DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims Status
Claims 4, 6 and 15 have been cancelled.
Claims 1-3, 5, 7-14 and 16-22 are pending.
Claims 18-20 stand withdrawn as set forth previously.
Claims 1-3, 5, 7-14, 16-17 and 21-22 stand rejected.
Claim Objection – Minor Informalities
Claim 1 is objected to because it includes an errant bracket “]the regression model configured to…” in line 26. Appropriate correction is required.
Response to Arguments
I. Applicant’s arguments made with respect to the rejection under 35 USC 101 have been fully considered but re not persuasive.
The Examiner first acknowledges the newly amended features emphasized by Applicant, including generate one or more trained neural networks using a reiterative training process to train one or more untrained neural networks based on a training dataset, wherein the training dataset includes one or more variant item features, wherein the one or more trained neural networks includes a relevance model, a regression model, and a ranking model, and wherein the reiterative training process, for each of a plurality of iterations, comprises:
applying the one or more untrained neural networks to the training dataset to generate training output data;
comparing the training output data to the training dataset to generate a cost value; and
based on a comparison of the cost value to a minimum value, adjusting parameters of the one or more untrained neural networks, wherein the reiterative training process is complete when the cost value is less than the minimum value, and wherein the adjusted parameters define the one or more trained neural networks when the reiterative training process is complete.
That is, the Examiner acknowledges that the claimed invention recites generating a trained neural network by the process above. Applicant then compares the claimed invention to that of Desjardins. The Examiner respectfully disagrees.
Initially, the Examiner holds that the limitations comparing the training output data to the training dataset to generate a cost value, and, based on a comparison of the cost value to a minimum value, adjusting parameters of the one or more untrained neural networks, wherein the reiterative training process is complete when the cost value is less than the minimum value are abstract.
The limitation comparing the training output data to the training dataset to generate a cost value is an evaluation or judgement capable of being made in the human mind. Likewise, based on a comparison of the cost value to a minimum value, adjusting parameters of the one or more untrained neural networks, wherein the reiterative training process is complete when the cost value is less than the minimum value are also evaluations or judgement performable in the human mind.
With respect to adjusting parameters of the one or more untrained neural networks, this adjustment stems specifically from the evaluation of the cost value and is manually performable by a human user. Neither the claims nor the specification restrict the adjustment to a particular manner of adjusting such that it precludes manual human adjustment. As one example, Li (US 2023/0125022) specifically enables a human user to adjust training parameters and perform retraining (e.g., 0080, 0139). A second example includes PTO 892-U, which demonstrates a GUI for manually adjusting weights (parameters) of a neural network by a human user (see annotations). Even the presumption that a human user would adjust parameters through use of a computer is little more than performing the mental evaluation to adjust the parameters on a generic computer. This is addressed further with respect to Prong Two.
Turning to Prong Two, the claimed adjustment fails to integrate the recited abstract idea into a practical application. The claimed adjustment is at best the mere automation of a manual process (see MPEP 2106.05(a)(I), Examples that the courts have indicated may not be sufficient (iii)). Taken individually or as a whole, the combination of elements emphasized by Applicant merely invoke the computer as a tool to improve the “diversity in displayed content” that is provided responsive to a product search request. Leveraging trained machine learning models to improve the diversity of product search results is an improvement to the abstract idea itself. The use of computers, including machine learning techniques, to this effect is nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea). Moreover, claiming the 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.
With respect to Applicant’s arguments concerning Ex Parte Desjardins, the Examiner also disagrees. In Desjardins, the ARP determined under Step 2A (Prong Two) that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” reflected the improvement disclosed in the specification. No such problem is described by the current application, nor does the application provide evidence of improvements specifically to the field of machine learning as with Desjardins.
To the contrary, the claimed parameter adjustment results in a trained neural network that is used expressly for abstract, commercial purposes. This is not akin to the problem of catastrophic forgetting in Desjardins. As argued by Applicant (Remarks, p. 13), the specific improvement manifested by the use of the trained model relates specifically to the “diversity in displayed content” – i.e., diversity in product results returned in response to a product search.
