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 .
Response to Amendment
This Final office action is responsive to Applicant’s amendment filed on
December 23, 2025. With the Amendment, Applicant has amended claims 1-3, 5-10 and 12-20. Claim 11 was canceled. Claims 1-10 and 12-20 remain pending and are under examination. Applicant’s amendment do not overcome the 35 U.S.C. §101 rejection. The 101 rejection is maintained below.
Response to Arguments
Applicant’s arguments with respect to the 35 U.S.C. §101 rejection filed on December 23, 2025 have been fully considered but they are not persuasive.
Applicant has amended independent claims 1, 14 and 20 and asserted on
page 14 that the claims, as amended herein, are not directed to the judicial exception without significantly more. Applicant further supported his assertion by arguing that “limitations of amended claim 1, 14 and 20 integrate the alleged judicial exception into a practical application of a computer system that generates, in real time, an output for another computing system based on data collected and processed by the computer system in real time. These limitations do not merely link the judicial exception to a technical field but instead add a meaningful limitation in that the computer system employs information provided by the judicial exception (i.e., replacement score, replacement item, engagement data) to generate, in real time, the output for the computing system that prompts one or more immediate actions to occur. Also, the limitations of amended claim 1 require the computer system to integrate specific physical devices to collect real time data, apply specific operations on the collected real time data to generate the real time output, and communicate the real time output to the computing system for performing one or more immediate actions”.
In response, the Examiner respectfully disagrees, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because :
The additional elements when considered both individually and as a combination do not amount to significantly more than the abstract idea. The claims recite the additional elements of a network, device, machine learning model, user interface and computing system. The network, device, machine learning model, user interface and computing system elements taken individually or as a whole is seen as general purpose computer or a computer system (see the applicant’s specification). These claimed devices are noted to perform routine computer functions such as receiving data, identifying data, applying data, selecting data, causing data, accessing data and generate data.
The claimed network, device, machine learning model, user interface and computing system are seen as a generic computer performing generic functions without an inventive concept as such does not amount to significantly more. These devices are simply a field of use that attempts to limit the abstract idea to a particular environment. The type of data being manipulated does not impose meaningful limitations. Looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore the claims do not amount to significantly more than the abstract idea itself. The claims are not patent eligible.
Furthermore, in Core Wireless Licensing S.A.R.L. v. LG Electronics, Inc., the Courts held that claims to a method for making websites easier to navigate on a small-screen device were not directed to an abstract idea. 880 F.3d 1356, 1363 (Fed. Cir. 2018). Here, the claims are not drafted in the format CoreWireless. Rather than providing a technical solution that improves the way the computing device, the applicant is merely using alternate ways of using a computer for generating recommendations for adjustment actions. The network, or device, or machine learning model, or user interface or computing system is then applied to the abstract idea. The claims do not provide sufficient details to transform the abstract idea into patent eligible subject matter. See, e.g. Alice, 134 S. Ct. at 2360 (explaining that claims that “amount to ‘nothing significantly more’ than an instruction to apply the abstract idea…using some unspecified, generic computer” is not ‘enough’ to transform an abstract idea into a patent-eligible invention” (quoting Mayo, 566, U.S. at 77, 79)); Intellectual Ventures LLC v. Capital One Fin.Corp., 850 F. 3d 1332, 1342 (Fed. Cir. 2017) (“The claim language here provides only a result-oriented-solution with insufficient detail for how a computer accomplishes it”).
Claim Rejections - 35 USC§ 101
4. 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.
5. Claims 1-10 and 12-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step One: Under Step one of an analysis, claim 1 does belong to a statutory category, namely it is a method claim. Likewise, claim 14 is a computer program product comprising a non-transitory computer readable storage medium, and claim 20 is a system claim. Each of the claims falls under one of the four statutory classes of invention.
Step 2A. Prong 1:
The claims disclose the abstract idea of generating market adjustment recommendations for a retailer associated with an online concierge system
The claims recite the limitations below in the abstract idea indicated in non-bold and the additional elements in bold.
