DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 8/10/2026 has been entered.
Claims Status
Claim 6 is cancelled.
Claims 1-5 and 7-20 remain pending.
Claims 10-12 and 14-19 stand withdrawn as set forth previously.
Claims 1-5, 7-9, 13 and 20 stand rejected.
Response to Affidavit/Declaration under 37 C.F.R. 1.132
The declaration under 37 CFR 1.132 filed 8/10/2026 is insufficient to overcome the rejection of claims 1-5, 7-9, 13 and 20 under 35 USC 101 as set forth in the last Office action. The Examiner observes the following:
Although factual evidence is preferable to opinion testimony, such testimony is entitled to consideration and some weight so long as the opinion is not on the ultimate legal conclusion at issue. While an opinion as to a legal conclusion is not entitled to any weight, the underlying basis for the opinion may be persuasive. The Examiner notes that many of points of evidence offered in the Declaration are directed to legal conclusions. The declaration has been fully considered with each section addressed as follows:
Sections 1-6: These sections detail the experience of Jorge Trujillo (Declarant). The Examiner acknowledges Declarant’s assertions of background and experience/expertise.
Section 7-20: This is opinion evidence used to provide a legal conclusion concerning the analysis under the Two-Part Alice/Mayo framework. Opinion testimony is entitled to consideration and some weight so long as the opinion is not on the ultimate legal conclusion at issue. The legal conclusion at issue is addressed further under the heading Response to Arguments, which has taken into consideration the discussion provided in sections 7-20 of the Declaration.
Response to Arguments
I. Applicant’s arguments with respect to the rejection under 35 USC 101 have been fully considered but are not persuasive.
Applicant initially argues that the Examiner has erred in finding, under Prong One, that the claims “recite” an abstract idea. Applicant alleges that there “is no reasonable interpretation” that expands the claim scope to encompass merely human activity. To this, the Examiner reminds Applicant that, in accordance with MPEP 2106.04(II)(A)(1), a claim recites a judicial exception (i.e. a law of nature, natural phenomenon, or abstract idea) is set forth or described in the claim. The Examiner continues to acknowledge that the claims do recite additional elements; however, this does not negate the myriad of limitations that clearly set forth or describe the abstract idea of implementing remediations to item listing data.
Further, Applicant’s argument that “literally every step of claim 1 confirms this fact by reciting hon-human “responsive to” actions with meaningful technical improvements”. The Examiner strongly disagrees, and asserts that the majority of the claims actually specifically describe the abstract, commercial process to be undertaken, and ultimately the organization of human activities undertaken in the commercial realm. For example, and referencing some of the highlighted limitations by Applicant:
receiving a topic for a change in item listing data, wherein the topic indicates that item listing data of an item listing record has changed
The added portion in bold does nothing to move this limitation from abstract to additional, and only refine that the topic is indicative in a change to an item listing record.
retrieving, by the computing system from a data store, at least one model that was trained using machine learning techniques to identify changes in other item listing data, determine at least one suggested remediation to the changes in the other item listing data to generate accurate item listing data, and determine at least one confidence metric indicating a likelihood that the at least one suggested remediation will result in generating the accurate item listing data
Here, the bolded portion clearly lays out a steps that describe analysis for performing the commercial process of implementing remediations to item listing data. The description that at least one model is retrieved by a computer to perform these operations does not change the underlying nature of the operations being performed – i.e., that they describe the abstract idea.
Each of at least one confidence metric satisfies a threshold confidence condition, generating instructions to update the item listing record, determining a suggested remediation does not satisfy auto-remediation criteria, receiving input indicating a rejection of the suggested remediation and an identification of a user-defined remediation similarly set forth and describe in more detail the abstract idea itself.
Contrary to Applicant’s allegation that the claims merely “involve” an abstract idea, the Examiner holds that the evidence supports strongly that the crux of the invention is squarely in the abstract idea itself:
[0003] Online retail environments, such as retail websites, can sell items (e.g., products) to end consumers. End consumers can search for items on the online retail environments and decide whether to purchase items and which items to purchase based on information presented about the items thereon. Each item can have a listing on the online retail environment. The listing can include information about the item, such as a title, price, and description. Sometimes, item listings may be missing information, outdated, incomplete, or duplicative. When item listings are incomplete and/or inaccurate, end consumers may be less inclined to purchase those items and may experience a lower shopping experience. Sometimes, the end consumers may have less confidence in the items that are being sold and therefore may not purchase items from that online retail environment.
