Detailed Action
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 Application and Claims
This action is in reply to the application filed on 6/10/2025.
This communication is the first action on the merits.
IDS filed on 4/16/2026 is acknowledged and considered by the Examiner.
Claims 1-20 is/are currently pending and have been examined.
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).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-20 of U.S. Patent Application No. 17897045, US Patent US12354056. Although the claims at issue are not identical, they are not patentably distinct from each other because the Claims reciter the same inventive concept with the same features being used in the same field of endeavor.
Table 1:
Instant Application: 19212409
Claim 1, 11
Application: 17897045, US12354056
Claim 1, 12, 20
training a delivery prediction model to predict a delivery metric based on a plurality of training examples, wherein the delivery prediction model comprises a machine-learning model, and the delivery prediction model is trained based on a training process comprising, for each training example of the plurality of training examples:
training a delivery prediction model to generate one or more predicted delivery metrics based on a plurality of training examples, wherein the delivery prediction model comprises a neural network and wherein training the delivery prediction model based on a training example of the plurality of training examples comprises:
accessing the training example of the plurality of training examples, wherein the training example comprises a set of input features for the delivery prediction model and wherein the training example further comprises a label for the delivery metric;
accessing a training example of the plurality of training examples, wherein the training example comprises a set of input features for the delivery prediction model, wherein the set of input features comprise order data describing an order placed in the past by a user, and wherein the training example further comprises a label for each of one or more delivery metrics, wherein the one or more delivery metrics include: a predicted time to accept orders by one of a plurality of shoppers; or a predicted percentage of orders delivered after a stated delivery time;
applying the delivery prediction model to the set of input features to generate one or more predictions for the one or more delivery metrics;
comparing each of the one or more predictions to the corresponding label of the training example; and
applying the delivery prediction model to the set of input features to generate a prediction for the delivery metric;
applying the trained delivery prediction model to order data associated with the request to determine one or more predicted delivery metrics;
comparing the prediction to the label of the training example; and
comparing the one or more predicted delivery metrics with one or more thresholds;
updating a set of parameters of the machine-learning model of the delivery prediction model based on the comparison of the prediction to the label of the training example;
updating a set of parameters of the neural network of the delivery prediction model based on the comparison of the one or more predictions to the corresponding labels of the training example;
receiving, through a graphical user interface of a client application operating on a client device associated with a user, a request to place an order by the user;
receiving, through a graphical user interface of a client application operating on a client device associated with a user, a request to place an order by the user for fulfillment by a shopper;
applying the trained delivery prediction model to order data associated with the request to generate a predicted delivery metric;
applying the trained delivery prediction model to order data associated with the request to determine one or more predicted delivery metrics;
comparing the predicted delivery metric with a threshold;
comparing the one or more predicted delivery metrics with one or more thresholds;
determining to provide the arrival prediction service for the request based on whether the predicted delivery metric meets the threshold; and
determining to provide the arrival prediction service for the request based on whether the one or more predicted delivery metrics meet the one or more thresholds; and
responsive to determining to provide the arrival prediction service, providing the arrival prediction service for the request, wherein the arrival prediction service generates a predicted time window for delivery of the order, and wherein providing the arrival prediction service comprises:
responsive to determining to provide the arrival prediction service based on the one or more predicted delivery metrics meet the one or more thresholds, providing the arrival prediction service for the request, wherein the arrival prediction service guarantees delivery of the order within a time window, wherein providing the arrival prediction service comprises:
transmitting instructions to the client device associated with the user to update the graphical user interface of the client application to include a user interface element describing the time window, wherein the instructions further cause the client device to display the updated graphical user interface through a display of the client device.
transmitting instructions to the client device associated with the user to update the graphical user interface of the client application to include a user interface element describing the time window, wherein the instructions further cause the client device to display the updated graphical user interface through a display of the client device.
Claims 1-20 of the Instant Application are substantially similar to Claims 1-20 of U.S. Patent Application 17897045, US12354056.
