DETAILED ACTION
This action is in reply to the Amendments filed on 01/21/2026.
Claims 1-20 are rejected.
Claims 1-20 are currently pending and have been examined.
Response to Amendment
Applicant’s amendment, filed 01/21/2026, has been entered. Claims 1, 8, and 15 have been amended.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/21/2026 has been entered.
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 .
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under Step 1 of the Subject Matter Eligibility Test for Products and Processes, the claims must be directed to one of the four statutory categories (see MPEP 2106.03). All the claims are directed to one of the four statutory categories (YES).
Under Step 2A of the Subject Matter Eligibility Test, it is determined whether the claims are directed to a judicially recognized exception (see MPEP 2106.04). Step 2A is a two-prong inquiry.
Under Prong 1, it is determined whether the claim recites a judicial exception (YES). Taking Claim 1 as representative, the claim recites limitations that fall within the certain methods of organizing human activity groupings of abstract ideas, including:
-obtaining, by an online concierge system from a customer client device, a shopping list that contains one or more items selected by a customer and a pickup time selected by the customer for self-service fulfillment at a retail store;
-determining, by the online concierge system, a subset of the items from the shopping list for prepopulating to a shopping cart reserved for the customer at the retail store, wherein the subset of the items is identified from application of one or more machine learning models to score and rank the items in the shopping list in accordance with one or more scoring metrics, wherein at least one of the one or more machine learning models is trained on [utilizes] historical data captured by sensors coupled to shopping carts, wherein the historical data tracks movement of the shopping carts through the retail store and items added into the shopping carts the historical data associated with a first set of instances where items were prepopulated to the shopping cart and a second set of instances where items were not prepopulated to the shopping cart, each instance associated with one or more scoring metrics based at least on time spent in association with the respective instance;
-assigning, by the online concierge system, one or more pickers to pick the subset of the items from shelf locations at the retail store and prepopulate the shopping cart reserved for the customer;
-causing a picker client device associated with each of the assigned one or more pickers to display the subset of the items to facilitate picking thereof by the one or more pickers;
-receiving, from one or more sensors coupled to the shopping cart, sensor data indicative of items physically added to the shopping cart, wherein the one or more sensors include an integrated camera at the shopping cart;
-determining one or more remaining items from the shopping list that are not physically in the shopping cart based on the sensor data indicative of items physically added to the shopping cart;
-causing the customer client device, upon the customer arriving at the retail store, to display [of] a map of locations of each of the one or more remaining items from the shopping list that are not physically in the shopping cart, to facilitate procurement thereof by the customer; and
-logging information about a checkout process conducted by the customer at the retail store for one or more items on the shopping list
The above limitations recite the concept of facilitating shopping by pickers and customers. The above limitations fall within the “Certain Methods of Organizing Human Activity” groupings of abstract ideas, enumerated in MPEP 2106.04(a).
Certain methods of organizing human activity include:
fundamental economic principles or practices (including hedging, insurance, and mitigating risk)
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; and business relations)
managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)
The limitation of logging information about a checkout process conducted by the customer at the retail store for one or more items on the shopping list is a process that, under their broadest reasonable interpretation, cover a commercial interaction. For example, “logging” in the context of this claim encompass advertising, and marketing or sales activities.
