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 . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
DETAILED ACTION
This Final Office Action is in response Applicant communication filled on 12/17/2025.
Status of Claims
Claims 1, 16, 19, and 20 have been amended and Claim 15 has been canceled by Applicant.
Claims 8-14 remain withdrawn from consideration as directed to non-elected inventions.
Claims 1-7 and 16-20 are currently under examination and have been rejected as follows.
# 1. Response to Applicant’s rebuttal of 35 U.S.C. 112 rejections
- 112(b) rejection in the previous act is withdrawn in view of Applicant’s amending independent Claims 1,9,20 in a manner suggested by Examiner at Non-Final Act 10/17/2025 p.4.
# 2. Response to Applicant’s rebuttal of 35 U.S.C. 101 rejection
- 101 rejection in the previous act is maintained and detailed as follows:
Remarks 12/17/2025 p.10 ¶4-p.11 ¶1 argues that analogous to Example 39, the current claims allow predictions to be made for a client, even when that client does not have a significant amount of data, while improving predictions over time as client-specific data become available. Specifically, Applicant argues that in the current claims a base predictive model, trained on a general training dataset that is derived from a plurality of clients, is used for prediction until there is sufficient client specific training data to train a client-specific sales prediction model for a specific client, at which point, the client-specific sales prediction model is used for prediction for that client.
Remarks 12/17/2025 p.11 ¶2 also argues that similar to Example 39, the claimed model improve over time and evolve as the client's data evolve. In particular, Applicant argues that the client-specific sales prediction model is continually fine-tuned "over each of one or more retraining cycles" using new client-specific data and replaced with the retrained model when the performance of the retrained model represents an improvement over the prior model.
Examiner fully considered the 101 arguments but disagrees finding them unpersuasive by first submitting that USPTO Example 39, as relied by Remarks 12/17/2025 p. 10 ¶4-p.11 ¶2, is, along with all other 101 examples merely illustrative and non-precedential. See USPTO “2019 PEG, 101 Examples 37-42 document entitled “Subject Matter Eligibility Examples: Abstract Ideas” p.1 ¶1 2nd sentence. “The examples below are hypothetical and only intended to be illustrative of the claim analysis under the 2019 PEG” corroborating “May 2016 Update: Memorandum- Formulating a Subject Matter Eligibility Rejection and Evaluating the Applicant’s Response to a Subject Matter Eligibility Rejection”, p.5 ¶2 Section C: “USPTO issued examples in conjunction with the Interim Eligibility Guidance, including […] July 2015 Update Appendix I: Examples […]; These examples, many of which are hypothetical, were drafted to show exemplary analyses under the Interim Eligibility Guidance and are intended to be illustrative of the analysis only. While some of the fact patterns draw from U.S. Supreme Court and U.S. Court of Appeals for the Federal Circuit decisions, the examples do not carry the weight of court decisions. Therefore, the examples should not be used as a basis for a subject matter eligibility rejection.
In any event, recitations of “train[ing] the sales prediction model using a general training dataset that comprises labeled activity data for a plurality of clients, to produce a base sales prediction model” and “fine-tun[ing] the base sales prediction model, using a client-specific training dataset that consists of labeled activity data for only the at least one client, to produce a client-specific sales prediction model” as raised by Applicant at Remarks 12/17/2025 p.10 last ¶ - p.11 ¶1 and recitations of “fine-tune the base sales prediction model” “over each of one or more retraining cycles”, with the model fine-tuned “over each of one or more retraining cycles” using new client-specific data, and replaced with the retrained model when the performance of the retrained model represents an improvement over the prior model, as raised by Applicant at Remarks 12/17/2025 p.11 ¶2 are irreconcilably different than the technological details provided by the hypothetical and nonprecedential USPTO’s Example 39 in “applying one or more transformations to each digital facial image including mirroring, rotating, smoothing, or contrast reduction to create a modified set of digital facial images; creating a first training set comprising the collected set of digital facial images, the modified set of digital facial images, and a set of digital non-facial images; training the neural network in a first stage using the first training set”.
