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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/12/2026 has been entered.
Status of Claims
Claims 1-20 are pending and examined herein per Applicant’s 03/12/2026 filing with the USPTO. Claims 1, 4, 8, 11, 15, and 18 are amended. No claims are canceled, added, or withdrawn.
Response to Arguments
Applicant's arguments filed with respect to the 35 USC 101 rejection of the previous Office action have been fully considered but they are not persuasive.
Claims 1, 8, and 15 cannot be "performed in the human mind." At minimum, the amended claims require machine-collected telemetry from network-deployed sensors/agents and tracked license-claim state across an enterprise network-data that is not available to a human "by thinking," and that is specifically generated by networked computing infrastructure. Moreover, the amended claims do not merely recite an outcome ("a likelihood"), but a particular model architecture and execution flow (classifier thresholding + conditional regression + defined fallback), as well as a specific operational use of the result (action recommendations tied to license/device lifecycle telemetry).
Respectfully, the Office disagrees with Applicant’s position. The MPEP provides “the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind."” MPEP 2106.04(a)(2). That same section of the MPEP also provides few different concept categories of a mental process – (1) Performing a mental process on a generic computer. (2) Performing a mental process in a computer environment. (3) Using a computer as a tool to perform a mental process.
The instant claims are found to fall in to mental process concept of Performing a mental process on a generic computer. The MPEP provides an example in Versata, in which the patentee claimed a system and method for determining a price of a product offered to a purchasing organization that was implemented using general purpose computer hardware. 793 F.3d at 1312-13, 1331, 115 USPQ2d at 1685, 1699. The Federal Circuit acknowledged that the claims were performed on a generic computer, but still described the claims as "directed to the abstract idea of determining a price, using organizational and product group hierarchies, in the same way that the claims in Alice were directed to the abstract idea of intermediated settlement, and the claims in Bilski were directed to the abstract idea of risk hedging." 793 F.3d at 1333; 115 USPQ2d at 1700-01.
In the instant specification provides, “Enterprise network operators perform manual analyses to understand network usage by their subscribers. By performing these manual analyses, network administrators can attempt to better understand their user behaviors and what features and functionalities different users need/want and how best to target their users for such features and functionalities” see Spec. [2] The claimed invention simply doing a manual process in a computing environment. It follows that any improvement comes from the capabilities of the computer rather than the claimed invention.
For the reasons given above the rejection of the previous Office is maintained as updated below.
Claims 1, 8, and 15 integrate any such concept into a practical application by requiring (1) Real-world telemetry acquisition and normalization from enterprise-network sensors/agents (not mere "data gathering" in the abstract, but collection of specific device/software usage data and license-state telemetry from a deployed enterprise network), (2) a constrained model architecture and execution sequence (e.g., a classifier generating a probability; a threshold decision making process; conditional regression predicting an adoption date and spend amount for accounts meeting the threshold; and explicit assignment of an adoption prediction value of zero otherwise), and (3) concrete, downstream operational outputs (e.g., action recommendations based on license expiration and device lifecycle status), which are used to drive enterprise-network upgrade/renewal targeting actions).
Respectfully, the Office disagrees with Applicant’s position. The claimed invention receives information applies the trained model to analyze the received data to generate and output. MPEP 2106.04(a)(2) provides examples a of cases that recite mental processes that includes Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 119 USPQ2d 1739, (Fed. Cir. 2016). The Court in EPG held a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind.
For the reasons given above the rejection of the previous Office is maintained as updated below.
Claims 1, 8, and 15, as amended, satisfy Step 2B because the additional elements, both individually and as an ordered combination, go beyond generic computer implementation. In particular, the amended claims recite (1) a specific two-stage model that first classifies, then conditionally regresses (and otherwise assigns zero), which is not the same as "apply machine learning" in the abstract, and (2) specific, enterprise-network-centric telemetry features (license expiration timing, active license claims by network nodes, days since last claim, and device lifecycle status such as end-of-sale/end-of-support) used to generate sales-play action recommendations.
Respectfully, the Office disagrees with Applicant’s position. Under Step 2B of the Alice/Mayo test is a search for an inventive concept in the claimed invention. The MPEP 2106.05 provides, “[the] inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself.” The MPEP further provides considerations as relevant to the evaluation of whether the claimed additional elements amount to an inventive concept. One of which is the “mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f))”.
The claimed invention’s under Step 2B are mere instructions to implement the abstract idea identified in Step 2A on a computer. MPEP 2106.05(f) provides “claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984 (warning against a § 101 analysis that turns on "the draftsman’s art").” In the instant claims the trained machine-learning model is simply applied to the first and second data to calculate a likelihood of a feature adoption then it provides an output display of the results of the application of the model to the user in the form of a dashboard recommendation.