Similarly, the limitations of obtaining, by an online concierge system from a customer client device, a shopping list that contains one or more items selected by a customer and a pickup time selected by the customer for self-service fulfillment at a retail store; determining, by the online concierge system, a subset of the items from the shopping list for prepopulating to a shopping cart reserved for the customer at the retail store, wherein the subset of the items is identified from application of one or more machine learning models to score and rank the items in the shopping list in accordance with one or more scoring metrics, wherein at least one of the one or more machine learning models is trained on [utilizes] historical data captured by sensors coupled to shopping carts, wherein the historical data tracks movement of the shopping carts through the retail store and items added into the shopping carts the historical data associated with a first set of instances where items were prepopulated to the shopping cart and a second set of instances where items were not prepopulated to the shopping cart, each instance associated with one or more scoring metrics based at least on time spent in association with the respective instance; assigning, by the online concierge system, one or more pickers to pick the subset of the items from shelf locations at the retail store and prepopulate the shopping cart reserved for the customer; causing a picker client device associated with each of the assigned one or more pickers to display the subset of the items to facilitate picking thereof by the one or more pickers; receiving, from one or more sensors coupled to the shopping cart, sensor data indicative of items physically added to the shopping cart, wherein the one or more sensors include an integrated camera at the shopping cart; determining one or more remaining items from the shopping list that are not physically in the shopping cart based on the sensor data indicative of items physically added to the shopping cart; and causing the customer client device, upon the customer arriving at the retail store, to display [of] a map of locations of each of the one or more remaining items from the shopping list that are not physically in the shopping cart, to facilitate procurement thereof by the customer are processes that, under their broadest reasonable interpretation, cover a commercial interaction. That is, other than reciting that the shopping list is obtained by an online concierge system from a customer client device, that the concierge is an online concierge system, that the subset of the items is identified from application of one or more machine learning models, that the one or more models are one of the one or more machine learning models that are trained, that the historical data is captured by sensors coupled to shopping carts, that the picker client device displays the subset of the items, that the data is sensor data that is received from one or more sensors coupled to the shopping cart, that the data is sensor data, that the one or more sensors include an integrated camera at the shopping cart, and that the customer client device displays the map locations, nothing in the claim element precludes the step from practically being performed by people. For example, but for the “online concierge system,” “customer client device,” “one or more machine learning models,” “trained,” “sensors coupled to shopping carts,” “one or more sensors coupled to the shopping cart,” “the one or more sensors include an integrated camera at the shopping cart,” “sensor data,” and “picker client device,” language, “obtaining,” “determining,” “assigning,” “causing,” “receiving,” “determining,” and “causing” in the context of this claim encompasses advertising, and marketing or sales activities.
Under Prong 2, it is determined whether the claim recites additional elements that integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application (NO).
-obtaining, by an online concierge system from a customer client device, a shopping list that contains one or more items selected by a customer and a pickup time selected by the customer for self-service fulfillment at a retail store;
-determining, by the online concierge system, a subset of the items from the shopping list for prepopulating to a shopping cart reserved for the customer at the retail store, wherein the subset of the items is identified from application of one or more machine learning models to score and rank the items in the shopping list in accordance with one or more scoring metrics, wherein at least one of the one or more machine learning models is trained on historical data captured by sensors coupled to shopping carts, wherein the historical data tracks movement of the shopping carts through the retail store and items added into the shopping carts the historical data associated with a first set of instances where items were prepopulated to the shopping cart and a second set of instances where items were not prepopulated to the shopping cart, each instance associated with one or more scoring metrics based at least on time spent in association with the respective instance;
-assigning, by the online concierge system, one or more pickers to pick the subset of the items from shelf locations at the retail store and prepopulate the shopping cart reserved for the customer;
-causing a picker client device associated with each of the assigned one or more pickers to display the subset of the items to facilitate picking thereof by the one or more pickers;
-receiving, from one or more sensors coupled to the shopping cart, sensor data indicative of items physically added to the shopping cart, wherein the one or more sensors include an integrated camera at the shopping cart;
-determining one or more remaining items from the shopping list that are not physically in the shopping cart based on the sensor data indicative of items physically added to the shopping cart;
-causing the customer client device, upon the customer arriving at the retail store, to display a map of locations of each of the one or more remaining items from the shopping list that are not physically in the shopping cart, to facilitate procurement thereof by the customer; and
-logging information about a checkout process conducted by the customer at the retail store for one or more items on the shopping list