Patents that do no more than claim the application of generic machine learning to new data environments (e.g., product recommendations) without disclosing improvements to the underlying processes for machine learning are not sufficient to confer eligibility to an otherwise ineligible claim (Recentive Analytics, Inc v. Fox Corp (Fed Cir, 2023-2437, 4/18/2025)).
Furthermore, the requirements that the machine learning model be iteratively trained or dynamically adjusted do not represent a technological improvement. As discussed in Recentive, iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning.
Regarding Ex party Carmody, the fact pattern employed by the PTAB in this non-precedential decision is not present when considering the current claims. The Examiner holds that, in view of the body of precedential case law, and in accordance with the guidance provided in the MPEP, the claims remain ineligible for patenting.
Turning to Example 37, the background specification emphasized that “traditional software does not automatically organize icons so that the most used icons are located near the “start” or “home” icon, where they can be easily accessed”. The specification further outlined that “the amount of use of each icon is automatically determined by a processor that tracks the number of times each icon is selected or how much memory has been allocated to the individual processes associated with each icon over a period of time (e.g., day, week, month, etc.).”. In finding claim 1 eligible, the analysis noted that claim 1 did recite a judicial exception, but found eligibility in step 2A (prong 2). The analysis emphasized that “the additional elements recite a specific manner of automatically displaying icons to the user based on usage which provides a specific improvement over prior systems, resulting in an improved user interface for electronic devices.”. The Examiner disagrees that the claimed invention achieves an analogous improvement. Applicant’s specification does not provide the requisite detail necessary such that one of ordinary skill in the art would recognize the claimed invention as providing a technical improvement. While the claims do recite generate an interface including a search results container interface elements representative of one or more item variants selected from the set of search results, wherein the interface elements include a link to an interface page for each variant item, the specification only describes the interface and its components at a high level of generality. The specification provides no further restriction on how the interface is generated other than the use of a generic container in which results – including links – are presented. No portion in the specification provides the requisite detail necessary to demonstrate to one of ordinary skill in the art that the container is provisioned in any particular manner or results in an improvement to a technical field such as user interfaces.
This is even more pronounced with the specification’s bare assertion that the invention is purportedly “beneficial for computing devices with small screens”. There is simply no detail in the specification that demonstrates to one of ordinary skill in the art how the generation of the container is performed in a manner specific to small screen devices.
Turning to Step 2B, similar logic as applied under Prong Two remains applicable herein. The Examiner again reiterates that the claims do not provide an improvement to the functioning of the computer itself or another technology or technical field. The high-level manner by which the claims recite the generation of the interface is at least similar to Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), where the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology. Like Trading Technologies, the claimed invention simply provides results with links within a region of an interface (e.g., webpage), which improves the business process of identifying item variants but not the underlying technology of the interface itself.
Nor do the claims move beyond the mere instructions to implement the abstract idea on generic computing components. The claims purport to solve problems confronting product searching – a commercial endeavor and abstract idea. The Examiner again draws Applicant’s attention to Recentive, which held the use of iterative training processes to be insufficient to confer eligibility on a claimed directed to improving an abstract idea. That is, the mere requirement for iterative training does not represent a technological improvement. Iterative training using selected training material (e.g., a training data set including variant item features) are incident to the very nature of machine learning.
While Applicant alleges that the preponderance of evidence indicates that the claims are directed to statutory subject matter, the Examiner disagrees and holds that the preponderance of evidence points to ineligibility in this case. Considered individually or as a whole, they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea). Moreover, multiple portions of the specification demonstrate the utilization of known neural network or training techniques, such as 0024 which provides a list of known trained functions that may be used, as well as known types of neural networks (see also: 0050).
Accordingly, the rejection under 35 USC 1010 has been maintained.
