DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The following is a Final Office Action in response to communications received on 1/28/2026. Claims 1-20 are currently pending and have been examined. Claims 1, 2, 5, 8, 13, 14, and 20 have been amended.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Step 1: The claims 1-12 are a method, claims 13-19 are a system, and claim 20 is a computer readable medium. Thus, each independent claim, on its face, is directed to one of the statutory categories of 35 U.S.C. §101. However, the claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong 1: The independent claims (1, 13 and 20, taking claim 1 as a representative claim) recite:
A method comprising:
receiving, by a computing system comprising one or more computing devices, data indicating a plurality of items associated with an online shopping concierge platform;
determining, by the computing system using an item availability machine learning model based on the data indicating the plurality of items, availability information of the plurality of items, wherein the item availability machine learning model is generated by:
training the item availability machine learning model, wherein the training comprises:
inputting factors for an item-warehouse pair into the item availability machine learning model to determine an availability prediction of the item-warehouse pair, the item-warehouse pair corresponding to a specific item and a warehouse, and adjusting parameters of the item availability machine learning model based on comparing the availability prediction to training datasets;
identifying, by the item availability machine learning model, a subset of items that are predicted to be unavailable;
scoring, by the computing system, the subset of the plurality of items that are predicted to be unavailable based on a tradeoff between (i) an estimated improvement to predictive performance of the item availability machine learning model resulting from obtaining verified availability information for a respective item and (ii) a quantified operational burden associated with having a shopper verify availability of the respective item;
selecting, based on the scoring, at least one item from the subset for which verified availability information is expected to improve the item availability machine learning model;
generating, by the computing system, communications comprising at least one of dispatching, instructing, incentivizing, or encouraging the one or more shoppers to check current availability of the selected at least one item that is based on the tradeoff at least;
and transmitting, by the computing system and to one or more computing devices associated with the one or more shoppers, the communications comprising at least one of dispatching, instructing, incentivizing, or encouraging the one or more shoppers to check the current availability of the selected at least one item that is based on the tradeoff;
receiving, by the computing system, observed availability information for the selected at least one item from the one or more shoppers and generating, based on the observed availability information, additional training data for retraining the item availability machine learning model; and
retraining the item availability machine learning model using the additional training data that is expected to improve item availability machine learning model, wherein the retraining comprises:
causing the item availability machine learning model to generate a confidence score indicative of an accuracy of a prediction of availability of a particular item in the additional training data, determining that the confidence score indicative of the accuracy of the prediction of availability of the particular item being below a threshold,
retrieving new information related to availability of the particular item from the additional training data that includes the observed availability information, and
adjusting parameters of the item availability machine learning model based on the new information.
These limitations, except for the italicized portions, under their broadest reasonable interpretations, recite certain methods of organizing human activity for managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) as well as commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). The claimed invention recites steps for determining the availability of an item and predicting the accuracy of the determined availability using additional information from shoppers checking the status of the item at the location. The steps under its broadest reasonable interpretation specifically fall under sales and marketing activities. The Examiner notes that although the claim limitations are summarized, the analysis regarding subject matter eligibility considers the entirety of the claim and all of the claim elements individually, as a whole, and in ordered combination.
Prong 2: This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements, as emphasized below, of
A method comprising: (claim 1)
A system comprising: one or more processors; and a memory storing instructions that when executed by the one or more processors cause the system to perform operations comprising: (claim 13)
One or more non-transitory computer-readable media comprising instructions that when executed by one or more computing devices cause the one or more computing devices to perform operations comprising: (claim 20)
receiving, by a computing system comprising one or more computing devices, data indicating a plurality of items associated with an online shopping concierge platform;
determining, by the computing system using an item availability machine learning model based on the data indicating the plurality of items, availability information of the plurality of items, wherein the item availability machine learning model is generated by:
training the item availability machine learning model, wherein the training comprises:
inputting factors for an item-warehouse pair into the item availability machine learning model to determine an availability prediction of the item-warehouse pair, the item-warehouse pair corresponding to a specific item and a warehouse, and adjusting parameters of the item availability machine learning model based on comparing the availability prediction to training datasets;
identifying, by the item availability machine learning model, a subset of items that are predicted to be unavailable;
scoring, by the computing system, the subset of the plurality of items that are predicted to be unavailable based on a tradeoff between (i) an estimated improvement to predictive performance of the item availability machine learning model resulting from obtaining verified availability information for a respective item and (ii) a quantified operational burden associated with having a shopper verify availability of the respective item;
selecting, based on the scoring, at least one item from the subset for which verified availability information is expected to improve the item availability machine learning model;
generating, by the computing system, communications comprising at least one of dispatching, instructing, incentivizing, or encouraging the one or more shoppers to check current availability of the selected at least one item that is based on the tradeoff at least;
and transmitting, by the computing system and to one or more computing devices associated with the one or more shoppers, the communications comprising at least one of dispatching, instructing, incentivizing, or encouraging the one or more shoppers to check the current availability of the selected at least one item that is based on the tradeoff;
receiving, by the computing system, observed availability information for the selected at least one item from the one or more shoppers and generating, based on the observed availability information, additional training data for retraining the item availability machine learning model; and
retraining the item availability machine learning model using the additional training data that is expected to improve item availability machine learning model, wherein the retraining comprises:
causing the item availability machine learning model to generate a confidence score indicative of an accuracy of a prediction of availability of a particular item in the additional training data, determining that the confidence score indicative of the accuracy of the prediction of availability of the particular item being below a threshold,
retrieving new information related to availability of the particular item from the additional training data that includes the observed availability information, and
adjusting parameters of the item availability machine learning model based on the new information.
