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 .
This Final Office Action is responsive to Applicant's amendment filed on 29 December 2025. Applicant’s amendment on 29 December 2025 amended Claims 1, 5, 7, 17, and 20. Claim Currently Claims 1-7, 9-13, and 15-20 are pending and have been examined. Claim 8 and 14 were previously canceled. The Examiner notes that the 101 has been maintained.
Response to Arguments
Applicant's arguments filed 29 December 2025 have been fully considered but they are not persuasive.
The Applicant argues on pages 15-16 that “with respect to claim 1, pages 13-14 of the Office Action cite to Pattabiraman in view of Ruhl, rather than Pattabiraman in view of Blassin. Furthermore, page 14 describes why it would have been obvious to combine Pattabiraman with Blassin to achieve limitations which are alleged to be taught by Pattabiraman in view of Ruhl. Finally, page 15 of the Office Action indicates that "Blassin discloses computing..." [remainder of limitation omitted for the sake of brevity], but does not provide a citation to the portions of Blassin that are being relied upon to teach this limitation.
With respect to claim 17, pages 19-21 include similar ambiguities with respect to the combination of Pattabiraman, Blassin, and Ruhl. Furthermore, page 21 of the Office Action indicates that "Blassin discloses generating..." [remainder of limitation omitted for the sake of brevity], but does not provide a citation to the portions of Blassin that are being relied upon to teach this limitation. Finally, the Office Action indicates that "Referring to Claim 17, Pattabiraman in view of Blassin in further view of Ruhl teaches a method for segment size estimation. Claim 18 recites the same or similar limitations as those addressed above in claim 1, Claim 18 is therefore rejected for the same reasons as set forth above in claim 1, except for the following noted exception..." However, the Office Action does not appear to address the limitation "predicting, by the prediction component, a segment return value for the future time period based on the first return value and the second return value", which is not recited in claim”.
The Examiner respectfully disagrees.
With respect to the argument the Examiner first thanks the Applicant for point out the typographical error of the order of references listed in the notice of rejection, however it is pointed out that the correct references are listed as well as in the actual rejection the correct paragraphs sited from the reference as well as the reasons to be combined are also correct as such it is unclear as to how it would be unreasonable to expect that their would be fair opportunity to understand and respond as all the information was available. Upon review of the case it appear that all limitations have been addressed with so with respect to the Applicant assertion that “but does not provide citation to the portions of Blassin that are being relied upon to teach this limitation”, however as mentioned above all citation appear to be listed.
Additionally, with respect to Claim 18 is therefore rejected for the same reasons as set forth above in claim 1, except for the following noted exception..." However, the Office Action does not appear to address the limitation "predicting, by the prediction component, a segment return value for the future time period based on the first return value and the second return value", which is not recited in claim, that was addressed in claim 1 and therefore the limitation was addressed. Therefore, the rejection is maintained.
The Applicant argues on page 10-11 that on “page 7 of the Office Action indicates that claim 1 recites a mental process. In response, Applicant respectfully submits that at least the limitation of "providing computing resources for the website based on the segment return value, wherein the computing resources are allocated for use by the website during the future time period when the segment return value indicates the segment of users will have a repeat interaction with the website during the future time period" as recited in claim 1 is not directed to a mental process because the limitation could not practically be performed in the human mind.
Page 7 of the Office Action provides that "the independent claims 1, 17, and 20 recite a mental process as drafted, the claim recites the limitation of identifying a segment and predicting a segment return which is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a processor nothing in the claim precludes the determining step from practically being performed in the human mind. For example, for the processor language, the claim encompasses the user manually collecting and analyzing previously collected data regarding user interacting with a specific content. The mere nominal recitation of a generic processor does not take the claim limitation out of the mental processes grouping. This limitation is a mental process. While the Guidance provides that claims do not recite a mental process when they contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations (GPS position calculation, network monitoring, data encryption for communication, rendering images."
In response, Applicant respectfully submits that the claim is directed to providing computing resources for a website based on a segment return value, which is distinct from "the user manually collecting and analyzing previously collected data regarding user interacting with a specific content." The Office Action does not address how or why "providing computing resources for the website" would be considered merely "collecting" and "analyzing" data, nor how "providing computing resources for a website" would be performed mentally.
Therefore, because claim 1 is not directed to a mental process, and the Office Action does not provide evidence that claim 1 is directed to a mental process, Applicant respectfully requests that the rejection of claim 1 under 35 U.S.C. 101 be withdrawn. Dependent claims 2-7, 9-13, and 15-16 are therefore also not directed to a judicial exception and are patent eligible under 35 U.S.C. 101.
Claims 17 and 20 recite similar subject matter as claim 1 and are patent eligible for similar reasons. Applicant therefore requests that the rejection of claims 17 and 20 and claims 18-19 respectively dependent thereon under 35 U.S.C. 101 be withdrawn”.
The Examiner Respectfully disagrees.
With respect to the argument the Examiner notes that the rejection of claims 1–7, 9–13, 15–20 under 35 U.S.C. 101 is maintained. Applicant's arguments have been considered but are not found persuasive for the following reasons: Applicant argues that the limitation "providing computing resources for the website based on the segment return value" cannot practically be performed in the human mind and, therefore, claim 1 is not directed to a mental process. The Examiner acknowledges that the "providing computing resources" limitation, considered in isolation, may not be performable mentally. However, this argument is unpersuasive because it mischaracterizes the nature of the Prong 1 inquiry. The correct analysis under MPEP 2106.04(a)(2) is not whether every limitation in the claim is a mental process, but rather whether the claim contains any limitation or combination of limitations that can practically be performed in the human mind. A claim recites a mental process when it contains limitations that can practically be performed in the human mind, including observations, evaluations, and judgments, even if other limitations in the claim are not themselves mental processes. The presence of one non-abstract limitation does not negate the finding that the claim as a whole recites an abstract idea where the heart of the claimed method remains abstract.
The core limitations driving the 101 rejection are the steps of (1) "identifying, by a segmentation component, a segment of a plurality of users for a first time period based on time series data," and (2) "computing, by a prediction component, a segment return value for a future time period based on computing a first subset and a second subset of the segment." Under the broadest reasonable interpretation, the identifying step encompasses the mental process of observing interaction data and evaluating which users share common attributes to group them into a segment precisely the kind of observation, evaluation, and judgment that the courts and USPTO guidance recognize as abstract mental processes. See MPEP 2106.04(a)(2), subsection III (citing Electric Power Group v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016), which held that "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, can practically be performed in the human mind). Similarly, the computing step encompasses the mental process of evaluating a set of users, dividing them into two groups based on a frequency threshold, and forming a judgment about predicted future interactions all of which can practically be performed by a person reviewing user interaction records with pen and paper. The claim does not specify any particular algorithm, formula, or technical mechanism by which the segment identification or return value computation must be carried out; it is claimed at a high level of generality that encompasses human mental performance. The mere recitation that these steps are performed "by a segmentation component" and "by a prediction component" does not remove them from the mental process grouping, as the nominal recitation of a generic computer component does not take a claim limitation out of the mental processes grouping where the claim encompasses performance of the limitation in the human mind. See MPEP 2106.04(a)(2), subsection III.B (a claim that encompasses a human performing the steps mentally with or without a physical aid recites a mental process). Applicant's argument that the Office Action does not address how "providing computing resources" would be performed mentally does not overcome the rejection because the rejection is premised on the identifying and computing limitations being the abstract mental process, not the resource-provisioning step and Applicant has not addressed those limitations at all. The rejection is therefore maintained.
The Applicant argues on pages 11-13 that the “Applicant respectfully submits that at least the additional elements of "providing computing resources for the website based on the segment return value, wherein the computing resources are allocated for use by the website during the future time period when the segment return value indicates the segment of users will have a repeat interaction with the website during the future time period" integrate claim 1, when read as a whole, into a practical application of improving website technology, and specifically to improving the efficiency of allocating resources to a website.
Accordingly, when read as a whole, the additional elements enable claim 1 to recite providing computing resources for the website based on the segment return value, wherein the computing resources are allocated for use by the website during the future time period when the segment return value indicates the segment of users will have a repeat interaction with the website during the future time period, and therefore to recite details of how computing resources are efficiently provided to a website.
Pages 8-9 of the Office Action assert that "It has been determined that based on the disclosure does not provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. It has not been provided clearly in the disclosure that the alleged improvement would be apparent to one of ordinary skill in the art, but is instead in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art, and therefore does not improve the technology. Second, in the instance, which in this case it is not clear that the specification sets forth an improvement in technology, the claim must not reflect the disclosed improvement (the claims must include components or steps of the invention that provide the improvement described in the specification)."
In response, Applicant respectfully submits that at least the aforementioned paragraphs [0015]-[0016], [0022], and [0027] clearly describe both an improvement to website technology, and how it is achieved, e.g., "By estimating a segment return value that includes a number of users who are in the segment and who are predicted to interact with the content channel during a second time period subsequent to the first time period, the prediction component therefore provides information that is useful in efficiently allocating resources to users in the segment and/or the content channel, or in determining that the segment should be exported to a data collection system. By basing the segment return value on the first subset and the second subset, the system provides a more accurate prediction of a size of a future segment of users than conventional techniques can provide". A description of how the segment return value is computed is provided throughout the disclosure, and especially in paragraphs [0099]-[0131] of the Specification, with the detail necessary for a person having ordinary skill in the art to understand the advantages of computing a more accurate segment return value based on the first subset and the second subset, and therefore of providing computing resources for the website according to the more accurate segment return value.
The Examiner Respectfully disagrees.
With respect to the argument the Examiner notes that the while the Applicant argues that the limitation "providing computing resources for the website based on the segment return value, wherein the computing resources are allocated for use by the website during the future time period when the segment return value indicates the segment of users will have a repeat interaction with the website during the future time period" integrates the claimed judicial exception into a practical application by improving the efficiency of allocating resources to a website. In support, Applicant relies on paragraphs [0015]–[0016], [0022], and [0027] of the specification and argues that paragraphs [0099]–[0131] provide the technical detail necessary for a person of ordinary skill in the art to understand the advantages of computing a more accurate segment return value. These arguments are not persuasive for the following reasons.
First, the alleged improvement is an improvement to the abstract idea itself, not an improvement to computer technology or a technical field. The improvements consideration under MPEP 2106.05(a) is expressly limited to improvements to the functioning of a computer or to another technology or technical field. Critically, an improvement in the abstract idea itself does not qualify as an improvement in technology. See Trading Technologies Int'l v. IBG), where the court held that a claimed user interface that gave traders more information to facilitate market trades improved the business process of market trading, but did not improve computers or technology itself. Here, what Applicant characterizes as an "improvement" is specifically the claim that the two-subset methodology produces a more accurate prediction of future user segment size than conventional techniques. This is precisely an improvement to the accuracy of the abstract mathematical/mental process of identifying user segments and computing return values it is an improvement to the predictive analysis itself, not to how a computer functions, how a website operates technically, or to any underlying computer architecture or network infrastructure. The specification paragraphs cited by Applicant confirm this characterization: paragraph [0022] states that "by basing the segment return value on the first subset and the second subset, the system provides a more accurate prediction of a size of a future segment of users than conventional techniques can provide," and paragraph [0016] describes the benefit as the system being able to "more accurately predict a size of a segment of users who will interact with the content channel during the future time period." Both of these characterizations are directed to the accuracy of the prediction which is a property of the abstract computation rather than to any technical improvement in how computational resources function, how servers allocate bandwidth, or how any specific computer component performs its operations.
Second, the specification paragraphs cited by Applicant set forth the alleged improvement only in a conclusory manner that is insufficient to satisfy the improvements consideration. Under MPEP 2106.05(a) and the established two-step framework for evaluating improvements, the specification must provide sufficient detail such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement to the functioning of a computer or another technology. If the specification explicitly sets forth an improvement but only in a conclusory manner i.e., as a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art the examiner should not determine that the claim improves technology. See also Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB, Sept. 26, 2025, precedential), confirming that the specification must identify the improvement to technology by explaining how the specific technical mechanism achieves it, not merely by asserting that it does. Paragraphs [0015]–[0016], [0022], and [0027] of Applicant's specification do not explain how any component of the computing infrastructure such as a server, a network, a memory system, or any other technological element is technically modified, reorganized, or improved in its operation. Instead, these passages uniformly describe the benefit of the invention in terms of prediction accuracy and resource efficiency at a functional, outcome-level. For example, paragraph [0027] states that "another example of an efficient allocation of resources is determining an amount of bandwidth, servers, cloud-based services, and the like that should be provided for the content channel based on the segment return value" but this says nothing about how the computing resources themselves operate differently, how they are technically configured, or what technical mechanism makes the allocation more efficient at the hardware or software infrastructure level. This is a bare assertion of a beneficial outcome, not a technical explanation of an improvement to the underlying technology.
