DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 4/17/2026 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 .
Priority
Applicant has not claimed priority to another application. Application 18/217,329 was filed 6/30/2023.
Information Disclosure Statement
The IDSs submitted on 2/11/2025 was previously considered.
Interview
Examiner invites the representative of this application to contact the Examiner to schedule an interview to expedite prosecution of this application.
Status of Claims
Applicant’s amended claims, filed 4/17/2026, have been entered. Claims 1, 3, 4, 10, 12, 13, and 19 have been amended. Claims 2, 11, and 20 have been cancelled. Claims 21-23 are new. Claims 1, 3-10, 12-19, and 21-23 are currently pending in this application and have been examined.
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, 2-10, 12-19, and 21-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) an abstract idea. This judicial exception is not integrated into a practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Under Step 1 of the Alice/Mayo test the claims are directed to statutory categories. Specifically, the method, as claimed in claims 1 and 2-9, are directed to a process, the computer program product, as claimed in claims 10 and 12-18, are directed to an article of manufacture, and the system, as claimed in claims 19 and 21-23, are directed to a machine (see MPEP 2106.03).
Under Step 2A (prong 1), claim 1, taken as representative, recites at least the following limitations (emphasis added) that recite an abstract idea:
A method, comprising:
determine a probability of an input user performing a specific action with an item of an input set of items based on an input search query attributes, item attributes of the item, and a position in a ranking of item in the input set of items, comprising:
obtaining a training dataset including a plurality of training examples, each training example including a position, input search query attributes and item attributes, each training example having a label indicating whether the specific action was performed,
applying the position modification model to each training example of the training dataset to generate a predicted probability of the specific action being performed for the training example,
updating one or more parameters of the position modification model based on the predicted probability;
receiving a search query from a user the search query associated with a retailer;
selecting a set of items from a catalog associated with the retailer based on the search query;
determining a ranking of items of the set based on a relevance score for each item of the set to the search query, the ranking identifying an order in which the items of the set are displayed to the user;
selecting an item of the set having a predicted availability at the retailer that is lower than a threshold predicted availability;
applying a position modification model to each of a set of candidate positions for the selected item to determine a position modification for the selected item based on one or more search query attributes of the search query and one or more item attributes of the received item, wherein the one or more item attributes of the received item includes whether the item was previously included in one or more prior orders received from the user;
modifying a position of the selected item in the ranking of items based on the determined position modification; and
transmitting the modified ranking of the items to a user, the transmitting causing the user to display the modified ranking of the items for presentation to the user.
These limitations recite certain methods of organizing human activity, such as performing commercial interactions (see MPEP 2106.04(a)(2)(II)). Certain methods of organizing human activity are defined by MPEP 2106.04 as including “fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).” In this case, the abstract ideas recited in representative claim 1 are certain methods of organizing human activity because generating search results displaying items offered by a retailer accounting for predicted availabilities of items at the retailer and search query attributes of a search query (see Specification [0064]) is a commercial or legal interaction because it is a advertising, marketing or sales activity, or business relations.
Thus, claim 1 recites an abstract idea.
Independent claims 10 and 19 recite the same abstract idea as recited in independent claim 1. As such, the analysis under Step 2A, Prong 1 is the same for independent claims 10 and 19 as described above for independent claim 1.