Here, the asserted improvements address the lack of data, known as cold start problem, in generating a sales prediction for an account. Thus, the asserted improvements are at best entrepreneurial and abstract rather than technological. While the Applicant’s solution proposes a compromise for the cold start or lack of initial data, by initially utilizing a base predictive model, trained on a general training dataset derived from a plurality of clients, used for prediction until there is sufficient client specific training data to train a client-specific sales prediction model for a specific client, (Remarks 12/17/2025 p. 10 ¶4-p.11 ¶1), the Examiner finds that such solution still addresses the abstract sales prediction model, not an improvement in actual technology or the computer itself. The same ineligibility rationale applies to the argument of the sales prediction model being continually fine-tuned over each of one or more retraining cycles using new client-specific data and replaced with the retrained model when the performance of the retrained model represents an improvement over the prior model, as argued Applicant at Remarks 12/17/2025 p.11 ¶2. Examiner justifies such rationale by pointing to SAP Am, Inc v InvestPic as cited by MPEP 2106.04(a)(2) I. C (i). Specifically, similar to how the Applicant alleges an improvement in predictions over time as client-specific data become available (Remarks 12/17/2025 p.10 ¶4-p.11 ¶1) and sales model replacement as improvement over the prior sales prediction model (Remarks 12/17/2025 p.11 ¶2), the ‘291 patent of SAP supra, described aspects of existing practices declared in need of improvement, that performed rudimentary statistical functions not useful to investors in forecasting the behavior of financial markets because they relied upon assumptions that the underlying probability distribution function (‘PDF’) for the financial data follows a normal or Gaussian distribution.” (’291 patent, col. 1, lines 24–36). Yet, “the PDF for financial market data is heavy tailed (i.e., the histograms of financial market data typically involve many outliers containing important information),” rather than symmetric like a normal distribution. Id., col. 1, lines 36– 37, 41–44. To remedy those deficiencies, the patent in “SAP” proposed utilization of resampled statistical methods for analysis of financial data, which did not assume a normal probability distribution. One such method is a bootstrap method, which estimates distribution of data in a pool (a sample space) by repeated sampling of the data in the pool. A sample space in a boot-strap method can be defined by selecting a specific investment or a particular period of time. Data samples are drawn from the sample space with replacement: samples are drawn from the sample space and then returned to the pool before next sample is drawn. Yet, the Federal Circuit noted: “Dependent method claims 2-7 and 10 add limitations… [that] require the resampling method to be a bootstrap method." SAP, 260 F. Supp. 3d at 715 . Likewise, "[c]laims 8 and 9 add limitations that the statistical method is a jackknife method and a cross validation method." Id. at 716. Because bootstrap, jack-knife, and cross-validation methods are all "particular methods of resampling," those features simply provide further narrowing of what are still mathematical operations. They add nothing outside the abstract realm. See Mayo, 566 U.S. at 88-89 (stating that narrow embodiments of ineligible matter, citing mathematical ideas as an example, are still ineligible); buySAFE, 765 F.3d at 1353 (same). Dependent method claims 12-21 are no different”. “Here, the focus of the claims is not any improved computer or network, but the improved mathematical analysis”. In a similar vein, the Supreme Court also found that an iterative formula for computing an alarm limit, by reputedly substituting the model with a most recent model, remained patent ineligible. see Parker v. Flook, 437 U.S. 584, 585, 198 USPQ 193, 195 (1978), as cited by MPEP 2106.04(a)(2) I. Specifically, in Flook, the process was repeated at the selected time intervals, and in each updating computation, the most recently calculated alarm base and the current measurement of the process variable was substituted for the corresponding numbers in the original calculation.
