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 08/22/2025.
Status of Claims
Claims 1 and 3-7, 9-15 were amended by Applicant with the 08/22/2025 amendment.
Claims 1-16 remain pending and have been rejected as follows.
Terminal Disclaimer/ Double Patenting
- The terminal disclaimer filed on 08/22/2025 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of Patent 11562386 has been reviewed and is accepted. The terminal disclaimer has been recorded. Thus, the non-statutory double patenting rejection in Non-Final Act 04/24/2025 p.2-p.3 ¶2 has been withdrawn.
Response to Applicant’s rebuttal of Claim Rejections under 35 USC 112
- 112(b) and (d) rejections in the previous act are withdrawn in view of Applicant’s amendment as suggested by examiner.
Response to Applicant’s rebuttal of Claim Rejections under 35 USC 101
- Step 2A Prong one: Remarks 08/22/2025 p.11 ¶4-p.12 cites Fig.4 and Spec. ¶ [0065]-[0066] to argue that the amended claims are directed to simulation aspects of the intelligent agent, not to actual retailer system or environment 450 of Fig.4. Remarks 08/22/2025 p.13-p.14 further argues the claims are not directed to improvement to an abstract entrepreneurial concept through optimization or simulation for promotional performance (Remarks 08/22/2025 p.13 ¶1), as identified by Non-Final Act 04/24/2025 p.7 but rather to a simulation, which is argued by Applicant as not one of the enumerated sub-grouping of fundamental economic principles or practices, commercial or legal interactions, etc. or an enumerated sub-group of the broader Certain Methods of Organizing Human Activities but rather alleged by Applicant at Remarks 08/22/2025 p.14 ¶3-p.15 ¶1 as a technical operation that involves the intelligent agent. Further, Remarks 08/22/2025 p.15 ¶2-p.16 ¶2 asserts that the technical problem is that retailers consider one promotion at a time and do not consider the impact of a sequence of promotions on total sales over a time period, and the technical solution is the use of simulation based on prior actions of the system, a simulated state of an environment, and a promotional model, with the Remarks 08/22/2025 p.16 ¶3-¶4 further comparing the current claims to DDR and Enfish. Further, Remarks 08/22/2025 p.16 last ¶-p.17 ¶3 further asserts “providing for an expected reward for each of the one or more candidate itemsets”, “simulating a sales metric of the plurality of product records based on the respective candidate itemset” is not directed to certain methods of organizing human activities, entrepreneurial or other abstract solutions. Then, Remarks 08/22/2025 p.17 ¶5-p.19 ¶4 argues the claims are similar to Examples 38,39. Lastly, Remarks 08/22/2025 p.19 ¶5-mid-p.20, emphasizes the “reinforcement learning algorithm”, “a simulated state of an environment”, “storing the sensor data in the short-term” and “long-term memory”, and “correcting the simulated state” to argue that the steps recited in independent Claim 1 cannot be practically performed in the human mind.
- Examiner fully considered the Step 2A prong 1 arguments but respectfully disagrees finding them unpersuasive, starting from Applicant’s own admission that the claims are directed to simulation for promotional performance, with the problem being that retailers do not consider the impact of a sequence of promotions on the total sales over a period of time, and the solution being use of simulation based on prior actions of the system, a simulated state of an environment, and a promotional model. Based on Applicant admission Examiner reincorporates all findings of Non-Final At 04/24/2025 p.5 last ¶-p.6 ¶1, to resubmit that “simulating promotional performance”, including “providing for an expected reward for each of the one or more candidate itemsets”, “simulating a sales metric of the plurality of product records based on the respective candidate itemset”, as raised by Remarks 08/22/2025 p.15 ¶2-p.16 ¶2, p.16 last ¶-p.17 ¶3, is not meaningfully different than the offer-based optimization of OIP Techs Inc v Amazon.com, Inc., 788 F.3d 1359,1362-63, 115 USPQ2d 1090, 1092 (Fed. Cir. 2015) cited under MPEP 2106.04(a)(2) II B iii, which falls under the Certain Methods of Organizing Human Activities grouping. Here, the current, “simulating promotional performance”, are similar to the OIP’ claims supra because the simulation is interpreted as an optimization example of an offer, which, at its turn is interpreted as a promotion. Also, similar to the current claims, the OIP’s claims did not necessarily focus on the actual retailing system or environment. Yet, OIP’s claims were nonetheless deemed patent ineligible by the Federal Circuit in OIP Techs Inc v Amazon.com supra.
It then follows that here the analogous argument that the claims are not recited to the retailer system or environment, as made by Applicant at Remarks 08/22/2025 p.12 ¶7, p.16 ¶3 would also not preclude the current claims from reciting, describing or setting forth the abstract exception.
For example, at no point does the MPEP 2106.04(a)(2) II B preclude optimization of promotions (i.e. offers) as not being an integral part, of the marketing activities. Similarly, at no point does MPEP 2106.04(a)(2) II A preclude the simulation of promotions or offers as not being part of the fundamental economic practices or principles. In fact, MPEP 2106.04(a)(2) II A ¶2 cited by Non-Final Act 04/24/2025 p.6 ¶2 last sentence to p.7, is clear that building blocks of the modern economy remain ineligible, with the term “fundamental” not used in the sense of necessarily being "old" or "well-known" citing again to OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1364, 115 U.S.P.Q.2d 1090, 1092 (Fed Cir. 2015) which ruled that a new method of price optimization was still a fundamental economic concept. Here, the simulation or optimizations of promotions or offers, and their subsequent reward based correction represent such fundamental building blocks of the modern economy, and thus remain ineligible, no matter whether or not such simulation or optimizations of promotions / offers is old or well-known as contested by Applicant in at least Remarks 08/22/2025 p.15 ¶2-p.16 ¶2.