Under Step 2A (prong 2), if it is determined that the claims recite a judicial exception, it is then necessary to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of that exception (see MPEP 2106.04). As stated in the MPEP, when “an additional element merely recites the words ‘apply it (or an equivalent) with the judicial exception, or merely uses a computer as a tool to perform an abstract idea,” the judicial exception has not been integrated into a practical application. In this case, representative claim 1 includes additional elements such as (additional elements are bolded):
A method, performed at a computer system comprising a processor and a computer-readable medium, comprising:
training a position modification model configured to determine a probability of an input user performing a specific action with an item of an input set of items based on an input search query attributes, item attributes of the item, and a position in a ranking of item in the input set of items, comprising:
obtaining a training dataset including a plurality of training examples, each training example including a position, input search query attributes and item attributes, each training example having a label indicating whether the specific action was performed,
applying the position modification model to each training example of the training dataset to generate a predicted probability of the specific action being performed for the training example,
updating one or more parameters of the position modification model by backpropagation based on the predicted probability
receiving a search query from a user, at an online system, the search query associated with a retailer;
selecting a set of items from a catalog associated with the retailer based on the search query;
determining a ranking of items of the set based on a relevance score for each item of the set to the search query, the ranking identifying an order in which the items of the set are displayed to the user;
selecting an item of the set having a predicted availability at the retailer that is lower than a threshold predicted availability;
applying a position modification model to each of a set of candidate positions for the selected item to determine a position modification for the selected item based on one or more search query attributes of the search query and one or more item attributes of the received item, wherein the one or more item attributes of the received item includes whether the item was previously included in one or more prior orders received from the user;
modifying a position of the selected item in the ranking of items based on the determined position modification; and
transmitting the modified ranking of the items to a user client device, the transmitting causing the user device to display the modified ranking of the items for presentation to the user.
Although reciting these additional elements, taken alone or in combination these elements are not sufficient to integrate the abstract idea into a practical application. These additional elements merely amount to the general application of the abstract idea to a technical environment (“performed at a computer system comprising a processor and a computer-readable medium”, “receiving…at an online system”, “transmitting…to a user client device, the transmitting causing the user device to display…”) and insignificant pre-and-post solution activity (receiving, transmitting, displaying). The specification makes clear the general-purpose nature of the technological environment. This is because the additional elements of claim 1 are recited at a high level of generality (i.e., as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform the abstract idea) (see Fig. 1; paragraphs[0020], [0022], [0025], [0033], [0055]-[0058], [0112]-[0114]). The specification indicates that while exemplary general-purpose systems may be specific for descriptive purposes, any elements capable of implementing the claimed invention are acceptable. That is, the technology used to implement the invention is not specific or integral to the claim. The description demonstrates that these additional elements are merely generic devices such as a generic computer. Further, the additional elements do no more than generally link the use of a judicial exception to a particular environment or field of use (such as the Internet or computing networks).
Therefore, considered both individually and as an ordered pair, the additional elements do no more than generally link the use of the abstract idea to a particular technological environment or field of use. That is, given the generality with which the additional elements are recited, the limitations do not implement the abstract idea with, or use the abstract idea in conjunction with, a particular machine or manufacture that is integral to the claim. Additionally, the claims do not reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, do not transform or reduction of a particular article to a different state or thing; and do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technology environment, such that the claim as a whole is more than a drafting effort designed to monopolize the abstract idea into a practical application, and is therefore “directed to” the abstract idea.
In addition to the above, the recited receiving, transmitting, and displaying steps (even assuming arguendo they do not form part of the abstract idea, which the Examiner does not acquiesce), are at best little more than extra-solution activity (e.g., data gathering, presentation of data) that contributes nominally or insignificantly to the execution of the claimed system (see MPEP 2106.05(g)).
In view of the above, under Step 2A (prong 2), claim 1 does not integrate the recited exception into a practical application.
Independent claims 10 and 19 recite the additional elements of “computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor” and “a processor; and a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by the processor, cause the processor to perform steps” in addition to the additional elements already addressed in the rejection for independent claim 1. However, these additional elements taken alone or in combination are not sufficient to integrate the abstract idea into a practical application as these additional elements are merely generic devices and do no more than generally link the use of a judicial exception to a particular environment or field of use (such as the Internet or computing networks). Therefore, under Step 2A (prong 2), claims 10 and 19 do not integrate the recited exception into a practical application.
Under Step 2B, examiners should evaluate additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). In this case, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Returning to claims 1, 10, and 19, taken individually or as a whole the additional elements of claims 1, 10, and 19 do not provide an inventive concept (i.e. they do not amount to “significantly more” than the exception itself). As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment.
Furthermore, the additional elements fail to provide significantly more also because the claim simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. For example, the additional elements of claims 1, 10, and 19 utilize operations the courts have held to be well-understood, routine, and conventional (see: MPEP 2106.05(d)(II)), including at least:
receiving or transmitting data over a network,
storing or retrieving information from memory,
presenting offers
Even considered as an ordered combination (as a whole), the additional elements of claims 1, 10, and 19 do not add anything further than when they are considered individually.
