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 .
The following is a Final Office Action in response to communications received on 2/23/2026. Claims 1-6, 8-16, and 18-20 are currently pending and have been examined. Claims 1, 11, and 20 have been amended. Claims 7 and 17 have been cancelled.
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.
Step 1: The claims 1-6 and 8-10 are a method, claims 11-16 and 18-10 are a computer readable medium and claim 20 is a system. Thus, each independent claim, on its face, is directed to one of the statutory categories of 35 U.S.C. §101. However, the claims 1-6, 8-9, 11-16, and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong 1: The independent claims (1, 11 and 20, taking claim 1 as a representative claim) recite:
A method performed at a computer system comprising a processor and a computer-readable medium, the method comprising:
generating location features from entries of a log associating times, locations, and actions;
receiving, via a network and from a device associated with a user, a request to create an order for items from an entity;
responsive to receiving the request, identifying location features corresponding to the order within the generated location features, wherein the identified location features include user residence features for a residence of the user and store features for a store associated with the entity;
accessing a machine learning model trained to predict a metric for the user quantifying an effect a first service option would have on the user to request one or more future orders when the first service option is visually emphasized to the user;
applying the machine learning model to the user residence features and the store features to generate a scalar value for the user indicative of a change in metric for the user due to visually emphasizing the first service option in a user interface of the device associated with the user;
determining, using the scalar value, that the change in the metric for the user is positive indicating that the first service option will have a positive effect on the user to request the one or more future orders when the first service option is visually emphasized to the user;
after determining that the change in the metric for the use is positive, sending, via the network and to the device associated with the user, a signal that causes the device associated with the user to visually emphasize the first service option to the first user in the user interface of the device associated with the user by displaying a visual highlight around an indicator of the first service option in the user interface, adding additional material to the user interface referring to the first service option, and displaying the indicator of the first service option at a ranked position of the user interface according to the change in metric is higher than each of one or more other ranked positions of the user interface occupied by one or more other indicators of one or more other service options different from the first service option;
receiving, via the network and from the device associated with the user, information about the user selecting the first service option for servicing the order and placing the order including a set of items associated with the entity;
responsive to the user selecting the first service option and placing the order, assigning a servicing of the order to a picker;
and upon assigning the servicing of the order, instructing, via instructions stored at the computer-readable medium and executed by the processor, the picker to collect the set of items in the store and deliver the set of items to the residence of the user.
These limitations, except for the italicized portions, under their broadest reasonable interpretations, recite certain methods of organizing human activity for managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) as well as commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). The claimed invention recites steps for determining and providing service options to a user for a current order based on historical data of the user. As stated in the specification, the concierge system assists customers with information such as delivery information and pricing options for their orders. Certain information is promoted or emphasized based on the likelihood a user will accept the information ([003]). The steps under its broadest reasonable interpretation specifically fall under marketing and advertising activities. The Examiner notes that although the claim limitations are summarized, the analysis regarding subject matter eligibility considers the entirety of the claim and all of the claim elements individually, as a whole, and in ordered combination.
Prong 2: This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of
at a computer system comprising a processor and a computer-readable medium, (claim 1)
A computer-readable storage medium storing instructions that when executed by a computer processor perform actions comprising: (claim 11)
A computer system comprising: a computer processor; and a computer-readable storage medium storing instructions that when executed by a computer processor perform actions comprising: (claim 20)
machine learning […] that is trained
a network and from a device associated with the user,
applying the machine learning
the network and to the device associated with the user,
the device
the user interface of the device
accessing a machine learning model trained
instructions stored at the computer-readable medium and executed by the processor.
The additional elements emphasized above are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of processing data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application – MPEP 2106.05(f).
Accordingly, these additional elements when considered individually or as a whole do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The independent claims are directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A Prong two, the additional elements in the claims amount to no more than mere instructions to apply the judicial exception using a generic computer component.
Even when considered as an ordered combination, the additional elements of claim 1, 11, and 20 do not add anything that is not already present when they are considered individually. Therefore, under Step 2B, there are no meaningful limitations in claims 1, 11, and 20 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (see MPEP 2106.05).
As such, independent claims 1, 11, and 20 are ineligible.
Dependent claims 2-6, 8-16, and 18-20 when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. §101 because the additional recited limitations fail to establish that the claims are not directed to the same abstract idea of Independent Claims 1, 11 and 20 without significantly more.
Claim 2 recites wherein generating the location features comprises generating the user residence features including at least one of: a time to deliver the items from the store to the residence of the user , a time to find a parking location for the residence of the user, a time to carry the items from the parking location to the residence of the user, or a number of unsuccessful delivery attempts to a neighborhood of the residence of the user. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 3 recites wherein generating the location features comprises generating the store features including at least one of: a time to find parking at the store, a time from parking to entering the store, a time from parking to picking a first item of the order, or a degree of crowdedness of the store. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 4 recites wherein generating the location features comprise generating the user residence features including a time for deliveries from the store to the residence of the user. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 5 recites further comprising: identifying location features for prior orders; determining degrees of activity of users associated with the prior orders; determining indications of whether the first service option was emphasized as part of the prior orders; and training the machine learning model by providing the location features, the degrees of activity, and the indications of whether the first service option was emphasized, to a machine learning algorithm of the machine learning model. The limitation merely further limits the abstract idea and recites that machine learning algorithm at a high level. Therefore, it does not integrate the judicial exception into a practical application.