The additional elements of the italicized emphasized above are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of processing data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application – MPEP 2106.05(f).
The recitation of the training and retraining of the data of the machine learning model merely indicates a field of use or technological environment in which the judicial exception is performed. Although these additional elements limit the identified judicial exception, which involves generating a training machine learning model using data regarding the availability of an item and retraining the machine learning model based on new information received, this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore does not integrate the abstract idea into a practical application (see MPEP 2106.05(g)).
Accordingly, these additional elements when considered individually or as a whole do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The independent claims are directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A Prong two, the additional elements in the claims amount to no more than mere instructions to apply the judicial exception using a generic computer component and generally linking the judicial exception to a particular technological environment.
Even when considered as an ordered combination, the additional elements of claim 1, 13, and 20 do not add anything that is not already present when they are considered individually. Therefore, under Step 2B, there are no meaningful limitations in claims 1, 13, and 20 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (see MPEP 2106.05).
As such, independent claims 1, 13, and 20 are ineligible.
Dependent claims 2-12 and 14-19 when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. §101 because the additional recited limitations fail to establish that the claims are not directed to the same abstract idea of Independent Claims 1, 13 and 20 without significantly more.
Claim 2 recites further comprising: receiving, by the computing system and from the one or more computing devices associated with the one or more shoppers, data indicating observed current availability of the at least a portion of the subset of the plurality of items at the one or more warehouse locations; and generating, by the computing system and based at least in part on the data indicating the observed current availability, training data for the item availability machine learning model configured to determine at least one of estimated or projected current availability for the plurality of items associated with the online shopping concierge platform. The limitation merely further limits the abstract idea and the recitation of the machine learning is recited at the apply it level and therefore does not integrate the judicial exception into a practical application.
Claim 3 recites further comprising filtering, by the computing system and prior to determining the subset of the plurality of items, the plurality of items based at least in part on one or more of their estimated or projected current availability determined based at least in part on the item availability machine learning model configured to determine the at least one of the estimated or projected current availability for the plurality of items associated with the online shopping concierge platform. The limitation merely further limits the abstract idea and the recitation of the machine learning is recited at the apply it level and therefore does not integrate the judicial exception into a practical application.
Claim 4 recites wherein scoring the subset of the plurality of items comprises determining the subset of the plurality of items based at least in part on one or more of their estimated or projected current availability determined based at least in part on the item availability machine learning model configured to determine the at least one of the estimated or projected current availability for the plurality of items associated with the online shopping concierge platform. The limitation merely further limits the abstract idea and the recitation of the machine learning is recited at the apply it level and therefore does not integrate the judicial exception into a practical application.
Claim 5 recites further comprising comparing, by the computing system, the data indicating the observed current availability of the at least a portion of the subset of the plurality of items against different data indicating the observed current availability, the different data being based at least in part on one or more of subsequent customer order data or data indicating the observed current availability received from one or more computing devices associated with one or more different shoppers. The limitation merely further limits the abstract idea and therefore does not integrate the judicial exception into a practical application.
Claim 6 recites further comprising: determining, by the computing system and for each item in the subset of the plurality of items, at least one of an estimated or projected allocation of resources required for a shopper of the one or more shoppers to check current availability of the item; and determining the subset of the plurality of items based at least in part on, for each item in the subset of the plurality of items, the at least one of the estimated or projected allocation of resources required for the shopper of the one or more shoppers to check the current availability of the item. The limitation merely further limits the abstract idea and therefore does not integrate the judicial exception into a practical application.
Claim 7 recites further comprising determining the at least one of the estimated or projected allocation of resources based at least in part on an estimated physical distance between the item and one or more different items for which the shopper is tasked with retrieving as part of a current customer order, the item not being among the items for which the shopper is tasked with retrieving. The limitation merely further limits the abstract idea and therefore does not integrate the judicial exception into a practical application.