For the reasons given above the rejection of the previous Office is maintained as updated below.
Claim Objections
Claim 11 is objected to because of the following informalities: the claim was amended to “the method of claim 1, wherein the analysis is visually presented on [ [a] ] dashboard”. In similarly amended limitation in claims 4 and 18 the “a” was replaced by “the”. It is recommended that “the” be added to the limitation of claim 11 to help with the flow and anteceding basis understand in the claim. Appropriate correction is required.
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 (i.e. mental processes) without practical application or significantly more when the elements are considered individually and as an ordered combination.
Step 1: Is the claimed invention to a process, machine, manufacture or composition of matter?
Yes, the claims fall within at least one of the four categories of patent eligible subject. Claims 1-7 are to a method (process), claims 8-14 are a device (machine), and claims 15-20 are to a medium (manufacture).
Step 2A, prong 1: Does the claim recite an abstract idea, law or nature, or natural phenomenon?
Yes, the claims are found to recite an abstract idea. Where mental processes relates to concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
Claim 1 (as a representative claim) recites the following, where the limitations found to contain elements of the abstract idea are in bold italics:
1. A method comprising:
receiving first data for a plurality of accounts, the first data including information related to software subscriptions by each of the plurality of accounts, wherein each account utilizes one or more devices and subscribes to one or more software products of an enterprise network;
establishing a communication session with a plurality of sensors and software agents deployed on the one or more devices or in the one or more software products;
receiving, from the plurality of sensors and software agents, second data for the plurality of accounts, the second data including telemetry information on usage of the one or more devices and the one or more software products in association with each of the plurality of accounts;
generating, using a trained machine-learning model, an analysis of the plurality of accounts, wherein the trained machine-learning model receives the first data and the second data as input and provides a likelihood of feature adoption by each of the plurality of accounts, wherein
the trained machine-learning model includes (a) a classifier configured to output for each account, a probability value indicating whether a corresponding account will adopt a network device or a software feature within a specified period, and (b) a regression model configured to predict, for accounts whose probability value satisfies a threshold, an adoption date within the specified period and an associated spend amount, and
when the probability value does not satisfy the threshold, the method includes assigning an adoption prediction value of zero to the corresponding account without applying the regression model; and
generating, for display in a dashboard, at least one action recommendation for each account based on corresponding analysis of the trained machine-learning model for each of the plurality of accounts.
The claims are directed to predicting if an account (user) will adopt a network device or software based on the analysis of known information (received data). While the method uses a trained machine learning model, the model does what the human brain does in this context. The Office finds that but for the recitation of the generic trained machine learning model (computer processor component) in the generation step to analyze of the plurality of accounts to provides a likelihood of feature adoption by each of the plurality of accounts can be done in the human mind using its ability to make evaluations and judgments based on the known first data and the second data inputs. Therefore the claims are found to be direct to an abstract idea. A human given the same input data could classify account and apply a regression an analysis to the data to determine a probability.
Step 2A, prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claimed invention does not recite additional elements that integrate the abstract idea into a practical application. Where a practical application is described as integrating the abstract idea by applying it, relying on it, or using the abstract idea in a manner that imposes a meaningful limit on it such that the claim is more than a drafting effort designed to monopolize it, see October 2019: Subject Matter Eligibility at p. 11.
The identified judicial exception is not integrated into a practical application. In particular, the claims recites the additional limitations see non-bold-italicized elements above. The receiving elements are determined to be insignificant extra-solution activity – data gathering. While the establishing elements are found to be a standard feature of the commuting operating system.
Where 2106.05(g) MPEP states, “term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent.”
The Office finds that merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea; adding insignificant extra solution activity to the judicial exception; or only generally linking the use of the abstract idea to a particular technological environment or field is not sufficient to integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the abstract idea?
No, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and as part of the ordered combination.
Where 2106.05(d)(I)(2) of the MPEP states, “A factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity. Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018). However, this does not mean that a prior art search is necessary to resolve this inquiry. Instead, examiners should rely on what the courts have recognized, or those in the art would recognize, as elements that are well-understood, routine, conventional activity in the relevant field when making the required determination. For example, in many instances, the specification of the application may indicate that additional elements are well-known or conventional. See, e.g., Intellectual Ventures v. Symantec, 838 F.3d at 1317; 120 USPQ2d at 1359 ("The written description is particularly useful in determining what is well-known or conventional"); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015) (relying on specification’s description of additional elements as "well-known", "common" and "conventional"); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (Specification described additional elements as "either performing basic computer functions such as sending and receiving data, or performing functions ‘known’ in the art.").”