Specifically here, far from an actual technological solution to an actual technological problem, both the asserted problem of the retailers not considering the impact of a sequence of promotions on the total sales over a period of time (Remarks 08/22/2025 p.15 ¶2- ¶3) and the asserted solution of using of simulation based on prior actions of the system, a simulated state of an environment, and a promotional model (Remarks 08/22/2025 p.16 ¶2), remain entrepreneurial and abstract, as building blocks of the modern economy, and thus remain ineligible right from the onset, no matter whether or not such the asserted solution in simulating promotions or offers and their subsequent correcti[on] “comprising the measured reward” would be intelligent over what is old and well-known in retail.
In fact, MPEP 2106.04 I, as cited by Non-Final At 04/24/2025 p.7-p.8¶1, stresses that even a “groundbreaking, innovative, or even brilliant discovery does not by itself satisfy the 101 inquiry” citing Myriad,569 U.S at 591, 106 USPQ2d at 1979. The “Myriad” rationale was corroborated by “SAP Am Inc v InvestPic” which is also cited by MPEP 2106.04(a)(2) I.C(i). Digging deeper into the Court’s rationale in SAP supra, the Examiner finds that 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 the rationale of SAP Am Inc v. InvestPic, LLC 890 F.3d 1016,126 USPQ.2d 1638 (Fed. Cir. 2018), no matter how much intelligence or advance the “agent” would provide by use of simulation based on prior actions of the system, a simulated state of an environment, and a promotional model, as alleged by Remarks 08/22/2025 p.16 ¶3-¶4, said advance would still lie entirely within the abstract marketing 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 and actual improvement to actual technology. MPEP 2106.04. This finding is also applicable to the Applicant’s criticism at Remarks 08/22/2025 p.15 ¶4, against the prior art of Ouimet; Kenneth J. US 20150324828 A1 noted by Non-Final At 04/24/2025 p.15, because once again the Examiner stresses that building blocks of the modern economy remain ineligible, with the term “fundamental” not used in the sense of necessarily being "old" or "well-known" citing a OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1364, 115 U.S.P.Q.2d 1090, 1092 (Fed Cir. 2015) from MPEP 2106.04(a)(2) II A ¶2.
To be also clear, considerations of novelty (35 USC 102) and non-obviousness (35 USC 103) over the prior art (argued here on Ouimet) still pertain to features that are abstract, or incapable to integrate the abstract idea or provide significantly more, which do not render the claims patent eligible (35 USC 101). Simply said, the novel and non-obviousness rationale above do not necessarily render the claims patent eligible. See for example 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”.
As per Applicant’s allegation at Remarks 08/22/2025 p.16 ¶3-¶4 that the claims are similar to DDR and Enfish, the Examiner submits that the current legal findings of the present claims are irreconcilably different than what was found eligible in, Enfish and DDR because here, the limitations argued at Remarks 08/22/2025 p.19 are asserted by Applicant as directed to “simulating promotional performance”, including “providing for an expected reward for each of the one or more candidate itemsets”, “simulating a sales metric of the plurality of product records based on the respective candidate itemset. At no point do the amended claims provide anything remotely analogous to the plurality of classification structures for repeated extraction and importing as required precursors for the mapping, for the referential data structures, as was the case in Enfish, 822 F.3d 1327, 1336, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016) as cited by MPEP 2106.04(a). Also, at no point do the amended claims provide anything remotely analogous to the systems and methods of generating a composite webpage that combines certain visual elements of a host website with the content of a third-party merchant, as in DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 113 USPQ2d 1097 (Fed. Cir. 2014), as cited by MPEP 2106.05(d). Digging deeper into DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d at 1248, 113 USPQ2d at 1099, the Examiner finds the Court ruled that the eligible claim had additional elements that amounted to significantly more than the abstract idea, because they modified conventional Internet hyperlink protocol to dynamically produce a dual-source hybrid webpage, which differed from the conventional operation of Internet hyperlink protocol that transported the user away from the host’s webpage to the third party’s webpage when the hyperlink was activated. Here, there is nothing similar to such patent eligible technological arrangement.
As per the argued use of computer components of: “reinforcement learning algorithm”, “short-term”, and “long-term” memories in “simulating promotional performance”, with “expected reward simulating a sales metric of the plurality of product records based on the respective candidate itemset” (independent Claims 1,9), as raised by Remarks 08/22/2025 p.19 ¶5-mid-p.20, the Examiner initially points to Non-Final At 04/24/2025 p.8 ¶2 to argue that as broadly recited, such computerization can be argued as not meaningfully different than use of computer tools or computer environments in determining a price, using organizational and product group hierarchies, as was the case in Versata, 793 F.3d at 1312-13, 1331, 115 USPQ2d at 1685, 1699 as cited by MPEP 2106.04(a)(2) III C #1.
Such computerized price determination in Versata supra, would analogously correspond here to the measured reward simulation, while the use of organizational and product group hierarchies would correspond here to “the one or more selected itemsets identifying two or more products in the plurality of product records” (independent Claims 1,9), and simulated “first” and “second” “category” “hierarchy” (dependent Claims 3-5, 11-13).
Since such level of computerization did not save the claims in Versata from patent ineligibility, the Examiner reasons that here, the asserted level of computerization at Remarks 08/22/2025 p.19 ¶5-mid-p.20, in “simulating promotional performance”, with “expected reward simulating a sales metric of the plurality of product records based on the respective candidate itemset” (independent Claims 1,9) would similarly not prelude the current claims from reciting, describing or setting forth the abstract exception. In fact, the Examiner will further demonstrate at the subsequent steps that as instructed by MPEP 2106.05(f)(2)(i)1 performing a business method and underlining mathematical algorithm on a computer does not integrate the abstract idea into a practical application or provides significantly more, because it would represent mere invocation of computer components or other machinery.
