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 .
Drawings
The drawings as submitted by Applicant on 04/04/2024 have been accepted by the examiner.
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 towards an abstract idea without significantly more or transforms the abstract idea into a practical application.
The independent claim 1 is directed towards scheduling pickers in accordance with shopper preferences, under broadest reasonable interpretation, are directed towards an abstract idea through the system merely gathering data (shopper preference data), generally analyzing the data (applying prediction model), and displaying results based on the analysis (providing a response to the request to the interface of the client device of the fulfillment agent). All the elements are directed towards the abstract idea of “certain method of organizing human activity” where the system is merely determining scheduling availability for a service provider (fulfillment agent) based on customer preferences. The analysis techniques are merely generic analysis techniques that are applying a general purpose business method to a computer. That general business method being the Scheduling of a worker based on predetermined and historical preferences. The independent claims are merely providing this business practice using generic analysis techniques (comparing, matching, and other various criterion) that are performed in determining a work schedule.
Step 2(a)(II) analysis considers the additional elements of the independent claim as to whether or not they are directed towards a practical application. The additional elements of “client device”, and an “interface”. The additional elements are described in Figs 1A, and at least paragraph [0064] in the originally filed specification. Based on the paragraphs and figures, there is no indication that the additional elements are integrating the abstract idea into a practical application. These technological elements are merely applying the abstract idea using the computer/system as a tool for the abstract idea (MPEP 2106.05(f)). Originally filed specification further discusses the processor in terms of standard components of a processor which further emphasizes that the additional elements are merely generic elements.
Step 2(b) analysis considers the additional elements in regards to being significantly more than the abstract idea identified. The additional elements of “client device” and a “interface”. The additional elements are described in Figs 1A, and at least paragraph [0064] in the originally filed specification. Based on the paragraphs and figures, as well as the analysis above, the additional elements are not significantly more than the abstract idea as they recite general links to a field of use and merely using the additional elements as a tool to implement the abstract idea (MPEP 2106.05(f) and 2106.05(h)).
The examiner further submits that the machine learning technique is described in the originally filed specification [0064], however, the description does not provide any specific examples or algorithms that show the machine learning technique is more than generically linking the additional element to a field of use (trained prediction model). The examiner submits that the machine learning is a generic recitation of an analysis and determination technique that provides no indication of what specific technique is being used within the system. Machine learning is merely an umbrella term that includes specific and non-specific (generic) techniques, and because broadest reasonable interpretation includes these generic techniques, then the determination using machine learning is a generic analysis technique.
Merely having the determination based on training the model does not preclude the technique to be indicative of applying the additional element in some meaningful way.
Machine learning includes generic and specific techniques, and without further discussion provided in the originally filed specification, then the broadest reasonable interpretation like that of exemplary independent claim 1 is merely generic analysis techniques that are not indicative of a practical application or significantly more than the abstract idea identified.
Similarly recited independent claim 11, as well as dependent claims 2-10 and 12-20 are directed towards an abstract idea without significantly more or transformative into a practical application. Therefore, claims 1-20 are rejected under 35 U.S.C. 101 for being directed towards non-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 Krishnan et al (US 2024/0144191) in view of Kassam (US 2022/0101240).
Regarding claim 1, the prior art discloses a computer-implemented method comprising:
receiving a request from a client device of a fulfillment agent (see at least paragraph [0069] to Krishnan et al, wherein there is a request for the picker to build their own schedule);
based on receipt of the request, applying a trained prediction model to each of a plurality of discretized time slots of a time period to predict a likelihood that the fulfillment agent receives, during the discretized time slot (see at least paragraph [0057] to Krishnan et al, wherein the earnings module 250 may then apply the order availability model to predict the likelihood for each time slot-location pair included in a set of time slot-location pairs described by availability information for a picker), and training the trained prediction model based on order histories of the one or more requesting users that have favorited the fulfillment agent (see at least paragraph [0053] to Krishnan et al, wherein the machine learning training module 230 may train the order availability model via supervised learning based on order data describing orders previously placed with the online concierge system 140 stored in the data store 240); generating an interface displaying the plurality of discretized time slots with a visual indication for each discretized time slot based on the predicted likelihood for the discretized time slot; and providing, in response to the request, the interface to the client device of the fulfillment agent (see at least paragraph [0080] to Krishnan et al, wherein the suggested schedule 440 may be sent 350 to the picker client device 110 in association with a map generated by the online concierge system 140… the map generated by the online concierge system 140 may correspond to a heat map, in which the amounts of earnings for different locations 420 depicted in the heat map are represented visually (e.g., with different colors, shades, etc.).
Krishnan et al does not appear to explicitly disclose a favorite order from a requesting user that has favorited the fulfillment agent, wherein the trained prediction model is trained by: retrieving a profile for the fulfillment agent comprising a list of one or more requesting users that have favorited the fulfillment agent.
However, Kassam discloses a shopping system and method with improved matching, further comprising a favorite order from a requesting user that has favorited the fulfillment agent, wherein the trained prediction model is trained by: retrieving a profile for the fulfillment agent comprising a list of one or more requesting users that have favorited the fulfillment agent (see at least paragraphs [0063] and [0068] to Kassam, wherein a customer may select a preferred picker and the picker has an associated profile).
The examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). The examiner submits that the combination of the teaching of the system and method for generating a schedule for a picker of an online concierge system based on an earnings goal and availability information, as disclosed by Krishnan et al and the shopping system and method with improved matching, as taught by Kassam, in order to increase the probability of increasing customer satisfaction and delivery order fulfillment outcomes (see at least paragraph [0038] to Kassam) could have been readily and easily implemented, with a reasonable expectation of success. As such, the aforementioned combination is found to be obvious to try, given the state of the art at the time of filing.