The respective corresponding Dependent Claims recite substantially similar limitations and are therefore also obvious between the US Application and the Instant Application.
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 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 1 (similarly 11) recites,
training a delivery prediction model to predict a delivery metric based on a plurality of training examples, wherein the delivery prediction model comprises a … model, and the delivery prediction model is trained based on a training process comprising, for each training example of the plurality of training examples:
accessing the training example of the plurality of training examples, wherein the training example comprises a set of input features for the delivery prediction model and wherein the training example further comprises a label for the delivery metric;
applying the delivery prediction model to the set of input features to generate a prediction for the delivery metric;
comparing the prediction to the label of the training example; and
updating a set of parameters of the … model of the delivery prediction model based on the comparison of the prediction to the label of the training example;
receiving, through … a user, a request to place an order by the user;
applying the trained delivery prediction model to order data associated with the request to generate a predicted delivery metric;
comparing the predicted delivery metric with a threshold;
determining to provide the arrival prediction service for the request based on whether the predicted delivery metric meets the threshold; and
responsive to determining to provide the arrival prediction service, providing the arrival prediction service for the request, wherein the arrival prediction service generates a predicted time window for delivery of the order, and wherein providing the arrival prediction service comprises:
transmitting instructions to … the user to update the … describing the time window, wherein the instructions further cause the …. to display the updated ….
Analyzing under Step 2A, Prong 1:
The limitations regarding, …selectively providing an arrival prediction service.… training a delivery prediction model to predict a delivery metric based on a plurality of training examples, wherein the delivery prediction model comprises a … model, and the delivery prediction model is trained based on a training process comprising, for each training example of the plurality of training examples: accessing the training example of the plurality of training examples, wherein the training example comprises a set of input features for the delivery prediction model and wherein the training example further comprises a label for the delivery metric; applying the delivery prediction model to the set of input features to generate a prediction for the delivery metric; comparing the prediction to the label of the training example; and updating a set of parameters of the … model of the delivery prediction model based on the comparison of the prediction to the label of the training example; receiving, through … a user, a request to place an order by the user; applying the trained delivery prediction model to order data associated with the request to generate a predicted delivery metric; comparing the predicted delivery metric with a threshold; determining to provide the arrival prediction service for the request based on whether the predicted delivery metric meets the threshold; and responsive to determining to provide the arrival prediction service, providing the arrival prediction service for the request, wherein the arrival prediction service generates a predicted time window for delivery of the order, and wherein providing the arrival prediction service comprises: transmitting instructions to … the user to update the … describing the time window, wherein the instructions further cause the …. to display the updated…, under the broadest reasonable interpretation, can include a human using their mind and using pen and paper to perform the above identified limitations, therefore, the claims recite a mental process.
Further, …selectively providing an arrival prediction service.… training a delivery prediction model to predict a delivery metric based on a plurality of training examples, wherein the delivery prediction model comprises a … model, and the delivery prediction model is trained based on a training process comprising, for each training example of the plurality of training examples: accessing the training example of the plurality of training examples, wherein the training example comprises a set of input features for the delivery prediction model and wherein the training example further comprises a label for the delivery metric; applying the delivery prediction model to the set of input features to generate a prediction for the delivery metric; comparing the prediction to the label of the training example; and updating a set of parameters of the … model of the delivery prediction model based on the comparison of the prediction to the label of the training example; receiving, through … a user, a request to place an order by the user; applying the trained delivery prediction model to order data associated with the request to generate a predicted delivery metric; comparing the predicted delivery metric with a threshold; determining to provide the arrival prediction service for the request based on whether the predicted delivery metric meets the threshold; and responsive to determining to provide the arrival prediction service, providing the arrival prediction service for the request, wherein the arrival prediction service generates a predicted time window for delivery of the order, and wherein providing the arrival prediction service comprises: transmitting instructions to … the user to update the … describing the time window, wherein the instructions further cause the …. to display the updated…, are humans determining when and whether or not to provide human shoppers with arrival prediction service, which are, managing personal behavior or relationships or interactions between people, contractual relationships, therefore the claims recite certain methods of organizing human activities.