Claim 1 recites:
receiving, via a network and from a device associated with a user of an online system, a request from a user of an online system for an item; responsive to receiving the request, identifying that the requested item is unavailable at an entity a retailer associated with the online system; upon the identification identifying that the requested item is unavailable, accessing an item replacement computer model of the online system, wherein the item replacement computer model is a machine-learning model trained to generate a replacement score for each candidate replacement item from a set of candidate replacement items;
applying the item replacement computer model to generate, based at least in part on user data associated with the user and one or more features for each candidate replacement item in the set of candidate replacement items, to generate the replacement score for each candidate replacement item;
selecting, based on using the replacement score for each candidate replacement item, a replacement item from the set of candidate replacement items;
causing a user interface of the device associated with the user to display the replacement item for recommendation to the user and inclusion into a cart;
collecting receiving, in real time via the network and from the device associated with the engagement data with information about an engagement of the user in relation to with the replacement item via the user interface;
accessing a market adjustment computer model of the online system, wherein the market adjustment computer model is a machine-learning model trained to:
generate a score for the user indicative of an affinity of the user in relation to the replacement item, and generate one or more market adjustment recommendations a real time alert for the retailer entity;
applying the market adjustment computer model to generate, based on at least one of the engagement data, behavioral information of the user and contextual information associated with the user to generate the score for the user and the one or more market adjustment recommendations the real time alert for the retailer entity; and providing communicating, via the network, the one or more market adjustment recommendations the real time alert to a computing system associated with the retailer entity prompting the retailer entity to perform one or more market adjustment immediate actions.
Similar limitations comprise the abstract ideas of claims 14 and 20.
Claim 2 further recites clustering, based at least in part on the generated score for the user, the user into a cluster of users of the online system, each user in the cluster of users associated with a likelihood of switching from a conversion of the item to a conversion of the replacement item greater than a threshold value; and storing, in a database of the online computer system, information about the cluster of users.
Claim 3 further recites generating the behavioral information of the user, wherein the behavioral information comprises at least one of: information about a brand of the replacement item converted by the user relative to a brand of the requested item, information about a type of the replacement item converted by the user relative to a type of the requested item, a discussion between the user and a picker associated with the online system during a fulfillment process associated with the replacement item, information about a future reordering of the replacement item by the user, or an appeasement request from the user associated with the requested item.
Claim 4 further recites generating the contextual information associated with the user, wherein the contextual information comprises at least one of: demographic information about the user, a timestamp when the request for the item was made, content of the cart, or information about one or more other replacement items included in the cart.
Claim 5 further recites wherein applying the market adjustment computer model further comprises: applying the market adjustment computer model to generate, based on at least one of the engagement data, the behavioral information of the user and the contextual information associated with the user, the one or more market adjustment recommendations to generate the real time alert including information about one or more reasons for the user declining to convert the recommended replacement item.
Claim 6 further recites wherein applying the market adjustment computer model further comprises:
applying the market adjustment computer model to generate, based on at least one of the engagement data, the behavioral information of the user and the contextual information associated with the user, the one or more market adjustment recommendations to generate the real time alert including at least one of a first recommendation about an adjustment of a price of the replacement item or a second recommendation for generating a coupon for incentivizing a conversion of the replacement item.
Claim 7 further recites wherein applying the market adjustment computer model further comprises: applying the market adjustment computer model to generate, based on at least one of the engagement data, the behavioral information of the user and the contextual information associated with the user, the one or more market adjustment recommendations to generate the real time alert including a recommendation for at least one of branding or advertising a group of items including the replacement item which was converted by the user.
Claim 8 further recites wherein applying the market adjustment computer model further comprises: applying the market adjustment computer model to generate, based on at least one of the engagement data, the behavioral information of the user and the contextual information associated with the user, the one or more market adjustment recommendations to generate the real time alert including a recommendation for the retailer entity to combine the replacement item with one or more other items into a single item.