In view of the above, claims 1, 10, and 19 do not provide an inventive concept (“significantly more”) under Step 2B, and is therefore ineligible for patenting.
Regarding claims 3-9, 12-18, and 21-23
Dependent claim(s) 3-9, 12-18, and 21-23, 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 claim(s) 3-9, 12-18, and 21-23 merely further define the abstract limitations of claim(s) 1, 10, and 19 or provide further embellishments of the limitations recited in independent claim claim(s) 1, 10, and 19.
The limitations of claims 3-9, 12-18, and 21-23 merely embellish the abstract idea of generating search results displaying items offered by a retailer accounting for predicted availabilities of items at the retailer and search query attributes of a search query. The claims do not set forth any further additional limitations, and therefore such abstract embellishments are applied to the additional limitations recited in claim(s) 1, 10, and 19, which do no more than generally link the use of the abstract idea to a particular technological environment, do not integrate the abstract idea into a practical application, and do not provide an inventive concept. Accordingly, the claims do not confer eligibility on the claimed invention and is ineligible for similar reasons to claim(s) 1, 10, and 19.
Thus, dependent claims 3-9, 12-18, and 21-23 are ineligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 2-10, 12-19, and 21-23 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ruan et al. (US 2023/0078450 A1 [previously recited]).
Regarding claim 1, Ruan et al., hereinafter Ruan, discloses a method, performed at a computer system comprising a processor and a computer-readable medium (¶¶0070-0072), comprising:
training a position modification model configured to determine a probability of an input user performing a specific action with an item of an input set of items based on an input search query attributes, item attributes of the item, and a position in a ranking of item in the input set of items (Figs. 1-5; ¶¶0036-0037 [ranks items of the set based on their relevance scores and modifies positions of one or more items in the ranking based on the confidence scores for the one or more items in the ranking… calculates a combined score for each item of the set by combining a relevance score of the item of the set and a confidence score of the item of the set... ranks the items of the set based on their combined scores… The online concierge system 102 further includes a machine-learned item availability model 216, a modeling engine 218, and training datasets 220. The modeling engine 218 uses the training datasets 220 to generate the machine-learned item availability model 216. The machine-learned item availability model 216 can learn from the training datasets 220, rather than follow only explicitly programmed instructions. The inventory management engine 202, order fulfillment engine 206, and/or shopper management engine 210 can use the machine-learned item availability model 216 to determine a probability that an item is available at a warehouse 110, also referred to as a predicted availability of the item at the warehouse 110], ¶0041 [The trained purchase model may be any suitable machine learning model trained (e.g., using supervised learning, semi-supervised learning, etc.) from labeled data identifying embeddings of items previously displayed to the user], and [increase a likelihood of the user selecting an item offered by the warehouse 110 that is currently available at the warehouse from the set of items that at least partially match the search query, the online concierge system 102 determines 535 a confidence score for each item of the set based on a predicted availability of the item of the set… the ranking of items of the set, which was based on relevance scores of the items of the set, based on the confidence scores for different items of the set], ¶0044 [the item characteristics include an item popularity score. The item popularity score for an item may be proportional to the number of delivery orders received that include the item. An alternative or additional item popularity score may be provided by a retailer through the inventory management engine 202]; Examiner notes a popularity score is comparable to a position in a ranking of item in the input set of items), comprising:
obtaining a training dataset including a plurality of training examples, each training example including a position, input search query attributes and item attributes, each training example having a label indicating whether the specific action was performed (Figs. 1-5; ¶¶0044-0045 [The training datasets 220 may include additional item characteristics that affect the item availability, and can therefore be used to build the machine-learned item availability model 216 relating the delivery order for an item to its predicted availability. The training datasets 220 may be periodically updated with recent previous delivery orders. The training datasets 220 may be updated with item availability information provided directly from shoppers 108. Following updating of the training datasets 220, a modeling engine 218 may retrain a model with the updated training datasets 220 and produce a new machine-learned item availability model 216… the item characteristics include an item popularity score. The item popularity score for an item may be proportional to the number of delivery orders received that include the item. An alternative or additional item popularity score may be provided by a retailer through the inventory management engine 202] in view of ¶0036 [ranks items of the set based on their relevance scores and modifies positions of one or more items in the ranking based on the confidence scores for the one or more items in the ranking] and ¶0041 [machine learning model trained (e.g., using supervised learning, semi-supervised learning, etc.) from labeled data identifying embeddings of items previously displayed to the user, with the labels indicating whether the user included the item in an order after being displayed with the item]),