Since the implementation of resampled statistical model in SAP supra, and the iterative or repetitive process of model substitution in Flook supra, did not save their underlining claims from patent ineligibility, the Examiner analogously reasons that here, the analogous “fine-tun[ing] the base sales prediction model, using a client-specific training dataset that consists of labeled activity data for only the at least one client, to produce a client-specific sales prediction model” as raised by Applicant at Remarks 12/17/2025 p.10 last ¶ - p.11 ¶1, and “fine-tun[ing] the base sales prediction model” “over each of one or more retraining cycles”, with the model fine-tuned "over each of one or more retraining cycles" using new client-specific data, and replaced with the retrained model when the performance of the retrained model represents an improvement over the prior model as raised by Applicant at Remarks 12/17/2025 p.11 ¶2, would also not preclude the claims from reciting, describing or setting forth the abstract fundamental practices and underlining mathematical manipulations. Yet, MPEP 2106.04(a)(2) II A ¶2 is clear that the term "fundamental" is not used in the sense of necessarily being old or well-known1, but rather as a building block of modern economy. Here, the alleged improvement in the sales prediction model would present such fundamental, building block of modern regardless of whether or not such abstract improvement is old or well-known. Yet, MPEP 2106.04(d)(1) is clear that “improvement in the judicial exception itself is not an improvement in technology” and MPEP 2106.04 I ¶3 is also clear that claims directed to narrow laws that have limited applications, remain patent ineligible. Also, MPEP 2106.04 I, cites Myriad, 569 U.S at 591, 106 USPQ2d at 1979 to stress that even a “groundbreaking, innovative, or even brilliant discovery does not by itself satisfy the 101 inquiry”. The “Myriad” rationale was corroborated by “SAP Am Inc v InvestPic” cited by MPEP 2106.04(a)(2) I.C(i). Digging deeper into the Court’s rationale in SAP supra, Examiner finds the Court ruled that, “even if one assumes that the techniques claimed are groundbreaking, innovative, or even brilliant those features are not enough for eligibility because their innovation is innovation in ineligible subject matter. An advance of that nature is ineligible for patenting”. That is, “no matter how much of an advance in the field the claims” [would] “recite the advance” [would still] “lie entirely in the realm of abstract ideas” with no plausibly alleged innovation in non-abstract application realm. Here, as in SAP Am Inc v. InvestPic, LLC, 890 F.3d 1016, 126 USPQ.2d 1638 (Fed. Cir. 2018), no matter how much of an advance or improvement in the sales prediction the claims would recite, said advance would still lie entirely within the realm of Certain Methods of Organizing Human Activities with no plausibly of the alleged innovation entering the non-abstract realm. The “SAP” findings were corroborated by Versata Dev Grp Inc v SAP Am Inc 115 USPQ2d 1681 Fed Cir 2015 again undelaying the difference between improvement to entrepreneurial goal objective versus improvement to actual technology. see MPEP 2106.04.
Here, the features argued by Remarks 12/17/2025 p.10 ¶4-p.11 ¶2, would correspond to such improved abstract, mathematical analysis to achieve an equally abstract fundamental or economic concept of “sales prediction”, as a fundamental building block, which would be ineligible following the legal tests of at least SAP, Flook, Myriad, and Versata as cited by MPEP supra.
Also, the level of computerization used for performing such mathematical manipulations at best representing a mere application of the abstract exception [MPEP 2106.05(f)(2)(i)] and/or a narrowing of the abstract idea to a field of use or technological environment [MPEP 2106.05(h)], none of which would integrate the abstract exception into a practical appclaition, and for the same reasons none of which providing significantly more than what already found abstract. Thus, the subject matter eligibility arguments are unpersuasive and the claims are ineligible.
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#3. and #4 Applicant’s rebuttal of 35 U.S.C. 102 and 35 U.S.C. 103 rejections
Remarks 12/17/2025 p.11 last ¶-p.12 ¶1 states that independent Claims 1,19,20 have been amended to incorporate the subject matter of dependent Claim 15, which the Non-Final Act 10/17/2025 p.19 last ¶ - p.20 found allowable over the prior art.
Examiner fully considered the Applicant’s argument and finds it persuasive. Examiner confirms that by incorporating the allowable subject matter from now canceled dependent Claim 15 into parent independent Claims 1,19,20, said Claims 1,19,20 now overcome the prior art. Examiner also reincorporates all allowability rationales as articulated by Non-Final Act 10/17/2025 p.20.
Claims 2-7,16-18 are dependent and overcome the prior art by dependency to parent Claim 1.
To be clear, novelty (35 USC 102) and non-obviousness (35 USC 103) pertain to features that are mostly abstract, or incapable to integrate the abstract idea or provide significantly more, which do not render the claims patent eligible (35 USC 101). Simply put, the novel and non-obviousness rationale above do not necessarily render the claims patent eligible. See MPEP 2106.04 I ¶5, 3rd sentence citing Mayo, 566 U.S. 71, 101 USPQ2d at 1965); Flook, 437 U.S. at 591-92, 198 USPQ2d at 198 "the novelty of the mathematical algorithm is not a determining factor at all”.
