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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 .
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The IDS filed 2/12/2025 has been considered by examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
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Claim 1-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claim of U.S. Patent No. 12,222,937 in view of Cheng et al in US Patent Application Publication № 2021/0248321, hereinafter called Cheng.
In regard to claim 1, the reference claims recite a computer-implemented method, comprising:
wherein the machine-learned model is trained by:
generating training data comprising a plurality of examples, each example comprising a historical query received by an online system and an item with which a user of the online system performed a specific interaction, wherein a label applied to each example of the training data indicates whether the specific interaction was performed with the item after the online system received the historical query (“generating training data comprising a plurality of examples, each example comprising a query received by an online concierge system and an item with which a user of the online concierge system performed a specific interaction, wherein a label applied to each example of the training data indicates whether the specific interaction was performed with the item after the online concierge system received the query;” reference claim 1);
generating a noisy subset of training data and a high-quality subset of training data based on a metric that defines a quality of training data, wherein the high-quality subset has a higher metric value than the noisy subset (identically recited in reference claim 1);
initializing the machine-learned model of a plurality of layers, the machine- learned model configured to receive the historical query and the item of an example (“initializing the machine-learned model comprising a network of a plurality of layers, the machine-learned model configured to receive a query and an item and to generate a predicted measure of relevance of the item to the query;” reference claim 1);
generating predicted measures of the items of the examples (“for each of a plurality of the examples of the noisy subset of the training data: applying, by one or more processors, the machine-learned model to the query of the example of the noisy subset of the training data and to the item of the example of the noisy subset of the training data;” reference claim 1, note further that such predictions are expressly recited as being used in reference claim 1);
training the machine-learned model (i.e. by adjusting weights using example data) using the examples of the noisy subset of the training data (the examples are taught to be used, “the backpropagating performed […] based on a difference between the label applied to the example of the noisy subset of the training data and a predicted measure of relevance of the item of the example of the noisy subset of the training data” reference claim 1 ) to update a set of parameters of the machine-learned model (“update a set of parameters of the network”; further, “storing, in the computer readable storage medium, the set of parameters of the network that are updated in the one or more iterations;” reference claim 1) through
backpropagating error terms based on differences between the labels applied to the examples of the noisy subset of the training data and predicted measures of the examples of the noisy subset of the training data (“the backpropagating performed through the network and one or more of the error terms based on a difference between the label applied to the example of the noisy subset of the training data and a predicted measure of relevance of the item of the example of the noisy subset of the training data” reference claim 1)
storing the set of parameters of the machine-learned model that are trained by the noisy subset as a first set of trained parameters (“storing, in the computer readable storage medium, the set of parameters of the network that are updated in the one or more iterations” reference claim 1);
training the machine-learned model using the examples of the high-quality subset of the training data to update the first set of trained parameters through backpropagating error terms based on differences between the labels applied to the examples of the high-quality subset of the training data and predicted measures of the examples of the high-quality subset of the training data (“for each of the plurality of the examples of the high-quality subset of the training data: applying, by the one or more processors, the machine-learned model to the query of the example of the high-quality subset of the training data and to the item of the example of the high-quality subset of the training data;” and further “backpropagating, by the one or more processors, one or more error terms obtained from one or more loss functions to generate a modified set of parameters of the network, the backpropagating performed through the network and one or more of the error terms based on a difference between a label applied to the example of the high-quality subset of the training data and a predicted measure of relevance of the item of the example of the high-quality subset of the training data and to the query of the example of the subset of the training data;” reference claim 1);
storing updated set of parameters of the machine-learned model that are trained by the high-quality subset as a second set of trained parameters; and storing the second set of trained parameters trained from the high-quality subset of the training data as parameters of the machine-learned model (“and storing, in the computer readable storage medium, the modified set of parameters of the network trained from the subset of the training data as parameters of the machine-learned model.” Reference claim 1);
However, the reference claims fail to expressly recite receiving a query for retrieving one or more items; applying a machine-learned model to identify a plurality of candidate items; and returning one or more candidate items identified by the machine-learned model in response to the query.
Cheng teaches receiving a query for retrieving one or more items (Fig. 2, element 218); applying a machine-learned model to identify a plurality of candidate items (Fig. 2, element 220); and returning one or more candidate items identified by the machine-learned model in response to the query (Fig. 2, element 222).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant invention to modify the reference claims to include the receipt of a query that uses the trained model to identify candidate items, as taught by Cheng. It would have been obvious because it represents the application of a known technique (i.e. providing a query to a trained machine learning model, which is trained using example data from user behavior, to identify candidate items, as taught by Cheng in paragraphs 0051-0054, and Fig. 2) to improve similar systems (i.e. the machine-learning-model entity recognition system which is trained based on examples and , as taught by Cheng, and the machine-learning-model item identification system which is trained based on examples from historical queries, as recited in the reference claims) in the same way (i.e. the trained model is queried to identify candidate items based on the training data).