As per Applicant’s reliance at Remarks 08/22/2025 p.17 ¶5-p.19 ¶4 on USPTO’s Examples 38,39, the Examiner reminds the Applicant that all the examples [including Examples 38,39] issued by the Office in conjunction with the examining guidance are merely hypothetical and non-precedential and do not carry the weight of Court decisions and hence are not used as basis for a subject matter eligibility rejection. 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 here, the argued claims are irreconcilably different than USPTO’s Examples 38 and 39. For example, Example 38 did not recite an abstract idea because it employed a simulation to more closely replicate the sound quality of an analog audio mixer by accounting for slight variances in analog circuit values that generated during the circuit’s manufacturing. Here “simulating” [the entrepreneurial and abstract] “promotional performance” as argued by Applicant is far remote from the technological audio simulation to more closely replicate the sound quality of the technological analog audio mixer by accounting for slight variances in analog circuit values generated during the technological circuit’s manufacturing, as was the case in eligible, hypothetical and non-precedential Example 38. Similarly, the currently argued independent Claims 1,9 are irreconcilably different than the technological details of the 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, as was the case in
the eligible, hypothetical and nonprecedential USPTO’s Example 39.
Simply put here, the claims’ still recite, describe or set forth the abstract idea, with simulating promotional performance as contested by Applicant above being not technological but entrepreneurial, and with its computerized execution being, at most, an attempt at applying the abstract idea, [MPEP 2106.05(f)] such as applying or involving the intelligent agent and associated simulation as argued at Remarks 08/22/2025 p.14 ¶3, last sentence, and Remarks 08/22/2025 p.16 ¶2 respectively.
Such computerization could also be argued as a technological environment or a computational field of use [MPEP 2106.05(h)] upon which to narrow the abstract idea. These concepts will be more granularly tested and explained at the subsequent steps below.
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- Step 2A Prong two: Remarks 08/22/2025 mid-p.21 to p.22 ¶3 argues that even if the claims do a judicial exception, the emphasized limitations of “executing a reinforcement learning algorithm to simulate”, “an expected reward for each of the one or more candidate itemsets” and “wherein the sensor data is representative of a measured state of the environment”; “storing the sensor data in the short-term memory”; “storing the sensor data in the long-term memory”; “correcting the simulated state of the environment based on the sensor data comprising the measured reward” go beyond generally linking use of abstract idea to a particular technological environment, since they allegedly transform, through “reinforcement learning” and respective simulat[ion] any abstract concept into non-abstract computerized functions and simulations substantially distinct from any contemporary commercial practices, thus integrating the abstract idea into a practical application or providing significantly more.
Remarks 08/22/2025 p.22 ¶4-p.23 ¶1 further argue that the amended independent claims provide example of technological improvement that integrates the abstract idea into a practical application by incorporating rules similar to MPEP 2106.05(a)(II), recited here as “a reinforcement learning algorithm”, execut[ed], “to simulate”, “at the simulation component, based on the short-term memory of the memory component, the long-term memory of the memory component, a simulated state of an environment, and the retail promotional model, an expected reward for each of the one or more candidate itemsets, each expected reward simulating a sales metric of the plurality of product records based on the respective candidate itemset”.
Remarks 08/22/2025 p.23 ¶2-¶5 argues that the current claims are similar to DDR because even if they recite commercial factors, they analogously expand these factors to a technological extent in which the solution is claimed, namely by “executing a reinforcement learning algorithm to simulate”, “applying the current action to the simulated state of the environment” and “correcting the simulated state of the environment based on the sensor data comprising the measured reward”.
- Examiner fully considered the Step 2A prong 2 arguments but respectfully disagrees finding them unpersuasive, because here the level of automation or computerization, emphasized by Remarks 08/22/2025 mid-p.21 to p.22 ¶3, is a mere attempt at applying existing or abstract processes, as tested per MPEP 2106.05(f), and/or narrow them to a field of use or technological environment, as tested per MPEP 2106.05(h). None of these integrate the abstract idea into a practical application. For example, MPEP 2106.05(f)(2) ¶12 finds that use of a computer or other machinery in its ordinary capacity for economic or other tasks such as to store data does not integrate a judicial exception into a practical application. It then follows that here, “storing the sensor data in the short-term memory” and “storing the sensor data in the long-term memory” would represent such an example of using a computer or other machinery for economic or other tasks to store data, and thus would similarly not integrate the abstract exception into a practical application.
Similarly, the same MPEP 2106.05(f)(2) ¶13 states that claiming any improved speed or efficiency inherent with applying the abstract idea on a computer does not integrate a judicial exception into a practical application or provide an inventive concept. It then follows that here, far from a technological improvement asserted by Applicant at Remarks 08/22/2025 p.22 ¶4-p23 ¶1, any alleged efficiency inherent with applying the abstract idea with “providing, in a memory, an intelligent agent, the intelligent agent comprising: a retail promotional model; a simulation component, a memory component comprising a long-term memory, and a short-term memory”, and any alleged efficiency inherent with applying the abstract idea with “executing a reinforcement learning algorithm to simulate, at the simulation component, based on the short-term memory of the memory component, the long-term memory of the memory component, a simulated state of an environment, and the retail promotional model, an expected reward for each of the one or more candidate itemsets, each expected reward simulating a sales metric of the plurality of product records based on the respective candidate itemset” would analogously not integrate the above abstract exception into a practical application.
These findings are corroborated by MPEP 2106.05(f)(2) (iii)4 which states that a process for monitoring audit log data executed on a computer is merely an example of invoking computers or machinery as a tool to perform an existing process, which merely applies the abstract idea and thus does not integrate said abstract idea into a practical application. It then follows that here, the argued recitation of “wherein the sensor data is representative of a measured state of the environment”, and read in light of Original Specification ¶ [0014] as a retailer related state, would represent such an example of monitoring audit log data executed on a computer, invoking computers or machinery as a tool and thus merely applying the abstract idea without not integrating it into a practical application.
These findings are also corroborated by MPEP 2106.05(f)(2)(i)5 which similarly finds that applying a mathematical algorithm on a computer for an underlining a business method, is another an example of invoking computers or machinery as tools, which merely apply the abstract idea and thus does not integrate it into a practical application. It then follows that here, far from any patent eligible rules, as alleged by Applicant at Remarks 08/22/2025 p.22 ¶4-p.23 ¶1, the argued “executing a reinforcement learning algorithm to simulate”, “an expected reward for each of the one or more candidate itemsets” “correcting the simulated state of the environment based on the sensor data comprising the measured reward” would represent an example of applying a business related mathematical algorithm [MPEP 2106.05(f)(2) (i)], as an invocation of computers or machinery as a tool, which merely apply the abstract idea, and thus, does not integrate it into a practical application.