applying the position modification model to each training example of the training dataset to generate a predicted probability of the specific action being performed for the training example (Figs. 1-5; ¶0045),
updating one or more parameters of the position modification model by backpropagation based on the predicted probability (Figs. 1-5; ¶¶0044-0045 in view of ¶0037-0045);
receiving a search query from a user, at an online system, the search query associated with a retailer (Figs. 1-5; ¶0035 [the order fulfillment engine 206 generates an interface identifying items offered by an identified warehouse 110 in response to receiving a search query from a user for an item. The interface displays information identifying different items offered by the warehouse in different positions. As further described below in conjunction with FIG. 5 , in response to receiving a search query, the order fulfillment engine 206 selects a set of items offered by the warehouse 110 associated with information that at least partially matches the search query.] in view of ¶0054 [The online concierge system 102 receives 515 a search query for the warehouse 110 from the user and selects 520 a set of items from the item catalog of the warehouse 110 that at least partially match the search query]);
selecting a set of items from a catalog associated with the retailer based on the search query (Figs. 1-5; ¶0035 [the order fulfillment engine 206 generates an interface identifying items offered by an identified warehouse 110 in response to receiving a search query from a user for an item… in response to receiving a search query, the order fulfillment engine 206 selects a set of items offered by the warehouse 110 associated with information that at least partially matches the search query. For example, the set of items includes items with titles or names that match at least a portion of the search query.] in view of ¶0054 [The online concierge system 102 receives 515 a search query for the warehouse 110 from the user and selects 520 a set of items from the item catalog of the warehouse 110 that at least partially match the search query]);
determining a ranking of items of the set based on a relevance score for each item of the set to the search query, the ranking identifying an order in which the items of the set are displayed to the user (Figs. 1-5; ¶¶0035-0036 [For each item of the set, the order fulfillment engine 206 generates a relevance score that is based at least in part on an amount of information associated with the item of the set matching the search query… While the relevance score accounts for a probability of the user including an item of the set in the order or an amount of information associated with an item of the set matching the search query, the warehouse 110 for which the search query was received has varying availabilities of different items. Failing to account for predicted availabilities of different items at the warehouse 110 when ranking items of the set may result in items that are currently unavailable at the warehouse 110 but that have high relevance scores being displayed in prominent positions of the interface. Such prominent display of unavailable items in the interface may discourage subsequent orders from the user or prevent the user from creating an order in response to the search query] in view of ¶0055 [From the relevance scores generated 525 for various items, the online concierge system 102 ranks 530 the items. In various embodiments, the online concierge system 102 ranks 530 the items so items with larger relevance scores have higher positions in the ranking. As the relevance scores account for a quality of match between the search query and information describing items] and ¶0063 [the online concierge system 102 generates 545 an interface identifying items of the set. The interface displays information describing different items of the set in different positions, with a position in the interface in which information describing an item in the set is displayed corresponding to a position of the item of the set in the modified ranking. For example, the interface comprises a vertical list including multiple positions, with each position displaying information describing an item of the set. A position in the vertical list in which information describing an item of the set is displayed corresponds to a position in the modified ranking of the item of the set. Hence, items of the set having higher positions in the modified ranking are displayed in corresponding higher positions of the vertical list, so items having higher positions in the modified ranking are more visible to the user]);
selecting an item of the set having a predicted availability at the retailer that is lower than a threshold predicted availability (Figs. 1-5; ¶0036 […To account for predicted item availabilities at the warehouse, the online concierge system 102 applies the machine-learned item availability model 216, further described below in conjunction with FIG. 4 , to combinations of items of the set and the identified warehouse 110. The order fulfillment engine 206 receives the predicted availability of each item of the set from the machine-learned item availability model 216 and generates a confidence score for each item of the set from the predicted availability of the item of the set. As further described below in conjunction with FIG. 5 , the confidence score of an item of the set is a combination of the predicted availability of the item of the set and one or more other factors in various embodiments. In some embodiments, the order fulfillment engine 206 ranks items of the set based on their relevance scores and modifies positions of one or more items in the ranking based on the confidence scores for the one or more items in the ranking, as further described below in conjunction with FIG. 5 . For example, the order fulfillment engine 206 decreases a position in the ranking of an item having a confidence score less than a threshold value or having a confidence score within a specific range.] in view of ¶¶0056-0058 [In response to determining the confidence score does not equal or exceed the threshold value, the online concierge system 102 decreases a position in the ranking of the item of the set.]);