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 and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea, here abstract idea) without significantly more. The claims recite, describe, or set forth abstract idea of a “sales prediction” (Claims 1,16-20) and “alerting at least one recipient about” [of] a detect[ed] “spike in the sales prediction” “for at least one of the one or more accounts” (dependent Claim 18) as read in light of the Field of Invention at Original Specification ¶ [3]. These fall within the fundamental economic practices or principles sub-grouping of MPEP 2106.04(a)(2) II A, with the term “fundamental” clarified by MPEP 2106.04(a)(2) II A ¶2 as not used in the sense of necessarily being "old" or "well-known" but rather as a building block of modern economy. Such fundamental economic practices are set forth here by the “sales prediction” (Claims 1,16-20) and “alerting at least one recipient about” [of] a detect[ed] “spike in the sales prediction” “for at least one of the one or more accounts” are further based on receiv[ed] behavior or commercial “activity” [MPEP2106.04(a)(2) II B] “associated with one of one or more accounts” (independent Claims 1,19,20) when read in light of Original Specification ¶ [55] 5th sentence, as that of “existing or potential customer of the client”, and with further considerations for equally abstract economic or commercial principles of “quality-level specific products” (dependent Claim 5), “the quality level corresponding to each activity represents one of a persona level of a person” (dependent Claims 6,7). All these fundamental and commercial processes and principles fall within the broader abstract grouping of “Certain Methods of organizing Human Activities” [MPEP 2106.04(a)(2) II].
Further, it could perhaps also be argued that these fundamental economic practices and/or principles based on commercial concepts can be implemented using equally abstract2 computer-aided cognitive evaluation and judgement for a subsequent observation or “output” of abstract, mental processes as enumerated by MPEP 2106.04(a)(2) III ¶2 and 2106.04(a)(2) III C, such as, through the combination of collecting information, analyzing it, and displaying certain results of the collection and analysis3 listed by MPEP 2106.04(a)(2) III A, 5th bullet point.
At their turn, such computer-aided evaluation and judgment, can also be argued as achievable through equally abstract mathematical relationships expressed in words and its associated predictive calculations as tested per MPEP 2106.04(a)(2) I A,C. For example, MPEP 2106.04(a)(2) I A iv cites Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717,1721 (Fed Cir. 2014) to state that generating first and second data by taking existing information, manipulating the data using mathematical correlations, and organizing this information into a new form, still sets forth the abstract exception. Such mathematical relationships expressed in words and its associated calculations are preponderantly recited, described or set forth here as: “apply the sales prediction model to normalized metrics for the plurality of activity types to determine a sales prediction score for the account, wherein the sales prediction score for the account represents a probability that the account will produce a sales opportunity, and output” [or display certain results of] “the sales prediction score to one or more downstream functions” (independent Claims 1,19,20), “the normalized metric is based on the recency of the activities of the activity type in a lookback period, and wherein the lookback period comprises a plurality of time intervals that are numbered from most recent to least recent” (dependent Claim 2), “wherein calculating the normalized metric comprises calculating a normalization factor by subtracting a ratio, between a number of a most recent one of the plurality of time intervals in which activity of the activity type occurred to a total number of the time intervals in the lookback period, from a value of one” (dependent Claim 3), “wherein the normalized metric either consists of the normalization factor, or comprises a product of the normalized metric and another parameter” (dependent Claim 4), “classifying each time interval, which has at least one activity, in at least a most recent subset of time intervals in the lookback window as one of a plurality of quality levels, wherein each of the plurality of quality levels is associated with a respective weight; and calculating a normalization factor as a sum of a plurality of quality-level specific products, wherein each quality-level specific product is a product of the respective weight associated with a corresponding one of the plurality of quality levels and a difference between a value of one and a ratio between a number of a most recent one of the plurality of time intervals in which activity of the corresponding quality level occurred to a total number of the time intervals in the lookback period” (dependent Claim 5), “wherein the quality level corresponding to each activity represents one of a persona level of a person associated with that activity or a confidence in a mapping between that activity and the account” (dependent Claim 6), “wherein the quality level corresponding to each activity represents the persona level of the person associated with that activity when the person is identified in the activity data, and the confidence in the mapping between that activity and the account when the person is not identified in the activity data” (dependent Claim 7), “detect a spike in the sales prediction score output for at least one of the one or more accounts, based on the sales prediction score and one or more previously output sales prediction scores for the at least one account, wherein the spike is represented by a rate of increase in the sales prediction score, relative to the one or more previously output sales prediction scores, that is greater than a predefined threshold; and in response to detecting the spike, alerting at least one recipient about the spike” (dependent Claim 18)
As per the recitations of “train the sales prediction model using a general training dataset that comprises labeled activity data for a plurality of clients, to produce a base sales prediction model” “prior to applying the sales prediction model” (independent Claims 1,19,20) followed by “fine-tune the base sales prediction model, using a client-specific training dataset that consists of labeled activity data for only the at least one client, to produce a client specific sales prediction model” and “fine-tune the client-specific sales prediction model, using a client-specific retraining dataset that comprises labeled activity data that have been acquired for the at least one client since a most recent fine-tuning of the client-specific sales prediction model, to produce a new client-specific sales prediction model” (dependent Claims 1,19,20), Examiner submits that such training and subsequent fine-tuning could be argued as examples of a computerized environment or tool, which according to MPEP 2106.04(a)(2) III C #2, #3 would not preclude the claims from setting forth the evaluation and judging components of the abstract exception. Further, here, such computerized environment or tool, as listed by MPEP 2106.04(a)(2) III C #2,#3 above, appears to be corroborated by the subsequent recitations of “Tomek links” at dependent Claim 16 and “Bayesian- based generalized linear model” at dependent Claim 17.