Third, the claim itself does not reflect any technical improvement to computer functioning, even accepting arguendo that the specification described one. Under the second prong of the improvements analysis, the claim must be evaluated to ensure that it includes the components or steps of the invention that provide the improvement described in the specification. See MPEP 2106.05(a); Intellectual Ventures I LLC v. Symantec Corp. The "providing computing resources" limitation as recited in claim 1 is written at the highest level of generality it states only that "computing resources are allocated for use by the website during the future time period when the segment return value indicates the segment of users will have a repeat interaction with the website." The claim recites no specific technical mechanism for how resources are allocated, no particular configuration of server infrastructure, no specific protocol for provisioning bandwidth, and no particular way in which the computing environment is technically changed. Under the broadest reasonable interpretation, this limitation encompasses any act of using the segment return value as a basis for deciding to allocate any type of computing resource in any manner. An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem, as opposed to merely claiming the idea of a solution or outcome. See MPEP 2106.05(a); McRO, Inc. v. Bandai Namco Games Am. Inc. Here, the claim covers only the idea of the solution that resources be allocated based on the computed value but not any particular technical way of achieving that allocation.
Fourth, the "providing computing resources" step is more properly characterized as post-solution activity and a field-of-use limitation rather than a meaningful integration of the judicial exception into a practical application. Adding a step that directs the results of an abstract computation to be "applied" in a particular downstream context here, making a resource allocation decision is analogous to the "administering a treatment" step the courts have consistently found to be insufficient post-solution activity. See MPEP 2106.05(g). Under the broadest reasonable interpretation, the step of providing computing resources based on the segment return value is simply an instruction to use the output of the abstract predictive computation to make a resource allocation decision. This amounts to nothing more than saying "apply it" in the context of website resource management, which is a particular technological environment but not a meaningful technical improvement to that environment. See MPEP 2106.05(f) and (h). While paragraphs [0099]–[0131] of the specification do provide mathematical detail for how the segment return value is computed, that detail relates to the abstract mathematical computations themselves the first return value, the second return value, moving average estimators, ARIMA models, and seasonal parameters none of which constitute improvements to computer technology and none of which are recited with that level of specificity in claim 1. Accordingly, neither the specification nor the claims establish an improvement to the functioning of a computer or any other technology, and the rejection under 35 U.S.C. 101 at Step 2A, Prong 2 is maintained for claims 1–7, 9–13, and 15–20. The rejection is therefore maintained.
The Applicant argues on pages 13-14that on “page 8 of the Office Action provides that "additionally the data providing step required to use the identifying and prediction steps do not add a meaningful limitation to the method as they are insignificant extra-solution activity (including post solution activity)... The providing step is recited at a high level of generality (i.e., as a general means of displaying data), and amounts to mere data displaying, which is a form of insignificant extra-solution activity."
In response, Applicant notes that the 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. MPEP 2106.05(g).
Applicant respectfully submits that "providing computing resources for the website based on the segment return value, wherein the computing resources are allocated for use by the website during the future time period when the segment return value indicates the segment of users will have a repeat interaction with the website during the future time period" is not insignificant extra-solution activity because it is a direct statement of primary process of the claim; e.g., it is not a nominal or tangential addition to the claim, but is instead the "solution" itself. The "providing..." limitation is directly integrated into the claim as whole via the "for the website" and "the computing resources are allocated for use by the website during the future time period when the segment return value indicates the segment of users will have a repeat interaction with the website during the future time period," and characterizing the limitation as "mere data displaying, which is a form of insignificant extra-solution activity" improperly ignores this integration.
Therefore, because claim 1 integrates an alleged judicia exception into a practical application, Applicant respectfully submits that claim 1 is patent eligible under 35 U.S.C. 101. Dependent claims 2-7, 9-13, and 15-16 are therefore also patent eligible under 35 U.S.C. 101. Accordingly, Applicant requests that the rejection of claims 1-7, 9-13, and 15-16 under 35 U.S.C. 101 be withdrawn. Claims 17 and 20 recite similar subject matter as claim 1 and are therefore patent eligible for similar reasons. Accordingly, Applicant requests that the rejection of claims 17 and 20 and claims 18-19 respectively dependent thereon under 35 U.S.C. 101 be withdrawn”.
The Examiner respectfully disagrees.
With respect to the argument the Examiner notes that the Extra-Solution Activity Characterization Is Not the Sole Basis for the Rejection, and Applicant's Rebuttal Does Not Address the Full Scope of the Prong 2 Analysis. As a threshold matter, Applicant's argument is directed primarily at disputing the label of "extra-solution activity" as applied to the "providing computing resources" limitation, while framing that limitation as the "solution itself." However, the extra-solution activity consideration under MPEP 2106.05(g) is only one of several independent bases on which the Prong 2 rejection rests. As set forth in the prior Office Action and as maintained herein, the "providing computing resources" limitation also fails to establish integration into a practical application under the "apply it" consideration of MPEP 2106.05(f), the improvements consideration of MPEP 2106.05(a), and the field-of-use consideration of MPEP 2106.05(h). Applicant has not addressed any of these alternative bases in Argument 3, and accordingly those aspects of the rejection stand unrebutted and are fully maintained for the reasons set forth in the prior response to Argument 2.
Applicant argues that the "providing computing resources" step is not extra-solution activity because it constitutes the "solution itself" rather than a nominal or tangential addition. However, this recharacterization conflates the structural position of the limitation in the claim with the legal question of whether it meaningfully integrates the judicial exception into a practical application. Under MPEP 2106.05(g), the relevant inquiry is not merely whether a limitation appears at the end of the claim or is labeled as central to the invention it is whether the limitation imposes a meaningful limit on the claim, such that it is not nominally or tangentially related to the judicial exception. See Ultramercial, Inc. v. Hulu, LLC. Applicant's argument that the "providing" step is deeply integrated into the claim by virtue of the conditional language "when the segment return value indicates the segment of users will have a repeat interaction with the website during the future time period" does not advance the analysis, because that conditional clause merely links the abstract predictive computation directly to the resource-allocation step it describes when the output of the abstract computation is acted upon, not how the computing resources are technically structured, configured, or modified. This is directly analogous to the situation in Parker v. Flook, where the Supreme Court held that a post-solution step of adjusting an alarm limit variable to a figure computed according to a mathematical formula was insignificant post-solution activity, even though that step was the stated purpose of the entire method. The fact that a step represents the intended downstream use of an abstract computation does not render it a meaningful integration of that computation into a practical application.
With regard to the "Providing Computing Resources" Limitation Covers Any Act of Resource Allocation and Imposes No Meaningful Technical Constraints the more fundamental and dispositive problem with the "providing computing resources" limitation which Applicant's argument does not address is that under the broadest reasonable interpretation, it encompasses any act of allocating any type of computing resource in any manner, triggered by any result from the abstract predictive computation. The claim specifies no particular technical mechanism for how resources are allocated, no specific type of computing infrastructure, no particular protocol or system architecture, and no constraints on the quantity, nature, or configuration of the resources provided. Under MPEP 2106.05(g), factor (2), the key question is whether a limitation is significant i.e., whether it imposes meaningful limits on the claim such that it is not nominally or tangentially related to the invention. A limitation written at this level of generality does not impose meaningful limits, because it could be satisfied by any human decision to provision additional servers, adjust bandwidth settings, or allocate cloud services upon receiving any signal that more users may return to a website. The Supreme Court cautioned in Flook that "a competent draftsman could attach some form of post-solution activity to almost any mathematical formula," and that this should not transform an unpatentable principle into a patentable process. Here, the recitation that computing resources are "provided" or "allocated" based on the computed segment return value is precisely the type of broadly worded application step that the courts have consistently found insufficient. Furthermore, under MPEP 2106.05(g), factor (3), a limitation amounts to necessary data gathering or output if all uses of the recited judicial exception require such an output and here, the segment return value by its very nature as a predictive numerical output of user return behavior would always be used to make resource allocation decisions, making the "providing" step a necessary and expected application of the abstract computation rather than a meaningful additional limitation.
Notably, Applicant's own characterization of the limitation as "the 'solution' itself" is revealing and actually supports the rejection rather than rebutting it. Under the Alice/Mayo framework, the judicial exception the abstract idea of identifying a user segment and computing a segment return value is itself the "solution" to the stated problem of predicting future user interactions. Directing that the solution be "applied" by allocating computing resources accordingly is precisely what the courts mean when they require more than reciting the abstract idea with an instruction to "apply it." See MPEP 2106.05(f); Alice Corp. Pty. Ltd. v. CLS Bank Int'l. If the "providing" step is indeed the primary purpose of the claim, as Applicant argues, then the claim is at its core directed to using the output of an abstract predictive computation to make resource allocation decisions which is the abstract idea applied in a particular technological environment, not an integration of the abstract idea into a practical application that meaningfully limits it. Accordingly, the rejection under 101 at Step 2A, Prong 2 is maintained in full for claims 1–7, 9–13, and 15–20, as none of Applicant's three arguments, individually or collectively, have overcome the finding that the claims are directed to a judicial exception without integrating that exception into a practical application. The rejection is therefore maintained.
The Applicant argues on pages 14-15 that in “Step 2B: Even assuming arguendo that claim 1 does not integrate an alleged judicial exception into a practical application, Applicant respectfully submits that at least the additional element of "providing computing resources for the website based on the segment return value, wherein the computing resources are allocated for use by the website during the future time period when the segment return value indicates the segment of users will have a repeat interaction with the website during the future time period" is a specific limitation that is not well-understood, routine, conventional activity, is not recited at a high level of generality, and confines claim 1 to a particular and useful application of the alleged judicial exception, namely the application of the judicial exception to the improvement of the efficiency of website technology by providing computing resources for a website according to a more accurate segment return value.
Accordingly, Applicant submits that claim 1 incudes additional elements that are sufficient to amount to significantly more than the alleged judicial exception, and therefore respectfully requests that the rejection of claim 1 and claims 2-7, 9-13, and 15-16 dependent thereon under 35 U.S.C. 101 be withdrawn. Claims 17 and 20 recite similar subject matter as claim 1 and are therefore patent eligible for similar reasons. Accordingly, Applicant requests that the rejection of claims 17 and 20 and claims 18-19 respectively dependent thereon under 35 U.S.C. 101 be withdrawn”.
The Examiner respectfully disagrees.
With respect to the argument the Examiner notes that at the outset, it must be noted that Step 2B asks whether the claim as a whole recites additional elements, individually or in combination, that amount to significantly more than the judicial exception itself. See MPEP 2106.05. Critically, the Step 2B inquiry is broader than simply determining whether a limitation is well-understood, routine, or conventional. Even where an additional element is not well-understood, routine, or conventional, it still must provide a meaningful limitation beyond the abstract idea it must, for example, improve the functioning of a computer or another technology, apply the exception with a particular machine, or otherwise impose a meaningful constraint on the judicial exception. An element that is novel or non-conventional but that merely applies an abstract idea to a particular environment or recites the idea of a solution without specifying a particular technical means for achieving that solution still fails to amount to significantly more. See MPEP 2106.05; Alice Corp. Pty. Ltd. v. CLS Bank Int'l. Applicant's Step 2B argument rests almost entirely on the assertion that the "providing computing resources" limitation is not well-understood, routine, or conventional, but Applicant neither provides evidence supporting that assertion nor explains how the limitation amounts to significantly more under any of the other applicable Step 2B considerations. This alone is insufficient to overcome the rejection.
The Applicant asserts that the "providing computing resources" limitation is "not recited at a high level of generality," but this assertion is directly contradicted by the plain language of the claim. The limitation as recited states only that "computing resources are allocated for use by the website during the future time period when the segment return value indicates the segment of users will have a repeat interaction with the website during the future time period." There is no recitation of any specific type of computing resource, no particular server architecture, no specific provisioning protocol, no technical mechanism for how the allocation occurs, and no constraint on the quantity or nature of what is allocated. Under the broadest reasonable interpretation, this limitation encompasses any act by any entity of deciding to allocate any type of computing resources to a website based on any computed numerical representation of predicted user interactions. The courts have consistently held that claim limitations recited at this level of functional generality describing only the outcome or effect of a step without specifying how it is technically achieved cannot amount to significantly more than an abstract idea. See Internet Patents Corp. v. Active Network, Inc. (a recitation that describes an effect or result dissociated from any method by which it is accomplished does not provide a meaningful limitation). The fact that the step involves computing resources for a website rather than some other subject matter does not confer specificity it merely places the abstract computation in a particular technological environment, which the courts have consistently found insufficient. See MPEP 2106.05(h).