applying a position modification model to each of a set of candidate positions for the selected item to determine a position modification for the selected item based on one or more search query attributes of the search query and one or more item attributes of the received item (Figs. 1-5; ¶0007 [the online concierge system applies a ranking model to the search query and to information describing various items in the item catalog. The ranking model outputs the relevance score for an item that is based on an amount of information describing the item matched by the search query in some embodiments. The relevance score may also account for a probability of the user including the item in an order. For example, the online concierge system identifies items of the item catalog for the identified warehouse for which the search query matches at least a portion of information describing the items. For each identified item, the online concierge system determines a probability of the user including the identified item in an order, or purchasing the identified item, by applying a trained purchase model to the user and to the identified item], ¶¶0035-0044 [the order fulfillment engine 206 decreases a position in the ranking of an item having a confidence score less than a threshold value or having a confidence score within a specific range. The order fulfillment engine 206 generates the interface from the modified ranking, so positions in the interface of information describing items of the set accounts for the confidence score (based on predicted availability) of items of the set… The order fulfillment engine 206 ranks the items of the set based on their combined scores and generates the interface based on positions of the items of the set in the ranking] in view of ¶0044 [item attributes] and ¶¶0058-0060), wherein the one or more item attributes of the received item includes whether the item was previously included in one or more prior orders received from the user (Fig. 5; ¶¶0035-0036 [additionally, the order fulfillment engine 206 applies a trained purchase model to the user and an item of the set, generating a probability of the user including the item in an order. The relevance score of the item of the set may be the probability of the user including the item of the set in an order in some embodiments, while in other embodiments, the relevance score of the item of the set is a combination of the probability of the user including the item of the set in an order and an amount of information associated with the item of the set matched by the search query… the order fulfillment engine 206 calculates a combined score for each item of the set by combining a relevance score of the item of the set and a confidence score of the item of the set. The order fulfillment engine 206 ranks the items of the set based on their combined scores and generates the interface based on positions of the items of the set in the ranking], ¶0041 [The trained purchase model may be trained based on prior inclusion of items in orders received from the user from data in the training datasets 220], and ¶0060 [In some embodiments, the online concierge system 102 prevents decreasing the position of an item of the set in response to the position in the ranking of the item of the set equaling or exceeding a threshold position. For example, the online concierge system 102 determines whether the position in the ranking of the item of the set equals or exceeds the threshold position (e.g., determines whether the position in the ranking of the item of the set is not lower than a fifth position in the ranking). In response to determining the position in the ranking of the item of the set equals or exceeds the threshold position, the online concierge system 102 maintains the position in the ranking of the item of the set without decreasing the position of the item of the set. However, in response to determining the position in the ranking of the item of the set is less than the threshold position, the online concierge system 102 decreases the position of the item of the set, as further described above. This allows the online concierge system 102 to prioritize ranking based on relevance of the item of the set to the search query when the relevance score of an item is higher relative to other items of the set.]);
modifying a position of the selected item in the ranking of items based on the determined position modification (Figs. 1-5; ¶¶0035-0036 [the order fulfillment engine 206 decreases a position in the ranking of an item having a confidence score less than a threshold value or having a confidence score within a specific range. The order fulfillment engine 206 generates the interface from the modified ranking, so positions in the interface of information describing items of the set accounts for the confidence score (based on predicted availability) of items of the set… The order fulfillment engine 206 ranks the items of the set based on their combined scores and generates the interface based on positions of the items of the set in the ranking], ¶0041, and ¶¶0058-0063); and
transmitting the modified ranking of the items to a user client device, the transmitting causing the user device to display the modified ranking of the items for presentation to the user (Figs. 1-5; ¶¶0036-0037 [The machine-learned item availability model 216 may be used to predict item availability for items being displayed to or selected by a customer or included in received delivery orders. A single machine-learned item availability model 216 is used to predict the availability of any number of items] in view of ¶0063 [The online concierge system 102 transmits 550 the generated interface to a client device of the user for display, such as for display in the customer mobile interface 106]).