Further still, the fact that each of independent Claims 1,19,20 recites “when a performance of the new client-specific sales prediction model represents an improvement over a currently operational client-specific prediction model, replace the currently operational client-specific prediction model with the new client-specific sales prediction model” would not necessarily render said claims 1,19,20 less abstract and eligible. First, this is because such contingent “when” limitation in the process or method of dependent claims 1,19,20, can be argued to have limited patentable weight as tested per MPEP 2111.04 II. Second, and separate from the point just made above, and assessment over “improvement over a currently operational client-specific prediction model” would at most qualify as an improving on the abstract exception of sales prediction itself, rather than an improvement to anything that is non-abstract. To justify this, the Examiner points to MPEP 2106.04(a)(2) I C i. which cites SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65, 127 USPQ2d 1597, 1598-1600 (Fed. Cir. 2018), to state that a similar concept of performing resampled statistical analysis to generate resampled distribution represented abstract mathematical calculations. Digging deeper into the Court rationale in SAP supra, the Examiner finds that its invention focused on resampled statistical analysis using computerized algorithms of boot-strap, jackknife, cross validation, and resampling in the abstract modeling of financial data. Yet, the Federal Circuit ruled that “even if one assumes that the techniques claimed are groundbreaking, innovative, or even brilliant those features are not enough for eligibility because their innovation is innovation in ineligible subject matter. An advance of that nature is ineligible for patenting”. Folrling the Federal Circuit rationale in SAP, the Examiner reasons that here, even if one assumes, in the arguendo, that the recitation of “resampling training data based on Tomek links” to generate “the general training dataset, the client-specific training dataset, or the client-specific retraining dataset” at dependent Claim 16 and the recitation of “when a performance of the new client-specific sales prediction model represents an improvement over a currently operational client-specific prediction model, replace the currently operational client-specific prediction model with the new client-specific sales prediction model” at independent Claims 1,19,20 would somehow be groundbreaking, innovative, or even brilliant, their use would still represent innovation in the ineligible subject matter of sales prediction, as analogous to the abstract predictive improvement in the financial data of “SAP” supra. Thus here, as in SAP Am., Inc. v. InvestPic, LLC, 890 F.3d 1016, 126 U.S.P.Q.2d 1638 (Fed. Cir. 2018), “no matter how much of an advance in the field the claims” [would] “recite the advance” [would still] “lie entirely in the realm of abstract ideas” with no plausibly alleged innovation in non-abstract application realm. The “SAP” ruling was corroborated by Versata Dev Grp Inc v SAP Am, Inc 115 USPQ2d 1681 Fed Cir 2015, where the Court again underlined the vital difference between improvement to entrepreneurial goal objective and actual improvement to actual technology. MPEP 2106.04. In an abundance of caution, the claimed computerization, including use of training and fine-tuning will be more granularly tested at subsequent steps. For now, given the preponderance of legal evidence above, the character as a whole of the claims remains undeniably abstract, as recting, describing or setting forth certain methods of organizing human activities [MPEP 2106.04(a)(2) II) implementable through mental processes [MPEP 2106.04(a)(2) III] and/or mathematical concepts [MPEP 2106.04(a)(2) I].
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This judicial exception is not integrated into a practical application because per Step 2A prong two, the individual or combination of the additional, computer-based elements is/are found to merely apply the already recited abstract idea. Here, if not already computer aids as tested above, the computer-based components would at most qualify as additional elements such as: “processor” at independent Claims 1,19,20, and dependent Claim 18, and possibly the training, retraining / and fine-tuning of independent Claims 1,19,20, the use of “Tomek links” at dependent Claim 16 and “Bayesian- based generalized linear model” at dependent Claim 17.