Claim 8 recites further comprising determining, by the computing system, the estimated physical distance between the item and the one or more different items based at least in part on warehouse location information for a warehouse location of the one or more warehouse locations in which both the item and the one or more different items are physically located. The limitation merely further limits the abstract idea and therefore does not integrate the judicial exception into a practical application.
Claim 9 recites further comprising determining, by the computing system, the estimated physical distance between the item and the one or more different items based on their respective logical locations in a taxonomy of items offered in association with the online shopping concierge platform. The limitation merely further limits the abstract idea and therefore does not integrate the judicial exception into a practical application.
Claim 10 recites further comprising: determining, by the computing system and for each item in the subset of the plurality of items, an incremental gross market value for the item; and determining the subset of the plurality of items based at least in part on, for each item in the subset of the plurality of items, the incremental gross market value for the item. The limitation merely further limits the abstract idea and therefore does not integrate the judicial exception into a practical application.
Claim 11 recites further comprising determining the subset of the plurality of items based at least in part on a symmetrical optimization function for incremental gross market values for the subset of the plurality of items. The limitation merely further limits the abstract idea and therefore does not integrate the judicial exception into a practical application.
Claim 12 recites determining, by the computing system and for each item in the subset of the plurality of items, a level of third-party sponsorship for checking current availability of the item; and determining the subset of the plurality of items based at least in part on, for each item in the subset of the plurality of items, the level of third-party sponsorship for checking current availability of the item. The limitation merely further limits the abstract idea and therefore does not integrate the judicial exception into a practical application.
Claim 14-19 recite parallel claim language to claims 2-12 and there fore are rejected under 35 USC 101 for the reasons set forth above. For these reasons claims 1-20 are rejected under 35 USC 101.
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.
Claims 1-6, 13-16 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Rao (US 20190236740).
Regarding claims 1, 13 and 20 Rao discloses:
A method comprising: (claim 1)
A system comprising: one or more processors; and a memory storing instructions that when executed by the one or more processors cause the system to perform operations comprising: (claim 13) (Figure 2)
One or more non-transitory computer-readable media comprising instructions that when executed by one or more computing devices cause the one or more computing devices to perform operations comprising: (claim 20) (Figure 2)
receiving, by a computing system comprising one or more computing devices, data indicating a plurality of items associated with an online shopping concierge platform; [0017] FIG. 2 is a diagram of an online concierge system 102, according to one embodiment. The online concierge system 102 includes an inventory management engine 202, which interacts with inventory systems associated with each warehouse 110. In one embodiment, the inventory management engine 202 requests and receives inventory information maintained by the warehouse 110. The inventory of each warehouse 110 is unique and may change over time. The inventory management engine 202 monitors changes in inventory for each participating warehouse 110. The inventory management engine 202 is also configured to store inventory records in an inventory database 204.
determining, by the computing system using an item availability machine learning model based on the data indicating the plurality of items, availability information of the plurality of items, wherein the item availability machine learning model is generated by: [0019] The online concierge system 102 also includes an order fulfillment engine 206 which is configured to synthesize and display an ordering interface to each customer 104 (for example, via the customer mobile application 106). The order fulfillment engine 206 is also configured to access the inventory database 204 in order to determine which products are available at which warehouse 110. The order fulfillment engine 206 may supplement the product availability information from the inventory database 204 with an item availability predicted by the machine-learned item availability model 216. and see [0021, 0023, 0024]
training the item availability machine learning model, wherein the training comprises: inputting factors for an item-warehouse pair into the item availability machine learning model to determine an availability prediction of the item-warehouse pair, the item-warehouse pair corresponding to a specific item and a warehouse, ([0025] The machine-learned item availability model 216 contains a set of functions generated by the modeling engine 218 from the training datasets 220 that relate the item, warehouse, and timing information, and/or any other relevant inputs, to the probability that the item is available at a warehouse. Thus, for a given item-warehouse pair, the machine-learned item availability model 216 outputs a probability that the item is available at the warehouse. The machine-learned item availability model 216 constructs the relationship between the input item-warehouse pair, timing, and/or any other inputs and the availability probability that is generic enough to apply to any number of different item-warehouse pairs.) and adjusting parameters of the item availability machine learning model based on comparing the availability prediction to training datasets; [0027] The item characteristics may be determined by the machine-learned item availability model 216 to be statistically significant factors predictive of the item's availability. For different items, the item characteristics that are predictors of availability may be different. For example, an item type factor might be the best predictor of availability for dairy items, whereas a time of day may be the best predictive factor of availability for vegetables. For each item, the machine-learned item availability model 216 may weight these factors differently, where the weights are a result of a “learning” or training process on the training datasets 220.
