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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 1, 11, and 20 recite a “machine learned model”. The specification does not define this term. It is unclear what is comprised within a machine learned model. The claims are therefore indefinite. Claims 2-10 and 12-19 depend from claims 1 and 11, respectively and are rejected for the same reason.
For purposes of examination, a “machine learned model” will be interpreted as a machine learning model.
35 USC § 101
The claims are directed to order fulfillment, which is a sales method and, therefore, a Certain Method of Organizing Human Activities. MPEP § 2106.04(a). The claims are thus directed to an abstract idea. However, the claims recite an “order quality model”, which is an abstract idea. The limitations relating to training this model impose meaningful limits on the scope of the abstract idea so as to integrate it into a practical application. The claims are therefore directed to statutory subject matter.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 2025/0045813 A1 (hereinafter “Bowen”) in view of U.S. Patent Application Publication 2023/0237552 A1 (hereinafter “Arrabothu”) and further in view of U.S. Patent Application Publication 2019/0311210 A1 (hereinafter “Chatterjee”).
With respect to claim 1, Bowen discloses
“A method, performed at a computer system comprising a processor and a non-transitory computer readable medium, comprising”: Bowen, abstract, ¶ 0004;
“receiving an indication from a picker client device that a requested item from an order to be fulfilled at a source location is unavailable at the source location, wherein the order is associated with a user”; Bowen ¶ 0004 (unavailable item is unavailable at source location);
“retrieving model inputs based in part on the indication, wherein the model inputs include availability information for the requested item, at least one other source location, …, and a tenure of the user”; Bowen ¶¶ 0057, 0062 (inputs include availability, substitute items, which can be similar or identical items from other locations, and user data, which includes user tenure);
“selecting a quality improvement action for the requested item based on an output of an order quality model applied to the model inputs, wherein the order quality model is a machine learned model that was trained by”: Bowen ¶¶ 0057, 0062 (quality improvement action can be filling order with substitute item);
“accessing a set of training examples including appeasement training data, picker fulfillment training data, user satisfaction training data, and user tenure training data”, Bowen ¶¶ 0057, 0060, 0062 (training examples are accessed);
“applying the order quality model to the set of training examples to generate a training output corresponding to a predicted quality improvement action”, Bowen ¶¶ 0057, 0060, 0062 (model generates quality improvement action such as filling order with substitute item); and
“performing the quality improvement action”. Bowen ¶¶ 0057, 0060, 0062 (e.g., order is filled with substitute item).
Bowen does not explicitly disclose a foundational item. Arrabothu discloses
“a foundational item status of the requested item”. Arrabothu ¶¶ 0033, 0057, 0072, 0093 (anchor item is foundational item).
Both Bowen and Arrabothu relate to recommending substitutions in fulfilling orders. Bowen, abstract; Arrabothu, abstract. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the foundational item feature as taught by Arrabothu in the method of Bowen with the motivation of improving order fulfillment by recommending more suitable substitute items. Arrabothu ¶¶ 0002, 0003.
Bowen and Arrabothu do not explicitly disclose back-propagation. Chatterjee discloses
“back-propagating one or more error terms obtained from one or more loss functions to update a set of parameters of the order quality model, and one or more of the error terms are based on a difference
between a label applied to a test interaction of the set of training examples and the predicted quality improvement action”; Chatterjee ¶ 0068 (back-propagation is used to improve error rates); and
“stopping the back-propagation after the one or more loss functions satisfy one or more criteria”. Chatterjee ¶ 0068 (back-propagation is stopped based on early stopping condition).
Bowen, Arrabothu, and Chatterjee relate to the sale of products online. Bowen, abstract; Arrabothu, abstract; Chatterjee, abstract. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the back-propagation feature as taught by Chatterjee in the method of Bowen/Arrabothu with the motivation of assisting with item selection and improving customer satisfaction. Chatterjee ¶ 0001.
With respect to claims 2 and 12, Bowen discloses
“wherein determining the quality improvement action for the requested item using the order quality model and the model inputs, further comprises: generating a plurality of quality improvement actions that each have a respective quality score, wherein a quality score for a given quality improvement action
is based on a cost to perform the given quality improvement action and a predicted user satisfaction for performing the given quality improvement action; ranking the plurality of quality improvement actions based in part on their respective quality score; and selecting the quality improvement action based in part on the ranking”. Bowen ¶¶ 0057, 0060, 0062 (e.g., alternatives for providing substitute items are generated and highest ranking is chosen).