Regarding claim 2, the prior art discloses the method of claim 1, wherein generating the interface displaying the plurality of discretized time slots comprises displaying the likelihood for each discretized time slot (see at least paragraph [0053] to Krishnan et al, wherein the earnings module 250 accesses an order availability model that is trained to predict a likelihood that an order placed with the online concierge system 140 will be available for a picker to service for a time slot).
Regarding claim 3, the prior art discloses the method of claim 1, wherein generating the interface displaying the plurality of discretized time slots comprises: applying a thresholding filter to the likelihood of each discretized time slot to determine a display color as the visual indication (see at least paragraph [0037] to Krishnan et al, wherein the content presentation module 210 may filter out items from presentation to a customer based on whether the predicted availability of the item exceeds a threshold); and generating a heat map with the plurality of discretized time slots colored according to the determined display colors (see at least paragraph [0066] to Krishnan et al, wherein there is a heatmap represented visually).
Regarding claim 4, the prior art discloses the method of claim 1, wherein generating the interface displaying the plurality of discretized time slots comprises: identifying one or more of the discretized time slots as optimal time slots having highest likelihoods; and visually distinguishing the optimal time slots in the scheduling interface (see at least paragraph [0038] to Krishnan et al, wherein The content presentation module 210 also may present a scheduling interface for generating a suggested schedule for a picker based on information received from a picker client device 110 associated with the picker).
Regarding claim 5, the prior art discloses the method of claim 1, wherein generating the interface displaying the plurality of discretized time slots comprises: generating an option for each discretized time slot that, when selected, provides an indication that the fulfillment agent is available during that discretized time slot (see at least paragraph [0076] to Kassam et al, wherein if the consumer wishes to select a picker (step 709) then the application displays a GUI (such as GUI 400 and optionally one or more of window 500 and window 600) of available pickers (step 710) to allow selection of a picker. Upon receiving selection of a picker (step 712) the application 114 then associates the selected picker with the cart (step 716)).
Regarding claim 6, the prior art discloses the method of claim 1, wherein the trained prediction model is further trained by: retrieving user preference data for the fulfillment agent based on historical orders fulfilled for one or more requesting users during discretized time slots in prior periods, wherein at least one of the requesting users has favorited the fulfillment agent; and training the prediction model with the user preference data to predict the likelihood that the fulfillment agent is favorited by a requesting user upon completion of an order request during a discretized time slot (see at least paragraph [0053] to Krishnan et al, wherein the machine learning training module 230 may receive a set of training examples including attributes of time slot-location pairs associated with orders previously placed with the online concierge system 140. In this example, the machine learning training module 230 also may receive a label for each time slot-location pair indicating whether an order placed with the online concierge system 140 was available for a picker to service for the corresponding time slot-location pair).
Regarding claim 7, the prior art discloses the method of claim 1, wherein the trained prediction model is further trained by: retrieving other historical orders fulfilled by other fulfillment agents in one or more retailer locations where the fulfillment agent has also fulfilled historical orders, wherein the other historical order indicate which other historical order was favorited by the corresponding requesting user; and training the prediction model with the other historical orders. training the prediction model with the other historical orders (see at least paragraph [0052] to Krishnan et al, wherein the machine learning training module 230 trains a machine learning model based on a set of training examples. Each training example includes input data to which the machine learning model is applied to generate an output. For example, each training example may include customer data, picker data, item data, or order data. In some cases, the training examples also include a label which represents an expected output of the machine learning model. In these cases, the machine learning model is trained by comparing its output from input data of a training example to the label for the training example).
Regarding claim 8, the prior art discloses the method of claim 1, further comprising:
receiving requesting user feedback on one or more new orders during a first discretized time slot in the time period fulfilled by the fulfillment agent, wherein at least one new order is favorited by the corresponding requesting user; and retraining the trained prediction model with the one or more new orders and the likelihood of the first discretized time slot predicted by the trained prediction
model (see at least paragraph [0032] to Krishnan et al, wherein Order data may further include information describing how the order was serviced, such as which picker serviced the order, when the order was delivered, or a rating that the customer gave the delivery of the order).
Regarding claim 9, the prior art discloses the method of claim 1, wherein the request from the client device requests to set an availability schedule for the time period (see at least Figure 4A to Krishnan et al).
Regarding claim 10, the prior art discloses the method of claim 9, further comprising:
receiving, via the interface, an availability selection from the client device of the fulfillment agent indicating that the fulfillment agent is available for one or more of the discretized time slots; and
updating an availability schedule for the time period to reflect the availability of the fulfillment agent during the one or more discretized time slots (see at least paragraph [0076] to Kassam, wherein if the consumer wishes to select a picker (step 709) then the application displays a GUI (such as GUI 400 and optionally one or more of window 500 and window 600) of available pickers (step 710) to allow selection of a picker. Upon receiving selection of a picker (step 712) the application 114 then associates the selected picker with the cart (step 716)).
Claims 11-20 each contain recitations substantially similar to those addressed above and, therefore, are likewise rejected.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
The examiner has considered all references listed on the Notice of References Cited, PTO-892.
The examiner has considered all references cited on the Information Disclosure Statement submitted by Applicant, PTO-1449.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TALIA F CRAWLEY whose telephone number is (571)270-5397. The examiner can normally be reached on Monday thru Thursday; 8:30 AM-4:30 PM 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, Fahd A Obeid can be reached on 571-270-3324. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TALIA F CRAWLEY/ Primary Examiner, Art Unit 3627