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 1-3, 5, 7-14, 16-17, and 21-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more.
Regarding claims 1-3, 5, 7-14, 16-17, and 21-22, under Step 2A claims 1-3, 5, 7-14, 16-17, and 21-22 recite a judicial exception (abstract idea) that is not integrated into a practical application and does not provide significantly more.
Under Step 2A (prong 1), and taking claim 1 as representative, claim 1 recites
receive a request including a search query;
generate, by a first level search model, a ranked set of candidate items based on the search query, wherein the first level search model comprises:
the relevance model configured to generate a set of relevance label values for each item variant in the item catalog based on the search query, a set of user features, and a set of variant item features;
the regression model configured to generate a set of engagement label values for each of the item variants in the item catalog based on the search query;
wherein the first level search model generates a baseline value for each item variant in an item catalog by performing a weighted combination of the relevance label values and the engagement label values for each of the item variants in the item catalog using one or more predetermined weights, and wherein the set of candidate items is selected based on the baseline value;
generate, by a second level search model comprising the ranking model configured to re-rank the ranked set of candidate items using a set of ranking criteria, a set of search results based on the set of candidate items;
These limitations recite ‘certain methods of organizing human activity’, such as by setting forth or describing the performance commercial interactions (see: MPEP 2106.04(a)(2)(II)). This is because claim 1 sets forth or describes the process by which variant items are identified responsive to a user query. This represents the performance of marketing or sales activities or behaviors, which is a commercial interaction and falls under organizing human activity.
Accordingly, under step 2A (prong 1) claim 1 recites an abstract idea because claim 1 recites limitations that fall within the “Certain methods of organizing human activity” grouping of abstract ideas.
Additionally, claim 1 can also be understood to recite limitations that set forth or describe “mental processes” that are performable in the human mind, or by pen and paper:
comparing the training output data to the training dataset to generate a cost value; and
based on a comparison of the cost value to a minimum value, adjusting parameters of the one or more untrained neural networks, wherein the reiterative training process is complete when the cost value is less than the minimum value,
This is because the steps of claim 1 above can be accomplished in the human mind, or using a physical aid (such as pen and paper, or generic computer). They further represent observations, evaluations or judgments (see: MPEP 2106.04(a)(2)(III)). The Examiner incorporates the discussion on 3-4 above herein, and reiterates that the courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid to perform the claim limitation.
Accordingly, under step 2A (prong 1) claim 1 also recites an abstract idea because claim 1 recites limitations that fall within the “Mental processes” grouping of abstract ideas.
Under Step 2A (prong 2), the abstract idea is not integrated into a practical application. The Examiner acknowledges that representative claim 1 does recite additional elements, including:
a non-transitory memory;
a processor communicatively coupled to the non-transitory memory, wherein the processor is configured to read a set of instructions,
generate one or more trained neural networks using a reiterative training process to train one or more untrained neural networks based on a training dataset, wherein the training dataset includes one or more variant item features, wherein the one or more trained neural networks includes a relevance model, a regression model, and a ranking model, and wherein the reiterative training process, for each of a plurality of iterations, comprises:
applying the one or more untrained neural networks to the training dataset to generate training output data;
based on a comparison of the cost value to a minimum value, adjusting parameters of the one or more untrained neural networks,
wherein the adjusted parameters define the one or more trained neural networks when the reiterative training process is complete;
a first device,
one or more trained neural networks,
an interface,
generate an interface including a search results container interface elements representative of one or more item variants selected from the set of search results, wherein the interface elements include a link to an interface page for each variant item and,
transmit the interface to the first device.
Although reciting these additional elements, taken alone or in combination these elements are not sufficient to integrate the abstract idea into a practical application. This is because the additional elements of claim 1 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea). This is most notably true in reference to generate an interface, where neither the claims nor the specification provide further detail with respect to how this is accomplished (i.e., covers any manner of generating an interface with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result). Moreover, while the Examiner maintains that a first level search model, a second level search model, a relevancy model, and a regression model represent abstract models (e.g., informative and/or mathematical representations), they could arguably be construed as software models. In such a scenario, these models represent nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea).