The Applicant's bare assertion that the "providing computing resources" step is not well-understood, routine, or conventional activity is unsupported by any evidence, argument by reference to the prior art, or citation to the specification demonstrating that this specific functionality was unknown or unconventional at the time of filing. Under MPEP 2106.05(d), the examiner must support a finding of well-understood, routine, conventional activity with appropriate written support, which has been provided through the specification itself. Specifically, the specification of the present application treats the concept of allocating bandwidth, servers, and cloud-based services to a website as a routine and well-known function paragraph [0027] describes it as an "example of an efficient allocation of resources" using generic terms like "bandwidth, servers, cloud-based services, and the like," language that is consistent with describing a well-known, routine infrastructure management activity. The specification provides no technical explanation of any novel mechanism, protocol, or architecture for how computing resources are allocated precisely the type of treatment the courts have recognized as indicative of a well-understood, conventional function. See MPEP 2106.05(d) (a specification that describes additional elements as well-understood, commercially available, or in a manner that indicates the particulars need not be described to satisfy 35 U.S.C. 112(a) demonstrates the conventional nature of those elements). Furthermore, the courts have recognized that allocating computing resources such as server capacity and bandwidth in response to predicted demand is a well-known, routine activity in the field of website management and network provisioning, performed routinely by those skilled in the art. Applicant has not rebutted this finding with any specific argument or evidence to the contrary, and accordingly the examiner's position is maintained.
The Step 2B inquiry also requires consideration of all additional elements in combination. See MPEP 2106.05. Beyond the "providing computing resources" limitation, the other additional elements in claim 1 are the recitations of a segmentation component, a prediction component, a web browser, and a website all of which are generic computer components and technological environments recited at a high level of generality that perform only their expected, basic functions. The segmentation component identifies user segments, the prediction component computes predictions, and the website and web browser are the environment in which user interactions occur. None of these elements, individually or in combination, performs functions that are anything other than generic. The combination of a generic segmentation component, a generic prediction component, and a generic resource allocation step, all directed toward performing the abstract mental processes of user segmentation and future interaction prediction, does not produce a non-conventional or non-generic arrangement that could provide a technical improvement of the kind found sufficient in BASCOM Global Internet Services v. AT&T Mobility LLC. In BASCOM, the inventive concept arose from the specific, unconventional arrangement of a filtering tool at a particular network location with customizable, user-specific features a concrete, technical departure from conventional architecture. No comparable technical specificity or unconventional arrangement is present here. The claims cover the broadest possible implementation of the abstract idea of user segment prediction applied to any website computing environment, which is precisely the type of over-broad, result-oriented claim that fails at Step 2B. Accordingly, the rejection under 35 U.S.C. 101 at Step 2B is maintained in full for claims 1–7, 9–13, and 15–20, and Applicant's request that the rejection be withdrawn is denied and the rejection is maintained.
The Applicant argues on pages 15-16 that “Pattabiraman, Blassin, and Ruhl do not teach or suggest at least the segment return value comprises a numerical representation of users in the segment who are predicted to interact with the website during the future time period as recited in claim 1. In the Office Action, claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over Pattabiraman, Blassin, and Ruhl. Pages 13-14 concede that Pattabiraman cannot be relied upon to teach or suggest the segment return value comprises a numerical representation of users in the segment who are predicted to interact with the website during the future time period, and instead relies on Ruhl to teach this limitation. Ruhl is generally directed to a graphical user interface for presenting time series data. See Ruhl, Abstract. Specifically, the Office Action indicates that paragraphs [0050], [0068], [0093], and [0101] of Ruhl teach least the segment return value comprises a numerical representation of users in the segment who are predicted to interact with the website during the future time period. However, Applicant respectfully submits that paragraphs [0050], [0068], [0093], and [0101] of Ruhl are silent on this limitation. For example, paragraph [0068] of Ruhl discloses that "This lower-bound on the generation of time series reduces not only the statistical noise level of the detected events of potential interest but also the storage needed for storing the time series." Even assuming arguendo an interpretation that Ruhl discloses user segments, Applicant respectfully submits that storing time series data, which necessarily has already been received by the system in order to be stored, does not imply any future prediction of a numerical representation of users in a segment who are predicted to interact with the website during a future time period.
Blassin does not remedy the deficiencies of Pattabiraman and Ruhl with respect to claim 1. For example, Blassin is generally directed to managing language translation resources. See Blassin, Abstract. However, Applicant respectfully submits that Blassin is silent on at least the segment return value comprises a numerical representation of users in the segment who are predicted to interact with the website during the future time period. While page 15 of the Office Action indicates that Blassin teaches this limitation, the Office Action does not provide a citation to the portion of Blassin which is being relied upon.
Therefore, because Pattabiraman, Blassin, and Ruhl cannot be relied upon to teach or suggest each limitation of claim 1, claim 1 is allowable over Pattabiraman, Blassin, and Ruhl. Claims 2-3 and 5-6 inherit the allowable features of claim 1 by virtue of depending from claim 1. Accordingly, Applicant respectfully requests that the rejection of claims 1-3 and 5-6 under 35 U.S.C. 103 be withdrawn.
Claims 17 and 20 are also rejected under 35 U.S.C. 103 as being unpatentable over
Pattabiraman, Blassin, and Ruhl. Claims 17 and 20 recite similar subject matter as claim 1, and claims 17 and 20 are therefore allowable over Pattabiraman, Blassin, and Ruhl for similar reasons. Accordingly, Applicant respectfully requests that the rejection of claims 17 and 20 under 35 U.S.C. 103 be withdrawn”.
The Examiner respectfully disagrees.
With respect to the argument the Examiner notes that the Applicant's argument that the cited paragraphs of Ruhl fail to teach a segment return value comprising a numerical representation of users predicted to interact with a website during a future time period is not persuasive for the following reasons. Regarding the Ruhl Citations the Applicant's response improperly focuses on paragraph [0068] in isolation and mischaracterizes how it was applied. As set forth in the Office Action, the rejection relies on paragraphs [0068], [0050], [0101], and [0093] of Ruhl in combination, not on any single paragraph standing alone.
Paragraph [0050] of Ruhl explicitly teaches that "the model-based event detection method described herein applies one or more statistical models to a time series to forecast or predict or estimate one or more values for a future time period." This is a direct and unambiguous teaching of prediction for a future time period the core concept Applicant argues is absent. Applicant's response is entirely silent as to this paragraph and has therefore failed to distinguish it.
Paragraph [0068] of Ruhl was cited, as stated in the Office Action, for the teaching that the predicted values are specifically numerical representations of user visits i.e., "a website receives at least 100 visits per day or 50 visits from distinct IP addresses." These are explicit numerical counts of users interacting with a website. The Office Action cited this paragraph for the proposition of "a number of visitors making a decision based on which side of the threshold estimate (i.e. prediction)" not merely for the concept of data storage as Applicant characterizes it.
Applicant's characterization that paragraph [0068] is "silent" on future prediction because it relates to data that "has already been received" misreads how the paragraph was applied. The paragraph establishes the numerical nature and threshold character of the user interaction counts being tracked. When read in combination with paragraph [0050]'s explicit teaching of applying statistical models to predict future time period values, and paragraph [0093]'s teaching that such values are "derived from user interaction data," the combination clearly teaches predicting a numerical representation of users who will interact with a website during a future time period.
Paragraph [0101] further supports the rejection by disclosing that the system processes and counts events associated with user visits "identifying and counting the events whose respective significance factors are at least equal to or higher than the user-specified sensitivity threshold" confirming the numerical, countable nature of the user interaction representations in Ruhl's system.
With regard to the Blassin Issue the Examiner acknowledges that the Office Action contained an internal inconsistency in its claim 1 rejection, wherein the phrase "Blassin discloses computing, by a prediction component, a segment return value..." appeared without a supporting citation to Blassin. This was a drafting error. The Office Action's actual evidentiary basis for the "numerical representation of users predicted to interact with the website during the future time period" limitation is the combination of Ruhl paragraphs [0068], [0050], [0101], and [0093], as clearly identified by specific paragraph citations in the rejection. Blassin's role in the rejection of claim 1, as stated in the Office Action, is directed to the resource allocation limitations, supported by Blassin paragraphs [0391] and [0395]-[0396]. The teachings of Ruhl at the cited paragraphs are independently sufficient to teach the disputed limitation, and the drafting error with respect to the attribution of this limitation to Blassin does not undermine the rejection.
Accordingly, because Ruhl at paragraphs [0068], [0050], [0101], and [0093], read in combination as cited in the Office Action, teaches a numerical representation of users predicted to interact with a website during a future time period, the rejection of claims 1-3, 5-6, 17, and 20 under 35 U.S.C. 103 rejection is maintained.
The Applicant argues on pages 16-19 Pattabiraman, Ruhl, and Blassin do not teach or suggest at least the computing resources are allocated for use by the website during the future time period when the segment return value indicates the segment of users will have a repeat interaction with the website during the future time period as recited in claim 1. Pages 15-16 of the Office Action concede that Pattabiraman and Ruhl cannot be relied upon to teach or suggest providing computing resources for the website based on the segment return value, wherein the computing resources are allocated for use by the website during the future time period when the segment return value indicates the segment of users will have a repeat interaction with the website during the future time period, and instead relies upon paragraphs [00391], [0395]-[0396], and [0460] of Blassin to teach these limitations. However, these cited portions of Blassin do not appear to disclose at least the computing resources are allocated for use by the website during the future time period when the segment return value indicates the segment of users will have a repeat interaction with the website during the future time period.
For example, paragraphs [0391] and [0395]-[0396] of Blassin relate to providing translation resources in response to a request for a translation. Specifically: Paragraph [0391] discloses that "A next step may include determining a state of translator availability for translators in a pool of translation resources who can translate between languages in the language pair. Of this pool, only a subset may be suitable for being offered to translate the content. A price for using one of the resources in this subset to perform the translation may be set. However, a time-dependent model of translator availability and quality may be applied to a portion of the pool (e.g., a portion that is otherwise qualified to translate among the language pair) to determine at least two distinct prices for the translation request-a price for using current resources and a price for using resources predicted to be available at a point in time in the future. A future timeframe for which resources are predicted to be available may align with a delivery timing requirement of the translation request, although a future prediction may cover a wide range of future time frames that may overlap but may cover resource availability at times other than those that satisfy the delivery timing requirement. To address the specific request, the method may include presenting at least one of the two distinct prices for the translation based on a timing requirement for delivery of the translated segment to the requester."
Paragraph [0395] discloses that "The system may provide a translation resource availability computer model that uses gathered data about a pool of translation resources to predict future availability of human translation resources that are capable of translating from the source language to the target language. The system may further use the output from the computer model to generate at least one translation resource utilization routing option and at least one price for the translation that is based on the predicted availability of the resources. The resource availability prediction model may generate a prediction of availability based on the source language, the target language, the time of day at which translation may take place, the location of translation resources, any combination thereof, or other factors related to translation performance, quality, profitability, client preferences, and the like that impact translator or translation resource selection described elsewhere herein."
Paragraph [0396] discloses that "The system may further include within the computer model for future resource availability or as a separate model, a capability to predict a future demand for translations from the source language to the target language. This prediction may be based on direct past translation request experience of the system, in-direct translation experience (e.g., industry reports), trends in language utilization, population growth estimates, third-party data (e.g., breaking news stories, news cycle, political events), and the like. Such a demand prediction may be based on time of day (e.g., as the sun starts to rise over China, a demand for Chinese source language translators may be predicted to increase due to the China business day starting). The demand range may be modeled so as to be based on a range of prices for the translation. Since demand for translation resources may impact market pricing for these resources, demand prediction may impact a price estimate and/or quote that the system may provide in response to a translation request. Alternatively, demand pricing may result in certain resources being filtered out from the available resources if the pricing for these certain resources is not compatible with a pricing guidance or requirement of the translation request."