identifying, by the item availability machine learning model, a subset of items that are predicted to be unavailable; [0052] For example, the inventory management engine 202 may determine item availability probabilities for all items within a delivery order transmitted to a picker. If the probability indicates that an item should be available, the online concierge system 102 may provide this information to the picker through the PMA 112. If the probability indicates that an item might be unavailable, the online concierge system 102 may transmit a warning or other indication to the picker that the item might be unavailable. In some examples, if the item probability indicates that an item is unavailable, the PMA 112 may instruct the picker to limit the amount of time the picker looks for the item in the warehouse, and/or to pick a replacement item. And see [0044]
scoring, by the computing system, the subset of the plurality of items that are predicted to be unavailable based on a tradeoff between (i) an estimated improvement to predictive performance of the item availability machine learning model resulting from obtaining verified availability information for a respective item and (ii) a quantified operational burden associated with having a shopper verify availability of the respective item; [0051] If the probability that the item is available is below a threshold value, then the picker management engine 210 instructs 610 the picker to stop looking for the item. The picker management engine 210 may transmit the instruction through the PMA 112. The picker management engine 210 may add the item-warehouse pair and any associated time or item information to the training dataset 220 indicating that the item was not found at the warehouse. The picker management engine 210 may then instruct the picker to look for the next item in a delivery order, or for a replacement item that has a high availability probability. [0052] In some examples, the online concierge system 102 may determine 604 a probability that an item is available at a warehouse and compare 606 the availability probability to a threshold before receiving an indication 602 from a picker that he or she cannot find an item. For example, the inventory management engine 202 may determine item availability probabilities for all items within a delivery order transmitted to a picker. If the probability indicates that an item should be available, the online concierge system 102 may provide this information to the picker through the PMA 112. If the probability indicates that an item might be unavailable, the online concierge system 102 may transmit a warning or other indication to the picker that the item might be unavailable. In some examples, if the item probability indicates that an item is unavailable, the PMA 112 may instruct the picker to limit the amount of time the picker looks for the item in the warehouse, and/or to pick a replacement item. In some examples, the item availability probabilities provided to the picker may include location information, such as where in a warehouse the item is most likely to be located, such as an aisle or department and see [0044-0046]
selecting, based on the scoring, at least one item from the subset for which verified availability information is expected to improve the item availability machine learning model; [0044] For each possible warehouse, the order fulfillment engine 206 identifies 502 an item-warehouse pair with one of the items in the delivery order. Thus a set of item-warehouse pairs is identified for each of the grated mozzarella, pizza dough and tomato sauce. The online concierge system 102 retrieves 406 the machine-learned item availability model 216 that predicts a probability that an item is available at the warehouse. The online concierge system 102 inputs the item, warehouse, and timing characteristics for each of the identified item-warehouse pairs into the machine-learned item availability model 216. The machine-learned item availability model 216 predicts 408 the probability that each of the grated mozzarella, pizza dough and tomato sauce are available at the identified warehouses. For each of the availability probabilities, the online concierge system 102 also determines 504 a confidence score associated with the probability from the machine-learned item availability model 216. [0045] The online concierge system 102, using the picker management engine 210, instructs 506 a picker to collect new information about pizza dough at one or more of the warehouses. The picker management engine 210 may identify an off-duty picker, or a picker already at one of the warehouses identified 502 in an item-warehouse pair to collect information about whether or not pizza dough is available at the warehouse. The picker management engine 210 transmits this instruction through the PMA 112. The picker 108 may find that pizza dough is in fact available, and transmit the availability to the online concierge system 102 through the PMA 112. This new information is used to update 508 the training dataset 220 and the inventory database 204. The picker management engine 210 may transmit the same instruction to multiple pickers 108 at different warehouses, or at different times, such that there is a larger set of data about pizza dough availability added to the training dataset 220, and more recent data in the inventory database 204.
The Examiner notes that the language “is expected to improve the item availability machine learning model” is intended use language and thereby given little patentable weight. However, the language has been addressed with prior art for compact prosecution.
generating, by the computing system, communications comprising at least one of dispatching, instructing, incentivizing, or encouraging the one or more shoppers to check current availability of the selected at least one item that is based on the tradeoff [0051-0052]; and
transmitting, by the computing system and to one or more computing devices associated with the one or more shoppers, the communications comprising at least one of dispatching, instructing, incentivizing, or encouraging the one or more shoppers to check the current availability of the selected at least one item that is based on the tradeoff; [0051-0052] [0045] It is possible that the confidence score for pizza dough confidence score at one or more of the warehouses is below a threshold, given that people frequently make their own pizza dough and it may not be frequently ordered. Thus pizza dough may have a relatively small and/or old associated dataset in the training dataset 220, leading to a low confidence score on the pizza dough availability probability within the machine-learned item availability model 216. The online concierge system 102, using the picker management engine 210, instructs 506 a picker to collect new information about pizza dough at one or more of the warehouses. The picker management engine 210 may identify an off-duty picker, or a picker already at one of the warehouses identified 502 in an item-warehouse pair to collect information about whether or not pizza dough is available at the warehouse. The picker management engine 210 transmits this instruction through the PMA 112. The picker 108 may find that pizza dough is in fact available, and transmit the availability to the online concierge system 102 through the PMA 112.