Claim 9 further recites wherein applying the market adjustment computer model further comprises: applying the market adjustment computer model to generate, based on at least one of the engagement data, the behavioral information of the user and the contextual information associated with the user, the one or more market adjustment recommendations to generate the real time alert including an advertisement of the replacement item.
Claim 10 further recites wherein applying the market adjustment computer model further comprises: applying the market adjustment computer model to generate, based on at least one of the engagement data, the behavioral information of the user and the contextual information associated with the user, the one or more market adjustment recommendations to generate the real time alert including a recommendation for adjusting stocking of at least one of the requested item or the replacement item.
Claim 12 further recites collecting data with information about one or more actions conducted by the retailer entity in response to the one or more market adjustment recommendations the real time alert; and
re-training the market adjustment computer model by updating, using the collected data, a set of parameters of the market adjustment computer model.
Claim 13 further recites wherein providing communicating the one or more market adjustment recommendations the real time alert comprises:
providing communicating the one or more market adjustment recommendations the real time alert to the computing system associated with the retailer entity prompting the retailer entity to perform the one or more market adjustment immediate actions including at least one of adjusting a stock of one or more items, generating advertisements for the one or more items, providing discount coupons for conversion of the one or more items, or generating one or more new items.
Claim 15 further recites clustering, based at least in part on the generated score for the user, the user into a cluster of users of the online system, each user in the cluster of users associated with a likelihood of switching from a conversion of the item to a conversion of the replacement item greater than a threshold value; and
storing, in a database of the online a computer system, information about the cluster of users.
Claim 16 further recites applying the market adjustment computer model to generate, based on at least one of the engagement data, the behavioral information of the user and the contextual information associated with the user, the one or more market adjustment recommendations to generate the real time alert including information about one or more reasons for the user declining to convert the recommended replacement item.
Claim 17 further recites applying the market adjustment computer model to generate, based on at least one of the engagement data, the behavioral information of the user and the contextual information associated with the user, the one or more market adjustment recommendations to generate the real time alert including a recommendation for the retailer entity to combine the replacement item with one or more other items into a single item.
Claim 18 further recites applying the market adjustment computer model to generate, based on at least one of the engagement data, the behavioral information of the user and the contextual information associated with the user, the one or more market adjustment recommendations to generate the real time alert including a recommendation for adjusting stocking of at least one of the requested item or the replacement item.
Claim 19 further recites collecting data with information about one or more actions conducted by the entity in response to the real time alert; and applying re-training the market adjustment computer model to generate, based on at least one of the engagement data, the behavioral information of the user or the contextual information associated with the user, the one or more market adjustment recommendations including one or more real time messages for the retailer in relation to the replacement item by updating, using the collected data, a set of parameters of the market adjustment model.
Step 2A, Prong One:
Here, the claimed concept falls into the category of functions of performing mental processes such as concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because it amounts to the function of:
applying the market adjustment computer model to generate, based on at least one of the engagement data, behavioral information of the user and contextual information associated with the user to generate the score for the user and the one or more market adjustment recommendations the real time alert for the retailer entity; and providing communicating, via the network, the one or more market adjustment recommendations the real time alert to a computing system associated with the retailer entity prompting the retailer entity to perform one or more market adjustment immediate actions.
.
Step 2A, Prong Two of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.2019 PEG Section III(A)(2), 84 Fed. Reg. at 54-55.
Clams 1-10 and 12-20 above recite the following bolded limitations understood to be the additional limitations:
In addition to the abstract ideas recited in the claims, the claims recite additional elements including a generic data gathering step of a processor and a non-transitory non-volatile data memory and a power tool.
As per claims 1, 14 and 20, the claimed receiving, a network and from a device associated with a user of an online system, computer model, a user interface of the device, a machine-learning model, a computing system.
are similarly understood in light of applicant's specification as mere usage of any arrangement of computer software or hardware intermediate components potentially using networks to communicate with instructions are properly understood to be mere instructions to apply the abstraction using a computer or device or computer system. Claims 1, 14 and 20 do not recite any computer structure to perform the claimed steps. Performing steps or functions by a processor merely limits the abstraction to a computer field by execution by generic computers to process data (i.e. generating recommendations for adjustment actions data).