These limitations are not indicative of integration into a practical application 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 mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea) as supported by paragraph [0079] of Applicant’s specification – “Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In some embodiments, a software module is implemented with a computer program product comprising one or more computer-readable media storing computer program code or instructions, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described. In some embodiments, a computer-readable medium comprises one or more computer-readable media that, individually or together, comprise instructions that, when executed by one or more processors, cause the one or more processors to perform, individually or together, the steps of the instructions stored on the one or more computer-readable media. Similarly, a processor comprises one or more processors or processing units that, individually or together, perform the steps of instructions stored on a computer-readable medium.” Specifically, the additional elements of a computer system, a processor, a computer-readable medium, an online concierge system, a customer client device, one or more machine learning models, models being trained, sensors coupled to shopping carts, a picker client device, one or more sensors coupled to the shopping cart, that the one or more sensors include an integrated camera at the shopping cart, and sensor data are recited at a high-level of generality (i.e. as a generic processor performing the generic computer functions of obtaining data, determining data, assigning data, causing display of data, receiving data, and logging data) such that they amount do no more than mere instructions to apply the exception using generic computer components. Accordingly, these 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. The claim is directed to an abstract idea. Further, the additional elements do no more than generally link the use of the judicial exception to a particular technological environment or field of use (such as computers or computing networks). Employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not integrate the exception into a practical application.
Additionally, 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 another technology or technical field, ii) apply 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) apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, the judicial exception is not integrated into a practical application.
Under Step 2B, it is determined whether the claims recite additional elements that amount to significantly more than the judicial exception. The claims of the present application do not include additional elements that are sufficient to amount to significantly more than the judicial exception (NO).
In the case of claim 1, taken individually or as a whole, the additional elements of claim 9 do not provide an inventive concept. As discussed above under step 2A (prong 2) with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed functions amount to no more than a general link to a technological environment.
Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually.
Claim 8 is a non-transitory computer-readable storage medium reciting similar functions as claim 1. Examiner notes that claim 8 recites the additional elements of a non-transitory computer-readable storage medium, one or more processors, an online concierge system, a customer client device, one or more machine learning models, models being trained, sensors coupled to shopping carts, a picker client device, one or more sensors coupled to the shopping cart, that the one or more sensors include an integrated camera at the shopping cart, and sensor data, however, claim 8 does not qualify as eligible subject matter for similar reasons as claim 1 indicated above.
Claim 15 is a computer system reciting similar functions as claim 1. Examiner notes that claim 8 recites the additional elements of computer system, one or more processors, a non-transitory computer-readable storage medium, an online concierge system, a customer client device, one or more machine learning models, models being trained, sensors coupled to shopping carts, a picker client device, one or more sensors coupled to the shopping cart, that the one or more sensors include an integrated camera at the shopping cart, and sensor data, however, claim 15 does not qualify as eligible subject matter for similar reasons as claim 1 indicated above.
Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually.
Therefore, claims 8 and 15 do not provide an inventive concept and do not qualify as eligible subject matter.
Dependent claims 2-7, 9-14, and 16-20, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. § 101 because they do not add “significantly more” to the abstract idea. More specifically, dependent claims 2-7, 9-14, and 16-20 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas in that they recite commercial interactions. Dependent claims 2, 4, 7, 9, 11, 14, 16, and 18, do not recite any farther additional elements, and as such are not indicative of integration into a practical application for at least similar reasons discussed above. Dependent claims 3, 5-6, 10, 12-13, 17, and 19-20 recite the additional elements of the one or more machine learning models, but similar to the analysis under prong two of Step 2A these additional elements are used as a tool to perform the abstract idea. As such, under prong two of Step 2A, claims 2-7, 9-14, and 16-20 are not indicative of integration into a practical application for at least similar reasons as discussed above. Thus, dependent claims 2-7, 9-14, and 16-20 are “directed to” an abstract idea. Next, under Step 2B, similar to the analysis of claims 1, 8, and 15, dependent claims 2-7, 9-14, and 16-20 when analyzed individually and as an ordered combination, merely further define the commonplace business method (i.e. facilitating shopping by pickers and customers) being applied on a general-purpose computer and, therefore, do not amount to significantly more than the abstract idea itself. Accordingly, the Examiner concludes that there are no meaningful limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amounts to significantly more than the judicial exception itself. The analysis above applies to all statutory categories of invention.