Further, the additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (such as the Internet or computing networks).
Lastly, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
In view of the above, under Step 2A (prong 2), claim 1 does not integrate the recited exception into a practical application.
Under Step 2B, examiners should evaluate additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). In this case, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Returning to representative claim 1, taken individually or as a whole the additional elements of claim 1 do not provide an inventive concept (i.e. they do not amount to “significantly more” than the exception itself). As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment.
Furthermore, the additional elements fail to provide significantly more also because the claim simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. For example, the additional elements of claim 1 utilize operations the courts have held to be well-understood, routine, and conventional (see: MPEP 2106.05(d)(II)), including at least:
receiving or transmitting data over a network
storing or retrieving information from memory
providing offers
Even considered as an ordered combination (as a whole), the additional elements of claim 1 do not add anything further than when they are considered individually.
In view of the above, representative claim 1 does not provide an inventive concept (“significantly more”) under Step 2B, and is therefore ineligible for patenting.
Regarding dependent claims 2-3, 5, 7-9 and 21, dependent claims 2-3, 5, 7-9 and 21 recite more complexities descriptive of the abstract idea itself, and at least inherit the abstract idea of claim 1. Moreover, the claims 2-3, 5, 7-9 and 21 further define the search models as comprising a relevance model, ranking model, and regression model. Each of these more specific types of models represent mathematical models, or models based upon mathematical operations, further underscoring the abstract nature of the search models themselves. Lastly, certain dependent claims (e.g., claims 5, 9) expressly set forth mathematical concepts such as mathematical relationships, mathematical formulas or equations, mathematical calculations.
Accordingly, claims 2-3, 5, 7-9 and 21 are understood to recite an abstract idea under step 2A (prong 1) for at least similar reasons as discussed above.
Under prong 2 of step 2A, the additional elements of dependent claims 2-3, 5, 7-9 and 21 also do not integrate the abstract idea into a practical application, considered both individually or as a whole. More specifically, claims 2-3, 5, 7-9 and 21 rely upon at least similar additional elements as recited in claim 1, and are recited only at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea). Further, the additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (such as the Internet or computing networks).
Lastly, under step 2B, claims 2-3, 5, 7-9 and 21 also fail to result in “significantly more” than the abstract idea under step 2B. This is again because the claims merely apply the exception on generic computing hardware, generally link the exception to a technological environment, and append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually.
In view of the above, claims 2-3, 5, 7-9 and 21 do not provide an inventive concept (“significantly more”) under Step 2B, and are therefore ineligible for patenting.
Regarding claims 10-14, 16-17 and 22 claims 10-14, 16-17 and 22 recite at least substantially similar concepts and elements as recited in claims 1-3, 5, 7-9 and 21 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 10-14, 16-17 and 22 are rejected under at least similar rationale.
Subject Matter Allowable Over the Prior Art
Claims 1-2, 5, 7-14, 16-17 and 21-22 are rejected on other grounds; however, similar to the previous discussion of allowable subject matter, the claims are allowable over the prior art (see: Non-Final Action mailed 1/29/2026, p. 15-16).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Li (US 2023/0125022) specifically enables a human user to adjust training parameters and perform retraining (e.g., 0080, 0139)
PTO 892-U (https://www.youtube.com/watch?v=rti0Ozfeqn8) demonstrates a GUI for manually adjusting weights (parameters) of a neural network by a human user (see annotations)
Applicant's amendment necessitated any changes to the 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 WILLIAM J ALLEN whose telephone number is (571)272-1443. The examiner can normally be reached Monday-Friday, 8:00-4:00.
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WILLIAM J. ALLEN
Primary Examiner
Art Unit 3625
/WILLIAM J ALLEN/Primary Examiner, Art Unit 3619
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