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 .
Notice to Applicant
This is the first Non-Final Office Action in response to Application Serial Number: 18/233,252, filed on August 11, 2023. Claims 1-20 are pending in this application and have been rejected below.
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 claimed subject matter falls within the four statutory categories of patentable subject matter.
Claims 1-10 are directed towards a method, claims 11-19 are directed towards a computer program product and claim 20 is directed towards a computer system, which are among the statutory categories of invention.
Step 2A – Prong One: The claims recite an abstract idea.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite predicting the likelihood that a picker servicing a batch of existing orders placed with the online concierge system will accept a batch of new orders for servicing.
Claim 1 recites limitations directed to an abstract idea based on certain methods of organizing human activity and mental processes. Specifically, receiving information describing a progress of a picker servicing a set of existing orders; predicting a first likelihood that the picker will finish servicing the set of existing orders within a threshold amount of time, wherein predicting the first likelihood is based at least in part on the progress of the picker and information describing the set of existing orders; determining whether the first likelihood exceeds a threshold likelihood; matching a plurality of sets of new orders with a plurality of pickers based at least in part on the second likelihood; determining whether one or more sets of new orders are matched with the picker; and responsive to determining the one or more sets of new orders are matched with the picker, sending one or more requests to service the one or more sets of new orders to a picker constitutes methods based on commercial interactions and managing personal behavior, as well as, methods based on observations, evaluations, judgements and/or opinion that can be performed mentally by a combination of the human mind and a human using pen and paper. The recitation of a computer system comprising a processor and computer-readable medium, online concierge system, machine learning model, and client device does not take the claim out of the certain methods of organizing human activity and mental processes groupings. Thus the claim recites an abstract idea. Claims 11 and 20 recite certain method of organizing human activity and mental processes for similar reasons as claim 1.
Step 2A – Prong Two: The judicial exception is not integrated into a practical application.
The judicial exception is not integrated into a practical application. In particular, claim 1 recites sending one or more requests to service the one or more sets of new orders to a client device associated with the picker, which is considered to be an insignificant extra-solution activity of collecting and delivering data; see MPEP 2106.05(g). Additionally, claim 1 recites a computer system comprising a processor and computer-readable medium, online concierge system, and client device at a high-level of generality such that they amount to no more than generic computer components used as tools to apply the instructions of the abstract idea; see MPEP 2106.05(f). Additionally, claim 1 recites responsive to determining the first likelihood exceeds the threshold likelihood, accessing a machine learning model trained to predict a second likelihood that the picker will accept a set of new orders for servicing while servicing the set of existing orders, wherein the machine learning model is trained by: receiving historical data describing acceptance, by pickers, of requests to service sets of new orders while servicing sets of existing orders, and training the machine learning model based at least in part on the historical data; applying the machine learning model to a set of inputs to predict the second likelihood that the picker will accept the set of new orders for servicing while servicing the set of existing orders, wherein the set of inputs comprises a set of attributes of the picker and the progress of the picker. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, the machine learning model disclosed in the claims are solely used as a tool to perform the instructions of the abstract idea. Thus, the additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limitations on practicing the abstract idea. Claim 1 as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and therefore is directed to an abstract idea. The computer program product comprising a non-transitory computer-readable storage medium having encoded instructions executable by a processor recited in claim 11 and the computer system comprising a processor and a non-transitory computer-readable storage medium storing instructions executable by the processor in claim 20 also amount to no more than mere instructions to apply the exception using a generic computer component; see MPEP 2106.05(f). Thus, the additional elements recited in claims 11 and 20 do not integrate the abstract idea into practical application for similar reasons as claim 1.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements in the claims other than the abstract idea per se, including a computer system comprising a processor and computer-readable medium, online concierge system, machine learning model, and client device amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); performing repetitive calculations, Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims."); electronic recordkeeping, Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log) and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; see MPEP 2106.05(d)(II). The machine learning techniques recited in the claim are disclosed at a high-level of generality (see at least Specification [0066]; [0100]) and does not amount to significantly more than the abstract idea. Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Therefore, since there are no limitations in the claim that transform the abstract idea into a patent eligible application such that the claim amounts to significantly more than the abstract idea itself, the claims are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
§ 101 Analysis of the dependent claims.