Regarding claim 3, Ruan discloses the method of claim 1, wherein selecting the position of the set of candidate positions for the selected item based on the probabilities of the specific action being performed with at least one item of the set based on application of the position modification model comprises:
selecting a position of the set of candidate positions corresponding to a maximum probability of the specific action being performed with at least one item of the set (Figs. 5 and 6; ¶0036, ¶054 [applies a ranking model to the search query and to information describing various items in the item catalog. The ranking model outputs the relevance score for an item that is based on an amount of information describing the item matched by the search query in some embodiments. The relevance score may also account for a probability of the user including the item in an order], and ¶0068 in view of ¶0008).
Regarding claim 4, Ruan discloses the method of claim 1, wherein determining the probability of the user performing the specific action comprises determining a probability of the user including at least one item of the set in an order (Figs. 5 and 6; ¶0035, ¶054 [applies a ranking model to the search query and to information describing various items in the item catalog. The ranking model outputs the relevance score for an item that is based on an amount of information describing the item matched by the search query in some embodiments. The relevance score may also account for a probability of the user including the item in an order]).
Regarding claim 5, Ruan discloses the method of claim 1, wherein determining the position modification for the selected item based on one or more search query attributes of the search query and one or more item attributes of the received item comprises determining the position modification for the selected item based on a search query attribute that comprises a query entropy of the search query that provides a measure of a breadth of the search query relative to a diversity of items included in the catalog for the identified retailer (Figs. 2 and 5; ¶0052 [The online concierge system 102 obtains 505 an item catalog of items offered by one or more warehouses 110. In some embodiments, the online concierge system 102 obtains 505 an item catalog from each warehouse 110, with an item catalog from a warehouse identifying items offered by the warehouse 110. The item catalog includes different entries, with each entry including information identifying an item (e.g., an item identifier, an item name) and one or more attributes of the item. Example attributes of an item include: one or more keywords, a brand offering the item, a manufacturer of the item, a type of the item, a price of the item, a quantity of the item, a size of the item and any other suitable information. Additionally, one or more attributes of an item may be specified by the online concierge system 102 for the item and included in the entry for the item in the item catalog. Example attributes specified by the online concierge system 102 for an item include: a category for the item, one or more sub-categories for the item, and any other suitable information for the item]).
Regarding claim 6, Ruan discloses the method of claim 1, wherein determining the position modification for the selected item based on one or more search query attributes of the search query and one or more item attributes of the received item comprises determining the position modification for the selected item based on one or more item attributes that include one or more of: the relevance score for a combination of the item and the search query, a length of time the item has been included in the catalog for the retailer, an amount of the search query matched by one or more other item attributes of the item, or an indication whether the item was previously included in one or more prior orders received from the user (Figs. 2 and 5; ¶¶0035-0036).
Regarding claim 7, Ruan discloses the method of claim 1, wherein determining the position modification for the selected item based on one or more search query attributes of the search query and one or more item attributes of the received item comprises:
determining the position modification for the selected item based on search query attributes of the search query, one or more item attributes of the received item, and one or more user characteristics of the user (Figs. 2 and 5; ¶¶0035-0036).