[0004] The document generally relates to identifying and remediating changes in item listings on online retail environments to provide accurate and useful information to end consumers. More specifically, the disclosed technology provides for automatically detecting changes to item listing data, running the changed data through various different models for identifying potential remediations, and providing options for auto-remediation, user-initiated remediation, and user override of system decisions to fix the changed data and ensure its accuracy, completeness, validity, timeliness, and/or consistency.
[0010] In some implementations, the at least one model could have been trained to identify, in the other item listing data, changes in at least one of item: accuracy, completeness, timeliness, uniqueness, validity, and consistency. A change in the item accuracy can include at least one of an inaccurate item type and inaccurate package dimensions, a change in the item completeness can include a missing merchant type attribute that is required for the other item listing data, a change in the item timeliness can include a threshold amount of time that passed since the other item listing data was updated, a change in the item uniqueness can include an item identifier or
an item title that is identical to another item identifier or another item title, a change in the item validity can include at least one of an invalid brand and an invalid item taxonomy, and a change in the item consistency can include an inconsistency of at least one of brand and item taxonomy for the other item listing data across data systems associated with the online retail environment.
[0021] The devices, system, and techniques described herein may provide one or more of the following advantages. For example, the disclosed technology provides for distilling an abundance of complex information in quality, performance, and decision intelligence into digestible content that is simple to publish, consume, and verify. Automated processes to distill this information can provide more accurate insights about accuracy in item listing data and remediation of inaccuracies in such item listing data. Automation can also improve ability to check an abundance of information and correct that information with minimal human intervention and/or feedback. This can improve not only efficiency of a data remediation process but also improve the humans' time efficiency and ability to perform other tasks.
The specification repeatedly indicates that the intent is an improvement of the listing remediation process itself, resulting only in commercial benefits to human users such as consumers or users performing listing remediation. This is echoed within the accompanying declaration as well (e.g., Section 9: “identifying and remediating changes in
item listings on online retail environments to provide accurate and useful information to end
consumers”). Moreover, each of these paragraphs demonstrate that the remediation is human performable, referencing specific user-performed operations. This is echoed byo paragraphs 0105-0106, which provide the majority of support for the “orchestration module” and its functioning:
[0105] As an illustrative example, an item listing can be flagged for having potentially inaccurate package dimensions. A notification (e.g., email) can be transmitted as the topic 232 via the communication API 230 to one of the external authoring systems 234A-N associated with the item listing. The external authoring system, such as a vendor, can review the notification and respond with input indicating correct package dimensions. This input can be received via the communication API 230. The computer system 102 can then update the item listing to include the correct package dimensions, based on the input. One or more other operations are also possible, as described herein. As another example, the compliance message can be published by the communication API 230 to one or more external authoring systems 234A-N associated with the online retail environment. Such compliance message can be checked by the relevant user, such as a quality control analyst, at the external authoring systems 234A-N to determine whether a remediation should be made, whether to implement a suggested remediation, and/or whether to override a suggested remediation. The relevant users at any of the external authoring systems 234A-N can then reject or close out of decisions made by the intelligence platform 200, as described in reference to FIG. 1B.
[0106] Referring back to the decision topic 222, the decision topic 222 can also indicate that an auto-remediation should be made by the computer system 102. This decision topic 222 can be transmitted to an orchestration module 226. The orchestration model 226 can be configured to take action on one or more decisions that are published via the decision topic 222. For example, the module 226 can determine whether a decision from the topic 222 should be passed to a user interface (UI) frontend 228 and/or the communication API 230. Flagged decisions, for example, may be passed via the orchestration module 226 to the communication API 230, such that the flagged decisions can be communicated to appropriate parties to review the decision and accept, reject, and/or close the decisions. In some implementations, auto-remediation decisions can also be transmitted to the communication API 230 and to relevant parties. Auto-remediation decisions, in some implementations, may be passed via the orchestration module 226 to the UI frontend 228 so that a relevant use can view an auto-remediation that was automatically implemented by the computer system 102. The UI frontend 228 can be presented to relevant users associated with the online retail environment, such as quality control analysts and employees of the retail environment. The UI frontend 228 can present information indicating one or more suggested remediations, flagged decisions for user review, and/or indications of auto- remediation actions that were performed. Refer to FIG. 12 for example output that can be presented at the UI frontend 228.
