind. The claims and the Specification provide no evidence that the claimed techniques provide a benefit to a computer technology or other technology or technical field. Although a specification need not explicitly set forth the improvement, such improvement must be readily apparent to the ordinarily skilled artisan. MPEP § 2106.05(a). Here, the claims recite automating the process of making a determination regarding infrastructure planning that previously was performed by a user. Because the training step lacks sufficient technical details regarding how the model itself is improved iteratively, the claimed neural network predictive model does not amount to a specific and meaningful integration of the technical element and is construed as merely a tool used to implement the abstract idea without transforming the underlying abstract idea into patent eligible subject matter. Similarly, the step for detecting an interaction is broadly and generically recited because the technical details specifying the type of interaction, the specific interface element receiving the interaction, and the functional change resulting from the interaction are not included in the claim language.
The additional limitation for performing a task for configuring/providing cloud resources is broadly and generically claimed without specific technical details regarding what resource is provisioned and how the resource is provisioned in an automated manner. As drafted, the claim merely requires that the task performed be related to cloud resources that are capable of providing any type of service or capable of being accessed or used by a client device. Providing access to a network or an application or the generic transmission of data meets this limitation. Both may be reasonably construed as insignificant extra solution activity. Without more technical details to show technical elements are used to provide a technical solution to a technical problem in a meaningful manner, the claimed task related intended results does not advance subject matter eligibility under 35 U.S.C. 101. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of a processor, storage device, graphical user interface, and neural network predictive model amount to no more than mere instructions to apply the exception using a generic computer component (using computer as a tool to perform the abstract concept) which cannot provide an inventive concept. See MPEP 2106.05(f).
Dependent claims 2 through 12, 14 through 18 and 20 include the abstract ideas of the independent claims. The limitations of the dependent claims merely narrow the fundamental economic activity abstract idea by describing the type of employment data, infrastructure data, and payroll data, and using weighting and ranking to index the predicted infrastructure projects by geographical regions used for analysis. The limitations of the dependent claims are not integrated into a practical application because none of the additional elements set forth any limitations that meaningfully limit the abstract idea implementation. Therefore the claims are directed to an abstract idea. There are no additional elements that transform the claim into a patent eligible idea by amounting to significantly more. The analysis above applies to all statutory categories of invention. Therefore claims 1 - 20 are ineligible under 35 U.S.C. 101.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Dasgupta et al. (US 11,755,377) - Blueprint configuration parameters may define infrastructure and data locality. For instance, the configuration parameters may provide for geographical (or other locational affinity) constraints due to data locality, compliance and regulatory constraints, which is typically a consideration for security/audit administration clients. In yet another embodiment, Blueprint configuration parameters may define disaster recovery considerations (e.g., availability zones). In still another embodiment, Blueprint configuration parameters may define power (or other types of infrastructure costs) as driving factors in the matching management controller 210 and infrastructure controllers 221 instances. Based on all of the defined Blueprint configuration parameters, mapper 610 maps available management instances to one or more infrastructure controllers 221 that satisfy the configuration parameter constraints. Thus, management controller 210 performs a search of database 350 to find the infrastructure controllers 221 having resources that satisfies the criteria, and assigns those resources to a management controller 210 instance. According to one embodiment, solver engine 320 also implements a learning model 615 to assist in the resource mapping performed by mapper.
Lin (US 2017/0134304) - resource planning method, system, and apparatus for a cluster computing architecture are provided. The resource planning apparatus establishes at least one training model based on a training platform and corresponding setting values and algorithm features, such that a master node apparatus operates based on each training model to obtain operating time, and the corresponding operating time of each training model is stored in a proposal database. Afterwards, a proposal is obtained from the proposal database according to a task condition and an expected total work time, such that the master node apparatus decides a resource allocation of the cluster computing architecture.
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 LETORIA G KNIGHT whose telephone number is (571)270-0485. The examiner can normally be reached M-F 9am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao WU can be reached at 571-272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/L.G.K/Examiner, Art Unit 3623 /RUTAO WU/Supervisory Patent Examiner, Art Unit 3623