Regarding claim 8, Ruan discloses the method of claim 7, wherein one or more user characteristics include one or more of: a type of user client device from which the search query was received, or an amount of time the user has maintained an account with the computing system (¶0026 [The environment 100 includes an online concierge system 102. The system 102 is configured to receive orders from one or more customers 104 (only one is shown for the sake of simplicity). An order specifies a list of goods (items or products) to be delivered to the customer 104. The order also specifies the location to which the goods are to be delivered, and a time window during which the goods should be delivered. In some embodiments, the order specifies one or more retailers from which the selected items should be purchased. The customer may use a customer mobile application (CMA) 106 to place the order; the CMA 106 is configured to communicate with the online concierge system 102]).
Regarding claim 9, Ruan discloses the method of claim 1, wherein determining the position modification for the selected item based on one or more search query attributes of the search query and one or more item attributes of the received item comprises:
retrieving a set of rules maintained by the computing system, each rule including a candidate position modification and a corresponding set of criteria for the search query attributes and the one or more item attributes (¶0028 [The inventory database 204 may store information in separate records—one for each participating warehouse 110—or may consolidate or combine inventory information into a unified record. Inventory information includes both qualitative and qualitative information about items, including size, color, weight, SKU, serial number, and so on. In one embodiment, the inventory database 204 also stores purchasing rules associated with each item, if they exist. For example, age-restricted items such as alcohol and tobacco are flagged accordingly in the inventory database 204. Additional inventory information useful for predicting the availability of items may also be stored in the inventory database 204. For example, for each item-warehouse combination (a particular item at a particular warehouse), the inventory database 204 may store a time that the item was last found, a time that the item was last not found (a shopper looked for the item but could not find it), the rate at which the item is found, and the popularity of the item]); and
determining the position modification for the identified modification as a candidate position modification included in a rule including at least a threshold amount of criteria satisfied by the search query attributes and the one or more item attributes (Figs. 2 and 5; ¶¶0035-0036).
Regarding claims 10, 12-19, and 21-23, the claim discloses substantially the same limitations, as claims 1 and 2-9, except claims 1 and 2-9 are directed to a process while claims 10 and 12-18 are directed to an article of manufacture and claims 19 and 21-23 are directed to a machine. The added element of “a computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon” is also taught by Ruan (claims 17 and 23). Therefore, claims 10, 12-19, and 21-23 are rejected for the same rational over the prior art cited in claims 1 and 2-9.
Response to Arguments
Applicant’s arguments, on page 13 of the Remarks filed 4/17/2026, with respect to the previous double patenting rejections have been fully considered and are persuasive in view of the amendments to the claims and the claim amendments in application 17/474,408. Accordingly, the previous double patenting rejections have been withdrawn.
Applicant’s arguments, on pages 13-15 of the Remarks filed 4/17/2026, with respect to the previous 35 USC §101 rejections have been fully considered but they are not persuasive. Applicant argues the amended claims recites a specific technical process for training and applying a machine-learned position modification model that integrates any alleged abstract idea into a practical application and amounts to significantly more than the alleged exception. Examiner respectfully disagrees.
As noted above and in the previous office action, the claims recite generating search results displaying items offered by a retailer while accounting for predicted availabilities of items at the retailer and search query attributes of a search query. Generating search results and displaying items offered by a retailer are sales activities or behaviors and business relations between a user and a retailer of items from a catalog. Sales activities or behaviors and business relations are considered a method of organizing human activity (i.e., one of the groupings of abstract ideas enumerated in MPEP 2106.04(a)(2)) and an abstract idea. MPEP 2106.04(d) uses the term additional elements to refer to claim features, limitations, and/or steps that are recited in the claim beyond the identified judicial exception. Examiner notes the arguments directed to additional elements such as “training a position modification model” and “backpropagation” are analyzed under Step 2A, Prong Two and not within Step 2A, Prong One.
Accordingly, with regard to Step 2A, Prong One, Examiner maintains the amended claims recite a judicial exception.
