DETAILED ACTION
Status of Claims
This action is in reply to the response received on 05 February 2026.
Claims 1, 9, and 17 have been amended.
Claims 1-20 are pending and have been examined.
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 .
Allowable Subject Matter
Claims 1-20 recite allowable subject matter and would be allowable if the claims were amended or Terminal Disclaimer filed to overcome the Double Patenting rejection in the Office Action below.
Reasons for Allowable Subject Matter
Eligibility considerations:
In light of the amendments to the claims, the claims recite eligible subject matter under 35 U.S.C. 101. Specifically, the claims now recite the features of applying a machine learning model to rank the set of candidate items, the machine learning model is trained by: initiating a set of weights of the machine learning model, receiving a training set that comprises inventory information and past outcomes of delivery orders, updating the training set based on the item’s availability across multiple warehouses, adjusting the weights of the machine learning model based on the training set, causing, responsive to the generic item description being inputted into the search field of the interface, the interface to automatically provide the one or more specific items for display without the user providing additional inputs to identify the specific item, thereby reducing a number of steps that the user needs to navigate in the interface. Under Step 2A, Prong Two of the eligibility analysis, the claims now recite a combination of additional elements that demonstrate that they are more than just merely applying the abstract idea with generic computing components. The steps of the claims of that also include that when the interface automatically includes the specific item in a user-selected list without any type of user providing additional input, is a feature that in combination with other machine learning model functions for predicting availabilities of items, is demonstrating integration of the abstract idea into a practical application by reflecting an improvement to the technical field of engine queries and machine learning models. The features are now more than just peripherally incorporated into the claims in order to implement the abstract idea. Thus, the additional elements and features in the amended claims recite eligible subject matter and therefore the 101 rejection has been withdrawn.
Prior Art Considerations:
Upon review of the evidence at hand, it is concluded that the totality of evidence in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of the Applicant’s invention.
Regarding the independent claims, the features are as follows:
wherein the machine learning model is trained by: initiating a set of weights of the machine learning model, receiving a training set that comprises inventory information and past outcomes of delivery orders, comparing an item's availability across multiple warehouses to determine whether the item is chronically unavailable, updating the training set based on the item's availability across multiple warehouses, and adjusting the weights of the machine learning model based on the training set; and causing, responsive to the generic item description being inputted into the search field of the interface, the interface to automatically provide the one or more specific items for display without the user providing additional inputs to identify the specific item, thereby reducing a number of steps the user needs to navigate in the interface to include a specific item.
The most apposite prior art of record includes Lazaro, S. (PGP No. US 2015/0012381 A1), in view of Barton, E., et al. (PGP No. US 2022/0067642 A1). Cameron, D., et al. (PGP No. US 2022/0027915 A1) Agarwal, V., et al. (Patent No. US 10,242,336 B1), to teach a system for improving a query for online shopping.
Lazaro discloses an online shopping system for grocery delivery that allows a user to search for items that are shopped for, such as milk, which is considered a generic item description (Lazaro, see: paragraph [0044] and [0070]). When the user types the text of ‘Milk’ into the text box, the system of Lazaro displays all the different categories and other relevant products related to milk, such as different brands related to milk products (Lazaro, see: paragraph [0042] and [0070]). Lazaro further describes that the system may use algorithms to predict what elements will go into the list of items and will automatically list the items to provide a product virtualization based on relevance to the user, such as a specific type of milk product, and can also take into account previous purchases or previously listed items in past shopping sessions (Lazaro, see: paragraphs [0055]-[0056]). Lazaro also describes that a probability is determined by an algorithm that is capable of predicting the likelihood of that specific item being shopped by the user (Lazaro, see: paragraph [0055]). Although Lazaro discloses these features, Lazaro does not disclose the allowable features indicated above.
Next, the reference of Barton describes a system that generates product identification information by utilizing the SKU sensor and the UPC, where the controller of the system scans for identifying indicia, such as a barcode, RFID or NFC identifier (Barton, paragraph [0034]). Barton also describes that the scanning of the identifying attributes such as the SKU number or barcode is used to generate the product identity data, is encompassing the instant feature of traversing paths of the items to identify one or more specific items that are associated with corresponding serial numbers (Barton, paragraph [0034]). The system of Barton results in larger sets of reference imaging data that is used to train a computer vision and machine learning model to detect the presence or identify a large variety of different products for purchase (Barton, paragraph [0059]).