Additionally and/or alternatively, when tested per MPEP 2106.05(h), it can also be argued that the level of automation or computerization as argued by Applicant at Remarks 08/22/2025 mid-p.21 to p.22 ¶3, represents a mere technological environment or field of use, upon which the abstract idea is being performed, such as narrowing the combination of collecting information, analyzing it, and displaying certain results of the collection and analysis to a computerized technological environment. According to MPEP 2106.05(h) and 2106.05(h)(vi)6 such narrowing to a computerized etchnoglical environment or field of use also does not integrate the abstract exception into a practical application.
As per the Applicant’s allegation at Remarks 08/22/2025 p.23 ¶2-¶5 that by “executing a reinforcement learning algorithm to simulate”, “applying the current action to the simulated state of the environment” and “correcting the simulated state of the environment based on the sensor data comprising the measured reward”, the claims are similar to DDR, the Examiner submits that the current legal findings of the present claims are irreconcilably different than what was found eligible in, “DDR Holdings” because, as previously identified above, the claims’ character as a whole remains directed to “simulating promotional performance”, including “providing for an expected reward for each of the one or more candidate itemsets”, “simulating a sales metric of the plurality of product records based on the respective candidate itemset. At no point do the amended claims provide anything remotely analogous to the systems and methods of generating a composite webpage that combines certain visual elements of a host website with the content of a third-party merchant, as in DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 113 USPQ2d 1097 (Fed. Cir. 2014), as cited by MPEP 2106.05(d).
Digging deeper into DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d at 1248, 113 USPQ2d at 1099, the Examiner finds the Court ruled that the eligible claim in DDR had additional elements that amounted to significantly more than the abstract idea, because they modified conventional Internet hyperlink protocol to dynamically produce a dual-source hybrid webpage, which differed from the conventional operation of Internet hyperlink protocol that transported the user away from the host’s webpage to the third party’s webpage when the hyperlink was activated.
Here, there is nothing similar to DDR’s eligible technological arrangement.
In conclusion the Examiner submits that the claims’ character as a whole still recites or at the minimum describes or sets forth the abstract idea (Step 2A prong one), with no additional computer-based elements capable to, either alone or in combination integrate the abstract idea into a practical application (step 2A prong two), and for the same reasons also, incapable to provide significantly more (Step 2B) than what was already found as the abstract idea itself. Thus, the claims are patent ineligible.
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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 1-16 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 claim(s) recite(s) describe, set forth the abstract Certain Methods of Organizing Human Activities including fundamental economic practices and/or commercial interactions [MPEP 2106.04(a)(2) II], including but not limited to offer-based optimization7 set forth here by recitation of “simulating promotional performance” as summarized by the preamble of independent Claims 1,9 and detailed throughout the body of the Claims 1-16. Specifically, the Examiner follows the USPTO’s latest 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (2024 AI SME update). (Effective July 17, 2024) and the corresponding sections of the MPEP. For example, MPEP 2106.04(a)(2) II B iii. cites OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1362-63, 115 USPQ2d 1090, 1092 (Fed. Cir. 2015) to state that offer-based optimization, pertains to marketing and thus falls within the abstract commercial interactions. Further, MPEP 2106.04(a)(2) II A again cites OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1364, 115 U.S.P.Q.2d 1090, 1092 (Fed Cir. 2015) to analogously state that a new method of price optimization was found to be an abstract, fundamental economic concept.
Examiner, tests the claims against to the fundamental economic practices and/or commercial interactions of MPEP 2106.04(a)(2) II A and B supra, and finds that here, the commercial interactions are similarly described or set forth by recitation of: “determining” “one or more candidate itemsets, each of the one or more candidate itemsets identifying two or more products in a plurality of product records”, “selecting” “one or more selected itemsets from the one or more candidate itemsets, each of the one or more selected itemsets identifying two or more products in the plurality of product records” (independent Claims 1,9), “the plurality of product records further comprises a product category hierarchy” (dependent Claims 3,11), “a first product belongs to the first level of the product category hierarchy and a first candidate itemset in the one or more candidate itemsets comprises the first product” (dependent Claims 3,11), “a second product belongs to the second level of the product category hierarchy and the first candidate itemset in the one or more candidate itemsets comprises the second product” (dependent Claims 4,12), “receiving, from a retailer system, retail data for a current time period” (dependent Claims 7,14) etc. Further the offer-based optimization is set forth here as simulation of a retail “environment”, and associated “retail promotional model” and “an expected reward” [i.e. offer result] “for each of the one or more candidate itemsets”, “generating a current” [offer based] “action corresponding to the one or more selected itemsets from the one or more candidate itemsets and each corresponding expected reward”; “applying the current action to the simulated state of the environment” for a further optimization by “correcting the simulated state” associated with the “retail promotional model”, “measured reward” and “expected reward simulating a sales metric of the plurality of product records based on the respective candidate itemset” (independent Claims 1,9), “the simulating the expected reward is for a first level of the product category hierarchy” (dependent Claims 3,11), “simulating a second level of the product category hierarchy, the second level at a lower level than the first level in the product category hierarchy” (dependent Claims 4,12), “simulating a first product set in the one or more selected itemsets, the first product set having a first product in the first level of the product category hierarchy and a second product in the second level of the product category hierarchy” (dependent Claims 5,13). Such further optimization is also set forth by “determining one or more solution increments for the one or more time periods”; “and” “simulating an addition or removal of product records to the one or more selected itemsets” (dependent Claims 6,14) “updating the retail promotional model based on the retail data for the current time period and the one or more selected itemsets” (dependent Claims 7,14). Yet, MPEP 2106.04(a)(2) II A ¶2 is clear that building blocks of the modern economy remain ineligible, with the term “fundamental” not used in the sense of necessarily being "old" or "well-known" citing again to OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1364, 115 U.S.P.Q.2d 1090, 1092 (Fed Cir. 2015) which ruled that a new method of price optimization was still a fundamental economic concept. In fact, MPEP 2106.04 I stresses that even a “groundbreaking, innovative, or even brilliant discovery does not by itself satisfy the 101 inquiry” citing Myriad,569 U.S at 591, 106 USPQ2d at 1979. The “Myriad” rationale was corroborated by “SAP Am Inc v InvestPic” which is also cited by MPEP 2106.04(a)(2) I.C(i). Digging deeper into the Court’s rationale in SAP supra, the Examiner finds that 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 in “simulating promotional performance” 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 and actual improvement to actual technology. MPEP 2106.04.