Applicant respectfully submits that paragraphs [0391] and [0395]-[0396] of Blassin are silent on the translation resources are allocated for use by a website when a segment return value indicates a segment of users will have a repeat interaction with the website during the future time period. Meanwhile, paragraph [0460] of Blassin discloses that "Methods and systems are provided herein for employing multi-jurisdictional crowd recruiting with automated quality checks upon recruiting." Applicant respectfully submits that this does not appear to relate to allocating resources for use by a website.
Therefore, because Pattabiraman, Blassin, and Ruhl acannot be relied upon to teach each element of claim 1, claim 1 is allowable over Pattabiraman, Blassin, and Ruhl. Claims 2-3 and 5- 6 inherit the allowable features of claim 1 by virtue of depending from claim 1. Accordingly, Applicant respectfully requests that the rejection of claims 1-3 and 5-6 under 35 U.S.C. 103 be withdrawn.Claims 17 and 20 are also rejected under 35 U.S.C. 103 as being unpatentable over Pattabiraman, Blassin, and Ruhl. Claims 17 and 20 recite similar subject matter as claim 1, and claims 17 and 20 are therefore allowable over Pattabiraman, Blassin, and Ruhl for similar reasons. Accordingly, Applicant respectfully requests that the rejection of claims 17 and 20 under 35 U.S.C. 103 be withdrawn”.
The Examiner respectfully disagrees.
With respect to the arguments the Examiner noted that the Applicant's argument that Blassin fails to teach allocating computing resources for use by a website during a future time period when a segment return value indicates users will have a repeat interaction with the website is not persuasive for the following reasons. Regarding the Blassin citations Applicant argues that paragraphs [0391] and [0395]-[0396] of Blassin relate only to translation resources and therefore cannot teach allocation of computing resources for a website. This argument improperly applies a narrow, literal reading to the cited teachings rather than considering what the references would suggest to a person of ordinary skill in the art in the context of an obviousness combination under 35 U.S.C. 103.
The rejection does not require that Blassin teach website computing resource allocation in the specific context of the claimed invention. Rather, as stated in the Office Action, the rejection relies on the combination of Pattabiraman, Blassin, and Ruhl, where each reference contributes specific teachings that together render the claimed subject matter obvious.
Paragraph [0391] of Blassin expressly teaches applying "a time-dependent model of translator availability and quality... to determine at least two distinct prices for the translation request a price for using current resources and a price for using resources predicted to be available at a point in time in the future." The core concept taught here is allocating resources specifically, making resource allocation decisions based on a predictive model for a future time period. This is precisely the type of resource allocation based on future prediction that is relevant to the claim limitation at issue. The fact that Blassin applies this concept in the context of translation resources rather than website computing resources does not defeat the teaching for purposes of an obviousness analysis. A person of ordinary skill in the art, who is presumed to have knowledge of all the relevant art, would readily recognize that the principle of allocating resources in advance based on a predictive model of future demand is directly applicable to the allocation of computing resources for a website based on a predicted number of returning users.
Paragraph [0395] further reinforces this teaching by disclosing that the system "uses gathered data about a pool of... resources to predict future availability" and "uses the output from the computer model to generate at least one... resource utilization routing option" based on that prediction. Again, the concept of using a predictive model to make forward-looking resource allocation decisions is squarely taught.
Paragraph [0396] extends this further by teaching that the system predicts "future demand" based on past experience and trends, and that this demand prediction directly drives resource allocation decisions. The parallel to the claimed limitation where the segment return value (representing predicted future user interactions) drives the allocation of computing resources is clear and would be apparent to a person of ordinary skill in the art.
Regarding Paragraph [0460], the Examiner acknowledges that paragraph [0460] of Blassin, which relates to multi-jurisdictional crowd recruiting, was cited in the Office Action as supporting "automatically adjusting resources for website use." The Examiner acknowledges this specific citation was not well-supported by the text of paragraph [0460] and constitutes a drafting error in the Office Action. However, this error does not undermine the overall rejection because paragraphs [0391] and [0395]-[0396] independently provide sufficient support for the resource allocation concept, as explained above.
Regarding the Obviousness Combination, as stated in the Office Action, Blassin was found to be analogous art because it is "comparable in certain respects to Pattabiraman which optimizes network utilization based on segmenting users of online resources" and is "reasonably pertinent to the problem faced by the inventor." Both Pattabiraman and Blassin involve using predictive models and segmentation data to make resource distribution decisions for future time periods. It would have been obvious to a person of ordinary skill in the art to apply Blassin's principle of future-oriented, prediction-based resource allocation as taught in paragraphs [0391] and [0395]-[0396] to Pattabiraman's system of user segmentation and interaction prediction, since both references address the same fundamental problem of efficiently allocating resources based on predicted future demand. The results of such a combination would have been entirely predictable. The rejection is therefore maintained.
The Applicant argues on pages 19-20 that “Pattabiraman, Ruhl, and Blassin do not teach or suggest at least generating, by the prediction component, a first return value for the first subset and a second return value for the second subset, respectively, wherein the first return value comprises a numerical representation of users in the first subset who are predicted to interact with the website during a future time period subsequent to the first time period and the second return value comprises a numerical representation of users in the second subset who are predicted to interact with the website during the future time period as recited in claim 17. Page 20 of the Office Action concedes that Pattabiraman cannot be relied upon to teach or suggest generating, by the prediction component, a first return value for the first subset and a second return value for the second subset, respectively, wherein the first return value comprises a numerical representation of users in the first subset who are predicted to interact with the website during a future time period subsequent to the first time period and the second return value comprises a numerical representation of users in the second subset who are predicted to interact with the website during the future time period and instead relies upon paragraphs [0050]-[0051], [0068], [0101], and [0142] of Ruhl to teach this limitation.
Paragraph [0142] of Ruhl discloses that "As shown in FIG. 15B, for the respective time- value pair and the particular attribute, the server system determines a significance factor (1511). In some embodiments, the significance factor is chosen such that, when the error-variance for each of the forecasting models is multiplied by the significance factor, the value of the time- value pair is inside the factored error-variance of a corresponding estimated metric value for at least a second subset of the forecasting models and the first subset is within the second subset." Applicant respectfully submits that, even assuming arguendo that Ruhl discloses a first subset of users who visit a website after a threshold number of website visits is reached and a second subset of users who visit a website before the threshold is reached, that paragraph [0068] of Ruhl indicates that time series data starts being recorded once the threshold is reached; therefore, paragraph [0142] of Ruhl cannot disclose computing a second return value for the second segment of users, because there is no stored data from which to compute the second return value. Furthermore, paragraph [0142] of Ruhl discloses that the "first subset is within the second subset". However, the claimed first and second subsets of users are mutually exclusive, and therefore Ruhl's subsets do not teach the claimed first and second subsets of users, or the first and second return values.
Blassin does not remedy the deficiencies of Pattabiraman and Ruhl with respect to claim 17. For example, Applicant respectfully submits that Blassin is silent on at least generating, by the prediction component, a first return value for the first subset and a second return value for the second subset, respectively, wherein the first return value comprises a numerical representation of users in the first subset who are predicted to interact with the website during a future time period subsequent to the first time period and the second return value comprises a numerical representation of users in the second subset who are predicted to interact with the website during the future time period. Page 21 indicates that Blassin discloses this limitation, but does not provide a citation to the portions of Blassin that are alleged to disclose the limitation.
Therefore, because Pattabiraman, Ruhl, and Blassin cannot be relied upon to teach each element of claim 17, claim 17 is allowable over Pattabiraman, Blassin, and Ruhl. Accordingly, Applicant respectfully requests that the rejection of claim 17 under 35 U.S.C. 103 be withdrawn”.
The Examiner respectfully disagrees.
With respect to the argument the Examiner notes that the Applicant's argument that Ruhl fails to teach generating a first return value for a first subset and a second return value for a second subset of users, respectively, each comprising a numerical representation of users predicted to interact with a website during a future time period, is partially persuasive with respect to paragraph [0142], but does not overcome the rejection as a whole based on the remaining cited paragraphs. Regarding Paragraph [0142] of Ruhl the Examiner acknowledges that Applicant raises a facially valid point regarding paragraph [0142]. That paragraph states that "the significance factor is chosen such that, when the error-variance for each of the forecasting models is multiplied by the significance factor, the value of the time-value pair is inside the factored error-variance of a corresponding estimated metric value for at least a second subset of the forecasting models and the first subset is within the second subset." As Applicant correctly notes, the "first subset" and "second subset" in paragraph [0142] refer to subsets of forecasting models, not subsets of users.
However, even so if paragraph [0142] as a supporting citation does not overcome the rejection because the Office Action additionally relied upon paragraphs [0050]-[0051], [0068], and [0101] of Ruhl, which Applicant has not adequately addressed.
Regarding Paragraphs [0050]-[0051] of Ruhl, Paragraph [0050] of Ruhl expressly teaches that the model-based event detection method "applies one or more statistical models to a time series to forecast or predict or estimate one or more values for a future time period." This establishes the foundational teaching that Ruhl's system generates predictive numerical values for future time periods directly relevant to the concept of generating return values representing predicted future user interactions.
Paragraph [0051] further teaches that "the model-based or rule-based event detection method can also be performed on a collection of time series data...to not only predict anomalies in the future... but also identify anomalies in the past." This confirms that Ruhl's predictive system operates across the range of collected time series data to generate predictions, which supports the teaching of generating separate predictive values based on different portions of the collected data. Applicant's response is entirely silent as to paragraphs [0050]-[0051] and has therefore failed to distinguish these teachings.
Regarding Paragraph [0068] of Ruhl, the Applicant argues that paragraph [0068] indicates that time series data only begins being recorded once a visit threshold is reached, and therefore there would be no stored data from which to compute a second return value for users below the threshold. This argument is viewed that the Applicant is misreading the paragraph. Paragraph [0068] states that "the time series gathering servers 170 may set a threshold such that no time series is generated for a website until the website's associated web analytics data reaches the threshold" specifically "at least 100 visits per day or 50 visits from distinct IP addresses." This threshold applies to whether a time series is generated for a given website, not to whether individual user interaction records are stored. Once the website-level threshold is met and the time series is being generated, the system collects and analyzes all user interactions within that time series. The paragraph does not state that individual user records below any threshold are excluded from the data set. Rather, it sets a minimum website activity level before any time series is generated at all. Applicant's interpretation would require reading limitations into the paragraph that are not present in the text.
Furthermore, paragraph [0068] teaches that the numerical threshold 100 visits per day or 50 visits from distinct IP addresses is specifically a count of users interacting with the website, which is precisely a numerical representation of user interactions of the type recited in claim 17. The system tracks these numerical counts and uses them as the basis for its predictive models, supporting the teaching of generating numerical return values representing predicted user interactions.
Regarding Paragraph [0101] of Ruhl, specifically paragraph [0101] teaches "identifying and counting the events whose respective significance factors are at least equal to or higher than the user-specified sensitivity threshold." This teaches the system generates and operates on numerical counts of user interaction events again directly supporting the teaching that Ruhl generates numerical representations of user interactions that can serve as the basis for predictive return values.
Regarding the Blassin Citation Issue, the Examiner acknowledges that page 21 of the Office Action attributed the disputed limitation to Blassin without providing a specific paragraph citation, which constitutes a drafting error. However, as explained above, paragraphs [0050]-[0051], [0068], and [0101] of Ruhl, read in combination as cited in the Office Action, are independently sufficient to teach the concept of generating numerical predictive values for user interaction subsets during a future time period. The drafting error regarding Blassin does not undermine the rejection based on the Ruhl teachings.
Regarding Mutual Exclusivity of Subsets, Applicant argues that claim 17 requires mutually exclusive first and second user subsets, which Ruhl allegedly cannot teach. The Examiner notes that the broader teachings of Ruhl regarding first and second groups of users specifically, those above and below a visit threshold do encompass the concept of distinct, non-overlapping user groupings for analytical purposes. Paragraphs [0050]-[0051] and [0068], read together, teach applying predictive models to distinct portions of the time series data corresponding to different user interaction levels, which in combination with Pattabiraman's explicit teaching of creating first and second mutually exclusive user subsets based on session duration thresholds (paragraphs [0009] and [0042]) renders the claimed subject matter obvious to one of ordinary skill in the art. The rejection is maintained.
The Applicant on pages 20-21 that “Pattabiraman, Ruhl, and Blassin do not teach or suggest predicting, by the prediction component, a segment return value for the future time period based on the first return value and the second return value as recited in claim 17.
Applicant respectfully submits that Pattabiraman, Ruhl, and Blassin are silent on at least predicting, by the prediction component, a segment return value for the future time period based on the first return value and the second return value, and the Office Action does not indicate otherwise. See Office Action, pages 19-21.