receiving, by the computing system, observed availability information for the selected at least one item from the one or more shoppers and generating, based on the observed availability information, additional training data for retraining the item availability machine learning model; and [0049] The online concierge system 102 (e.g., the picker management engine 210) receives an indication 602 from a picker that he or she cannot find an item at the warehouse. The picker may transmit this information to the online concierge system 102 through the PMA 112, which communicates it to the picker management engine 210. The picker may input the item information into the PMA 112. In some examples, the picker may also provide additional information about where they have already looked for the item within the warehouse, such as aisles in which the item was not found, departments in which the item was not found, the amount of time he or she spent looking for the item, etc. In response, the online concierge system 102 inputs the item, warehouse, and timing characteristics for the item received from the picker and the warehouse in which the picker is unable to find the item into the machine-learned item availability model 216 with. In some embodiments, the online concierge system 102 may incorporate the information provided by the picker through the PMA 112 into the training datasets 220, which may be later used by the modeling engine 218 to update the machine-learned item availability model 216.
retraining the item availability machine learning model using the additional training data that is expected to improve item availability machine learning model, wherein the retraining comprises: [0049]In response, the online concierge system 102 inputs the item, warehouse, and timing characteristics for the item received from the picker and the warehouse in which the picker is unable to find the item into the machine-learned item availability model 216 with. In some embodiments, the online concierge system 102 may incorporate the information provided by the picker through the PMA 112 into the training datasets 220, which may be later used by the modeling engine 218 to update the machine-learned item availability model 216.
causing the item availability machine learning model to generate a confidence score indicative of an accuracy of a prediction of availability of a particular item in the additional training data, determining that the confidence score indicative of the accuracy of the prediction of availability of the particular item being below a threshold, [0049]The online concierge system 102 determines 604 a probability that the item is available at the warehouse from the probability output by the machine-learned item availability model 216. The online concierge system 102 then compares the output probability against a threshold and determines 606 if the item availability probability is above the threshold. In some examples, this threshold value may be an item availability probability of 0.3. Additionally or alternatively, the online concierge system 102 may compare a confidence score associated with the item availability probability to a threshold value. and see [0050-51] for discussion of threshold scenarios
retrieving new information related to availability of the particular item from the additional training data that includes the observed availability information, and [0049]In response, the online concierge system 102 inputs the item, warehouse, and timing characteristics for the item received from the picker and the warehouse in which the picker is unable to find the item into the machine-learned item availability model 216 with. In some embodiments, the online concierge system 102 may incorporate the information provided by the picker through the PMA 112 into the training datasets 220, which may be later used by the modeling engine 218 to update the machine-learned item availability model 216.
adjusting parameters of the item availability machine learning model based on the new information. [0045] This new information is used to update 508 the training dataset 220 and the inventory database 204. The picker management engine 210 may transmit the same instruction to multiple pickers 108 at different warehouses, or at different times, such that there is a larger set of data about pizza dough availability added to the training dataset 220, and more recent data in the inventory database 204. [0046] In this example, the modeling engine 218 uses the updated training datasets 220 to retrain the machine-learned item availability model 216.
Regarding claims 2 and 14, Rao discloses the limitations set forth above and further discloses:
receiving, by the computing system and from the one or more computing devices associated with the one or more shoppers, data indicating observed current availability; and [0045] It is possible that the confidence score for pizza dough confidence score at one or more of the warehouses is below a threshold, given that people frequently make their own pizza dough and it may not be frequently ordered. Thus pizza dough may have a relatively small and/or old associated dataset in the training dataset 220, leading to a low confidence score on the pizza dough availability probability within the machine-learned item availability model 216. The online concierge system 102, using the picker management engine 210, instructs 506 a picker to collect new information about pizza dough at one or more of the warehouses. The picker management engine 210 may identify an off-duty picker, or a picker already at one of the warehouses identified 502 in an item-warehouse pair to collect information about whether or not pizza dough is available at the warehouse. The picker management engine 210 transmits this instruction through the PMA 112. The picker 108 may find that pizza dough is in fact available, and transmit the availability to the online concierge system 102 through the PMA 112.
generating, by the computing system and based at least in part on the data indicating the observed current availability, training data for the item availability machine learning model configured to determine at least one of estimated or projected current availability for the plurality of items associated with the online shopping concierge platform. [0049] The online concierge system 102 determines 604 a probability that the item is available at the warehouse from the probability output by the machine-learned item availability model 216. The online concierge system 102 then compares the output probability against a threshold and determines 606 if the item availability probability is above the threshold. In some examples, this threshold value may be an item availability probability of 0.3. Additionally or alternatively, the online concierge system 102 may compare a confidence score associated with the item availability probability to a threshold value. [0050] If an availability probability is above the threshold, this indicates that the item is predicted to be available at the warehouse. The picker management engine 210 then instructs 608 a picker to continue looking for the item.