Subject Matter Allowable Over the Prior Art
In the present application, claims 1-20 would be allowable if rewritten or amended to overcome the rejections under 35 USC § 101 set forth in this Office action. The following is the Examiner's statement of reasons of allowance:
Regarding 35 U.S.C. §103, upon review of the evidence at hand, it is hereby concluded that the totality of the evidence, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of the applicant’s invention. Claims 1-20 are allowable over the prior art as follows:
Claims 1-20 are allowable over 35 U.S.C. §103 as follows:
The most relevant prior art made of record includes Hershtik et al. (US 2002/0019782 A1), Gibbon et al. (US 2022/0309553 A1), Viale et al. (US 2022/0261672 A1), and newly cited Stanley et al. (US 2019/0236525 A1). Hershtik teaches obtaining, by an online concierge system from a customer client device, a shopping list that contains one or more items selected by a customer and a pickup time selected by the customer for self-service fulfillment at a retail store (Hershtik, see at least: [0044] and [0043]); determining a subset of the items from the shopping list for prepopulating to a shopping cart reserved for the customer at the retail store (Hershtik, see at least: [0044] and [0026]); one or more pickers to pick the subset of the items from shelf locations at the retail store and prepopulate the shopping cart reserved for the customer (Hershtik, see at least: [0051]); causing a picker client device associated with each of the assigned one or more pickers to display the subset of the items to facilitate picking thereof by the one or more pickers (Hershtik, see at least: [0051] and [0044]); upon the customer arriving at the retail store, display information about the one or more remaining items from the shopping list that are not physically in the shopping cart, to facilitate procurement thereof by the customer (Hershtik, see at least: [0031]); and logging information about a checkout process conducted by the customer at the retail store for one or more items on the shopping list (Hershtik, see at least: [0044]).
Hershtik is deficient in a number of ways. As written, the claims require determining, by the online concierge system, a subset of the items from the shopping list, wherein the subset of the items is identified from application of one or more machine learning models to score and rank the items in the shopping list in accordance with one or more scoring metrics; wherein at least one of the one or more machine learning models is trained on historical data captured by sensors coupled to shopping carts, wherein the historical data tracks movement of the shopping carts through the retail store and items added into the shopping carts the historical data associated with a first set of instances where items were prepopulated to the shopping cart and a second set of instances where items were not prepopulated to the shopping cart, each instance associated with one or more scoring metrics based at least on time spent in association with the respective instance; assigning one or more pickers being by the online concierge system; receiving, from one or more sensors coupled to the shopping cart, sensor data indicative of items physically added to the shopping cart, wherein the one or more sensors include an integrated camera at the shopping cart; determining one or more remaining items from the shopping list that are not physically in the shopping cart based on the sensor data indicative of items physically added to the shopping cart; and causing the customer client device to display a map of locations of each of the one or more remaining items from the shopping list that are not physically in the shopping cart.
Regarding Gibbon, Gibbon teaches determining, by the online concierge system, a subset of the items from the shopping list, wherein the subset of the items is identified from application of one or more machine learning models to score and rank the items in the shopping list in accordance with one or more scoring metrics (Gibbon, see at least: [0163], [0074], and [0162]); and assigning, by the online concierge system, one or more pickers (Gibbon, see at least: [0074]).
Though disclosing these features, Gibbon does not disclose or render obvious the features discussed above.