Therefore, because Pattabiraman, Ruhl, and Blassin cannot be relied upon to teach each element of claim 17, claim 17 is allowable over Pattabiraman, Blassin, and Ruhl. Accordingly, Applicant respectfully requests that the rejection of claim 17 under 35 U.S.C. 103 be withdrawn”.
The Examiner respectfully disagrees.
With respect to the argument the Examiner notes that while the Applicant argues that the Office Action is entirely silent on the limitation "predicting, by the prediction component, a segment return value for the future time period based on the first return value and the second return value" as recited in claim 17, and that therefore claim 17 is allowable. This argument is not persuasive for the following reasons. Regarding the scope of the claim 17 rejection as explicitly stated in the Office Action at page 20, "Claim 17...recites the same or similar limitations as those addressed above in claim 1" except for specifically noted exceptions. The Office Action expressly incorporated the full claim 1 rejection analysis into the claim 17 rejection. The limitation of predicting a segment return value based on the first and second return values was addressed within that incorporated analysis. Applicant's assertion that the Office Action "does not indicate otherwise" with respect to this limitation is therefore incorrect the Office Action addressed this limitation through the explicit cross-reference to the claim 1 rejection combined with the additional Ruhl citations at paragraphs [0050]-[0051], [0068], and [0101].
Furthermore, regarding the teachings of the cited references the Office Action cited paragraphs [0018]-[0019] of Pattabiraman for the limitation of "computing, by a prediction component, a first subset of a segment of the plurality of users and a second subset of the segment of the plurality of users based on the time series data." Paragraphs [0018]-[0019] of Pattabiraman teach "segmenting the aggregate set of user session data into two or more subsets" and using those subsets to "improve prediction models for auto segmentation of a population of users." Critically, paragraph [0018] teaches that "segmenting the aggregate set of user session data into two or more subsets can improve prediction models for auto segmentation of a population of users" this directly teaches that the purpose of computing the individual subsets is to arrive at an improved overall predictive value for the segment as a whole. The concept of combining subset-level predictions into a single overall segment prediction is therefore directly taught by Pattabiraman's stated purpose of subset segmentation.
Additionally, Ruhl at paragraphs [0050]-[0051], as cited in the Office Action, teaches that "the model-based event detection method...applies one or more statistical models to a time series to forecast or predict or estimate one or more values for a future time period and then compares the predicted values with the actual value when available." The use of multiple predicted values which correspond to the first and second return values in the claim that are then compared and combined to arrive at a final determination is precisely the concept of generating a segment-level prediction based on individual subset predictions. The combination of these values into a final determination is the natural and predictable result of the process taught in Ruhl's paragraphs [0050]-[0051].
Additionally, paragraph [0068] of Ruhl was cited in the Office Action for "based on the number of visits and then making a decision" this teaching of making an overall determination based on numerical visit counts drawn from the analyzed data directly supports the concept of generating a segment-level return value based on the individual subset return values.
Finally, regarding the argument of obviousness even if no single cited reference explicitly teaches combining a first return value and a second return value to predict an overall segment return value, this combination would have been obvious to a person of ordinary skill in the art. As stated in the Office Action, "the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately." Where Pattabiraman teaches creating first and second user subsets for predictive segmentation purposes and Ruhl teaches generating numerical predictions from time series data across different portions of the data, it would have been entirely predictable to a person of ordinary skill to combine the individual subset predictions into an overall segment-level prediction. This is the straightforward and expected mathematical result of applying the subset-level analysis taught by the combined references. The rejection is therefore maintained.
The Applicant argues on pages 21-22 that “Dependent claims 4, 7, 9-13, 15-16, and 18-19 are allowable by virtue of depending from allowable claims 1 and 17, respectively.
In the Office Action, claims 4, 7, 9-10, 15-16, and 19 were rejected under 35 U.S.C. 103, with claims 4, 7, 9-10 15-16, and 19 being unpatentable over Pattabiraman, Blassin, Ruhl, and Storan, claims 11 and 18 being unpatentable over Pattabiraman, Blassin, Ruhl, Storan, and Mappus, claim 12 being unpatentable over Pattabiraman, Blassin, Ruhl, Storan, Mappus, and Davies, and claim 13 being unpatentable over Pattabiraman, Blassin, Ruhl, Storan, Mappus, and Klarfield. Claims 4, 7, 9-13, and 15-16 depend from claim 1, and claims 18-19 depend from claim 17. However, as no combination of Pattabiraman, Blassin, Ruhl, Storan, Mappus, Davies, and Klarfield remedies the deficiencies of Pattabiraman, Blassin, and Ruhl with respect to claim 1, claims 4, 7, 9-13, and 15-16 are allowable over Pattabiraman, Storan, Mappus, Davies, and Klarfield, respectively. Furthermore, as no combination of Pattabiraman, Blassin, Ruhl, Storan, and Mappus remedies the deficiencies of Pattabiraman, Blassin, and Ruhl with respect to claim 17, claims 18-19 are allowable over Pattabiraman, Blassin, Ruhl, Storan, and Mappus, respectively.
For example, Storan is generally directed to delivering television advertising content to a segment of viewers (see Storan, Abstract), Mappus is generally directed to scheduling delivery of content during a future time period (see Mappus, Abstract), Davies is generally directed to generating a forecast for a telecast using a machine learning model (see Davies, Abstract), and Klarfield is generally directed to displaying television content according to viewer preferences (see Klarfield, Abstract). However, Applicant respectfully submits that each of Storan, Mappus, Davies, and Klarfield respectively are silent on at least the segment return value comprises a numerical representation of users in the segment who are predicted to interact with the website during the future time period the computing resources are allocated for use by the website during the future time period when the segment return value indicates the segment of users will have a repeat interaction with the website during the future time period as recited in claims 1 and 17, and that Storan and Mappus are silent on at least generating, by the prediction component, a first return value for the first subset and a second return value for the second subset, respectively, wherein the first return value comprises a numerical representation of users in the first subset who are predicted to interact with the website during a future time period subsequent to the first time period and the second return value comprises a numerical representation of users in the second subset who are predicted to interact with the website during the future time period and predicting, by the prediction component, a segment return value for the future time period based on the first return value and the second return value as recited in claim 17.
Therefore, because Pattabiraman, Blassin, Ruhl, Storan, Mappus, Davies, and Klarfield cannot be relied upon to teach each element of claim 1, claim 1 is allowable over Pattabiraman, Blassin, Ruhl, Storan, Mappus, Davies, and Klarfield. Claims 4, 7, 9-13, and 15-16 inherit the allowable features of claim 1 by virtue of depending from claim 1. Accordingly, Applicant respectfully requests that the rejection of claims 4, 7, 9-13, and 15-16 under 35 U.S.C. 103 be withdrawn.
Likewise, because Pattabiraman, Blassin, Ruhl, Storan, and Mappus cannot be relied upon to teach each element of claim 17, claim 17 is allowable over Pattabiraman, Blassin, Ruhl, Storan, and Mappus. Claims 18-19 inherit the allowable features of claim 17 by virtue of depending from claim 17. Accordingly, Applicant respectfully requests that the rejection of claims 18-19 under 35 U.S.C. 103 be withdrawn”.
The Examiner respectfully disagrees
With respect to the argument the Examiner notes that the Applicant's argument that dependent claims 4, 7, 9-13, 15-16, and 18-19 are allowable solely by virtue of depending from claims 1 and 17 is not persuasive because it is entirely derivative of Arguments 1-4, each of which has been fully addressed and overcome above. Since the rejections of independent claims 1 and 17 are maintained for the reasons set forth in the Examiner's responses to Arguments 1-4, the dependent claims do not inherit any allowable subject matter from those base claims.
Regarding the derivative nature of the argument the Applicant does not present any independent argument against the specific rejections of claims 4, 7, 9-13, 15-16, 18, and 19 based on the additional references Storan, Mappus, Davies, and Klarfeld. Applicant's sole basis for allowability of these dependent claims is that claims 1 and 17 are alleged to be allowable. As set forth in the responses to Arguments 1-4, claims 1 and 17 are not allowable, and therefore this derivative argument provides no basis for withdrawing the rejections of the dependent claims.
Furthermore regarding the additional references, with respect to Applicant's contention that Storan, Mappus, Davies, and Klarfeld are each silent on the limitations of claims 1 and 17 that Applicant disputes, the Examiner notes that these additional references were not cited in the Office Action to remedy any alleged deficiency with respect to the limitations of claims 1 and 17. Rather, as clearly set forth in the Office Action, each additional reference was cited specifically and only for the additional limitations introduced by the respective dependent claims:
Storan was cited specifically for claims 4, 7, 9, 10, and 16 to teach identifying and selecting user attributes for segmentation (claim 4), generating customized content based on segment return value (claim 7), generating first and second return values for the first and second subsets (claims 9-10), and identifying a frequency and selecting a model based on that frequency (claim 16). The Office Action cited the Abstract, paragraphs [0008], [0012], [0041], and [0085]-[0086] of Storan for these specific dependent claim limitations. These citations stand independently of the claim 1 and 17 analysis.
Mappus was cited specifically for claims 11 and 18 to teach a moving average estimator for the first return value based on a plurality of time periods before the first time period. The Office Action cited columns 2 and 3 of Mappus for this specific limitation. This citation likewise stands independently.
Davies was cited specifically for claim 12 to teach that the moving average estimator comprises an autoregressive moving average, as taught in the Abstract of Davies. This citation stands independently.
Klarfeld was cited specifically for claim 13 to teach computing a seasonal parameter of the time series data wherein the moving average estimator is based on the seasonal parameter, as taught in paragraph [0090] of Klarfeld. This citation stands independently.
Applicant has not challenged any of these specific citations on their merits with respect to the dependent claim limitations for which they were applied. Applicant's only argument is that the additional references fail to remedy deficiencies in the base claim rejections a proposition that is both legally incorrect as a matter of how obviousness combinations operate, and factually incorrect because no such deficiencies exist in the base claim rejections as demonstrated in the responses to Arguments 1-4.
Finally, regarding the legal standard Under 103 prior art analysis, each dependent claim must be evaluated on the full combination of references applied to it. A dependent claim is not allowable merely because its base independent claim is argued to be allowable, particularly where the Examiner maintains the rejection of the independent claim. See MPEP 2143. The specific limitations added by each dependent claim were each individually addressed in the Office Action with specific citations to the additional references, and Applicant has not challenged those specific teachings on their individual merits. The rejection is therefore maintained.
The remaining Applicant's arguments filed 29 December 2025 have been fully considered but they are moot in view of new grounds of rejection as necessitated by amendment.
Claim Rejections - 35 USC 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-7, 9-13, and 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter because the claim(s) 1-7, 9-13, and 15-20 as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. The claim(s) 1-7, 9-13, and 15-20 is/are directed to the abstract idea of estimating segment size during time periods of content to provide customized content. The claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more than the judicial exception itself. Claim(s) (1-7, 9-13, and 15-20) is/are directed to an abstract idea without significantly more.
Step 1
Regarding Step 1 of the Subject Matter Eligibility Test for Products and Processes, claim(s) (1-7, 9-13, 15, 16 and 17-19) is/are directed to a method, and claim(s) (20) is/ are directed to an apparatus and therefore the claims recites a series of steps and, therefore the claims are viewed as falling in statutory categories.
Step 2A Prong 1
The claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) a mental process. Specifically, the independent claims 1, 17, and 20 recite a mental process as drafted, the claim recites the limitation of identifying a segment and predicting a segment return which is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a processor nothing in the claim precludes the determining step from practically being performed in the human mind. For example, but for the processor language, the claim encompasses the user manually collecting and analyzing previously collected data regarding user interacting with a specific content. The mere nominal recitation of a generic processor does not take the claim limitation out of the mental processes grouping. This limitation is a mental process. While the Guidance provides that claims do not recite a mental process when they contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations (GPS position calculation, network monitoring, data encryption for communication, rendering images. However with regard to the instant application the Examiner has reviewed the disclosure and determined that the underlying claimed invention is described as a concept that is performed in the human mind and/or with the aid of a pen and paper, and thus it is viewed that the applicant is merely claiming that concept performed 1) on a generic computer, 2) in a computer environment or 3) is merely using a computer as a tool to perform the concept, and therefore is considered to recite a mental process.