Regarding claim 3 , Rao discloses the limitations set forth above and further discloses:
further comprising filtering, by the computing system and prior to determining the subset of the plurality of items, the plurality of items based at least in part on one or more of their estimated or projected current availability determined based at least in part on the item availability machine learning model configured to determine the at least one of the estimated or projected current availability for the plurality of items associated with the online shopping concierge platform. ([0041] This list of item-warehouse pairs may be filtered, e.g., based on item popularity, predicted items to be ordered, warehouse, or one or more other factors. And Figure 7 Probability that the item is available the warehouse is below a threshold step 708- allow customer to add item or 712 notify customer through interface that the item is likely to be unavailable)
Regarding claims 4 and 15 , Rao discloses the limitations set forth above and further discloses:
wherein scoring the subset of the plurality of items comprises determining the subset of the plurality of items based at least in part on one or more of their estimated or projected current availability determined based at least in part on the item availability machine learning model configured to determine the at least one of the estimated or projected current availability for the plurality of items associated with the online shopping concierge platform. [0045] It is possible that the confidence score for pizza dough confidence score at one or more of the warehouses is below a threshold, given that people frequently make their own pizza dough and it may not be frequently ordered. Thus pizza dough may have a relatively small and/or old associated dataset in the training dataset 220, leading to a low confidence score on the pizza dough availability probability within the machine-learned item availability model 216.
Regarding claim 5, Rao discloses the limitations set forth above and further discloses:
further comprising comparing, by the computing system, the data indicating the observed current availability against different data indicating the observed current availability, the different data being based at least in part on one or more of subsequent customer order data or data indicating the observed current availability received from one or more computing devices associated with one or more different shoppers. [0045] It is possible that the confidence score for pizza dough confidence score at one or more of the warehouses is below a threshold, given that people frequently make their own pizza dough and it may not be frequently ordered. Thus pizza dough may have a relatively small and/or old associated dataset in the training dataset 220, leading to a low confidence score on the pizza dough availability probability within the machine-learned item availability model 216. The online concierge system 102, using the picker management engine 210, instructs 506 a picker to collect new information about pizza dough at one or more of the warehouses. The picker management engine 210 may identify an off-duty picker, or a picker already at one of the warehouses identified 502 in an item-warehouse pair to collect information about whether or not pizza dough is available at the warehouse. The picker management engine 210 transmits this instruction through the PMA 112. The picker 108 may find that pizza dough is in fact available, and transmit the availability to the online concierge system 102 through the PMA 112.
Regarding claims 6 and 16, Rao discloses the limitations set forth above and further discloses:
determining, by the computing system and for each item in the subset of the plurality of items, at least one of an estimated or projected allocation of resources required for a shopper of the one or more shoppers to check current availability of the item; and determining the subset of the plurality of items based at least in part on, for each item in the subset of the plurality of items, the at least one of the estimated or projected allocation of resources required for the shopper of the one or more shoppers to check the current availability of the item. [0045] It is possible that the confidence score for pizza dough confidence score at one or more of the warehouses is below a threshold, given that people frequently make their own pizza dough and it may not be frequently ordered. Thus pizza dough may have a relatively small and/or old associated dataset in the training dataset 220, leading to a low confidence score on the pizza dough availability probability within the machine-learned item availability model 216. The online concierge system 102, using the picker management engine 210, instructs 506 a picker to collect new information about pizza dough at one or more of the warehouses. The picker management engine 210 may identify an off-duty picker, or a picker already at one of the warehouses identified 502 in an item-warehouse pair to collect information about whether or not pizza dough is available at the warehouse. The picker management engine 210 transmits this instruction through the PMA 112. The picker 108 may find that pizza dough is in fact available, and transmit the availability to the online concierge system 102 through the PMA 112. and see Figure 6.
Claim Rejections - 35 USC § 103
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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 10, 11, 17, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Rao in view of Ouimet (US 20130066740).
Regarding claims 10 and 17, Rao discloses the limitations set forth above. While Rao determines the availability of items within a store, the reference does not expressly disclose:
determining, by the computing system and for each item in the subset of the plurality of items, an incremental gross market value for the item; and
determining the subset of the plurality of items based at least in part on, for each item in the subset of the plurality of items, the incremental gross market value for the item.