Specifically, here, even when tested per MPEP 2106.05(f)(2)(i), such elements would represent mere computer components used or invoked as tools, upon which the business process of sales prediction is being applied, using the training, retraining / and fine-tuning of independent Claims 1,19,20, use of “Tomek links” at dependent Claim 16 and “Bayesian- based generalized linear model” at dependent Claim 17, which tested per MPEP 2106.05(f)(2)(i) does not integrate the abstract idea into a practical application. Also, MPEP 2106.05(f)(2) ¶1 is clear that use of a computer or other machinery in its ordinary capacity for economic or other tasks such as to: receive, store, or transmit data, does not integrate a judicial exception into a practical application.
Similarly, MPEP 2106.05(f)(2)(iii),(v) states that monitoring audit log data executed on general-purpose computer, and requiring use of software to tailor information and provide it to the user on a generic computer, do not integrate the abstract exception into a practical application.
It then follows that here, any asserted computerized functionality such as “processor” in receiv[ing] or monitoring “activity data” of “each of the plurality of records” [that] “represents an activity of one of a plurality of activity types, and wherein each of the plurality of records is associated with one of one or more accounts” (independent Claims 1,19,20) for subsequent tailoring or “output the sales prediction score to one or more downstream functions” (independent Claims 1,19,20) and “alerting at least one recipient about the spike” “in response to detecting the spike” (dependent Claim 18) would represent mere invocation of computer components to merely apply the abstract exception above, without integrating it into a practical application. Also, MPEP 2106.05(h) vi. cites Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016), to state that limiting the combination of collecting, analyzing and displaying certain results of collection and analysis, to data related to technological environment or field of use, does not integrate the abstract idea into a practical application. It then follows that here, narrowing the aforementioned collecting, analyzing and displaying certain results of the collection and analysis to data related to a predictive field of use, such as the one narrowed at dependent Claims 16-17, would also not integrate the abstract idea into a practical application.
Thus here, there is a preponderance of legal evidence for the claims not reciting additional, computer-based elements capable to integrate the abstract exception into a practical application.
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The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as shown above, the additional computer-based elements merely apply the already recited abstract idea and link the use of abstract idea to a field of use or technological environment. Examiner follows MPEP 2106.05 (d) II and carries over the findings tested per MPEP 2106.05 (f),(h) to submit that the additional computer-based elements also do not provide significantly more. Even assuming arguendo, that further evidence would be required to demonstrate conventionality of the additional, computer-based elements, Examiner would also point as evidence to the high level of generality of the additional elements read in light of Original Disclosure:
- Original Specification ¶ [35] reciting at high level of generality: “Fig.2 is a block diagram illustrating an example wired or wireless system 200 that may be used in connection with various embodiments described herein. For example, system 200 may be used as or in conjunction with one or more of the processes, methods, or functions (e.g., to store and/or execute the software) described herein, and may represent components of platform 110,user system(s) 130, external system(s) 140, and/or other processing devices described herein. System 200 can be any processor-enabled device (e.g., server, personal computer, etc.) that is capable of wired or wireless data communication. Other processing systems and/or architectures may also be used, as will be clear to those skilled in the art”.
- Original Specification ¶ [105] reciting at high level of generality: “The above description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles described herein can be applied to other embodiments without departing from the spirit or scope of the invention. Thus, it is to be understood that the description and drawings presented herein represent a presently preferred embodiment of the invention and are therefore representative of the subject matter which is broadly contemplated by the present invention. It is further understood that the scope of the present invention fully encompasses other embodiments that may become obvious to those skilled in the art and that the scope of the present invention is accordingly not limited”
If necessary, Examiner would also point to MPEP 2106.05(d). I 2. c. as a test for the following publications demonstrating the conventionality of “Tomek links” (dependent Claim 16) “Bayesian-based generalized linear model” (dependent Claim 17).
- US 20180322958 A1 ¶ [0262] The choice of algorithm may be …Tomek links, …, or any other similar technique known in prior art
- US 20150235239 A1 entitled Predicting demand of a newly introduced short lifecycle product within an assortment reciting at ¶ [0034] last sentence: a method in one embodiment initializes, then continually updates the estimates of these parameters over time, e.g., using known methods such as Bayesian updating or replacing the like-item sales for the first t weeks with the actual observed sales, and re-initializing by complete re-estimation.
- US 20220292407 A1 reciting a ¶ [0159] In several embodiments, system 300 can use two sets of threshold optimizations to achieve maximum recall, given a certain pre-defined value of precision can be run: one threshold at a seller level, another threshold at an overall level, which are again combined using Bayesian Model Combination, to obtain the appropriate probability threshold for each seller, which can provide an advantage over the conventional methods.