Paragraph 105 clear articulates the user-performable operations for remediation, while paragraph 106 underscores at least certain aspects to be specifically performed by users, with other aspects potentially automatically performed. Simply automating such actions, or using models or computing components to do so, etc. as claimed 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). The link to “online” environments is also just generally links the use of a judicial exception to a particular technological environment or field of use (such as the Internet or computing networks). Each of these are addressed further concerning Prong Two.
Accordingly, the Examiner maintains that the claims “recite” an abstract idea under Prong One.
Turning to Prong Two, Applicant argues that the claims manifest an improvement to “conventional remediation tools that did not provide the claimed computer-implemented workflow…”, and implies that the Examiner has disregarded the written descriptions recitation of advantages. This incorrectly characterizes the Examiner’s previous analysis, to which the Examiner previously provided:
“The Examiner disagrees that the claims provide an improvement to computing technology. This is because the claims, at best, provide an improvement to the abstract idea itself (i.e., remediating item listings), not the functioning of the computer itself or another technology or technical field. This is also because Applicant’s arguments – in view of the specification - fail to provide more than a bare assertion of such an improvement.”
Furthermore, as evidenced by the citations above, the Examiner has fully considered the “advantages” of the claimed invention and found them to be insufficient to demonstrate that the claims improve the functioning of the computer itself or another technology or technical field. This is again because the improvements alleged by Applicant and clarified by the specification itself clearly admit to improving the abstract process for implementing remediations to item listings, resulting in more accurate item listing data. This speak not to any improvement in the technology itself, but only to an improvement to the abstract idea.
The opinions reflected in the Declaration do not suffice to provide evidence to the contrary. In fact, as indicated above, they reiterate the improvements already addressed by the Examiner from the specification. Notably, the Declaration does add arguments that “The specification further explains that the disclosed technology monitors incremental changes to item listing data before running models to check whether to remediate the changes to that data, which can improve efficiency and use of available compute resources and processing power. See Specification, [0022]”. This is, at best, a conclusory statement of an improvement.
Furthermore, any improvement to “computer resources” stems only from the potential that they are used less – not from any alteration of how the computer operates in any technical way. This is underscored by paragraph [0024], which provides:
“Since the disclosed techniques provide for improving item listing data by ensuring the item listing data is complete, accurate, consistent, timely, unique, and valid, consumers can search and more easily find the items they wish to purchase. This can further improve consumer confidence, their purchasing decisions, and their overall shopping experiences. This improved searchability can also decrease the amount of time that each consumer engages with the system during a particular session, thereby reducing processing and communication bandwidth requirements for the system, which frees up these resources for other purposes.”.
As seen from the above, the invention improves “searchability” by ensuring accurate listing data. The byproduct, then, is that the resources are used less – not that the computer’s ability to function in a technical capacity is improved. Paragraph 0025 offers similar disclosure and is treated consistent as above.
There is also no nexus between the claimed invention and “improved processing power”. Here again, this statement in the specification and opinion offered in the Declaration stands only as a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art. Applicant is reminded that f it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement.
Lastly, with respect to improved “efficiency”, the only improvement to efficiency is due to the automation itself, and not in the computer’s ability to process any information more efficiently. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See 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). Similarly, “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. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
With respect to Step 2B, the Examiner again disagrees. Considered both individually and as a whole, the claims do not provide “significantly more”.
First, as discussed above with respect to Prong Two, the claims do nothing other than invoke computers or other machinery merely as a tool to perform the abstract idea of implementation remediations to listing data in order to address issues of accuracy, completeness, or the like of the listing data.
Applicant also alleges that the current amendments “do not merely recite receiving data, outputting a suggestion, or using machine learning in an online environment”. This, however, is precisely what they do, and the addition of further generic computing components such as an API, a UI frontend, an orchestration module does not move beyond the “apply it” realm.