Following such legal precedents, as underlined by MPEP 2106.04 above, the Examiner reasons that here, the claims would at most improve an entrepreneurial and abstract concept through optimization or simulation for “promotional performance”. Yet, as identified above and confirmed by MPEP 2106.04 (d)(1) a claim is not patent eligible merely because it applies an abstract idea in a narrow way; that is, an “improvement in the judicial exception itself” “is not an improvement in technology”. Specifically, MPEP 2106.04 I ¶5 states that the Supreme Court’s decisions made it clear that judicial exceptions need not be old or long-prevalent, and that even newly discovered or novel judicial exceptions are still exceptions. For example, both Myriad and Flook were novel, but nonetheless considered by the Supreme Court to be judicial exceptions because they were basic tools of scientific and technological work’ that lie beyond the domain of patent protection. Here when considered in light of the above legal findings, the simulation of a retail “environment”, and associated “retail promotional model” and “an expected reward” [i.e. offer result] “for each of the one or more candidate itemsets”, “generating a current” [offer based] “action corresponding to the one or more selected itemsets from the one or more candidate itemsets and each corresponding expected reward”; “applying the current action to the simulated state of the environment” for a optimization by “correcting the simulated state” associated with the “retail promotional model”, “measured reward” and “expected reward simulating a sales metric of the plurality of product records based on the respective candidate itemset” (independent Claims 1,9) etc., would at most correspond to such narrowing or improvement to the abstract exception, namely the building blocks of modern economy8 or the fundamental economic practices or principles of MPEP 2106.04(a)(2) II A and/or the customer behavior-based marketing activities / business relations of MPEP 2106.04(a)(2) II B. Yet, these building blocks of modern economy remain patent ineligible as iterated by MPEP 2106.04(a)(2) II A ¶2.
As per the level of computerization in the claims, the Examiner points to MPEP 2106.04(a)(2) II ¶6, 4th sentence, which states that certain activity between a person and a computer may still fall within the "certain methods of organizing human activity" grouping. In a similar vein9, the computer-aided, evaluation and judgement of mental processes (MPEP 2106.04(a)(2) III) do not preclude the claims from reciting, describing or setting forth the abstract exception, because MPEP 2106.04(a)(2) III. C. clearly states that: #1. Performing a mental process on a generic computer, # 2. Performing a mental process in a computer environment, or #3. Using a computer as a tool to perform a mental process, do not preclude the claim from reciting a mental process. For example, MPEP 2106.04(a)(2) III C #1 cites Versata, 793 F.3d at 1312-13, 1331, 115 USPQ2d at 1685, 1699, to state that using general purpose computer hardware for determining a price, using organizational and product group hierarchies still recites the attract exception. Such price determination would analogously correspond here to the measured reward simulation, while the use of organizational and product group hierarchies would correspond here to “the one or more selected itemsets identifying two or more products in the plurality of product records” (independent Claims 1,9), and simulated “first” and “second” “category” “hierarchy” (dependent Claims 3-5, 11-13). Following, the MPEP 2106.04(a)(2) III. C. test, Examiner finds that here, it could be argued that the “simulating” of “promotional performance” could be executed by computer components to aid the human evaluation and judgment of a retail environment with respect to a “measured reward”, “an expected reward for each of the one or more candidate itemsets” and “a current action corresponding to the one or more selected itemsets from the one or more candidate itemsets and each corresponding expected reward”. Indeed, MPEP 2106.04(a)(2) III is clear that the abstract combination of collecting information, analyzing it, and displaying certain results of the collection and analysis remains directed to the abstract exception10. It then follows that here “receiving” “retail data for current time period” (dependent Claims 7, 15), “receiving” “measured reward” “representative of a measured state of the environment” and “correcting the simulated state of the environment based on the sensor data comprising the measured reward” (independent Claims 1,9) followed by “simulating an addition or removal of product records to the one or more selected itemsets” (dependent Claims 6,14), “updating the retail promotional model based on the retail data for the current time period and the one or more selected itemsets” (dependent Claims 7,15), as an example of certain results of the collection and analysis, would also fall within the realm abstract exception. Thus here, there is preponderance of legal evidence demonstrating that the claims recite, describe or set forth abstract Certain Methods of Organizing Human Activities practically implementable through equally abstract Mental Processes. Step 2A prong one.
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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 and/or narrow it to a field of use or technological environment. Here, the additional elements are: “server” (independent Claim 9), “memory component comprising” “long-term” “and” “short-term memory” (independent Claims 1,9, dependent Claims 2,10), “processor in communication with the memory” (independent Claim 1) “network device” (independent Claim 9), “intelligent agent module” (independent Claims 1,9), “simulation component” (independent Claims 1,9), possibly the “reinforcement learning algorithm” (independent Claims 1,9), narrowed as “genetic algorithm” (dependent Claims 8,16), “sensor data” (independent Claims 1,9, dependent Claims 2,10), “retailer system” (dependent Claims 7,15).