Regarding Viale, Viale teaches wherein at least one of the one or more machine learning models is trained on historical data captured by sensors coupled to shopping carts, wherein the historical data tracks movement of the shopping carts through the retail store and items added into the shopping carts (Viale, see at least: [0077], [0084], [0071], and [0025]); receiving, from one or more sensors coupled to the shopping cart, sensor data indicative of items physically added to the shopping cart, wherein the one or more sensors include an integrated camera at the shopping cart (Viale, see at least: [0071] and [0076]); determining one or more remaining items from the shopping list that are not physically in the shopping cart based on the sensor data indicative of items physically added to the shopping cart (Viale, see at least: [0071]); and causing the customer client device to display a map of locations of each of the one or more remaining items from the shopping list that are not physically in the shopping cart (Viale, see at least: [0076], [0025], [0045], [0046] and Fig. 9).
Though disclosing these features, Viale does not disclose or render obvious the features discussed above.
Regarding Stanley, Stanley teaches the historical data associated with a first set of instances where items were prepopulated to the shopping cart and a second set of instances where items were not prepopulated to the shopping cart, each instance associated with one or more scoring metrics based at least on time spent in association with the respective instance (Stanley, see at least: [0028], [0029], [0058], [0060], [0061], and [0054]).
Though disclosing these features, Stanley does not disclose or render obvious the features discussed above.
Ultimately, the particular combination of limitations as claimed, is not anticipated nor rendered obvious in view of Hershtik, Gibbon, Viale, and Stanley, and the totality of the prior art. While certain references may disclose more general concepts and parts of the claim, the prior art available does not specifically disclose the particular combination of these limitations.
Hershtik, Gibbon, Viale, and Stanley, however, do not teach or suggest, alone or in combination the claimed invention. Examiner emphasizes that the prior art/additional art 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).
Cited NPL reference U (cited 03/10/2026 on PTO-892) teaches an automated shopping cart that allows items to be billed to a customer when they complete shopping, but does not teach or suggest the recited limitations.
The Examiner further emphasizes the claims as a whole and hereby asserts that the totality of the evidence fails to set forth, either explicitly or implicitly, an appropriate rationale for further modification of the evidence at hand to arrive at the claimed invention. The combination of features as claimed would not be obvious to one of ordinary skill in the art as combining various references from the totality of evidence to reach the combination of features as claimed would be a substantial reconstruction of Applicant’s claimed invention relying on improper hindsight bias.
It is thereby asserted by Examiner that, in light of the above and further deliberation over all of the evidence at hand, that the claims are allowable as the evidence at hand does not anticipate the claims and does not render obvious any further modification of the references to a person of ordinary skill in the art.
Response to Arguments
Rejections under 35 U.S.C. §101
Applicant argues that the claimed invention recites an improvement to the technical field of computer implemented systems by integrating sensor-driven data collection, machine learning models trained on that sensor data, and dynamic device interaction to optimize partial prepopulation of a customer's shopping cart for self-service pickup. In particular, the claims do not merely use a generic computer to perform known operations, but instead implement a coordinated technical solution that leverages historical sensor data from shopping carts, machine learning models trained on such data, and dynamic communication with picker and customer devices in order to improve the efficiency and accuracy of in-store order fulfillment. The training of the machine learning models on historical cart movement and item detection data captured by integrated cameras and other sensors directly ties the claimed method to a technological advance in sensor assisted item handling, enabling predictive identification of which items should be prepopulated for a given customer to minimize fulfillment time and increase store throughput (Remarks, page 15).
Examiner respectfully disagree. Self-service pickup is not a technical field and improving the efficiency and accuracy of in-store order fulfillment, minimizing fulfillment time and increasing store throughput are business improvements, not technical improvements. Additionally, merely utilizing picker and customer devices for communication, amount to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea). Furthermore, sensor data is not an additional element, it is data, and merely improving the data utilized to train a machine learning model does not improve the machine learning technology itself. Accordingly, the amended claims are ineligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
-Verma et al. (US 10,999,416 B1) teaches applying priority scores to products in a list.
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/ARIELLE E WEINER/ Primary Examiner, Art Unit 3689