As per the contingent limitation of “when a performance of the new client-specific sales prediction model represents an improvement over a currently operational client-specific prediction model, replace the currently operational client-specific prediction model with the new client-specific sales prediction model” at independent Claims 1,19,20, even assuming, in the arguendo that it would carry patentable weight, it would not provide significantly more than what was already identified as abstract because MPEP 2106.05(d) II ii. has established that performing repetitive calculations, are well‐understood, routine, and conventional functions citing the ruling in recomputing or readjusting alarm limit values in Flook, 437 U.S. at 594, 198 USPQ2d at 199; Specifically when more granularly investigating Flook supra, the Examiner finds that its process was repeated at the selected time intervals. In each updating computation, the most recently calculated alarm base and the current measurement of the process variable will be substituted for the corresponding numbers in the original calculation. It follows that here, the comparable replac[ing] “the currently operational client-specific prediction model with the new client-specific sales prediction model” “when a performance of the new client-specific sales prediction model represents an improvement over a currently operational client-specific prediction model” at claims 1,19,20 would similarly not render the claims patent eligible.
In conclusion, Claims 1-7 and 16-20 although directed to statutory categories (“method” or process at Claims 1-7, 16-18, “system” or machine at Claim 19, “non-transitory storage medium”, or article of manufacture at Claim 20,) they still recite or set forth the abstract idea (Step 2A prong one), with their additional, computer based elements not integrating the abstract idea into a practical application (Step 2A prong two) or providing significantly more than the abstract idea itself (Step 2B). Thus, the Claims 1-7 and 16-20 are patent ineligible.
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Conclusion
The following art is made of record and considered pertinent to Applicant's disclosure:
- Aguilar Palacios et al, Cold-start promotional sales forecasting through gradient boosted-based contrastive explanations, IEEE Access, 8, p137574-p137586, Jul 27, 2020
- WO 2008008278 A2 A Promotions system and method
- US 20160225063 A1 ¶ [0165] In addition, a system in accordance with the present disclosure may support a data dimension that may be referred to as “Cold-Start”, which may show sales (e.g., per year) of items by volume groups to show how recommendations work in “Cold-Start”. Indications of “0-10”, “11-25”, “26-50”, “51-100”, “101-500”, “501-1000”, “1001-5000”, and “5000+” may be provided.
- US 20160379265 A1 ¶ [0023] 2nd sentence: A cold-start problem occurs when a sales representative has no historical data or very little historical data
- US 20160379266 A1 ¶ [0023] 2nd sentence: A cold-start problem occurs when a sales representative has no historical data or very little historical data.
- US 20140229406 A1 ¶ [0058] processor 44 trains the classifier using on-line learning. In this embodiment, the on-line learning scheme uses two parameters--A default heuristic h and a cold-start number C. The first C profiles (potential leads) are selected for evaluation using the default heuristic h. Since focused querying is applied to these C profiles, it is known which of them are leads and which are not. As such, the first C profiles can be used as a training set for training a classifier. Processor 44 performs this training before selecting the (C+1).sup.th profile.
- US 20150012345 A1 ¶ [0002] Collaborative filtering methods are widely used by recommendation services to predict the items that users are likely to enjoy. These methods rely on the consumption history of users to determine the similarity between users (or items), with the premise that similar users consume similar items. Collaborative filtering approaches are highly effective when there is sufficient data about user preferences. However, they face a fundamental problem when new users who have no consumption history join the recommendation service. A new user needs to enter a significant amount of data before collaborative filtering methods start providing useful recommendations. The specific problem of recommending items to new users is referred to as the "cold-start" recommendation problem.