However Ouimet teaches:
determining, by the computing system and for each item in the subset of the plurality of items, an incremental gross market value for the item; and [0309] In block 1084, an evaluation is made of purchases of product P1 by consumers 1070-1074 of offer group 1068 to determine the incremental revenue or profit to retailers 190-194.
determining the subset of the plurality of items based at least in part on, for each item in the subset of the plurality of items, the incremental gross market value for the item. [0302] The sharing percentage, incremental revenue or profit, or performance based fee (sharing percentage times incremental profit) can be used as a basis for prioritizing the products from retailers 190-194 on optimized shopping list 144. The retailer that is positioned to achieve the highest incremental revenue or profit or that is offering consumer service provider 72 the highest sharing percentage can be placed in first position on optimized shopping list 144.
Therefore 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 determining of available items of Rao to include determining, by the computing system and for each item in the subset of the plurality of items, an incremental gross market value for the item; and determining the subset of the plurality of items based at least in part on, for each item in the subset of the plurality of items, the incremental gross market value for the item, as taught in Ouimet, in order to achieve the highest incremental revenue or profit (paragraph 0302).
Regarding claims 11 and 18, Rao discloses the limitations set forth above. While Rao determines the availability of items within a store, the reference does not expressly disclose:
further comprising determining the subset of the plurality of items based at least in part on a symmetrical optimization function for incremental gross market values for the subset of the plurality of items
However Ouimet teaches:
further comprising determining the subset of the plurality of items based at least in part on a symmetrical optimization function for incremental gross market values for the subset of the plurality of items. [0313] Retailers 190-194 can monitor the incremental profit in block 1084, as well as the statistical correlation between the incremental profit and the individualized offers. T-LOG data 20 shows that the consumers purchased product P1 with an individualized discounted offer 1080 that is less than the maximum retailer acceptable discount. Consumer service provider 72 is paid a performance based fee 1086 from the incremental revenue or profit, e.g., a percentage of the incremental revenue or profit. If the evaluation demonstrates that the purchasing decisions made by consumers 1070-1074 in offer group 1068 is primarily attributed to the individualized discounted offer 1080, i.e., because consumers 1062-1066 of control group 1060 did not purchase the product when no discounted offer was made, then consumer service provider 72 receives a full share of the incremental profit. The incremental profit can be statistically correlated to the individualized discounted offer 1080 as being the primary motivational influence in the purchasing decision.
Therefore 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 determining of available items of Rao to include further comprising determining the subset of the plurality of items based at least in part on a symmetrical optimization function for incremental gross market values for the subset of the plurality of items, as taught in Ouimet, in order to achieve the highest incremental revenue or profit (paragraph 0302).
Subject Matter Free of Prior Art
Claims 7-9, 12, and 19 are determined to recite subject matter free of prior art, however remain rejected under 35 USC 101 and objected to as they depend from claims 1 and 13 which are rejected under 35 USC 102(a)(1).
The closest prior art of record was found to be Li (US 20180232755). Li discloses leveraging shoppers already present at a store who are completing another shopping task to shop of an additional shopping task. The shopper is provided with an incentive if they accept and complete the task (see Figure 3). Li also determines the optimal shopper to select based on location and the amount of time it might take of the shopper to complete the task [0040, 0046-48]. In [0030] and [0036] of Li it recites, “After store 106 receives the order 104, the store identifies a second shopper 110 in or near the store who may be available to collect the items in the online order. The store sends a notification or request, represented at 112, to this second shopper asking the shopper if he or she would be willing to collect those items, represented at 114, and this request includes a promotional offer, coupon or discount as an incentive for the shopper to collect those items. [0036] At 162, an incentive plan 164 is prepared for the potential shopper. This plan may involve a combination of coupons, discounts, promotions and other incentives. The incentive plan could be for current or future shopping based on insight from precision marketing.”
However, the references of Rao, Li, and/or Ouimet and those found in the prior art search, nor alone or in combination, render the following limitations anticipated nor obvious to one of ordinary skill in the art:
Claim 7: The method of claim 6, further comprising determining the at least one of the estimated or projected allocation of resources based at least in part on an estimated physical distance between the item and one or more different items for which the shopper is tasked with retrieving as part of a current customer order, the item not being among the items for which the shopper is tasked with retrieving.
Claim 8: The method of claim 7, further comprising determining, by the computing system, the estimated physical distance between the item and the one or more different items based at least in part on warehouse location information for a warehouse location of the one or more warehouse locations in which both the item and the one or more different items are physically located.
Claim 9: The method of claim 7, further comprising determining, by the computing system, the estimated physical distance between the item and the one or more different items based on their respective logical locations in a taxonomy of items offered in association with the online shopping concierge platform.
Claim 12: The method of claim 1, further comprising: determining, by the computing system and for each item in the subset of the plurality of items, a level of third-party sponsorship for checking current availability of the item; and determining the subset of the plurality of items based at least in part on, for each item in the subset of the plurality of items, the level of third-party sponsorship for checking current availability of the item.