More specifically, here, when tested per MPEP 2106.05(f)(2) such additional, computer-based elements, merely apply the abstract idea, such as the aforementioned business method [here identified above] and mathematical algorithm [here “simulation component”, “reinforcement learning algorithm” etc.] to use a computer11 [here “processor”, “intelligent agent” etc.] or other machinery in its ordinary capacity for economic tasks [here identified above] or other tasks to store, receive and transmit data12. Such storing is exemplified here by the capabilities of the “memory component” comprising “short-term” and “long-term” “memory” to “store/storing the sensor data” (independent Claims 1,9). The capabilities of the additional computer-based element to receive of data is exemplified here by recitations of: “receiving, at the intelligent agent, sensor data comprising a measured reward, wherein the sensor data is representative of a measured state of the environment” (Claims 1,9), “receiving, from a retailer system / using the network device/, retail data for a current time period” (dependent Claims 7,15). The capabilities of the additional computer-based element to transmit data are reflected here by: “updating the retail promotional model based on the retail data for the current time period and the one or more selected itemsets” (dependent Claims 7,15).
Further, MPEP 2106.05(f)(2) iii cites FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016), to state that a process for monitoring audit log data that is executed on a general-purpose computer, is also an example of invoking computers or machinery as a tool to perform an existing process, which does not integrate the abstract idea into a practical application. It then follows that here, recitations of “sensory data” such as “receiving, at the intelligent agent, sensor data comprising a measured reward, wherein the sensor data is representative of a measured state of the environment” at independent Claims 1,9 read in light of Original Specification ¶ [0067] would correspond to such an example of monitoring audit log data, which would not integrate the abstract idea into a practical application.
Further still, MPEP 2106.05(f)(2) v. cites Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363,1370-71,115 USPQ2d 1636,1642 (Fed. Cir. 2015) to state that requiring use of software to tailor information and provide it to user on a generic computer, is another example of applying the abstract idea, which does not integrate it into a practical application. It then follows that here requiring use of computer components for “correcting” or tailoring “simulated state of the environment based on the sensor data comprising the measured reward” (independent Claims 1,9), “updating the retail promotional model based on the retail data for the current time period and the one or more selected itemsets” (dependent Claims 7, 15) would similarly not integrate the abstract exception into a practical application.
In a similar vein, MPEP 2106.05(h) 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 information, analyzing it, and displaying certain results of collection and analysis [here identified and mapped above] to data related to a technological environment, is an example of limiting the identified abstract idea to a field of use or technological environment which again does not integrate it into a practical application. Step 2A prong two. Analogously, MPEP 2106.05(h) cites FairWarning v. Iatric Sys., 839 F.3d 1089, 1094-95, 120 USPQ2d 1293, 1295 (Fed. Cir. 2016) to state that specifying that the abstract idea of monitoring audit log data relates to transactions or activities executed in a computer environment is a requirement that merely limits the claims to the computer field, without integrating the abstract idea into a practical application. It then follows that here, narrowing the above, abstract economic and/or commercial transactions to the computerized implementation above would at most narrow the abstract idea to a field of use or technological environment without integrating it into a practical application. Similarly, MPEP 2106.05(h) x cites buySAFE Inc v Google, Inc 765 F.3d 1350,1354, 112 USPQ2d 1093,1095-96 (Fed Cir 2014) to state that performance of a transaction (a) using a computer that receives and sends information over a network, or (b) be limited to guaranteeing online transactions, represent limitations that limit the use of the abstract idea to computer environments, which would not integrate such abstract idea into a practical application. It then follows that here, the computerized capabilities to simulate promotional performance, as identified above; would represent analogous limitations that limit the use of the abstract idea to computer environments, which would similarly not integrate the current abstract idea into a practical application.
Thus here, there is preponderance of legal evidence for the additional, computer-based elements, to merely apply the abstract idea [MPEP 2106.05(f)] and/or narrow it to a technological environment or filed of use [MPEP 2106.05(h)], and thus not integrating it into a practical application. Step 2A prong two.
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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 per above, the additional computer-based elements merely apply the already recited abstract idea and link use of abstract idea to a field of use or technological environment. see MPEP 2106.05(f) and/or (h). Specifically Examiner follows the guidelines of MPEP 2106.05(d) II 2nd bullet point and caries over the conclusions reached on the MPEP 2106.05(f), and/or (h) tests to Step 2B, and submits that for the same reasons articulated above, said computer-based additional elements also do not provide significantly when considering MPEP 2106.05(f) and/or (h) as sufficient option(s) for evidence, without the need to rely on Berkheimer evidence for the well-understood, routine and conventional test. Yet, assuming arguendo, that further evidence would be required to demonstrate conventionality of the additional, computer-based elements, the Examiner would further rely on MPEP 2106.05(d) guidelines to demonstrate that said additional elements are also well-understood, routine, conventional. In such case, the Examiner would rely as evidence on Applicant’s own Specification:
- Original Specification ¶ [00055] 1st sentence states: “Reference is made to Fig. 1, which shows an autonomous enterprise planning system according to an embodiment of the present invention and indicated generally by reference 100”.
- Original Specification ¶ [00055] 6th sentence reciting at high level of generality: “As will be described in more detail below, the computing and processing facility 110 comprises computers and/or processors implemented in hardware and/or software configured to process the retail data and generate retail merchandise operational plans, sales/margins results measurements and forecasts, and a retailer merchandise plan tailored for one or more of the retailers”.
- Original Specification ¶ [0056] last sentence: “The particular implementation details will be within the understanding of those skilled in the art of computers and computer programming”.
- Original Specification ¶ [00085] last sentence: “The particular implementation details for the aprioriT/D algorithm will be readily within the understanding of those skilled in the art”.
- Original Specification ¶ [000120] states: “It will be further appreciated that in a practical system, the product selection solutions (or control inputs) will rarely be 100 per cent executed without …human intuition”.
- Original Specification ¶ [00124] 1st sentence, reciting at a high level: “Reference is next made to Fig. 6, which shows in diagrammatic form high level data platform architecture for the retail planning system 100 of Fig. 1, according to an exemplary implementation and indicated generally by reference 600”.
- Original Specification ¶ [00146] last sentence states: “The particular implementation details of the modules and functionality for the client access layer 930 will readily be within the understanding of one skilled in the art”.
- Original Specification ¶ [000155] last sentence states: “The specific implementation details of the software objects and/or program modules and/or hardware components will be within the knowledge and understanding of one skilled in the art”.