As an additional consideration, retrieving, by the computing system from a data store, at least one model… is analogous functionality to storing or retrieving information from memory. Each of transmitting, by the computing system, the decision topic to an orchestration module…, and publishing, by the computing system, a flagged-decision topic to the orchestration module for routing information… is receiving or transmitting data, as is transmitting, by the computing system, to the user device, the generated output. The use of a GUI to present information is akin to presenting offers (i.e., presenting data, data output) and tantamount to arranging transactional information on a graphical user interface (see Trading Technologies v. IBG LLC, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019)).
Additionally, the Examiner perceives nothing in the claims, whether considered individually or in their ordered combination, that would transform the training into something “significantly more” than the abstract idea of remediating changes in item listing data.
Considered both individually and as an ordered combination (as a whole), the additional elements of claim 1 do not add anything further than when they are considered individually. The evidence at hand demonstrates that the claimed invention is little more than the mere automation of manual processes by merely instructing the implementation of the abstract idea on generic computing hardware and software, and at best doing so utilizing well-understood, routine and conventional computing functions.
In view of all of the evidence presented, the Examiner maintains that – taken individually or as a whole – the claims of the instant application do not provide “significantly more” than the abstract idea itself.
As such, and in view of the above, the rejection has been maintained as updated below to account for the amended features.
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 1-5, 7-9, 13 and 20 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 1-5, 7-9, 13 and 20, under Step 2A claims 1-5, 7-9, 13 and 20 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 a method for implementing remediations to item listing data,
receiving a topic for a change in item listing data, wherein the topic indicates that item listing data of an item listing record has changed;
identify changes in other item listing data,
determine at least one suggested remediation to the changes in the other item listing data to generate accurate item listing data, and
determine at least one confidence metric indicating a likelihood that the at least one suggested remediation will result in generating the accurate item listing data
inputting the item listing data associated with the topic as input to the at least one model;
receiving output from the at least one model indicating at least one suggestion to remediate the item listing data and the at least one confidence metric;
determining that the at least one suggestion satisfies auto- remediation criteria, wherein determining that the at least one suggestion satisfies the auto- remediation criteria comprises determining that the at least one confidence metric satisfies a threshold confidence condition; and
auto-remediating, based on a determination that the at least one suggestion satisfies the auto-remediation criteria, the item listing data with the at least one suggestion, wherein auto-remediating the item listing data comprises generating instructions to update the item listing record with the at least one suggestion;
generating a decision topic indicating that an auto-remediation was performed for the item listing record;
in response to determining a suggested remediation does not satisfying the auto-remediation criteria:
flagging the item listing data as flagged item listing data;
generating, by the computing system, an output to be presented indicating the suggested remediation; and,
transmitting the generated output;
receiving user input indicating a rejection of the suggested remediation and an identification of a user-defined remediation;
implementing the user-defined remediation to update the item listing data.
These limitations recite ‘certain methods of organizing human activity, such as by performing commercial interactions and/or fundamental economic principals or practices (see: MPEP 2106.04(a)(2)(II)). This is because the claim sets forth or describes remediating changes in item listing data. This represents the performance of a marketing or sales activities or behaviors, which is a commercial interaction. This also describes concepts relating to the economy and commerce that represent fundamental economic practices.
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.
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:
an online retail environment,
a computing system,
a data management system, and,
retrieving, by the computing system from a data store, at least one model that was trained using machine learning techniques to, wherein the at least one model is one of a plurality of models each trained to analyze a different attribute of item listing data;
transmitting, by the computing system, the decision topic to an orchestration module configured to determine whether information corresponding to the decision topic is passed to a user interface (UI) frontend, a communication application programming interface (API), or both;
a graphic user interface (GUI) display at a user device,
publishing, by the computing system, a flagged-decision topic to the orchestration module for routing information corresponding to the flagged item listing data to the UI frontend, the communication API, or both;
training, by the computing system, the at least one model using the user-defined remediation to identify remediations for other item listing data that does not satisfy the auto- remediation criteria.