These limitations do NOT offer an improvement to another technology or technical field; improvements to the functioning of the computer itself; apply the judicial exception with, or by use of, a particular machine; effect a transformation or reduction of a particular article to a different state or thing; add a specific limitation other than what is well-understood, routine and conventional in the field, or add unconventional steps that confine the claim to a particular useful application; or other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment. Therefore, these additional limitations when considered individually or in combination do not provide an inventive concept that can transform the abstract idea into patent eligible subject matter.
The other independent claims recite similar limitations and are rejected for the same reasoning given above.
The dependent claims do not further limit the claimed invention in such a way as to direct the claimed invention to statutory subject matter.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipate by Pothula (US 2021/0174257A1).
Claims 1, 8, and 15
Pothula teaches a method (Pothula [110] “systems and methods described herein”) comprising:
receiving first data for a plurality of accounts, the first data including information related to software subscriptions by each of the plurality of accounts, wherein each account utilizes one or more devices and subscribes to one or more software products of an enterprise network (Pothula [38] “the sub-models 15 are configured to ingest data from a dataset 14, which in some cases may include tokens, telemetry data, risk data, third-party data, social data, and customer data.”, [50] “KPIs may include subscription” [101] “governance classes (7014) may capture the restrictions and business protocols for specific KPIs”, and fig 5, #1060 where capture is the functional equivalent of receiving);
establishing a communication session with a plurality of sensors and software agents deployed on the one or more devices or in the one or more software products (Pothula [113] “Network interface 1040 may support wired or wireless communication. The network may include an electronic communication network, such as the Internet, a local area network (LAN), a wide area network (WAN), a cellular communications network” and Fig. 5);
receiving, from the plurality of sensors and software agents, second data for the plurality of accounts, the second data including telemetry information on usage of the one or more devices and the one or more software products in association with each of the plurality of accounts (Pothula [38] “ sub-models 15 are configured to ingest data from a dataset 14, which in some cases may include . . . . telemetry data . . . and customer data”);
generating, using a trained machine-learning model, an analysis of the plurality of accounts, wherein the trained machine-learning model receives the first data and the second data as input and provides a likelihood of feature adoption by each of the plurality of accounts (Pothula abstract “machine learning model attributes from a collection of one or more of the sub-models”, [67] “some of the datasets may be updated in real time to capture new events and attributes”, [104] “may then determine, e.g., by sub-model 15 of node 17′, the set of actions to achieve (or increase the likelihood of achieving) the given targeted action”, and [106] “the output of the node 17′ may effectuate various types of actions to enhance the performance of businesses and enterprises . . . identify likely candidates for upgrade of service plan . . . improve conversion of new businesses . . . optimize the move to subscription models”), wherein
the trained machine-learning model includes (a) a classifier configured to output for each account, a probability value indicating whether a corresponding account will adopt a network device or a software feature within a specified period, (Pothula [45] “OOM module 22 may use ontology semantics by leveraging feature engineering to classify and sort different types of features (e.g. events and non-event attributes) from different entities.” Where one of ordinary skill in the art would recognize adopt or not adopt with in a time period is a binary type of decision) and (b) a regression model configured to predict, for accounts whose probability value satisfies a threshold, an adoption date within the specified period and an associated spend amount, (Pothula [88] “BLR analysis is threshold comparison of Cerebri values based no datasets 14. If the Cerebri value is below a threshold, in response, the controller 12 may the corresponding dataset are not merged or homomorphically transformed.”) and
when the probability value does not satisfy the threshold, the method includes assigning an adoption prediction value of zero to the corresponding account without applying the regression model (Pothula [42] “machine learning techniques that may be used in this system include the following: Ordinary Least Squares Regression (OLSR), Linear Regression, Logistic Regression, Stepwise Regression, Multivariate Adaptive Regression Splines (MARS), Locally Estimated Scatterplot Smoothing (LOESS), Instance-based Algorithms, k-Nearest Neighbor (KNN), Learning Vector Quantization (LVQ), Self-Organizing Map (SOM), Locally Weighted Learning (LWL), Regularization Algorithms, Ridge Regression, Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net, Least-Angle Regression (LARS), Decision Tree Algorithms, Classification and Regression Tree (CART)”, [88] “BLR analysis is threshold comparison of Cerebri values based no datasets 14. If the Cerebri value is below a threshold, in response, the controller 12 may the corresponding dataset are not merged or homomorphically transformed.”, and [92] “This class may hold one or more economic objectives and zero or more economic constraints related to a unitary set of objects (e.g. a person, an product, a service) or a finite set of unitary set of objects (e.g. persons and products) or a finite set of unitary sets complemented by geo-temporal domain (e.g. persons and products and labor day in Maryland) and uses an allocation algorithm to maximize the objectives.”); and
generating, for display in a dashboard, at least one action recommendation for each account based on corresponding analysis of the trained machine-learning model for each of the plurality of accounts (Pothula [26] “each of those sub-models may specify a model architecture (like a network of perceptrons in a neural network, such as a directed graph indicating which perceptron's outputs feed into which perceptron's inputs, or other types of models discussed below)”, [111] “perform functions by operating on input data and generating corresponding output” and [112] “I/O device interface 1030 may provide an interface for connection of one or more I/O devices 1060 to computer system 1000. I/O devices may include devices that receive input (e.g., from a user) or output information (e.g., to a user)”).