In regard to instant claims 11 and 20, they are substantially similar to instant claim 1, and accordingly are rejected under similar reasoning.
In regard to instant claim 2, the reference claims further recite that generating the high- quality subset of training data comprises: selecting examples of the training data including items with which the specific interaction was performed with at least a threshold frequency (“generating the high-quality subset of training data comprises: selecting examples of the training data including items with which the specific interaction was performed with at least a threshold frequency.” Reference claim 2).
In regard to instant claim 12, it is substantially similar to instant claim 2 and accordingly is rejected under similar reasoning.
In regard to instant claim 3, the reference claims further recite that generating the high- quality subset of training data further comprises: determining an example of the training data includes an item with which the specific frequency was performed with at least an additional threshold frequency; and including a specific number of replicas of the example determined to include the item with which the specific frequency was performed with at least the additional threshold frequency in the subset of the training data in response to the determining (identically recited in reference claim 3).
In regard to instant claim 13, it is substantially similar to instant claim 3 and accordingly is rejected under similar reasoning.
In regard to instant claim 4, the reference claims further recite that generating the high- quality subset of training data comprises: ranking examples of the training data based on frequencies with which the specific interaction was performed with items included in the examples of the training data; selecting examples of the training data having at least a threshold position in the ranking (identically recited in reference claim 4).
In regard to instant claim 14, it is substantially similar to instant claim 4 and accordingly is rejected under similar reasoning.
In regard to instant claim 5, the reference claims further recite that generating the high- quality subset of training data further comprises: determining an example of the training data includes an item with which the specific frequency was performed with at least a threshold frequency; and including a specific number of replicas of the example determined to include the item with which the specific frequency was performed with at least the threshold frequency in the subset of the training data in response to the determining (identically recited in reference claim 5).
In regard to instant claim 15, it is substantially similar to instant claim 5 and accordingly is rejected under similar reasoning.
In regard to instant claim 6, the reference claims further recite that the specific interaction comprises including the item in an order received by the online system (“the specific interaction comprises including the item in an order received by the online concierge system” reference claim 6).
In regard to instant claim 16, it is substantially similar to instant claim 6 and accordingly is rejected under similar reasoning.
In regard to instant claim 7, the reference claims further recite that backpropagating the error terms based on the differences between the labels applied to the examples of the noisy subset of the training data and predicted measures of the examples of the noisy subset of the training data comprises: generating the error terms from application of the machine-learned model to the example of the high-quality subset of the training data using an alternative loss function than a loss function generating the error term from application of the machine-learned model to the example of the training data (“backpropagating one or more error terms obtained from one or more loss functions to modify the set of parameters of the network comprises: generating the one or more error terms from application of the machine-learned model to the example of the high-quality subset of the training data using an alternative loss function than a loss function generating the error term from application of the machine-learned model to the example of the training data.” Reference claim 7).
In regard to instant claim 17, it is substantially similar to instant claim 7 and accordingly is rejected under similar reasoning.
In regard to instant claim 8, the reference claims further recite that the alternative loss function applies a higher weight to an error term from application of the machine-learned model to the example of the high-quality subset of the training data than the loss function generating the error term from application of the machine-learned model to the noisy subset of the training data (identically recited in reference claim 8).
In regard to instant claim 18, it is substantially similar to instant claim 8 and accordingly is rejected under similar reasoning.
In regard to instant claim 9, the reference claims further recite that generating the predicted measures of the items of the examples comprises: applying the machine-learned model with a particular architecture to the example of the noisy subset of the training data and to the item of the example of the noisy subset of the training data (“applying the machine-learned model to the query of the example of the noisy subset of the training data and to the item of the example of the noisy subset of the training data comprises: applying the machine-learned model with a particular architecture to the example of the noisy subset of the training data and to the item of the example of the noisy subset of the training data.” Reference claim 9).
In regard to instant claim 19, it is substantially similar to instant claim 9 and accordingly is rejected under similar reasoning.
In regard to instant claim 10, the reference claims further recite that generating the predicted measures of the items of the examples comprises: applying the machine-learned model with a different architecture than the particular architecture to the example of the high-quality subset of the training data and to the item of the high-quality subset of the example of the training data (“wherein applying the machine-learned model to the query of the example of the high-quality subset of the training data and to the item of the example of the high-quality subset of the training data comprises: applying the machine-learned model with a different architecture than the particular architecture to the example of the high-quality subset of the training data and to the item of the high-quality subset of the example of the training data.” Reference claim 10).
Conclusion
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/AJAY M BHATIA/Supervisory Patent Examiner, Art Unit 2156
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