Further, the reference of Cameron is relied upon to demonstrate that have previous placed a threshold number of orders in a shopping system can be received to ultimately provide relevant recommendations (Cameron, see: paragraph [0138]). The reference of Agarwal is relied upon to demonstrate that a system can determine a probability that local merchants that sell products can or cannot fulfill an order for a user, where the system compares probability to a threshold for the item to determine if delivering the item within a specific time frame should be pursued in an order or offered to the user (Agarwal, Col. 8, ln. 61-65).
Although the references describe these features, in combination, they do not teach or describe the now allowable features as indicated above. 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. Moreover, the combination of features of independent claims, would not have been obvious to one of ordinary skill in the art because any combination of evidence at hand to reach the combination of features as claimed would require substantial reconstruction of Applicant’s claimed invention relying on improper hindsight bias and resulting in an inappropriate combination.
It is hereby asserted by the Examiner, that in light of the above and in further deliberation over all of the evidence at hand, that the claims recite allowable subject matter 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.
Examiner’s Comment
The Examiner notes that the non-patent literature (NPL) document, titled Aramark Acquires ‘Good Uncle’ On-Demand Food Delivery Service, published in Wireless News (2019), documented on PTO-892 form as reference U, and hereinafter referred to as ‘Aramark’, describes an on-demand food delivery service for online users, via a shopping application that servers as a concierge service. The service includes allowing customers to choose food and premade meals to be either picked up at the location or to be delivered to an address. Although Aramark discloses these features, the reference does not disclose or teach the allowable features that are stated above, and does not remedy the deficiencies of the noted prior art.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claim 1 is rejected is rejected on the ground of nonstatutory double patenting as being unpatentable over Claim 1 of US Patent No. 11,995,700 B2, in view of Barton, E., et al. (PGP No. US 2022/0067642 A1).
Claim 1 of instant claims
Claim 1 of ‘700 claims
A method for improving a query engine, the method comprising:
A method comprising:
at an online system comprising memory and one or more processors:
receiving a generic item description from a user, the generic item description being a search query inputted into a search field of an interface;
receiving, at an online concierge system, a generic item description from a user of the online concierge system, the generic item description being a search query inputted into a search field of an interface;
retrieving a taxonomy stored by the online system, the taxonomy maintaining associations between generic item descriptions and attributes of specific items that are associated with serial numbers, the attributes comprising information of stores that offer one or more specific items;
retrieving a taxonomy stored by the online concierge system, the taxonomy associating a set of candidate items with the generic item description;
training a machine learning model that is configured to predict availabilities of items, wherein training of the machine learning model comprises:
identifying, based on the taxonomy, a set of candidate items that match the generic item description specified in the search field;
traversing one or more paths of the taxonomy from the set of candidate items to identify one or more specific items that are associated with corresponding serial numbers;
Although Patent ‘700 does not disclose traversing one or more paths of the taxonomy from the set of candidate items to identify one or more specific items that are associated with corresponding serial numbers, Barton does teach:
traversing one or more paths of the taxonomy from the set of candidate items to identify one or more specific items that are associated with corresponding serial numbers (Barton, see: paragraph [0034] teaching “a stock keeping unit (SKU) sensor, a barcode (e.g., electronic product code (EPC), universal product code (UPC),” and “controller 120 may be configured to scan an identifying indicia (e.g., two dimensional barcode, RFID, NFC identifier, ultra-wideband (UWB) identifier, Bluetooth identifier, image, etc.) of the consumer products 190a-190c that are detected by the controller 120, and, based on a scan of the identifier of the consumer product 190 [i.e., traversing one or more paths of the items] by the controller 120, to generate product identity data”) (Examiner’s note: As described by Barton, the scanning of the product with the identifying attributes, such as the SKU number or barcode, is similar to what is described as the claimed feature of traversing one or more paths of the items in paragraph [0041] of the Applicant’s specification.);
This step of Barton, is applicable to the method of Lazaro as they both share characteristics and capabilities, namely, they are directed to identifying products. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Lazaro to include the features items that are associated with serial numbers, traversing one or more paths of the items to identify one or more specific items that are associated with corresponding serial numbers, and applying a machine learning model to the items, as taught by Barton. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Lazaro to improve the identification of the products needed by a consumer (Barton, see: paragraph [0005]).
