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 .
Status of the Claims
This Office Action is in response to Applicant’s initially filed application dated 12/15/2024, claims 1-20 are currently pending and being examined in this reply.
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 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without “significantly more.” Claims 1-20 are directed to certain methods of organizing human activity and Mental Processes, which is considered an abstract idea. Further, the claim(s) as a whole, when examined on a limitation-by-limitation basis and in ordered combination do not include an inventive concept.
Step 1 – Statutory Categories
As indicated in the preamble of the claims, the examiner finds the claims are directed to a process, machine, or article of manufacture.
Step 2A – Prong One - Abstract Idea Analysis
Representative claim 15 recites the following abstract concepts, in italics below, which are found to include an “abstract idea”:
A computer system comprising: a processor; and a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by the processor, cause the computer system to perform steps comprising: generating a plurality of sequences for collecting a plurality of items of an order, where each sequence of the plurality of sequences has a different arrangement of the plurality of items; for each of the plurality of sequences, applying an appeasement model to predict a total appeasement value for the sequence; generating a predicted collection time for each of the plurality of sequences; scoring the plurality of sequences based in part on the total appeasement values and the collection times; selecting a sequence from the plurality of sequences based in part on the scoring; and sending the selected sequence to a device, wherein sending the selected sequence to the device causes the device to display the selected sequence for collecting the plurality of items.
The claim features in italics above as drafted, under its broadest reasonable interpretation, are certain methods of organizing human activity (fundamental economic practice, managing personal behavior or relationships or interactions between people), and Mental Processes performed by generic computer components. That is, other than reciting “processor, memory, device” nothing in the claim element precludes the step from practically being a method of organized human activity or Mental Process. For example, but for the “processor, memory, device” the above italicized limitations in the context of this claim encompasses certain methods of organizing human activity and mental processes. If the claim limitations, under its broadest reasonable interpretation, covers steps which could be a fundamental economic practice or managing personal behavior or relationships or interactions between people, or mental processes but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” and “mental processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A – Prong Two - Abstract Idea Analysis
This judicial exception is not integrated into a practical application. In particular, the claim only recites 3 additional elements – processor, memory, device. They are recited at a high-level of generality (i.e., as a generic processor performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.05(f)), data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)), and linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B - Significantly More Analysis
The claims do not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of processor, memory, device amounts to no more than mere instructions to apply the exception using a generic computer component, insignificant extra-solution activity, and linking the use of the judicial exception to a particular technological environment or field of use. Mere instructions to apply the exception using a generic computer component, insignificant extra-solution activity, and linking the use of the judicial exception to a particular technological environment or field of use, cannot provide an inventive concept. Further, the background does not provide any indication that the processor, memory, device is anything other than a generic, off-the-shelf computer components. For these reasons, there is no inventive concept.
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 United States Patent Application Publication No. 2019/0236525 A1 to Stanley et al. (“Stanley”), in view of United States Patent No. 11,887,021 B1 to Bales et al. (“Bales”), and further in view of United States Patent Application Publication No. 2022/0016779 A1 to Wang et al. (“Wang”).
In regards to claims 1, 8, and 15, Stanley discloses the following limitations:
A method, performed at a computer system comprising a processor and a computer-readable medium, comprising: (see at least Stanley ¶¶ 0080-0082, describing implementation of the disclosed operations as program instructions executed by one or more processors of a computer system stored on a tangible computer readable storage medium)
generating a plurality of sequences for collecting a plurality of items of an order, where each sequence of the plurality of sequences has a different arrangement of the plurality of items; (see at least Stanley ¶¶ 0046, 0049-0050 and FIG. 5B, describing that the system sorts a full list of items into a suggested picking sequence by iteratively predicting, for the remaining items, candidate next items and feeding the predicted next item back into the process to generate a sequence, and Stanley ¶ 0028, describing repeatedly applying the picking sequence model to arrange the items into a sequence)
generating a predicted collection time for each of the plurality of sequences; (see at least Stanley ¶¶ 0044, 0060, describing that the picked sequences include a time at which each item was picked or a measure of time between picking subsequent items, and that the model uses this timing information such that the predicted sequence reflects the amount of time a picker would spend, and Stanley ¶ 0028, describing that the model determines an optimized picking sequence directed to picking efficiency)
selecting a sequence from the plurality of sequences based in part on the scoring; and (see at least Stanley ¶¶ 0049, 0069-0072, describing that the candidate having the highest/most favorable score is selected as the predicted next item and that the system sorts the full list accordingly)
sending the selected sequence to a device, wherein sending the selected sequence to the device causes the device to display the selected sequence for collecting the plurality of items. (see at least Stanley ¶¶ 0039-0040, 0066, 0072, describing that the system transmits the determined sequence to the picker mobile application, which displays the list of items in the determined picking sequence to the picker)
Stanley discloses generating and scoring candidate sequences (see at least Stanley ¶¶ 0048-0049 and FIG. 6) however does not appear to specifically disclose the following limitations:
for each of the plurality of sequences, applying an appeasement model to predict a total appeasement value for the sequence; scoring the plurality of sequences based in part on the total appeasement values and the collection times;
The Examiner provides Bales to teach the following limitations:
for each of the plurality of sequences, applying an appeasement model to predict a total appeasement value for the sequence; (see at least Bales col. 11, ll. 23-40 and FIG. 9, describing a trained machine learning model (predictive model 910) that receives item, fulfillment, customer, and condition data and outputs a likelihood of damage, which is provided to a decision engine 920 that optimizes a set of parameters 930 including a predicted customer negative value action (NVA) cost and a customer concession cost; and Bales col. 12, ll. 1-20, describing that the customer concession cost is computed from the customer's history of orders, the number of reported damages, and the number of concessions — e.g., refunds, replacements, and discounts — offered and accepted in response to the reported damages, which under the broadest reasonable interpretation reads on a predicted value indicating whether an appeasement is probable, or a predicted cost to be paid, for an item)