- Original Specification ¶ [000156]: “The present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. Certain adaptations and modifications of the invention will be obvious to those skilled in the art”.
- Original Specification ¶ [0003] last sentence construed as admitting that [at least some] “retailers” [might not have the time but might have the] “tools to do any in depth analysis”, by virtue of using conjunction “or” as follows: “...retailers do not have the time and/or tools to do any in depth analysis”.
Additionally, or alternatively, Examiner also points to MPEP 2105.05(d) to show the conventionality of electronic recordkeeping13 as well as receiving/transmitting data including utilizing an intermediary computer to forward information14, arranging a hierarchy of groups, sorting information15 as examples that do not provide significantly more. These are especially relevant to the capabilities of the additional computer-based elements in “determining, one or more itemsets”, “store/storing the sensor data in the” “short-term” and “long-term” “memory”; (independent Claims 1,9), “updating the retail promotional model based on the retail data for the current time period and the one or more selected itemsets” (dependent Claims 7,15) etc. Examiner also points to MPEP 2105.05(d) showing conventionality performing repetitive calculations16 for updating alarm limits in a conversion process. especially relevant to the capabilities of the additional computer-based elements in simulat[ing] and then “correcting the simulated state of the environment based on the sensor data comprising the measured reward” at independent Claims,1,9 then narrowed at dependent Claims 3-6,11-14.
If necessary, the conventionality of the “genetic algorithm” of dependent Claims 8, 16 would be tested under MPEP 2106.05(d) I 2.c), and demonstrated by at least the following publications:
* US 20150242947 A1 ¶ [0041] “Another example of optimizing a combination of metrics is maximizing the monetary amount of sales subject to not exceeding a threshold number of seats sold. Optimization does not need to be a global optimization. For example, genetic algorithms, which are a common method of parameter optimization and could be used with this inventive system, are known to be non-exhaustive testing and thus provide only a local optimization with the certainty of having achieved the global optimization defined in terms of a confidence level” and ¶ [0060] “Box 201 starts off the auction processing and calculations of the sales parameter value for different candidate sales configurations. In box 202, an initial seed is determined. Part of this process is obtaining a random number. Because genetic algorithms typically require some degree of randomness to create variations in the progression of generations this seed may be a random number. Genetic algorithms are well-known in the art, and may optionally be utilized as a method for the optimization process. The auction host may seek to optimize a single sales parameter or perhaps some weighted combination of sales parameters, subject to some constraints. Options include maximizing total sales revenue, number of seats sold, and per-seat average revenue”.
* US 20100100418 A1 Adaptive self-learning marketing automation reciting at ¶ [0077] “Finally, the original marketer specified offers may carry through the system as effective or may be supplanted by alternate offers created by the system. In order to avoid obscuring the inventive subject matter, steps to collect feedback on each offer and to cross-breed the offers have been left out of the example. These steps are within the ability of one of ordinary skill in the art of genetic algorithms, and represented
simplistically in FIG. 4 item 430 and Fig.5 in its entirety”.
* US 20090259516 A1 teaching at ¶ [0082] “For some applications, central server 22 assigns a set of rules for each retail location, and uses the sets as DNA rule sets for input into a genetic (DNA) algorithm, as is known in the art of genetic algorithms. For example, the server may use techniques described in above-mentioned US Patent Application Publication 2003/0083936 to Mueller et al., mutatis mutandis. Each rule specifies a condition (such as a sale of a certain product) and an associated action to perform when the condition is satisfied…For some applications, the measure of performance to be optimized comprises sales (i.e., measured in dollars), such as total sales at the retail location, while for other applications, the server seeks to optimize sales of mid-selling items, or sales of best-selling items. For some applications, the measure of performance comprises gross margin, unit sales, and/or a combination of two or more of gross margin, unit sales, and sales. The server may also seek to minimize sales of certain products or types of products, for example if such a decrease is correlated with an increase in other more desirable products, e.g., which have higher gross margins”.
* US 20090070129 A1 ¶ [0152] “the response pattern learning module accesses customer records and analyzes the stored information which relates to responses to promotions. The pattern learning module identifies one or more common factors among all the customers who have responded to a certain promotion. Preferably, natural selection algorithms and genetic algorithms are used to identify the common factors”.
* US 20080065476 A1 ¶ [0056] “Analysis 301-Underlying the ODMS 100 is some software based system to predict customer behavior. The methodology for that system can be any one of several mathematical methods well known to those reasonably skilled in this area, including but not limited to RFM, regression, genetic algorithms, neural nets, or finite state machines. The analysis system uses the inputs described below and produces scores for each Customer 160 that are typically stored on a database server”.
* US 20070192168 A1 ¶ [0465] “In another embodiment, such” [advertisement] “information is determined and learned through use of the system over time through use of statistical methods and/or through use of neural nets, expert systems, genetic algorithms or any other known algorithms in the prior art”.
* US 20070143186 A1 [0250] “One exemplary method of implementing the present algorithm in Step A18 is to utilize the genetic algorithm to find the optimum or close to optimum combination of values of the Profit Maximization Variables that maximize Total Profitmax. The genetic algorithm method is well known in the programming art”.
* US 20040059549 A1 mid- ¶ [0048] “The generation, convergence assessment and modification operations of steps S12, S16 and S18 are performed according to any well-known optimization algorithm such as Genetic Algorithms”, and ¶ [0079] last sentence: “The system calculates gain/loss by using financial attributes, and calculates additional combinations of these near portfolios and the original portfolio by swapping through random selection and/or using conventional genetic algorithms”.
If necessary, the conventionality of the intelligent assistants would be tested under MPEP 2106.05(d) I 2.c), and demonstrated by at least the following publications:
* US 20180300337 A1 ¶ [0004] last sentence: in some conventional methods and systems, the system generates the score for each answers based on historical results obtained by a machine learning of the plurality of virtual assistants.