selecting the one or more specific items from the set of candidate items, wherein selecting the specific item comprises applying a machine learning model to rank the set of candidate items by availabilities, wherein the machine learning model is trained by:
selecting, by the online concierge system, a specific item from the set of candidate items, wherein selecting the specific item comprises applying the machine learning model to rank the set of candidate items by availabilities and user selection probabilities, wherein applying the machine learning model comprises:
initiating weights of the machine learning model,
initiating a set of weights of the machine learning model,
receiving a training set that comprises inventory information and past outcomes of delivery orders,
receiving a training set that comprises inventory information and past outcomes of delivery orders,
comparing an item’s availability across multiple warehouses to determine whether the item is chronically unavailable,
comparing an item's availability across multiple warehouses to determine whether the item is chronically unavailable, and
updating the training set based on the item’s availability across multiple warehouses, and
updating the training set based on the item's availability across multiple warehouses,
adjusting the weights f the machine learning model based on the training set; and
adjusting the weights of the machine learning model based on the training set;
receiving one or more factors associated with previous actions of the user,
receiving, for each candidate item in the set, one or more attributes of the candidate item,
inputting, for each candidate item in the set, one or more factors associated with the previous actions of the user and one or more attributes of the candidate item into the machine learning model to generate, for each candidate item, a prediction of availability of the candidate item and a probability that the user will select the candidate item,
generating a ranked list of candidate items based on predicted availabilities and the user selection probabilities of the candidate items in the set, and
selecting the specific item from the set of candidate items based on the ranked list of candidate items;
causing, responsive to the generic item description being inputted into the search field of the interface, the interface to automatically provide the one or more specific items for display without the user providing additional inputs to identify the specific item, thereby reducing a number of steps the user needs to navigate in the interface to include a specific item.
causing, responsive to the generic item description being inputted into the search field of the interface, the interface to automatically include the specific item in a user-selected list without the user providing additional inputs to identify the specific item, thereby reducing a number of steps the user needs to navigate in the interface to include the specific item in the user-selected list.
Claim 2 is rejected is rejected on the ground of nonstatutory double patenting as being unpatentable over Claim 2 of US Patent No. 11,995,700 B2, in view of Barton, et al.
Claim 2 of instant claims
Claim 2 of ‘700 claims
wherein selecting, by the online concierge system, the specific item from the set of candidate items comprises:
wherein selecting, by the online concierge system, the specific item from the set of candidate items comprises:
selecting the specific item that is included in at least a threshold number of orders the online concierge system previously received from the user.
selecting the specific item that is included in at least a threshold number of orders the online concierge system previously received from the user.
Claim 3 is rejected is rejected on the ground of nonstatutory double patenting as being unpatentable over Claim 3 of US Patent No. 11,995,700 B2, in view of Barton, et al.
Claim 3 of instant claims
Claim 3 of ‘700 claims
wherein selecting, by the online concierge system, the specific item from the set of candidate items comprises:
wherein selecting, by the online concierge system, the specific item from the set of candidate items comprises:
determining a probability of the user purchasing each candidate item in the set of candidate items; and
determining a probability of the user purchasing each candidate item in the set of candidate items; and
selecting an item having a maximum probability of being purchased by the user from the set.
selecting an item having a maximum probability of being purchased by the user from the set.
Claim 4 is rejected is rejected on the ground of nonstatutory double patenting as being unpatentable over Claim 4 of US Patent No. 11,995,700 B2, in view of Barton, et al.
Claim 4 of instant claims
Claim 4 of ‘700 claims
wherein determining the probability of the user purchasing each candidate item in the set of candidate items comprises:
wherein determining the probability of the user purchasing each candidate item in the set of candidate items comprises:
applying a trained purchase model to each combination of the user and the candidate item, the trained purchase model being part of the machine learning model, the trained purchase model generating the probability of the user purchasing the candidate item based on attributes of the candidate item and items included in orders the online concierge system previously received from the user.
applying a trained purchase model to each combination of the user and the candidate item, the trained purchase model being part of the machine learning model, the trained purchase model generating the probability of the user purchasing the candidate item based on attributes of the candidate item and items included in orders the online concierge system previously received from the user.