Step 2A Prong 2
Specifically, the determined judicial exception is not integrated into a practical application because the generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer and additionally the data providing step required to use the identifying and prediction steps do not add a meaningful limitation to the method as they are insignificant extra-solution activity (including post solution activity).
The claim recites the additional element(s): that a processor is used to perform both the identifying and predicting steps. The processor in both steps is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing data (the identifying and predicting in order to estimate the segment size and then provide customized content). This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component. 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. The claim is directed to the abstract idea.
The claim recites the additional element(s): providing customized content to a user is performed using the identifying and predicting steps. The providing step is recited at a high level of generality (i.e., as a general means of displaying data), and amounts to mere data displaying, which is a form of insignificant extra-solution activity. The processor that performs the identifying and predicting steps is also recited at a high level of generality, and merely automates the identifying and predicting steps. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component (the processor).
For further clarification the Examiner points out that the claim(s) 1-7, 9-13, and 15-20 recite(s) identifying a segment of a plurality of users, predicting a segment return value, and providing customized content which are viewed as an abstract idea in the form of a mental process. This judicial exception is not integrated into a practical application because the use of a computer for identifying, predicting, and providing which is the abstract idea steps of valuing an idea (to estimating segment size during time periods of content to provide customized content) in the manner of “apply it”.
Thus, the claims recite an abstract idea directed to a mental process (i.e. to estimating segment size during time periods of content to provide customized content). Using a computer to identifying, predicting, and providing the data resulting from this kind of mental process merely implements the abstract idea in the manner of “apply it” and does not provide 'something more' to make the claimed invention patent eligible. The claimed limitations of a computing device is not constraining the abstract idea to a particular technological environment and do not provide significantly more.
The estimating segment size during time periods of content to provide customized content would clearly be to a mental activity that a company would go through in order to decide which content to provide to users based on collected and analyzed data. The specification makes it clear that the claimed invention is directed to the mental activity data gathering and data analysis to determine estimate a segment size so as to provide customized content:
The dependent claims recite elements that narrow the metes and bounds of the abstract idea but do not provide ‘something more’.
The dependent claims do not remedy these deficiencies.
Claims 11, 16, and 18 recite limitations which further limit the claimed analysis of data.
Claims 4-7, 9, and 10 recites limitations directed to claim language viewed insignificantly extra solution activity.
Using a computer to perform the data processing as claimed is merely implementing the abstract idea in the manner of “apply it” and does not provide significantly more. Additionally with respect to the Berkheimer the Examiner points out that the steps of the claim are viewed to be to nothing more than spell out what it means to apply it on a computer and cannot confer patent-eligibility as there are no additional limitations beyond applying an abstract idea, restricted to a computer. As the claims are merely implementing the abstract idea in the manner of “Apply It” the need for a Berkheimer analysis does not apply and is not required. With respect to the currently filed claims the implementing steps can be found in Pattabiraman which discloses how the claims alone and in combination are viewed to be well understood, routine and conventional based on point 3 of the Berkheimer memo and subsequent evidence, complying with and providing evidence.
Claims 2, 3, 12, 13, 15, and 19 recites limitations directed to claim language viewed non-functional data labels.
Thus, the problem the claimed invention is directed to answering the question based on gathered and analyzed information about the users to determine customized content. This is not a technical or technological problem but is rather in the realm of data analysis for customer management in the media industry and therefore an abstract idea.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed with respect to Step 2A Prong Two, the additional element in the claim amounts to no more than mere instructions to apply the exception using a generic computer component.
The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. This is the case because in order for the claims to be viewed as significantly more the claims must incorporate the integral use of a machine to achieve performance of a method, in contrast to where the machine is merely an object on which the method operates, which does not provide significantly more in order for a machine to add significantly more, it must play a significant part in permitting the claimed method to be performed, rather than function solely as an obvious mechanism for permitting a solution to be achieved more quickly. Whether its involvement is extra-solution activity or a field-of-use, i.e., the extent to which (or how) the machine or apparatus imposes meaningful limits on the claim. Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more. Additionally, another consideration when determining whether a claim recites significantly more is whether the claim effects a transformation or reduction of a particular article to a different state or thing. "[T]ransformation and reduction of an article ‘to a different state or thing’ is the clue to patentability of a process claim that does not include particular machines. All together the above analysis shows there is not improvement in computer functionality, or improvement to any other technology or technical field. The claim is ineligible.
With respect to the Berkheimer as noted above the same analysis applies to the 2B where the claims are viewed as applying it and as such no further analysis is required. However, with respect to the current identifying and providing claims that are viewed as extra solution or post solution activity the Examiner notes that the claims are viewed as well-understood, routine, and conventional because a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s). An appropriate publication could include a book, manual, review article, or other source such as the current prior art Pattabiraman that describes the state of the art and discusses what is well-known and in common use in the relevant industry.
The dependent claims recite elements that narrow the metes and bounds of the abstract idea but do not provide ‘something more’. Specifically, the dependent claims do not remedy these deficiencies of the independent claims.
Claim Rejections - 35 USC 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent may not be obtained through the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 5, 6, 15, 17, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable in view of Pattabiraman (U.S. Patent Publication 2021/0337032 A1) in view of Blassin et al. (U.S. Patent Publication 2016/0162478 A1) (hereafter Blassin) in further view of Ruhl et al. (U.S. Patent Publication 2011/0119100 A1) (hereafter Ruhl).
Referring to Claim 1, Pattabiraman teaches a method for segment size estimation, said method comprising:
identifying, by a segmentation component, a segment of a plurality of users for a first time period based on time series data for a website, wherein the time series data includes a series of interactions between the plurality of users and the website via a web and wherein the segment includes a portion of the plurality of users interacting with the website during the first time period (see; par. [0029] of Pattabiraman teaches network utilization on a website interaction for a period of the user session, par. [0042] based a user length of time of the user session).
Pattabiraman does not explicitly disclose the following limitation, however,
Ruhl teaches computing, by a prediction component, a segment return value for a future time period based on computing a first subset and a second subset of the segment, wherein the first subset includes users of the segment that interact with the website greater than a threshold number of times during a range of the time series data and the second subset comprises users of the segment that interact with the website less than the threshold number of times during the range of the time series data, and wherein the segment return value comprises a numerical representation of users in the segment who are predicted to interact with the website during the future time period (see: par. [0068] of Ruhl teaches a number of visitors making a decision based on which side of the threshold estimate (i.e. prediction), par. [0050] during a future time period, par. [0101] based on a determined segment , par. [0093] derived from user interaction data).
The Examiner notes that Pattabiraman teaches similar to the instant application teaches optimizing network utilization based on segmenting users of online resources. Specifically, Pattabiraman discloses determining user sessions including number of times and actual amount of time and creating segmentation and analyzed data it is therefore viewed as analogous art in the same field of endeavor. Additionally, Blassin teaches information technology platform for language translation and task management and as it is comparable in certain respects to Pattabiraman which optimizing network utilization based on segmenting users of online resources and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Pattabiraman discloses determining user sessions including number of times and actual amount of time and creating segmentation and analyzed data. However, Pattabiraman fails to disclose computing, by a prediction component, a segment return value for a future time period based on computing a first subset and a second subset of the segment, wherein the first subset includes users of the segment that interact with the website greater than a threshold number of times during a range of the time series data and the second subset comprises users of the segment that interact with the website less than the threshold number of times during the range of the time series data, and wherein the segment return value comprises a numerical representation of users in the segment who are predicted to interact with the website during the future time period.
Blassin discloses computing, by a prediction component, a segment return value for a future time period based on computing a first subset and a second subset of the segment, wherein the first subset includes users of the segment that interact with the website greater than a threshold number of times during a range of the time series data and the second subset comprises users of the segment that interact with the website less than the threshold number of times during the range of the time series data, and wherein the segment return value comprises a numerical representation of users in the segment who are predicted to interact with the website during the future time period.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Pattabiraman computing, by a prediction component, a segment return value for a future time period based on computing a first subset and a second subset of the segment, wherein the first subset includes users of the segment that interact with the website greater than a threshold number of times during a range of the time series data and the second subset comprises users of the segment that interact with the website less than the threshold number of times during the range of the time series data, and wherein the segment return value comprises a numerical representation of users in the segment who are predicted to interact with the website during the future time period as taught by Blassin since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Pattabiraman and Blassin teach the collecting and analysis of data in order to determine based on analysis content to provide to a user and they do not contradict or diminish the other alone or when combined.
Pattabiraman in view of Blassin does not explicitly disclose the following limitation, however,
Ruhl teaches providing computing resources for the website based on the segment return value, wherein the computing resources are allocated for use by the website during the future time period when the segment return value indicates the segment of users will have a repeat interaction with the website during the future time period (see; par. [0391], par. [0395]-[0396] of Blassin teaches resource allocation for a future time period based on segmentation values based on par. [0460] automatically adjusting resources for website use).
The Examiner notes that Pattabiraman teaches similar to the instant application teaches optimizing network utilization based on segmenting users of online resources. Specifically, Pattabiraman discloses determining user sessions including number of times and actual amount of time and creating segmentation and analyzed data it is therefore viewed as analogous art in the same field of endeavor. Additionally, Blassin teaches information technology platform for language translation and task management and as it is comparable in certain respects to Pattabiraman which optimizing network utilization based on segmenting users of online resources and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Ruhl teaches method and system for displaying anomalies in time series data and as it is comparable in certain respects to Pattabiraman and Blassin which optimizing network utilization based on segmenting users of online resources and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Pattabiraman and Blassin discloses determining user sessions including number of times and actual amount of time and creating segmentation and analyzed data. However, Pattabiraman and Blassin fails to disclose providing computing resources for the website based on the segment return value, wherein the computing resources are allocated for use by the website during the future time period when the segment return value indicates the segment of users will have a repeat interaction with the website during the future time period.
Blassin discloses providing computing resources for the website based on the segment return value, wherein the computing resources are allocated for use by the website during the future time period when the segment return value indicates the segment of users will have a repeat interaction with the website during the future time period.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Pattabiraman and Blassin computing, by a prediction component, a segment return value for a future time period based on computing a first subset and a second subset of the segment, wherein the first subset includes users of the segment that interact with the website greater than a threshold number of times during a range of the time series data and the second subset comprises users of the segment that interact with the website less than the threshold number of times during the range of the time series data, and wherein the segment return value comprises a numerical representation of users in the segment who are predicted to interact with the website during the future time period as taught by Ruhl since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Pattabiraman, Blassin, and Ruhl teach the collecting and analysis of data in order to determine based on analysis content to provide to a user and they do not contradict or diminish the other alone or when combined.
Referring to Claim 2, see discussion of claim 1 above, while Pattabiraman in view of Blassin in further view of Ruhl teaches the method above, Pattabiraman further discloses a method having the limitations of,
the first subset includes users that interact with the website more than one time during the first time period (see; par. [0093]-[0095] of Pattabiraman teaches an example of counting the number of sessions on the website on different devices during a time period).
Referring to Claim 3, see discussion of claim 1 above, while Pattabiraman in view of Blassin in further view of Ruhl teaches the method above, Pattabiraman further discloses a method having the limitations of,
the first subset includes users that interact with the website at least one time during one or more time periods before the first time period (see; par. [0079] of Pattabiraman teaches noting a time stamp prior to a different time stamp related to a different time period).
Referring to Claim 5, see discussion of claim 1 above, while Pattabiraman in view of Blassin in further view of Ruhl teaches the method above, Pattabiraman further discloses a method having the limitations of:
generating, by a monitoring component, a cookie for the user based on an interaction of the user with the website (see; par. [0069] of Pattabiraman teaches monitoring users utilizing cookies to monitor the user interactions on the website).
determining, by the segmentation component, that the user is in the segment based on the cookie, (see; par. [0069]-[0071] of Pattabiraman teaches using cookies which is a user identifier that is used to determine segments, par. [0008] to provide personalized content).
Referring to Claim 6, see discussion of claim 1 above, while Pattabiraman in view of Blassin in further view of Ruhl teaches the method above, Pattabiraman does not explicitly discloses a method having the limitations of,
inserting, by a monitoring component, code for monitoring the website in the website (see; Abstract of Pattabiraman teaches code that provides, par. [0042] a website to monitor user sessions).
collecting, by the monitoring component, the time series data based on the code (see; par. [0069]-[0070] of Pattabiraman teaches collecting time stamps associated with the data (i.e. time series)).