Regarding the dependent claims, dependent claims 3, 5-8, 10 and 13, 14-18 recite limitations that are not technological in nature and merely limits the abstract idea to a particular environment. MPEP 2106.05(f). Claims 2, 5, 6, 12, 15 and 16 recites training an additional machine learning model and utilizing the machine learning model to predict likelihoods and set attributes, respectively. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, the machine learning models disclosed in the claims are solely used as a tool to perform the instructions of the abstract idea. Additionally, claims 4, 9, 10, 14 and 19 recite steps that further narrow the abstract idea. No additional elements are disclosed in the dependent claims that were not considered in independent claim 1. Therefore claims 2-10 and 12-19 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
Reasons Claims are Patentably Distinguishable from the Prior Art
Examiner analyzed claim 1 in view of the prior art on record and finds not all claim limitations are explicitly taught nor would one of ordinary skill in the art find it obvious to combine references with a reasonable expectation of success as discussed below.
Rajkhowa et al. (US 20190340561 A1) teaches a fulfillment optimization engine performs cross-load intermixing by integrating items from later orders into existing task queues while also optimizing picklists for efficient retrieval of items (see Abstract). Specifically, Rajkhowa discloses batching of the orders may be optimized as follows: items from a first customer order are assigned as a single batch. For each additional customer order the system uses proximity and capacity parameters to determine whether it is favorable to pick the items from the additional order separately or to add them into one of the already existing batches assigned to pickers. Put another way, the system determines whether a picker is physically “close enough” to the item (e.g. within a specified distance parameter), has the storage space available on their trolley/cart, and has the time to retrieve it while not violating the time constraints of the previous items the picker has been assigned to retrieve. The output of this approach is batch orders with maximum overlapping of items thereby increasing the density of pick-up points until the capacity of the tote/trolley for a picker is exhausted. Meta-heuristic methods are used to batch orders or work on a subset of orders and greedy and/or other methods are used to optimize the objective function within orders. In one embodiment, if multiple pickers are close enough and all have capacity and time, the system assigns the item from the additional order to the picker that is geographically closest to the item at some point during the performance of their picklist (see par. 0021) and for each item that the calculated distance met the threshold, and for which the fulfillment optimization engine identifies one or more workers that have capacity to add the one or more additional items to their existing batch, the fulfillment optimization engine determines whether the additional items can be retrieved without violating a completion time constraint for either the existing items or the one or more additional items (see par. 0044).
Chen et al. (US 20240220911 A1) teaches assignment engines configured to generate optimized assignments (see par. 0001). Specifically, Chen discloses generating and storing real-time data related to current activities for a driver. Real-time data can include, but is not limited to, current trip information, driver status, availability , and/or any other suitable real-time data (see par. 0052), a route optimization engine is configured to implement a route optimization process that optimizes driver-trip pairings for one or more features, such as, for example, total estimated travel time for all assigned deliveries (see par. 0059), a set of driver features, such as a set of driver historical behavior features and/or a set of driver real-time behavior features can be provided to one or more ETA adjustment engines configured to implement a trained machine learning model to generate an ETA adjustment based on the received set of features (see par. 0026-0027), a trained driver affinity model is configured to classify a selected driver-trip pair based on a set of historical driver reactions. A set of historical driver reactions can include, but is not limited to, accepting an offered trip, declining an offered trip, cancelling an accepted trip, completing an accepted trip, arriving late, arriving early, and/or any other suitable historical trip label (see par. 0098), a trained driver affinity model includes a trained machine learning model configured to generate a driver-trip affinity score that predicts the probability of a given driver, selected from the set of drivers, will accept an offer for a trip, selected from the set of trips(see par. 0108) and a specific trip is assigned to a specific driver and provided to the driver system by the route assignment system . A driver, through the driver system can select or decline an assigned trip provided by the route assignment system (see par. 0056).
Matsuoka et al. (US 20230060753 A1) teaches generating representative models configured for facilitating the functionality of the task-facilitation service (see par. 0002), Specifically, Matsuoka discloses the machine-learning models may include multiple machine-learning models each being configured to process a particular set of inputs, generate particular outputs, generate particular types of predictions, and/or the like. The machine-learning models may process input data according to a hierarchical design in which models may execute in a particular order. In some instances, when a set of input data indicative of a task is identified (e.g., with a likelihood that is greater than a threshold), data processing may temporary pause to prevent wasting processing resources and/or identifying too many possible tasks and overloading the member or representative). The one or more machine-learning models may also include classifiers that may predict a likelihood that a member will select a particular task. If the likelihood is greater than a threshold, then a task-specification according to the task may be generated. In some instances, the threshold may be set low (e.g., approximately 60%), to increase a quantity of possible tasks that can be provided to the member. The threshold may be dynamically determined based on a quantity of tasks that are being generated and the member feedback (see par. 0217-0218).