- US 20230368236 A1 [0003] 2nd sentence: To determine whether to apply a new treatment type to a user, the online concierge system may access a set of treatment models that generate treatment lift scores for treatments of different treatment types. mid-¶ [0040]:For example, for a customer user, a treatment may include notifying the user of a new product, sending a message to the user encouraging the user to submit an order, sending a coupon to the user, offering the user a temporary or permanent discount on orders, offering the user a free product, or offering to reduce or eliminate fees on a user's purchase. For a picker user or a runner user, a treatment may include notifying the user of a possible order for servicing, offering the user an additional service fee for servicing an order from another user, offering a temporary or permanent increase in a service fee or commission paid to the user for servicing orders, or offering a reward to the user for servicing a certain number of orders within a time period
- US 20230055699 A1 teaching Recommender system and method using shared neural item representations for cold-start recommendations
- US 20230368264 A1 teaching Machine learning recommendation engine with improved cold-start performance
- US 20050050158 A1 reciting at ¶ [0054] They are also used to determine the maximum (greatest) sum for each user, which become the denominator in each ratio. It should be understood in advance, however, that under the present invention there are at least two ways of calculating the relative longevity ratio/value and the relative recency ratio/value (described in greater detail below). The first is uni-directional where the ratio from user 14A to user 14B is different from the ratio from user 14B to user 14A. The second way is bi-directional where the ratio from user 14A to user 14B is equal to the ratio from user 14B to user 14A. The former is advantageous if the values need to be normalized around user 14A. The later is advantageous if the values need to be normalized around the relationship. For example if the results will be presented to user 14A then the uni-directional approach should be used. If the values will be used by an administrator who is looking at the social network as a whole then the bi-directional way is likely better. This notion applies to both the relative longevity value, as well as the relative recency value which will be further described below. However, for the illustrative examples described herein, the bi-directional method be shown. ¶ [0057] These MAX values are then used as the denominator for the three relative recency values (ratios). Thus, the relative recency value between users 14A and 14B=6.5/6.5=1 or 100%.
- US 20030120536 A1 reciting at ¶ [0040] It is to be appreciated that the recency frequency class denoted as `RF.sub.1x` for the numerator means that a purchase has occurred during the cycle `C.sub.t+1` (coded as the `1`); it does not matter whether a purchase has occurred during the cycle `C.sub.t` (coded as `x` which may be `1` or `0`). Similarly, the recency frequency class denoted as `RF.sub.xy` for the denominator means that a purchase has occurred or not during the cycle `C.sub.t` (coded as `x`) and that a purchase has occurred or not during the cycle `C.sub.t-1` (coded as `y`).
- US 20160260153 A1 reciting at ¶ [0006] calculating, for each consumer that has purchased the first product and the second product, an average time interval based on a difference in the transaction time and/or date included in a first transaction data entry including a consumer identifier associated with the respective consumer and the first product identifier and a second transaction data entry including the consumer identifier associated with the respective consumer and the second product identifier; calculating, by the processing device, a recency time based on a difference between the identified present time and the purchase time and/or date included in the received transaction data; and calculating, by the processing device, a probability that the specific consumer will purchase the second product based on at least the calculated ratio of consumers, the calculated average time interval, and the calculated recency time.
- US 20220335489 A1 reciting at ¶ [0039] last sentence: As further described below in conjunction with FIG. 5, the modeling engine 218 trains the purchase model based on prior purchases by users, which may modify or update the user model or the item model.
- US 20220414592 A1 reciting at mid-¶ [0015] the conversion rate is determined through another model trained from prior conversions by users of the online concierge system
- US 20180247189 A1 reciting at ¶ [0051] 3rd – 4th sentences: The unidirectional recurrent neural network may be trained with social media posts associated with a single user to allow the unidirectional recurrent neural network to learn patterns across social media posts (e.g., “a user may first express interest in a product before buying but is unlikely to express that they have purchased a product before they were interested.”) Based on these trained patterns, the unidirectional GRU may classify each of the social media posts into one of the AIDBUN classes based on the generated representation of each social media post and the classification or representation associated with one or more chronologically earlier social media posts in the sequence of social media posts.
- US 20250292291 A1 reciting at its claim 42: generating a second set of training items, the second set of training items comprising a set of homes listed for sale prior to being sold, each training item of the second set of training items including a synthetic sale price for a second home
- US 10528858 B1 column 12 lines 14-26: For example, some embodiments fully train the neural network 1017 with the transaction data from multiple customers prior to training an instance of the neural network 1017 with purchase history of a specific customer. Some embodiments fully train the neural network 1017 with the transaction data from multiple customers prior to training an instance of the neural network 1017 with purchase history of a subset of customers that exhibit similar behavioral patterns while the payment instrument is not malfunctioning. Other embodiments may not fully train the neural network 1017 prior to provision of an instance to train with purchase history of a specific customer or a subset of customers.
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 OCTAVIAN ROTARU whose telephone number is (571)270-7950. The examiner can normally be reached on 571.270.7950 from 9AM to 6PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PATRICIA H MUNSON, can be reached at telephone number (571)270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). 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) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/Octavian Rotaru/
Primary Examiner, Art Unit 3624
February 5th, 2026
1 OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1364, 115 U.S.P.Q.2d 1090, 1092 (Fed Cir. 2015) (a new method of price optimization was found to be a fundamental economic concept);
2 MPEP 2106.04(a): “examiners should identify at least one abstract idea grouping, but preferably identify all groupings to the extent possible”.
3 Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016);