Referring to Claim 15, see discussion of claim 1 above, while Pattabiraman in view of Blassin in further view of Ruhl teaches the method above, Pattabiraman further discloses a method having the limitations of:
the segment return value comprises a ratio between a number of users predicted to interact with the content channel during the second time period and a number of users in the segment during the first time period (see; par. [0018]-[0019] of Pattabiraman teaches segmentation based on a predicted outcomes and improve the prediction
Referring to Claim 17, Pattabiraman in view of Blassin in further view of Ruhl teaches a method for segment size estimation . Claim 18 recites the same or similar limitations as those addressed above in claim 1, Claim 18 is therefore rejected for the same reasons as set forth above in claim 1, except for the following noted exception,
monitoring, by a monitoring component, a website to collect time series data for a plurality of users (see; par. [0020] of Pattabiraman teaches the tracking of user activities in order to collect data regarding when and the amount of time a user commits to an online resource).
computing, by a prediction component, a first subset of a segment of the plurality of users and a second subset of the segment of the plurality of users based on the time series data (see: par. [0018]-[0019] of Pattabiraman teaches predict segmentation based on the amount of time a user spent during the session, par. [0093] taking into account the number of user sessions, par. [0016] where a website is utilized that indicates less than a threshold time of user sessions, par.[0093] taking into account number of sessions during a period of time (i.e. time series)).
Pattabiraman in view of Blassin does not explicitly disclose the following limitation, however,
Ruhl teaches generating, by the prediction component, a first return value for the first subset and a second return value for the second subset, respectively, wherein the first return value comprises a numerical representation of users in the first subset who are predicted to interact with the website during a future time period subsequent to the first time period and the second return value comprises a numerical representation of users in the second subset who are predicted to interact with the website during the future time period (see; par. [0142] of Ruhl teaches a first and second subsets based on estimated metrics, par. [0050]-[0051] during a prediction of future time, par. [0068] based on the number of visits and then making a decision, par. [0101] utilizing the determined segment).
The Examiner notes that Pattabiraman teaches similar to the instant application teaches optimizing network utilization based on segmenting users of online resources. Specifically, Pattabiraman discloses determining user sessions including number of times and actual amount of time and creating segmentation and analyzed data it is therefore viewed as analogous art in the same field of endeavor. Additionally, Blassin teaches information technology platform for language translation and task management and as it is comparable in certain respects to Pattabiraman which optimizing network utilization based on segmenting users of online resources and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Ruhl teaches method and system for displaying anomalies in time series data and as it is comparable in certain respects to Pattabiraman and Blassin which optimizing network utilization based on segmenting users of online resources and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Pattabiraman and Blassin discloses determining user sessions including number of times and actual amount of time and creating segmentation and analyzed data. However, Pattabiraman and Blassin fails to disclose generating, by the prediction component, a first return value for the first subset and a second return value for the second subset, respectively, wherein the first return value comprises a numerical representation of users in the first subset who are predicted to interact with the website during a future time period subsequent to the first time period and the second return value comprises a numerical representation of users in the second subset who are predicted to interact with the website during the future time period.
Blassin discloses generating, by the prediction component, a first return value for the first subset and a second return value for the second subset, respectively, wherein the first return value comprises a numerical representation of users in the first subset who are predicted to interact with the website during a future time period subsequent to the first time period and the second return value comprises a numerical representation of users in the second subset who are predicted to interact with the website during the future time period.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Pattabiraman and Blassin generating, by the prediction component, a first return value for the first subset and a second return value for the second subset, respectively, wherein the first return value comprises a numerical representation of users in the first subset who are predicted to interact with the website during a future time period subsequent to the first time period and the second return value comprises a numerical representation of users in the second subset who are predicted to interact with the website during the future time period as taught by Ruhl since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Pattabiraman, Blassin, and Ruhl teach the collecting and analysis of data in order to determine based on analysis content to provide to a user and they do not contradict or diminish the other alone or when combined.
Referring to Claim 20, Pattabiraman in view of Blassin in further view of Ruhl teaches a system. Claim 20 recites the same or similar limitations as those addressed above in claim 1, Claim 20 is therefore rejected for the same reasons as set forth above in claim 1, except for the following noted exception,
a processor, a memory including instructions executable by the processor (see; col. 15, lines (9-11) a process computer that executes a program.
Claims 4, 7, 9, 10, 15, 16, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable in view of Pattabiraman (U.S. Patent Publication 2021/0337032 A1) in view of Blassin et al. (U.S. Patent Publication 2016/0162478 A1) (hereafter Blassin) in further view of Ruhl et al. (U.S. Patent Publication 2011/0119100 A1) (hereafter Ruhl) in further view of Storan et al. (U.S. Patent Publication 2010/0269134 A1) (hereafter Storan).
Referring to Claim 4, see discussion of claim 1 above, while Pattabiraman in view of Blassin in further view of Ruhl teaches the method above, Pattabiraman in view of Blassin in further view of Ruhl does not explicitly disclose a method having the limitations of, however,
Storan teaches identifying, by the segmentation component, a plurality of attributes characterizing the plurality of users (see; Abstract of Storan teaches determining a segment based on a user profile data),
selecting, by the segmentation component, an attribute of the plurality of attributes, wherein the segment is identified based on the selected attribute (see; par. [0012] of Storan teaches proving content based on determined attributes).
The Examiner notes that Pattabiraman teaches similar to the instant application teaches optimizing network utilization based on segmenting users of online resources. Specifically, Pattabiraman discloses determining user sessions including number of times and actual amount of time and creating segmentation and analyzed data it is therefore viewed as analogous art in the same field of endeavor. Additionally, Blassin teaches information technology platform for language translation and task management and as it is comparable in certain respects to Pattabiraman which optimizing network utilization based on segmenting users of online resources and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Ruhl teaches method and system for displaying anomalies in time series data and as it is comparable in certain respects to Pattabiraman and Blassin which optimizing network utilization based on segmenting users of online resources and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Storan teaches television programing promotion and as it is comparable in certain respects to Pattabiraman, Blassin, and Ruhl which optimizing network utilization based on segmenting users of online resources and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Pattabiraman, Blassin, and Ruhl discloses determining user sessions including number of times and actual amount of time and creating segmentation and analyzed data. However, Pattabiraman, Blassin, and Ruhl fails to disclose identifying, by the segmentation component, a plurality of attributes characterizing the plurality of users and selecting, by the segmentation component, an attribute of the plurality of attributes, wherein the segment is identified based on the selected attribute.
Storan discloses identifying, by the segmentation component, a plurality of attributes characterizing the plurality of users and selecting, by the segmentation component, an attribute of the plurality of attributes, wherein the segment is identified based on the selected attribute.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Pattabiraman, Blassin, and Ruhl identifying, by the segmentation component, a plurality of attributes characterizing the plurality of users and selecting, by the segmentation component, an attribute of the plurality of attributes, wherein the segment is identified based on the selected attribute as taught by Storan since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally Pattabiraman, Blassin, Ruhl, and Storan teach the collecting and analysis of data in order to determine based on analysis content to provide to a user and they do not contradict or diminish the other alone or when combined.
Referring to Claim 7, see discussion of claim 1 above, while Pattabiraman in view of Blassin in further view of Ruhl teaches the method above, Pattabiraman in view of Blassin in further view of Ruhl does not explicitly disclose a method having the limitations of, however,
Storan teaches generating, by the content component, the customized content for the segment based on the segment return value (see; par. [0008] of Storan teaches determining optimal time and channels to deliver content to a specific user (i.e. customized content), Abstract based on a determined segment).
The Examiner notes that Pattabiraman teaches similar to the instant application teaches optimizing network utilization based on segmenting users of online resources. Specifically, Pattabiraman discloses determining user sessions including number of times and actual amount of time and creating segmentation and analyzed data it is therefore viewed as analogous art in the same field of endeavor. Additionally, Blassin teaches information technology platform for language translation and task management and as it is comparable in certain respects to Pattabiraman which optimizing network utilization based on segmenting users of online resources and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Ruhl teaches method and system for displaying anomalies in time series data and as it is comparable in certain respects to Pattabiraman and Blassin which optimizing network utilization based on segmenting users of online resources and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Storan teaches television programing promotion and as it is comparable in certain respects to Pattabiraman, Blassin, and Ruhl which optimizing network utilization based on segmenting users of online resources and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Pattabiraman, Blassin, and Ruhl discloses determining user sessions including number of times and actual amount of time and creating segmentation and analyzed data. However, Pattabiraman, Blassin, and Ruhl fails to disclose generating, by the content component, the customized content for the segment based on the segment return value.
Storan discloses generating, by the content component, the customized content for the segment based on the segment return value.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Pattabiraman, Blassin, and Ruhl generating, by the content component, the customized content for the segment based on the segment return value as taught by Storan since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Pattabiraman, Blassin, Ruhl, and Storan teach the collecting and analysis of data in order to determine based on analysis content to provide to a user and they do not contradict or diminish the other alone or when combined.
Referring to Claim 9, see discussion of claim 1 above, while Pattabiraman in view of Blassin in further view of Ruhl teaches the method above, Pattabiraman in view of Blassin in further view of Ruhl does not explicitly disclose a method having the limitations of, however,
Storan teaches generating, by the prediction component, a first return value for the first subset based on the time series data, wherein the segment return value is based on the first return value (see; par. [0086] of Storan teaches providing a predicted segment based on estimated segment size).
The Examiner notes that Pattabiraman teaches similar to the instant application teaches optimizing network utilization based on segmenting users of online resources. Specifically, Pattabiraman discloses determining user sessions including number of times and actual amount of time and creating segmentation and analyzed data it is therefore viewed as analogous art in the same field of endeavor. Additionally, Blassin teaches information technology platform for language translation and task management and as it is comparable in certain respects to Pattabiraman which optimizing network utilization based on segmenting users of online resources and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Ruhl teaches method and system for displaying anomalies in time series data and as it is comparable in certain respects to Pattabiraman and Blassin which optimizing network utilization based on segmenting users of online resources and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Storan teaches television programing promotion and as it is comparable in certain respects to Pattabiraman, Blassin, and Ruhl which optimizing network utilization based on segmenting users of online resources and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Pattabiraman, Blassin, and Ruhl discloses determining user sessions including number of times and actual amount of time and creating segmentation and analyzed data. However, Pattabiraman, Blassin, and Ruhl fails to disclose generating, by the prediction component, a first return value for the first subset based on the time series data, wherein the segment return value is based on the first return value.
Storan discloses generating, by the prediction component, a first return value for the first subset based on the time series data, wherein the segment return value is based on the first return value.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Pattabiraman, Blassin, and Ruhl generating, by the prediction component, a first return value for the first subset based on the time series data, wherein the segment return value is based on the first return value as taught by Storan since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Pattabiraman, Blassin, Ruhl, and Storan teach the collecting and analysis of data in order to determine based on analysis content to provide to a user and they do not contradict or diminish the other alone or when combined.
Referring to Claim 10, see discussion of claim 9 above, while Pattabiraman in view of Blassin in further view of Ruhl in further view of Storan teaches the method above, Pattabiraman in view of Blassin in further view of Ruhl does not explicitly disclose a method having the limitations of, however,
Storan teaches generating, by the prediction component, a second return value for the second subset based on the time series data, wherein the segment return value is based on the first return value and the second return value (see; par. [0086] of Storan teaches providing a predicted segment based on estimated segment size using models to predict the segments).
The Examiner notes that Pattabiraman teaches similar to the instant application teaches optimizing network utilization based on segmenting users of online resources. Specifically, Pattabiraman discloses determining user sessions including number of times and actual amount of time and creating segmentation and analyzed data it is therefore viewed as analogous art in the same field of endeavor. Additionally, Blassin teaches information technology platform for language translation and task management and as it is comparable in certain respects to Pattabiraman which optimizing network utilization based on segmenting users of online resources and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Ruhl teaches method and system for displaying anomalies in time series data and as it is comparable in certain respects to Pattabiraman and Blassin which optimizing network utilization based on segmenting users of online resources and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Storan teaches television programing promotion and as it is comparable in certain respects to Pattabiraman, Blassin, and Ruhl which optimizing network utilization based on segmenting users of online resources and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Pattabiraman, Blassin, and Ruhl discloses determining user sessions including number of times and actual amount of time and creating segmentation and analyzed data. However, Pattabiraman, Blassin, and Ruhl fails to disclose generating, by the prediction component, a second return value for the second subset based on the time series data, wherein the segment return value is based on the first return value and the second return value.