However, Rajkhowa, Chen and Matsuoka, individually or in combination, do not explicitly teach the combination of claim limitations as a whole as recited in independent claim 1. Specifically, predicting the likelihood a picker will finish servicing the set of existing orders within a threshold amount of time, wherein predicting the first likelihood is based at least in part on the progress of the picker and information describing the set of existing orders, determining whether the first likelihood exceeds a threshold likelihood and responsive to determining the first likelihood exceeds the threshold likelihood accessing a machine learning model trained to predict a second likelihood that the picker will accept a set of new orders for servicing while servicing the set of existing orders. Thus, claim is found to be distinguishable over the prior art. Claims 11 and 20 are distinguishable over the prior art for similar reasons as cited for claim 1. Dependent claims 2-10 and 12-19 are distinguishable because they depend on claims 1 and 11respectively.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Franey et al. (US 20220237530 A1) – Systems and methods for identifying an available product for an unavailable product to minimize a deviation or distance from an original planned pick path. A computer can transmit instructions corresponding to a path through a location where products are stored to collect a plurality of products stored at locations along the path. In response to receiving a notification for a particular product that the particular product is not at the location where it is stored, the computer can modify the path to include a location of a substitute product for the particular product. The computer can identify a substitute product for the particular product based on a distance from a location of the one more available products to a location along the path and determine a revised path with the substitute product. The path can be revised to include the location for the available product.
Esmalifalak et al. (US 20230027594 A1) – An industrial work order analysis system applies statistical and machine learning analytics to both open and closed work orders to identify problems and abnormalities that could impact manufacturing and maintenance operations. The analysis system applies algorithms to learn normal maintenance behaviors or characteristics for different types of maintenance tasks and to flag abnormal maintenance behaviors that deviate significantly from normal maintenance procedures. Based on this analysis, embodiments of the work order analysis system can identify unnecessarily costly maintenance procedures or practices, as well as predict asset failures and offer enterprise-specific recommendations intended to reduce machine downtime and optimize the maintenance process.
Rehn et al. (US 20200293988 A1) – Systems and methods related to providing delivery offers for use with a user interface. A method for providing delivery offers comprises receiving, from a mobile device, a request for one or more delivery tasks including a geographical area and a time frame, accessing a database storing delivery tasks, each delivery task associated with a status of fully assigned, partially assigned, or not assigned based on a comparison of a number of workers assigned to the task and a number of workers necessary to complete the task, determining which of the stored delivery tasks needing assignment have a delivery route in the received geographical area and time frame, selecting one or more delivery offers if a status of each determined delivery offers is equal to partially assigned or not assigned, and responding to the received request by transmitting the one or more selected delivery offers to the mobile device.
Rademaker et al. (CA 2831413 A1) – Systems and methods of processing orders are provided. In some embodiments, a retailer for fulfilling an order is chosen, and a predicted time of arrival for the customer to arrive at the retailer is determined. An order notification is transmitted to the retailer, which inserts the order into a preparation queue to be prepared and delivered to a pickup location by the predicted time of arrival. In some embodiments, a string of orders is created. A set of retailers capable of fulfilling orders for a set of products is determined. A set of pickup locations associated with the set of retailers is determined, and path.
Merkado et al. (CA 3177901 A1) – A server may, based on the selected retail store, select at least one available assignment in the selected retail store. Assignments may be selected randomly from a list of assignments, or selected based on user preferences and/or capabilities. In some embodiments, such as if retail store selection was not performed based on earning rate, server 135 may, at step 4106, determine an amount of time estimated to complete the selected at least one available assignment, and select assignments based on the amount of time estimated being less than an amount of available time provided by the user via a user device to server 135. The selected at least one available assignment may include, for example, purchasing an item at the selected retail store, taking a picture of a display at a store, checking inventory of an item at a store, interacting with store employees, or returning an item to a store.
Huq et al. (Profit and Satisfaction Aware Order Assignment for Online Food Delivery Systems Exploiting Water Wave Optimization) – In this paper, we develop a framework for optimal assignment of food delivery orders to workers as a multi-objective linear programming (MOLP) problem that makes a trade-off in between the worker profit and customer satisfaction. The results of simulation experiments depict that the WWOFooD offers competitive workers’ profit as well as significantly enhances customer satisfaction compared to the state-of-the-art works.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Crystol Stewart whose telephone number is (571)272-1691. The examiner can normally be reached 9:00am-5:00pm.
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/CRYSTOL STEWART/Primary Examiner, Art Unit 3624