Pothula further teaches the limitations of independent claims 8 and 15 that are substantially similar to those rejected above, these limitations are also rejected for the reasons given above.
Pothula additionally teaches the limitations of independent claim 8 to a device (Pothula fig. 5) comprising he also teaches the additional limitations of one or more memories having computer-readable instructions stored therein (Pothula [114] “System memory 1020 may be configured to store program instructions 1100 or data 1110. Program instructions 1100 may be executable by a processor (e.g., one or more of processors 1010a-1010n) to implement one or more embodiments of the present techniques”); and one or more processors configured to execute the computer-readable instructions (Pothula [111] “one or more processors (e.g., processors 1010a-1010n) coupled to system memory . . . processor capable of executing or otherwise performing instructions . . . processor may receive instructions and data from a memory (e.g., system memory”):
Pothula additionally teaches the limitations of independent claim 15 to one or more non-transitory computer-readable media comprising computer-readable instructions, which when executed by one or more processors of a network component, cause the network component (Pothula [115] “non-transitory computer readable storage medium may include a machine readable storage device . . . that may have program instructions stored thereon that are executable by a computer processor (e.g., one or more of processors 1010a-1010n) to cause the subject matter and the functional operations described herein.”):
Claims 2, 9, and 16
Pothula teaches all the limitations of the method of claim 1, wherein the first data further includes historical spend data for each of the plurality of accounts (Pothula [66], [86], and [106]).
Pothula further teaches the limitations of claims 9 and 16 that are substantially similar to those rejected above, these limitations are also rejected for the reasons given above.
Claims 3, 10, and 17
Pothula teaches all the limitations of the method of claim 1, wherein the second data is received via one or more sensors deployed throughout the enterprise network (Pothula [38] and [102]).
Pothula further teaches the limitations of claims 10 and 17 that are substantially similar to those rejected above, these limitations are also rejected for the reasons given above.
Claims 4, 11, and 18
Pothula teaches all the limitations of the method of claim 1, wherein the analysis is visually presented on the dashboard (Pothula [24] ).
Pothula further teaches the limitations of claims 11 and 18 that are substantially similar to those rejected above, these limitations are also rejected for the reasons given above.
Claims 5, 12, and 19
Pothula teaches all the limitations of the method of claim 1, wherein the analysis includes a ranking of the plurality of accounts according to the likelihood of adoption by each of the plurality of accounts (Pothula [106]).
Pothula further teaches the limitations of claims 12 and 19 that are substantially similar to those rejected above, these limitations are also rejected for the reasons given above.
Claims 6, 13, and 20
Pothula teaches all the limitations of the method of claim 1, wherein the analysis includes a predicted amount to be spent by each of the plurality of accounts (Pothula [106] and [125] at 11).
Pothula further teaches the limitations of claims 13 and 20 that are substantially similar to those rejected above, these limitations are also rejected for the reasons given above.
Claims 7 and 14
Pothula teaches all the limitations of the method of claim 1, wherein the likelihood of adoption is over a specified period of time (Pothula [31] and [87]).
Pothula further teaches the limitations of claim 14 that are substantially similar to those rejected above, these limitations are also rejected for the reasons given above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Helfman et al (US 2021/0192057 A1) teaches each of the events in List A and/or in List B may be converted into features per each day or prediction date for which scoring may be sought.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FOLASHADE ANDERSON whose telephone number is (571)270-3331. The examiner can normally be reached Monday to Thursday 12:00 P.M. to 6:00 P.M. CST.
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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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/FOLASHADE ANDERSON/Primary Examiner, Art Unit 3623
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