Claim 6 recites wherein the machine learning algorithm comprises a meta-learning algorithm for uplift modeling of the machine learning model. The limitation merely further limits the abstract idea and recites that machine learning algorithm at a high level. Therefore, it does not integrate the judicial exception into a practical application.
Claim 8 recites wherein determining that the predicted difference in activity of the
Claim 9 recites further comprising: displaying one or more of the location features to the user within the user interface. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 10 recites further comprising: using the location features to estimate a delivery cost of the items. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 12-16, 18, and 19 recite parallel claim language and therefore are rejected for the reasons set forth above.
For these reasons claims 1-6, 8-9, 11-16, and 18-20 are rejected under 35 USC 101.
Subject Matter Free of Prior Art
Claims 1, 11 and 20 are determined to have overcome the prior art of rejection and are free of prior art, however the claims remain rejected under 35 USC 101, as set forth above. All dependent claims are also free of prior art by virtue of dependency, but remain rejected under 35 USC 101.
Taking amended claim 1 as a representative claim, the claims as amended are found to overcome the prior art rejection for the reasons set forth below.
Claim 1 now recites:
determining, using the scalar value, that the change in the metric for the user is positiveindicating that the first service option will have a positive effect on the user to request the one or more future orders when the first service option is visually emphasized to the user;
responsive to after determining that the scalar value change in the metric for the user is positive,
sending, via the network and to the device associated with the user, a signal that causes the device associated with the user to visually emphasize the first service option to the user in the user interface of the device associated with the user by displaying a visual highlight around an indicator of the first service option in the user interface,
adding additional material to the user interface referring to the first service option, and displaying the indicator of the first service option at a ranked position of the user interface according to the change in the metric that is higher than each of one or more other ranked positions of the user interface occupied by one or more other indicators of one or more other service options different from the first service option;
The closest prior art of record was found to be as follows:
Pirhooshyaran US 12346867 discloses [Col. 5 lines 15-26] Order-level features may also include speed-related features such as different delivery times for different shipment options. Order-level features may also include merchant-related features like merchant type. Order-level features may also include location-related features like address type (e.g., apartment, single-family home), zip code, etc. For the user-level features, user historical ship option usage, shopping activities, device usage (e.g., mobile and PC, web browser or application, etc.) in recent time periods, along with tenure may be aggregated. The contextual features 102 may also include any other types of data. Additionally, the reference discloses [Col. 4 lines 52-67] The scores 105 may represent the probabilities that a user is likely to select any of the shipment options at the online checkout page. The probabilities may be based on aggregated user data, such that the scores are based on data associated with multiple users. However, in some cases, the scores 105 may also be tailored to specific users (using data associated with that specific user) to reduce the likelihood that a particular user “opts-out” of a default shipment option (for example, a user selecting one of the options that is not the default selected shipment option determined by the one or more machine learning models). Lastly, in Figure 1 a particular timeslot is highlighted based on received data. However, the reference does not disclose and displaying the indicator of the first service option at a ranked position of the user interface according to the change in the metric that is higher than each of one or more other ranked positions of the user interface occupied by one or more other indicators of one or more other service options different from the first service option as required by the claimed invention.
Boo US 20210073887 discloses [0088] At least one of the plurality of modification options is then selected using a trained machine learning model, and independent of user intervention, based on the user interaction data (block 806). The modification module 124 of the listing modification system 104, for instance, receives the user identifier 202, the interaction description 204, and the modification options 206 from the analysis module 120, as generated from the user interaction data 106. The modification module 124 then aggregates this received information together and provides the aggregated information as an input to the trained machine learning model 208, which is configured to recognize patterns form input data and automatically select one or more of the modification options 206. [0047] For instance, in response to determining that the interaction description 204 indicates that a location of the first computing device 108 is geographically close to a warehouse from which the subject item of the item listing is to be shipped, the analysis module 120 may identify a potential modification option 206 is to visually emphasize an option for local delivery that may not have been considered by a user of the first computing device 108. [0073] For instance, in the illustrated example of FIG. 6, interface 308 depicts modification option 508 as being selected to alter the item listing 110, as output at the first computing device 108, in a manner that incentives a user to continue engaging with the item listing 110. While the reference discloses visually emphasizing option for the local delivery, the reference does not disclose and displaying the indicator of the first service option at a ranked position of the user interface according to the change in the metric that is higher than each of one or more other ranked positions of the user interface occupied by one or more other indicators of one or more other service options different from the first service option as required by the claimed invention.
Wilkinson US 20170301002 discloses the selection of identified products from order and shipment in [0182]. However, the reference discloses visually emphasizing option for the local delivery, the reference does not disclose and displaying the indicator of the first service option at a ranked position of the user interface according to the change in the metric that is higher than each of one or more other ranked positions of the user interface occupied by one or more other indicators of one or more other service options different from the first service option as required by the claimed invention.