Claim 5 is rejected is rejected on the ground of nonstatutory double patenting as being unpatentable over Claim 5 of US Patent No. 11,995,700 B2, in view of Barton, et al.
Claim 5 of instant claims
Claim 5 of ‘700 claims
wherein selecting, by the online concierge system, the specific item from the set of candidate items comprises:
wherein selecting, by the online concierge system, the specific item from the set of candidate items comprises:
determining a probability of the user purchasing each candidate item in the set of candidate items;
determining a probability of the user purchasing each candidate item in the set of candidate items;
determining differences between a probability of the user purchasing a candidate item included in one or more previous orders the online concierge system received from the user and probabilities of the user purchasing each alternative item in the set; and
determining differences between a probability of the user purchasing a candidate item included in one or more previous orders the online concierge system received from the user and probabilities of the user purchasing each alternative item in the set; and
selecting an alternative item in the set having a minimum difference between the probability of the user purchasing the candidate item included in one or more previous orders the online concierge system received from the user and the probability of the user purchasing the alternative item in the set.
selecting an alternative item in the set having a minimum difference between the probability of the user purchasing the candidate item included in one or more previous orders the online concierge system received from the user and the probability of the user purchasing the alternative item in the set.
Claim 6 is rejected is rejected on the ground of nonstatutory double patenting as being unpatentable over Claim 6 of US Patent No. 11,995,700 B2, in view of Barton, et al.
Claim 6 of instant claims
Claim 6 of ‘700 claims
wherein selecting, by the online concierge system, the specific item from the set of candidate items comprises:
wherein selecting, by the online concierge system, the specific item from the set of candidate items comprises:
determining a predicted availability of each candidate item of the set of candidate items at a warehouse;
determining a predicted availability of each candidate item of the set of candidate items at a warehouse;
identifying a group of candidate items of the set having at least a threshold predicted availability; and
identifying a group of candidate items of the set having at least a threshold predicted availability; and
selecting a candidate item of the group having at least a threshold probability of being purchased by the user.
selecting a candidate item of the group having at least a threshold probability of being purchased by the user.
Claim 7 is rejected is rejected on the ground of nonstatutory double patenting as being unpatentable over Claim 7 of US Patent No. 11,995,700 B2, in view of Barton, et al.
Claim 7 of instant claims
Claim 7 of ‘700 claims
wherein selecting, by the online concierge system, the specific item from the set of candidate items comprises:
wherein selecting, by the online concierge system, the specific item from the set of candidate items comprises:
determining a predicted availability of each candidate item of the set of candidate items at a warehouse;
determining a predicted availability of each candidate item of the set of candidate items at a warehouse;
identifying a group of candidate items of the set having at least a threshold predicted availability;
identifying a group of candidate items of the set having at least a threshold predicted availability;
ranking the candidate items of the group based on probabilities of the user purchasing corresponding items of the group; and
ranking the candidate items of the group based on probabilities of the user purchasing corresponding items of the group; and
selecting a candidate item of the group having at least a threshold position in the ranking.
selecting a candidate item of the group having at least a threshold position in the ranking.
Claim 8 is rejected is rejected on the ground of nonstatutory double patenting as being unpatentable over Claim 8 of US Patent No. 11,995,700 B2, in view of Barton, et al.
Claim 8 of instant claims
Claim 8 of ‘700 claims
wherein selecting, by the online concierge system, the specific item from the set of candidate items comprises:
wherein selecting, by the online concierge system, the specific item from the set of candidate items comprises:
determining a predicted availability of each candidate item of the set of candidate items at a warehouse;
determining a predicted availability of each candidate item of the set of candidate items at a warehouse;
ranking the candidate items of the set based on their predicted availabilities;
ranking the candidate items of the set based on their predicted availabilities;
identifying a group of candidate items of the set having at least a threshold position in the ranking; and
identifying a group of candidate items of the set having at least a threshold position in the ranking; and
selecting an item of the group having a maximum probability of being purchased by the user.
selecting an item of the group having a maximum probability of being purchased by the user.