* US 10629191 B1 column 1 lines 59-63: Conventional virtual assistant platforms implement natural language conversations with end users in one of several ways, either via decision trees or finite state machines, menu-driven approaches, frame-slot approaches, or machine learning on existing conversation datasets.
* US 20190189132 A1 ¶ [0084] With technological advances in artificial intelligence (AI), the voice recognition is more commonplace as are voice-based devices, such as Amazon's Echo and Apple's Siri. Such voice-based devices include application programmable interfaces (APIs) so that voice-based devices can be integrated with and control any device, such as gateways, televisions (TVs), set top boxes, washing machines, dryers, refrigerators, lighting, window shades, microwaves, ranges, dishwashers, security systems, computers, laptops, tablets (tablet computers), PDAs, pagers, etc. Such voice-based devices may also be a separate device or may be integrated with the gateway itself. Irrespective of the deployment scenario, the voice-based commands enhance the user's experience. The voice-based commands can be categorized as normal commands that do not require any authentication and identified (specific, pre-defined) voice-based commands that require authentication.
* US 20210173377 A1 ¶ [0002] 1st sentence: Conventional machine learning technologies can allow intelligent systems such as robots and personal assistants to acquire knowledge and solve difficult problems by learning from examples or instruction.
* US 20210217409 A1 ¶ [0068] 2nd sentence: The artificial intelligence agent, as a dedicated program for providing an artificial intelligence based service (e.g., voice recognition service, personal assistant service, translation service, search service, etc.), may be executed by a conventional generic-purpose processor (e.g., CPU)
* US 20190084420 A1 ¶ [0005] To use smart devices as connected to vehicle head units that support phone projection such as Google's Android Auto, Apple's CarPlay and Nokia's Mirrorlink by a wired or wireless method, conventionally, users could use them after setting up (e.g., select Android Auto icon, select Mirrorlink connection, etc.) via an input device provided in the vehicle head unit or an input device provided in the smart device and executing phone projection.
* US 20190318035 A1 ¶ [0135] As a result of the foregoing, and in some embodiments, electronic digital assistants may computationally consider and provide assistance within multiple party conversations digitally captured and processed by the electronic digital assistant, allowing electronic digital assistant to provide more substantive responses that consider additional context and inter-party and role-based information compared to traditional single-person inquiries and responses processed by conventional electronic digital assistants, and without requiring large memory spaces and processing power required to store every possible situation and response, and without requiring large datasets and time-consuming training periods required by deep-learning and other machine learning mechanisms. Other features and advantages are possible as well.
In conclusion Claims 1-16 although directed to statutory categories (“method” or process, “system” or machine) they still recite, or at least set forth or describe 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). The claims are ineligible.
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- Reasons for overcoming the prior art -
Examiner submits that the closest prior art are: Ouimet; Kenneth J. US 20150324828 A1 and Zhou et al, US 20170221086 A1. Yet neither Ouimet now Zhou, nor any other prior art n record teaches either alone or together with adequate rationales the combination of the eight limitations below as recited at independent Claim 1 and similarly recited t sister independent Claim 9:
I. “executing a reinforcement learning algorithm to simulate, at the simulation component, based on the short-term memory of the memory component, the long-term memory of the memory component, a simulated state of an environment, and the retail promotional model, an expected reward for each of the one or more candidate itemsets, each expected reward simulating a sales metric of the plurality of product records based on the respective candidate itemset”;
II. “selecting, at the intelligent agent module, one or more selected itemsets from the one or more candidate itemsets, each of the one or more selected itemsets identifying two or more products in the plurality of product records”;
III. “generating a current action corresponding to the one or more selected itemsets from the one or more candidate itemsets and each corresponding expected reward”;
IV. “applying the current action to the simulated state of the environment”;
V. “receiving, at the intelligent agent, sensor data comprising a measured reward, wherein the sensor data is representative of a measured state of the environment;
VI. “store the sensor data in the short-term memory”;
VII. “store the sensor data in the long-term memory”; “and”
VIII. “correcting the simulated state of the environment based on the sensor data comprising the measured reward”
Claims 2-8, 10-16 are dependent and overcome prior art by dependency to parent Claims 1,9.
To be also clear, considerations of novelty (35 USC 102) and non-obviousness (35 USC 103) over the prior art (here Ouimet, Zhou ) still pertain to features that are abstract, or incapable to integrate the abstract idea or provide significantly more, which do not render the claims patent eligible (35 USC 101). Simply said, the novel and non-obviousness rationale above do not necessarily render the claims patent eligible. See for example 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”.
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Conclusion
The following art is made of record and considered pertinent to Applicant's disclosure:
WO 2010068551 A1 teaching Method For Determining Market Reference Price To Optimize Dynamic Pricing, Involves Determining Market Reference Price Based One Of Weighted Average Process, Percentile Process And Position-in-range Process
* Saarenvirta, Kari Tapio Evaluation of turbulence models for internal flows 2003 0258-0258 teaches several algorithmic concepts corresponding to the current Inventor’s own work
* US 20160055427 A1 teaching at ¶ [0036] FIG. 23 is an example illustrating a decision tree for a database maintained by an insurance company to predict a risk of an insurance contract based on a type of a car and a age of its driver; ¶ [0220]-¶ [0227] noting fraud detection)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to 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 A
September 29th, 2025
1 Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014);
Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972);
Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)
2 Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
3 Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015)
4 FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)
5 Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014);
Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972);
Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
6 Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
7 OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1362-63, 115 USPQ2d 1090, 1092-1093 (Fed. Cir. 2015).
8 MPEP 2106.04(a)(2) II A. ¶2 citing 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
9 MPEP 2106.04(a): “examiners should identify at least one abstract idea grouping, but preferably identify all groupings to the extent possible”.
10 Electric Power Group v Alstom S.A 830 F3d 1350,1353-54,119 USPQ2d 1739,1741-42 Fed Cir 2016
11 Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014);
Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972);
Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
12 Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone);
TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016)
Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
13 Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts");
Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755
14 Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016);
OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015);
buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)
15 Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015)
16 Flook, 437 U.S. at 594, 198 USPQ2d at 199 and Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012)
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