With respect to claims 3 and 13, Bowen discloses
“wherein performing the quality improvement action comprises: identifying a second source location where the requested item is available; selecting a second picker to obtain the requested item; and providing instructions to a second picker client device for a second picker associated with the second picker client device to fulfill the order for the requested item at the second source location”. Bowen ¶¶ 0057, 0060, 0062 (e.g., order for substitute item is generated).
With respect to claims 4 and 14, Bowen discloses
“wherein the second picker client device is the picker client device”. Bowen ¶¶ 0057, 0060, 0062 (e.g., user device can be utilized).
With respect to claims 5 and 15, Bowen discloses
“wherein identifying the second source location where the requested item is available, comprises: determining availability of the requested item at a first source location that is part of a same retailer as the source location; determining availability of the requested item at a second source location that is not part of the retailer; weighting the first source location more than the second source location; adjusting the weighting of the first source location and the weighting of the second source location based on their respective locations from a delivery location for the order; and selecting the source location from the first source location and the second source location based on the adjusted weightings”. Bowen ¶¶ 0057, 0060, 0062 (source of substitute item can be the same or a different retailer or location; highest ranking substitute is chosen).
With respect to claims 6 and 16, Arrabothu discloses
“further comprising: determining the foundational item status for the requested item using a foundational item model and items of the order, wherein the foundational item model is a machine learned model that determines what items are essential to an order”. Arrabothu ¶¶ 0033, 0057, 0072, 0093 (anchor item is determined using machine learning model).
With respect to claims 7 and 17, Bowen discloses
“further comprising: receiving user feedback on the order; and updating the quality improvement model based in part on the performed quality improvement action and the user feedback”. Bowen ¶ 0079 (feedback on substituted items is used by machine learning process).
With respect to claims 8 and 18, Bowen discloses
“further comprising: determining replacement item availability using a replacement model to identify
items that are suitable to replace the requested item and are available at the source location, wherein the model inputs further include the determined replacement item availability”. Bowen ¶¶ 0057, 0060, 0062 (suitable available items are identified as substitute items).
With respect to claims 9 and 19, Arrabothu discloses
“wherein the order is a first order, the method further comprising: receiving a second order for the source location that includes the requested item; determining the foundational item status for the requested item of the first order using a foundational item model and items of the first order, wherein the foundational item model is a machine learned model that determines what items are essential to an order, and the foundational item status indicates that the requested item is a foundational item for the first order; determining a second foundational item status for the requested item of the second order using the foundational item model and items of the second order, wherein the second foundational item status indicates that that the requested item is not a foundational item for the second order; and
prioritizing fulfillment of the requested item for the first order over fulfillment of the requested item for the second order based in part on the foundational item status of the requested item in the first order and the foundational item status of the requested item in the second order”. Arrabothu ¶¶ 0033, 0057, 0072, 0093 (anchor items are determined for any one or more related orders).
With respect to claim 10, Bowen discloses
“wherein performing the quality improvement action comprises: identifying a second source location where the requested item is available; reserving a unit of the requested item for the first order at the second source location where it is available; and providing instructions to the picker client device for a picker associated with the picker client device to fulfill the order for the requested item at the second
source location”. Bowen ¶¶ 0057, 0060, 0062 (substitute item is used to fill order).
With respect to claim 11, Bowen discloses
“A computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor of a computer system, cause the computer system to perform steps comprising”: Bowen ¶¶ 0007, 0044.
Claim 11 is otherwise rejected on the same basis as claim 1.
With respect to claim 20, Bowen discloses
“A computer system comprising”: Bowen, abstract;
“a processor”; Bowen ¶ 0045; and
“a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by the processor, cause the processor to perform steps comprising”: Bowen ¶¶ 0007, 0044.
Claim 20 is otherwise rejected on the same basis as claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Application Publication 2025/0292310 A1 (hereinafter “Rakshit”) discloses back propagation in machine learning models. Rakshit ¶ 0047.
U.S. Patent Application Publication 2022/0092670 A1 (hereinafter “Laserson”) discloses using a machine learning model to substitute out of stock items. Laserson ¶ 0012.
Maitra, Sarit et al., "Backorder Prediction in Inventory Management: Classification Techniques and Cost Considerations", arXiv:2309.13837v2 [cs.L] 2023 (hereinafter “Maitra”) discloses backorder prediction techniques. Maitra, abstract.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ETHAN D CIVAN whose telephone number is (571)270-3402. The examiner can normally be reached Monday-Thursday 8-6:30.
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ETHAN D. CIVAN
Primary Examiner
Art Unit 3688
/ETHAN D CIVAN/Primary Examiner, Art Unit 3688