Regarding claim 9, claim 9 s directed to a product of manufacture. Claim 9 recites limitations that are similar in nature to those addressed above for claim 1 which is directed towards a method. It is noted that claim 9 of the instant claims also recites a computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor of an online system, cause the processor to, which is also recited in claim 12 of Patent ‘700. Claim 9 is therefore rejected for the same reasons as set forth above for claim 1.
Regarding claim 10, claim 10 is directed to a product of manufacture. Claim 10 recites limitations that are parallel in nature to those addressed above for claim 2 which is directed towards a method. Claim 10 is therefore rejected for the same reasons as set forth above for claim 1.
Regarding claim 11, claim 11 is directed to a product of manufacture. Claim 11 recites limitations that are parallel in nature to those addressed above for claim 3 which is directed towards a method. Claim 11 is therefore rejected for the same reasons as set forth above for claim 3.
Regarding claim 12, claim 12 is directed to a product of manufacture. Claim 12 recites limitations that are parallel in nature to those addressed above for claim 4 which is directed towards a method. Claim 12 is therefore rejected for the same reasons as set forth above for claim 4.
Regarding claim 13, claim 13 is directed to a product of manufacture. Claim 13 recites limitations that are parallel in nature to those addressed above for claim 5 which is directed towards a method. Claim 13 is therefore rejected for the same reasons as set forth above for claim 5.
Regarding claim 14 claim 14 is directed to a product of manufacture. Claim 14 recites limitations that are parallel in nature to those addressed above for claim 6 which is directed towards a method. Claim 14 is therefore rejected for the same reasons as set forth above for claim 6.
Regarding claim 15, claim 15 is directed to a product of manufacture. Claim 15 recites limitations that are parallel in nature to those addressed above for claim 7 which is directed towards a method. Claim 15 is therefore rejected for the same reasons as set forth above for claim 7.
Regarding claim 16, claim 16 is directed to a product of manufacture. Claim 16 recites limitations that are parallel in nature to those addressed above for claim 8 which is directed towards a method. Claim 16 is therefore rejected for the same reasons as set forth above for claim 8.
Regarding claim 17, claim 17 is directed to a system. Claim 17 recites limitations that are similar in nature to those addressed above for claim 1 which is directed towards a method. It is noted that claim 17 of the instant claims also recites an online system comprising: one or more processors; and memory configured to store code comprising instructions, where the instructions, when executed by the one or more processors, cause the one or more processors to, which is also recited in claim 20 of Patent ‘700. Claim 17 is therefore rejected for the same reasons as set forth above for claim 1.
Regarding claim 18, claim 18 is directed to a system. Claim 18 recites limitations that are parallel in nature to those addressed above for claim 2 which is directed towards a method. Claim 18 is therefore rejected for the same reasons as set forth above for claim 2.
Regarding claim 19, claim 19 is directed to a system. Claim 19 recites limitations that are parallel in nature to those addressed above for claim 3 which is directed towards a method. Claim 19 is therefore rejected for the same reasons as set forth above for claim 3.
Regarding claim 20, claim 20 is directed to a system. Claim 20 recites limitations that are parallel in nature to those addressed above for claim 5 which is directed towards a method. Claim 20 is therefore rejected for the same reasons as set forth above for claim 5.
Response to Arguments
With respect to the rejections made under 35 USC § 101, the Applicant’s arguments filed on 05 February 2026, have been fully considered, and in light of the amendments, the claims now recite eligible subject matter, and therefore the 101 rejection has been withdrawn.
With respect to the rejections made under 35 USC § 103, the Applicant’s arguments filed on 05 February 2026, have been fully considered, and in light of the amendments to the claims, the claims now recite allowable subject matter, and therefore the 103 rejection is withdrawn.
With respect to the rejections made under the nonstatutory double patenting, when considering the amendments to the claims, the Examiner maintains the double patenting rejection for reasons stated in the Office Action above.
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.
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/ASHLEY D PRESTON/Primary Examiner, Art Unit 3688