The Examiner provides Wang to teach the following limitations:
scoring the plurality of sequences based in part on the total appeasement values and the collection times; (see at least Wang ¶ 0072-0074, describing scoring and ranking candidate placements by a scoring function; and Bales col. 11, l. 60 – col. 12, l. 20 and FIG. 9, describing that the decision engine generates a decision by computing a total cost as a weighted sum of optimization parameters including the predicted customer concession / NVA (appeasement) cost)
Therefore it would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to include in the system and method as taught by Stanley the trained damage/customer cost prediction as taught by Bales and the candidate scoring as taught by Wang in order to generate a collection method that accounts for picking efficiency but also the predicting the likelihood of item damage resulting in customer remediation. (See at least Bales col. 1 ll 13-20 and col. 2 ll 5-20)
In regards to claims 2, 9, and 16, Stanley further discloses the following limitations:
wherein generating a predicted collection time for each of the plurality of sequences comprises: applying a timing estimation model to predict a collection time for each of the sequences. (see at least Stanley ¶¶ 0044, 0060, describing that the machine-learned model is trained using the time at which each item was picked and the time gaps between sequentially picked items, such that the model predicts a sequence reflecting picking time, thereby teaching a timing estimation model that predicts a collection time)
In regards to claims 3, 10, and 17, Stanley further discloses the following limitations:
further comprising: retrieving a layout of a source location, wherein applying the timing estimation model to predict the collection time for each of the sequences, comprises applying the timing estimation model to the layout of the source location. (see at least Stanley ¶¶ 0028, 0056, describing that additional warehouse information including warehouse layout information, e.g., aisle information or department metadata, is provided as an input to the model used to determine the picking sequence)
In regards to claims 4, 11, and 18, The combination further teaches the following limitations:
wherein, for each of the plurality of sequences, applying the appeasement model to predict the total appeasement value for the sequence comprises: applying the appeasement model to generate, for the sequence, a predicted appeasement value for each the plurality of items in the sequence; and summing the predicted appeasement values for each the plurality of items of the sequence to generate the total appeasement value for the sequence. (see at least Bales col. 11 and 12, describing that a per-item predicted concession / NVA (appeasement) cost is computed for an individual item from that item's damage and concession history, in combination with Wang ¶¶ 0080-0082 and Eq. (12), describing that a placement score for an arrangement is computed by summing per-position contributions across the arrangement)
In regards to claims 5, 12, and 19, The combination further teaches the following limitations:
wherein applying the appeasement model to generate, for the sequence, the predicted appeasement value for each the plurality of items in the sequence comprises: applying the appeasement model to order data associated with the items, the order data including order histories for orders that included at least two of the items, wherein the order histories include: instances where the at least two items were added to a physical receptacle in a first sequence and there was an appeasement for an item of the at least two items, and instances where the at least two items were added to a physical receptacle in a second sequence that is different from the first sequence, and there was no appeasement for the at least two items. (see at least Stanley ¶¶ 0032, 0044, 0057-0059, describing training the model on data describing prior orders picked by pickers, including the sequences in which items in prior orders were collected; and Bales col. 5, l. 60 – col. 6, l. 35 and col. 12, ll. 1-20, describing that the model is trained on order data and damage data collected in association with the customer's orders and other order history)
In regards to claims 6, 13, and 20, The combination further teaches the following limitations:
wherein the appeasement model was trained by: accessing a set of training examples including training order data for sets of items that were collected in different sequences and training item data; applying the appeasement model to the set of training examples to generate a training output corresponding to sets of predicted appeasements for the different sequences; back-propagating one or more error terms obtained from one or more loss functions to update a set of parameters of the appeasement 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 set of predicted appeasements for the different sequences; and stopping the back-propagation after the one or more loss functions satisfy one or more criteria. (see at least Bales col. 6, ll. 60-67 and col. 10, ll. 25-34, describing that the predictive packaging model is implemented as a convolutional neural network trained by accessing a set of training examples (item descriptions, item images, package types, and observed damages), applying the model to generate predicted package decisions, minimizing a loss function defined as a function of a predicted output and the actual damage data, and implementing a back-propagation algorithm to iteratively tune the weights of the network until the loss is minimized; see also Stanley ¶¶ 0057-0061 and 0076, describing training a neural network model and adjusting the hidden layers using a cross entropy loss function based on the difference between a predicted output and a label)
In regards to claims 7 and 14, The combination further teaches the following limitations:
further comprising: generating, by the computer system, training examples based on appeasements made for an item in in a first set of orders having different sequences, and orders of a second set of sequences that included the item where no appeasement was made for the item; labeling each training example based on a comparison of a resolution of the training example to a metric associated with the computer system; and retraining the appeasement model using the labeled training examples. (see at least Bales col. 5, l. 35 – col. 6, l. 35, describing that, once deployed, measurements about the package decisions are tracked and collected over time for a continuous update or re-training of the model, and that the collected damage and concession data is labeled and added to the training data set; see further Stanley ¶¶ 0073-0077, describing generating training examples from completed orders, labeling them by comparison of the predicted result to the actual result, and retraining/updating the model at periodic intervals)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH M MUTSCHLER whose telephone number is (313)446-6603. The examiner can normally be reached 0600-1430.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Florian Zeender can be reached at (571)272-6790. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOSEPH M MUTSCHLER/Examiner, Art Unit 3627
/A. Hunter Wilder/Primary Examiner, Art Unit 3627
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