Applicant next argues even if some portion of the claimed subject matter where characterized as reciting an abstract idea, the claims satisfy Step 2A, Prong Two because they integrate any such idea into a practical application. Examiner respectfully disagrees. Applicant argues amended claim 1 recites a specific technical implementation solving the problem of “ranking items in a way that accounts for both predicted availability and the likelihood of a user performing a specific action at each candidate display position” and notes the Specification provides support in that “explaining that failing to account for predicted availabilities of items when ranking search results may result in unavailable items being displayed in prominent positions, discouraging subsequent orders from the user or preventing the user from creating an order in response to a search query” (see paragraph [0003]). Examiner respectfully disagrees. Ranking items in a way that accounts for both predicted availability and the likelihood of a user performing a specific action at each candidate display position is itself an abstract idea and is part of the abstract idea of “generating search results displaying items offered by a retailer accounting for predicted availabilities of items at the retailer and search query attributes of a search query”, and does not contain any additional elements, such as hardware, beyond the abstract idea itself. Abstract ideas are not patent eligible, therefore these “improvements” cannot provide integration. Generating search results displaying items offered by a retailer accounting for predicted availabilities of items at the retailer and search query attributes of a search query “to account for predicted availabilities of items when ranking search results may result in unavailable items being displayed in prominent positions, discouraging subsequent orders from the user or preventing the user from creating an order in response to a search query”” addresses a business challenge, not a technical improvement.
While the invention may improve the displayed search results (i.e., an abstract idea), unlike the claims in Desjardins, the claims and specification do not recite improving the performance of a machine learning model. Unlike Desjardins, the instant claims and specification do not constitute an improvement to how the machine learning model itself operates. In Desjardins, the Appeals Review Panel (APR) determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continued learning systems, which was directed to improvements in the machine learning technology itself and these improvements were additionally recited in the claimed invention (see MPEP 2106.05(a)). More similar to Recentive Analytics, Inc. vs. Fox Corp. Case No. 2023-2437 (Apr. 18, 2025), the specification of the instant invention and the currently recited claims makes clear that “any suitable machine learning technique” may be employed (see paragraphs [0055]-[0059]) and merely apply generic machine learning techniques to new data environments or fields of use—without disclosing specific improvements to the machine learning models or methods themselves—are not patent-eligible under § 101.
Accordantly, Examiner maintains the claims do not recite specific technological improvements and the additional elements do not integrate the abstract idea into a practical application.
In review of the claimed invention, and in consideration of the specification as originally filed, the Examiner asserts that:
(i) the claimed invention does not reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, but instead improves an abstract, commercial process, and,
(ii) the specification, as originally filed, does not provide sufficient discloser or technical explanation such that one of ordinary skill in the art would have determined that the disclosed invention provided an improvement to the functioning of a computer or another technology or technical field.
Therefore, the Examiner maintains the claims do not recite additional elements that integrate the judicial exception into a practical application of that exception, the additional elements are merely being used to apply the abstract idea to a technological environment, and the additional elements alone or in ordered combination do not render the claim as being significantly more than the underlying abstract idea.
Accordingly, the 35 USC §101 rejection is maintained.
Applicant’s arguments, on pages 15-16 of the Remarks filed 4/17/2026, with respect to the 35 USC §102 rejections have been fully considered but are not persuasive. Applicant argues Ruan does not explicitly disclose applying a position modification model to each of a set of candidate positions for the selected item, wherein the position modification model is trained by obtaining a training dataset in which each training example includes a position. Examiner respectfully disagrees. While the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Accordingly, as currently claimed, Ruan discloses item characteristics can include an item popularity score (i.e., a position) (see ¶0044 of Ruan) which can be used in training the model (see ¶0041 of Ruan) and applying a model to each candidate positions (ranks) for a selected item to determine the item’s rank (see ¶0007, ¶¶0035-0044, and ¶¶0058-0060 of Ruan). Accordingly, Examiner maintains, "given their broadest reasonable interpretation consistent with the specification," Ruan discloses the argued limitations.
Conclusion
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LINDSEY B. SMITH
Examiner
Art Unit 3688
/LINDSEY B SMITH/ Examiner, Art Unit 3688
/Jeffrey A. Smith/ Supervisory Patent Examiner, Art Unit 3688
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