Johnson WO2018185492A1 discloses improving the speed of ordering goods from a pre-selected list of goods from vendors. The pre-selected list based on user collected information (abstract). However, the reference discloses visually emphasizing option for the local delivery, the reference does not disclose and displaying the indicator of the first service option at a ranked position of the user interface according to the change in the metric that is higher than each of one or more other ranked positions of the user interface occupied by one or more other indicators of one or more other service options different from the first service option as required by the claimed invention.
Closest NPL of record: “Development of Recommendation System to Choose Best Courier Service” discloses providing recommended courier services to users based on set of criteria and collaborative filtering. However, the reference discloses visually emphasizing option for the local delivery, the reference does not disclose and displaying the indicator of the first service option at a ranked position of the user interface according to the change in the metric that is higher than each of one or more other ranked positions of the user interface occupied by one or more other indicators of one or more other service options different from the first service option as required by the claimed invention.
It was found that no references alone or in combination, neither anticipates, reasonable teaches, nor renders obvious the below noted features of Applicant’s invention. The features of claim 1 (and parallel claims 11 and 20) in combination that overcome the prior art are:
determining, using the scalar value, that the change in the metric for the user is positiveindicating that the first service option will have a positive effect on the user to request the one or more future orders when the first service option is visually emphasized to the user;
responsive to after determining that the scalar value change in the metric for the user is positive,
sending, via the network and to the device associated with the user, a signal that causes the device associated with the user to visually emphasize the first service option to the user in the user interface of the device associated with the user by displaying a visual highlight around an indicator of the first service option in the user interface,
adding additional material to the user interface referring to the first service option, and displaying the indicator of the first service option at a ranked position of the user interface according to the change in the metric that is higher than each of one or more other ranked positions of the user interface occupied by one or more other indicators of one or more other service options different from the first service option;
Therefore, none of the cited references disclose or render obvious each and every feature of the claimed invention and the claimed invention is determined to be free of the prior art. Although individually the claimed features could be taught, any combination of references would teach the claimed limitations using a piecemeal analysis, since references would only be combined and deemed obvious based on knowledge gleaned from the applicant's disclosure. Such a reconstruction is improper (i.e., hindsight reasoning). See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). The examiner emphasizes that it is the interrelationship of the limitations that renders these claims free of the prior art/additional art.
Therefore, it is hereby asserted by the Examiner that, in light of the above, that the claims are free of prior art as the references do not anticipate the claims and do not render obvious any further modification of the references to a person of ordinary skill in art.
Relevant Art Not Cited
Bedi (US11366872) discloses prioritizing selectable options based on user history
Iqbal (US 20160189099) discloses determining the optimal shipping options to display based on the user’s history
NPL: “Modeling and Analyzing Online Food Delivery Services Using Design Thinking: An Optimization Approach discloses using user feedback to optimize the user interface based on the indicated preferences.
Response to Arguments
With respect to the remarks filed 2/23/2026 directed to 35 USC 103, the remarks are found persuasive as the claims amended are not taught by the previously cited references. Further, after an updated search, the claimed invention was not found to be taught by prior art, as shown in the “Subject Matter Free of Prior Art” section.
Applicant's arguments filed 2/23/2026 have been fully considered but they are not persuasive for the reasons set forth below.
With respect to the remarks directed to 35 USC 101, the rejection has been updated above to address the claims as amended.
With respect to the remarks directed to the comparison of the instant application and Example 37, the remarks are not found to be persuasive. In Example 37, claim 1 is found to be eligible as the claim recites a specific manner of automatically displaying icons to the user based on usage which provides a specific improvement over prior systems. This was supported by the improvement set forth in the disclosure over the conventional system. This results in an improved user interface for electronic devices. Claim 2 was found to recite no abstract idea as the determining step now required action by the processor that could not be performed by the human mind. Claim 3 was determined to be ineligible. Here the claim recited a mental process and the processor was recited at a high level of generality. That is the processor was merely a tool to process the amount of use of each icon and ranking the icons based on the amount of use.
While the instant case recites the generating of a user interface based on the received scalar value, it does not follow the same fact pattern of claims 1 and 2 of the Example 37. It follows the fact pattern of claim 3 where the additional elements are recited at a high level of generality to carry out the abstract idea and present the result to the end user on the display. The user interface itself or the computer is not improved, but rather the output (part of the abstract idea- the service options to the user) is improved. The claimed invention does not recite an improvement to the computer system. The alleged improvement of how the content is displayed at specific positions is part of the abstract idea and thereby improves the information provided to the end user, allowing for more optimized service choices. This does not improve how the interface of the computer system functions.
For at least these reasons the claims remain rejected under 35 USC 101.
Conclusion
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 VICTORIA E. FRUNZI whose telephone number is (571)270-1031. The examiner can normally be reached Monday- Friday 7-4 (EST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Marissa Thein can be reached at (571) 272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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VICTORIA E. FRUNZI
Primary Examiner
Art Unit TC 3689
/VICTORIA E. FRUNZI/ Primary Examiner, Art Unit 3689 4/24/2026
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