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). Moreover, the term “model” in general refers to “a system of postulates, data, and inferences presented as a mathematical description of an entity or state of affairs” (https://www.merriam-webster.com/dictionary/model). Similarly, it may refer to “a simplified representation of a system or phenomenon, as in the sciences or economics, with any hypotheses required to describe the system or explain the phenomenon, often mathematically” (https://www.dictionary.com/browse/model). Even presuming the model is implemented as software, it’s use remains nothing more than mere instructions to implement or apply the abstract idea on generic computing hardware. That is, even in light of training the model or models that were previously trained for analyzing different attributes, the only thing the claims disclose about the use of machine learning technique is that the techniques are used to automate remediation of inaccurate listing data.
Concerning generating instructions that, when executed by the computing system, cause the computing system to update the item listing record, this is literally the mere instruction to implement an abstract idea on a computer.
With respect to (i) transmitting, by the computing system, the decision topic to an orchestration module configured to determine whether information corresponding to the decision topic is passed to a user interface (UI) frontend, a communication application programming interface (API), or both and (ii) publishing, by the computing system, a flagged-decision topic to the orchestration module for routing information corresponding to the flagged item listing data to the UI frontend, the communication API, or both, the claim leverages highly-generalized software as a tool in automating the abstract idea of implementing remediations to item listing data.
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). This is notably true in relation to placing the listings within an online retail environment, which is akin to a general link to the Internet or computing networks.
Secondly, 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 addition to the above, retrieving, by the computing system from a data store, at least one model that was trained using machine learning techniques… and transmitting, by the computing system, to the user device, the generated output represent (at best) little more than extra-solution activity (e.g. data gathering and output) that contributes only nominally or insignificantly to the execution of the claimed method (see: MPEP 2106.05(g)).
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,
presenting offers
More specifically, retrieving, by the computing system from a data store, at least one model… is analogous functionality to storing or retrieving information from memory. Each of transmitting, by the computing system, the decision topic to an orchestration module…, and
publishing, by the computing system, a flagged-decision topic to the orchestration module for routing information… is receiving or transmitting data, as is transmitting, by the computing system, to the user device, the generated output. The use of a GUI to present information is akin to presenting offers (i.e., presenting data, data output) and tantamount to arranging transactional information on a graphical user interface (see Trading Technologies v. IBG LLC, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019)).
Additionally, the Examiner perceives nothing in the claims, whether considered individually or in their ordered combination, that would transform the training into something “significantly more” than the abstract idea of remediating changes in item listing data.
Considered both individually and as an ordered combination (as a whole), the additional elements of claim 1 do not add anything further than when they are considered individually. The evidence at hand demonstrates that the claimed invention is little more than the mere automation of manual processes by merely instructing the implementation of the abstract idea on generic computing hardware and software, and at best doing so utilizing well-understood, routine and conventional computing functions.
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 2-5, 7-9 and 13, dependent claims 2-5, 7-9 and 13 recite more complexities descriptive of the abstract idea itself, and at least inherit the abstract idea of claim 2-5, 7-9 and 13. As such, claims 2-9 and 13 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 2-5, 7-9 and 13 also do not integrate the abstract idea into a practical application, considered both individually or as a whole. More specifically, 2-5, 7-9 and 13 rely on at least similar additional elements recited only a t ahigh level of generality as addressed with respect to claim 1. Further additional elements such as a graphical user interface (e.g., claim 4) and training the model (claim 5) are also 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 2-5, 7-9 and 13 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 2-5, 7-9 and 13 do not provide an inventive concept (“significantly more”) under Step 2B, and are therefore ineligible for patenting.
Regarding claim 20 (system), claim 20 recites at least substantially similar concepts and elements as recited in claim 1 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. With respect to additional elements such as one or more processors and one or more computer-readable device including instructions, the Examiner reiterates the discussion under Step 2A (Prong 2) and Step 2B above. Accordingly, claim 20 is rejected under at least similar rationale as discussed above.
Subject Matter Allowable Over the Prior Art
Claims 1-5, 7-9, 13, and 20 remain rejected on other grounds but contain subject matter allowable over the prior art. The reasons for indicating allowability over the prior art from pages 13-14 of the Final Office Action mailed 4/22/2026 are incorporated herein.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Dolan (US 2009/0198541) discloses brand label validation and remediating item data (see: 0006, 0026, 0055).
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. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anita Coupe can be reached at 571-270-3614. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WILLIAM J ALLEN/Primary Examiner, Art Unit 3619
WILLIAM J. ALLEN
Primary Examiner
Art Unit 3625