Performing steps by a generic machine, or server computing device merely limit the abstraction to a computer field by execution by generic computers. See MPEP 2106.05 (1 ).
The claimed limitations pertaining to a machine learning model amount merely to the very definition of the training aspect of supervised machine learning. As such, the independent claims do not reflect any improvement in machine learning (or in another technology/functioning of a computer), and the machine learning limitations are merely generic computer elements.
As noted in MPEP 2106.04(d), limitations which amount to instructions to implement an abstract idea on a computer or merely using a computer as a tool, limitations which amount to insignificant extra-solution activity, and limitations which amount to generally linking to a particular technological environment do not integrate a practical exception into a practical application.
Performance of the claimed steps or functions technologically may present a meaningful limit to the scope of the claims does not reasonably integrate the abstraction into a practical application.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus the claims are directed to an abstract idea.
Step 2B: The elements discussed above with respect to the practical application in Step 2A, prong 2 are equally applicable to consideration of whether the claims amount to significantly more. Accordingly, the clams fail to recite additional elements which, when considered individually and in combination, amount to significantly more. Reconsideration of these elements identified as insignificant extra-solution activity as part of Step 2B does not change the analysis.
Positively reciting a "processor" ” does not change the analysis as these aspects are properly considered as additional elements which amount to instructions to apply it with a computer.
These claimed elements also as found in the dependent claims are also recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using a generic component.
In processing the claims, it is noted that the recitation of these additional elements does not impact the analysis of the claims because these elements in combination are noted only to be one or more of a general purpose computer for performing basic or routine computer functions. The claimed processor is noted to a be a generic computer for performing one or more market adjustment immediate actions.
The additional elements do not overcome the analysis as these elements are merely considered as additional elements which amount to instructions to be applied to the generic computer.
The judicial exception is not integrated into a practical application. In particular, the claimed " online system”, “computer model”, “ user interface”, “machine-learning model”, and “computing system " are recited at a high level of generality such they amount to no more than mere instructions to apply the exception using generic components. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, claims 1, 14 and 20 are directed to an abstract idea.
Dependent claims 2-10 and 12-13 include additional elements beyond those recited by independent claim 1. Dependent claims 15-18 include additional elements beyond those recited by independent claim 14. The fact that the claims go into detail about these machine learning do not provide any counterargument to the foregoing points. The provision of additional detail of a generic computer element does not render the element any less generic. The claimed steps do not amount to significantly more than the abstract idea, because they are well-understood, routine, and conventional computer functions in view of MPEP 2106 .05(d)(11). The recited computer elements do not amount to significantly more than the abstract idea because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As a result, claims 2-10, 12-19 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1-10 and 12-20 are rejected under 35 USC. 101 as being directed to non-statutory subject matter.
6. Claims 1-10 and 12-20 would be allowable if overcome the 101 rejection.
- Cartoon et al (US Publication No. 2016/0299906) teach a content item recommendation module to provide the recommended content item sequence to the user as a recommendation, which the user can select to approve or deny. The content item recommendation module can present the recommended content item sequence and enable the user to select to approve or deny one or more or all of the recommended content items. If the user denies one or more of the recommended content items, content item recommendation module can select replacement content items based on the content item selection rules and/or the recommended content attribute sequence. This can include replacing only the denied content items or the denied content items and other content items in the recommended content item sequence. A selected replacement content item requires content item
recommendation module to replace an additional content item to conform to the conditions dictated by the content item selection rules.
- Joshi et al (US Patent No. disclose computing device can further determine recommended substitute items based on the test features or the control features wherein the recommended substitute items are intended to replace items ordered by the customer that are unavailable. The computing device can also determine one or more performance metrics of the test group and the control group.
7. 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.
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. As per attached PTO 892 form.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Romain Jeanty whose telephone number is (571)272-6732. The examiner can normally be reached M-F 9.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O'Connor can be reached at 571 272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RJ/
/ROMAIN JEANTY/Primary Examiner, Art Unit 3624