Claim 19: The system of claim 13, wherein the operations further comprise: determining, for each item in the subset of the plurality of items, a level of third-party sponsorship for checking current availability of the item; and determining the subset of the plurality of items based at least in part on, for each item in the subset of the plurality of items, the level of third-party sponsorship for checking current availability of the item.
Therefore, none of the cited references disclose or render obvious each and every feature of the claimed invention and the claimed invention is determined to be free of the prior art. Therefore, it is hereby asserted by the Examiner that, in light of the above, that the claims are free of prior art as the references do not anticipate the claims and do not render obvious any further modification of the references to a person of ordinary skill in art.
Relevant Art Not Cited
NPL: “Unify Online and In-Person Sales Today” discloses ways in which inventory accuracy can be improved in order to avoid over stocking, under stocking and prevent delays.
Response to Arguments
Applicant's arguments filed 1/28/2026 have been fully considered but they are not persuasive.
With respect to the remarks directed to 35 USC 101, the examiner asserts the claims as amended do not overcome the 101. The limitations such as the scores determining the tradeoff between an estimated improvement to the predictive performance of the model and the quantified operational burden associated with manual verification merely further limit the abstract idea. The same is asserted about generating communications to shoppers to verify the availability of the selected items and receiving related information. The machine learning is recited in a manner that is at the apply it level. Here the machine learning is used in a new environment (item availability), however, as set forth in Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205, 2025 U.S.P.Q.2D (BNA) 628 (Fed. Cir. 2025), The requirements that the machine learning model be “iteratively trained” or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement. Iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning [page 12]
That is, the claims do not delineate steps through which the machine learning technology achieves an improvement. [page 13] Stated differently, patents may be directed to abstract ideas where they disclose the use of an “already available [technology], with [its] already available basic functions, to use as [a] tool[] in executing the claimed process.” SAP Am., 898 F.3d at 1169–70. We think those cases are equally applicable in the machine learning context. [page 15].
With regards to Desjardins, Examiner notes that the fact patterns of the instant case are different from those set forth in Ex Parte Desjardins, and different fact patterns may have different eligibility outcomes. In Ex Parte Desjardins, the claimed invention was a method of training a machine learning model on a series of tasks, and technical improvements as a result of the model training were identified as reduced storage, reduced system complexity, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks. While the ARP in Ex Parte Desjardins determined that these improvements were sufficient to reverse the 101 rejection of the claims at hand in Ex Parte Desjardins, analogous improvements are not apparent in the instant claims. Furthermore, as discussed below, neither Applicant’s specification nor the instant claims set forth analogous improvements. Accordingly, under the analysis set forth according to the MPEP, discussed below, the amended claims stand as ineligible.
With respect to the remarks directed to the prior art rejection, the examiner has maintained the rejection citing Rao and Ouimet. The examiner asserts that read in light of the specification, trade-off, is interpreted as [0050] In some embodiments, the computing device(s) may determine, for each item in the determined subset, an estimated, projected, and/or the like allocation of resources (e.g., time, costs, and/or the like) required for a shopper of shoppers 506 to check the current availability of the item, and/or the like. In some of such embodiments, the subset of items 502 may be determined (e.g., by model(s) 508, and/or the like) based at least in part on such estimated, projected, and/or the like resource allocations. In some embodiments, the estimated, projected, and/or the like resource allocations may be determined based at least in part on an estimated physical distance between the item and one or more different items for which the shopper is tasked with retrieving, e.g., as part of a current customer order that does not include the subject item, and/or the like. Rao does teach this determination in at least the example of [0051] If the probability that the item is available is below a threshold value, then the picker management engine 210 instructs 610 the picker to stop looking for the item. The picker management engine 210 may transmit the instruction through the PMA 112. The picker management engine 210 may add the item-warehouse pair and any associated time or item information to the training dataset 220 indicating that the item was not found at the warehouse. The picker management engine 210 may then instruct the picker to look for the next item in a delivery order, or for a replacement item that has a high availability probability. In both the instant application and the cited reference of Rao, the determination is made as to whether the tradeoff of using the manual resources of the pickers/shoppers is the best use of the resources as opposed to the determination of the probability that the item is available in the warehouse.
To the same accord, the Rao reference then teaches using the collected information in the modeling to update the machine learning to re-train the model (see at least [0045 and 0049]]).
For at least these reasons the examiner has maintained the rejection under 35 USC 102 and 103.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VICTORIA E. FRUNZI whose telephone number is (571)270-1031. The examiner can normally be reached Monday- Friday 7-4 (EST).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Marissa Thein can be reached at (571) 272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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VICTORIA E. FRUNZI
Primary Examiner
Art Unit TC 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 4/1/2026
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