Storan discloses generating, by the prediction component, a second return value for the second subset based on the time series data, wherein the segment return value is based on the first return value and the second return value.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Pattabiraman, Blassin, and Ruhl generating, by the prediction component, a second return value for the second subset based on the time series data, wherein the segment return value is based on the first return value and the second return value as taught by Storan since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Pattabiraman, Blassin, Ruhl, and Storan teach the collecting and analysis of data in order to determine based on analysis content to provide to a user and they do not contradict or diminish the other alone or when combined.
Referring to Claim 16, see discussion of claim 1 above, while Pattabiraman in view of Blassin in further view of Ruhl teaches the method above, Pattabiraman in view of Blassin in further view of Ruhl does not explicitly disclose a method having the limitations of, however,
Storan teaches identifying, by the prediction component, a frequency for the time series data (see; par. [0041] of Storan teaches determining a frequency of a user watches and the changes when they watch during different time periods), and
selecting, by the prediction component, a model for computing the segment return value based on the frequency (see; par. [0085]-[0086] of Storan teaches a model that projects a segment based on analyzed data, par. [0041] based on determining a frequency of a user watches and the changes when they watch during different time periods).
The Examiner notes that Pattabiraman teaches similar to the instant application teaches optimizing network utilization based on segmenting users of online resources. Specifically, Pattabiraman discloses determining user sessions including number of times and actual amount of time and creating segmentation and analyzed data it is therefore viewed as analogous art in the same field of endeavor. Additionally, Blassin teaches information technology platform for language translation and task management and as it is comparable in certain respects to Pattabiraman which optimizing network utilization based on segmenting users of online resources and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Ruhl teaches method and system for displaying anomalies in time series data and as it is comparable in certain respects to Pattabiraman and Blassin which optimizing network utilization based on segmenting users of online resources and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Storan teaches television programing promotion and as it is comparable in certain respects to Pattabiraman, Blassin, and Ruhl which optimizing network utilization based on segmenting users of online resources and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Pattabiraman, Blassin, and Ruhl discloses determining user sessions including number of times and actual amount of time and creating segmentation and analyzed data. However, Pattabiraman, Blassin, and Ruhl fails to disclose identifying, by the prediction component, a frequency for the time series data, and selecting, by the prediction component, a model for computing the segment return value based on the frequency.
Storan discloses identifying, by the prediction component, a frequency for the time series data, and selecting, by the prediction component, a model for computing the segment return value based on the frequency.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Pattabiraman, Blassin, and Ruhl identifying, by the prediction component, a frequency for the time series data, and selecting, by the prediction component, a model for computing the segment return value based on the frequency as taught by Storan since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Pattabiraman, Blassin, Ruhl, and Storan teach the collecting and analysis of data in order to determine based on analysis content to provide to a user and they do not contradict or diminish the other alone or when combined.
Referring to Claim 19, see discussion of claim 17 above, while Pattabiraman in view of Blassin in further view of Ruhl teaches the method above Claim 19 recites the same or similar limitations as those addressed above in claim 15, Claim 19 is therefore rejected for the same or similar limitations as set forth above in claim 15.
Claims 11 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable in view of Pattabiraman (U.S. Patent Publication 2021/0337032 A1) in view of Blassin et al. (U.S. Patent Publication 2016/0162478 A1) (hereafter Blassin) in further view of Ruhl et al. (U.S. Patent Publication 2011/0119100 A1) (hereafter Ruhl) in view of Storan et al. (U.S. Patent Publication 2010/0269134 A1) (hereafter Storan) in further view of Mappus et al. (U.S. Patent 11,038,940 B2) (hereafter Mappus).
Referring to Claim 11, see discussion of claim 9 above, while Pattabiraman in view of Blassin in further view of Ruhl in further view of Storan teaches the method above, Pattabiraman in view of Blassin in further view of Ruhl in further view of Storan does not explicitly disclose a method having the limitations of, however,
Mappus teaches computing, by the prediction component, a moving average estimator for the first return value based on a plurality of time periods before the first time period, wherein the first return value is based on the moving average estimator (see; col. 2, lines (47-61) and col. 3, lines (27-39) of Mappus teaches a moving average based on time intervals (i.e. time periods) to predict the level of content distribution during the different time intervals).
The Examiner notes that Pattabiraman teaches similar to the instant application teaches optimizing network utilization based on segmenting users of online resources. Specifically, Pattabiraman discloses determining user sessions including number of times and actual amount of time and creating segmentation and analyzed data it is therefore viewed as analogous art in the same field of endeavor. Additionally, Blassin teaches information technology platform for language translation and task management and as it is comparable in certain respects to Pattabiraman which optimizing network utilization based on segmenting users of online resources and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Ruhl teaches method and system for displaying anomalies in time series data and as it is comparable in certain respects to Pattabiraman and Blassin which optimizing network utilization based on segmenting users of online resources and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Storan teaches television programing promotion and as it is comparable in certain respects to Pattabiraman, Blassin, and Ruhl which optimizing network utilization based on segmenting users of online resources and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Mappus teaches streaming content cache scheduling and as it is comparable in certain respects to Pattabiraman, Blassin, Ruhl and Storan which multiple dataset correlation and content delivery system and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Pattabiraman, Blassin, Ruhl and Storan discloses the targeted content delivery system which allows for content to be delivered based on collected and analyzed data. However, Pattabiraman, Blassin, Ruhl and Storan fails to disclose computing, by the prediction component, a moving average estimator for the first return value based on a plurality of time periods before the first time period, wherein the first return value is based on the moving average estimator.
Mappus discloses computing, by the prediction component, a moving average estimator for the first return value based on a plurality of time periods before the first time period, wherein the first return value is based on the moving average estimator.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Pattabiraman, Blassin, Ruhl and Storan computing, by the prediction component, a moving average estimator for the first return value based on a plurality of time periods before the first time period, wherein the first return value is based on the moving average estimator as taught by Mappus since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Pattabiraman, Blassin, Ruhl, Storan and Mappus teach the collecting and analysis of data in order to determine based on analysis content to provide to a user and they do not contradict or diminish the other alone or when combined.
Referring to Claim 18, see discussion of claim 17 above, while Pattabiraman in view of Blassin in further view of Ruhl teaches the method above Claim 18 recites the same or similar limitations as those addressed above in claim 11, Claim 18 is therefore rejected for the same or similar limitations as set forth above in claim 11.
Claim 12 is/are rejected under 35 U.S.C. 103 as being unpatentable in view of Pattabiraman (U.S. Patent Publication 2021/0337032 A1) in view of Blassin et al. (U.S. Patent Publication 2016/0162478 A1) (hereafter Blassin) in further view of Ruhl et al. (U.S. Patent Publication 2011/0119100 A1) (hereafter Ruhl) in further view of Storan et al. (U.S. Patent Publication 2010/0269134 A1) (hereafter Storan) in further view of Mappus et al. (U.S. Patent 11,038,940 B2) (hereafter Mappus) in further view of Davies et al. (U.S. Patent Publication 2022/0114472 A1) (hereafter Davies).
Referring to Claim 12, see discussion of claim 11 above, while Pattabiraman in view of Blassin in further view of Ruhl in further view of Storan in further view of Mappus teaches the method above, Pattabiraman in view of Blassin in further view of Ruhl in further view of Storan in further view of Mappus does not explicitly disclose a method having the limitations of, however,
Davies teaches the moving average estimator comprises an autoregressive moving average (see; Abstract of Davies teaches a media forecast using an auto regressive moving average).
The Examiner notes that Pattabiraman teaches similar to the instant application teaches optimizing network utilization based on segmenting users of online resources. Specifically, Pattabiraman discloses determining user sessions including number of times and actual amount of time and creating segmentation and analyzed data it is therefore viewed as analogous art in the same field of endeavor. Additionally, Blassin teaches information technology platform for language translation and task management and as it is comparable in certain respects to Pattabiraman which optimizing network utilization based on segmenting users of online resources and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Ruhl teaches method and system for displaying anomalies in time series data and as it is comparable in certain respects to Pattabiraman and Blassin which optimizing network utilization based on segmenting users of online resources and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Storan teaches television programing promotion and as it is comparable in certain respects to Pattabiraman, Blassin, and Ruhl which optimizing network utilization based on segmenting users of online resources and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Mappus teaches streaming content cache scheduling and as it is comparable in certain respects to Pattabiraman, Blassin, Ruhl and Storan which multiple dataset correlation and content delivery system and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Davies teaches generating machine learning driven telecast forecasts and as it is comparable in certain respects to Pattabiraman, Blassin, Ruhl, Storan and Mappus which multiple dataset correlation and content delivery system and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Pattabiraman, Blassin, Ruhl, Storan and Mappus discloses the targeted content delivery system which allows for content to be delivered based on collected and analyzed data. However, Pattabiraman, Blassin, Ruhl, Storan and Mappus fails to disclose the moving average estimator comprises an autoregressive moving average.
Davies discloses the moving average estimator comprises an autoregressive moving average.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Pattabiraman, Blassin, Ruhl, Storan and Mappus the moving average estimator comprises an autoregressive moving average as taught by Davies since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Pattabiraman, Blassin, Ruhl, Storan, Mappus and Davies teach the collecting and analysis of data in order to determine based on analysis content to provide to a user and they do not contradict or diminish the other alone or when combined.
Claim 13 is/are rejected under 35 U.S.C. 103 as being unpatentable in view of Pattabiraman (U.S. Patent Publication 2021/0337032 A1) in view of Blassin et al. (U.S. Patent Publication 2016/0162478 A1) (hereafter Blassin) in further view of Ruhl et al. (U.S. Patent Publication 2011/0119100 A1) (hereafter Ruhl) in view of Storan et al. (U.S. Patent Publication 2010/0269134 A1) (hereafter Storan) in further view of Mappus et al. (U.S. Patent 11,038,940 B2) (hereafter Mappus) in further view of Klarfeld et al. (U.S. Patent Publication 2003/0067554 A1) (hereafter Klarfeld).
Referring to Claim 13, see discussion of claim 11 above, Pattabiraman in view of Blassin in further view of Ruhl in further view of Storan in further view of Mappus teaches the method above, Pattabiraman in view of Blassin in further view of Ruhl in further view of Storan in further view of Mappus does not explicitly disclose a method having the limitations of, however,
Klarfeld teaches computing, by the prediction component, a seasonal parameter of the time series data, wherein the moving average estimator is based on the seasonal parameter (see; par. [0090] of Klarfeld teaches seasonal viewing parameters for providing personalized tv).
The Examiner notes that Pattabiraman teaches similar to the instant application teaches optimizing network utilization based on segmenting users of online resources. Specifically, Pattabiraman discloses determining user sessions including number of times and actual amount of time and creating segmentation and analyzed data it is therefore viewed as analogous art in the same field of endeavor. Additionally, Blassin teaches information technology platform for language translation and task management and as it is comparable in certain respects to Pattabiraman which optimizing network utilization based on segmenting users of online resources and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Ruhl teaches method and system for displaying anomalies in time series data and as it is comparable in certain respects to Pattabiraman and Blassin which optimizing network utilization based on segmenting users of online resources and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Storan teaches television programing promotion and as it is comparable in certain respects to Pattabiraman, Blassin, and Ruhl which optimizing network utilization based on segmenting users of online resources and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Karfeld teaches personalized tv based on segments and as it is comparable in certain respects to Pattabiraman, Blassin, Ruhl, Storan, and Mappus which multiple dataset correlation and content delivery system and method as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Pattabiraman, Blassin, Ruhl, Storan, and Mappus discloses the targeted content delivery system which allows for content to be delivered based on collected and analyzed data. However, Pattabiraman, Blassin, Ruhl, Storan, and Mappus fails to disclose computing, by the prediction component, a seasonal parameter of the time series data, wherein the moving average estimator is based on the seasonal parameter.
Karfeld discloses computing, by the prediction component, a seasonal parameter of the time series data, wherein the moving average estimator is based on the seasonal parameter.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Pattabiraman, Blassin, Ruhl, Storan, and Mappus the computing, by the prediction component, a seasonal parameter of the time series data, wherein the moving average estimator is based on the seasonal parameter as taught by Klarfeld since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Pattabiraman, Blassin, Ruhl, Storan, Mappus and Karfeld teach the collecting and analysis of data in order to determine based on analysis content to provide to a user and they do not contradict or diminish the other alone or when combined.
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 STEPHEN S SWARTZ whose telephone number is (571)270-7789. The examiner can normally be reached Mon-Fri 9:00 - 6:00.
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, Boswell Beth can be reached at 571 272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/S.S.S/Examiner, Art Unit 3625
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625
Prosecution Insights