Accordingly, the claims recite a mental process, certain methods of organizing human activities, and thus, the claims are directed to an abstract idea under the first prong of Step 2A.
Analyzing under Step 2A, Prong 2:
This judicial exception is not integrated into a practical application under the second prong of Step 2A.
In particular, the claims recite the additional elements beyond the recited abstract idea identified under Step 2A, Prong 1, such as:
Claim 1, 11: machine-learning, graphical user interface of a client application operating on a client device associated with a user, graphical user interface of the client application to include a user interface element, updated graphical user interface through a display of the client device, A non-transitory computer-readable medium storing instructions that, when executed by a computer system, cause the computer system to perform operations
, and pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components.
Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer.
Additionally, with respect to, “…training…”, “…accessing…”, “…updating…”, “…receiving…”, “….providing…”, “…transmitting…”, “…display…”, these elements do not add a meaningful limitations to integrate the abstract idea into a practical application because they are extra-solution activity, pre and post solution activity - i.e. data gathering – “…training…”, “…accessing…”, “…receiving…”, data output –“…updating…”, “….providing…”, “…transmitting…”, “…display…”
Analyzing under Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B.
As noted above, the aforementioned additional elements beyond the recited abstract idea are not sufficient to amount to significantly more than the recited abstract idea because, as an order combination, the additional elements are no more than mere instructions to implement the idea using generic computer components (i.e. apply it).
Additionally, as an order combination, the additional elements append the recited abstract idea to well-understood, routine, and conventional activities in the field as individually evinced by the applicant’s own disclosure, as required by the Berkheimer Memo, in at least:
[0018] The client devices 110 are one or more computing devices capable of receiving user input as well as transmitting and/or receiving data via the network 120. In one or more embodiments, a client device 110 is a computer system, such as a desktop or a laptop computer. Alternatively, a client device 110 may be a device having computer functionality, such as a personal digital assistant (PDA), a mobile telephone, a smartphone, or another suitable device. A client device 110 is configured to communicate via the network 120. In one or more embodiments, a client device 110 executes an application allowing a user of the client device 110 to interact with the online concierge system 102. For example, the client device 110 executes a customer mobile application 206 or a shopper mobile application 212, as shown in FIG. 2 and is further described below in conjunction with FIGS. 4A and 4B, respectively, to enable interaction between the client device 110 and the online concierge system 102. As another example, a client device 110 executes a browser application to enable interaction between the client device 110 and the online concierge system 102 via the network 120. In another embodiment, a client device 110 interacts with the online concierge system 102 through an application programming interface (API) running on a native operating system of the client device 110, such as IOS® or ANDROID™.
[0019] A client device 110 includes one or more processors 112 configured to control operation of the client device 110 by performing functions. In various embodiments, a client device 110 includes a memory 114 comprising a non-transitory storage medium on which instructions are encoded. The memory 114 may have instructions encoded thereon that, when executed by the processor 112, cause the processor to perform functions to execute the customer mobile application 206 or the shopper mobile application 212 to provide the functions further described above in conjunction with FIGS. 4A and 4B, respectively.
[0020] The client devices 110 are configured to communicate via the network 120, which may comprise any combination of local area and/or wide area networks, using both wired and/or wireless communication systems. In one or more embodiments, the network 120 uses standard communications technologies and/or protocols. For example, the network 120 includes communication links using technologies such as Ethernet, 802.11, worldwide interoperability for microwave access (WiMAX), 3G, 4G, 5G, code division multiple access (CDMA), digital subscriber line (DSL), etc. Examples of networking protocols used for communicating via the network 120 include multiprotocol label switching (MPLS), transmission control protocol/Internet protocol (TCP/IP), hypertext transport protocol (HTTP), simple mail transfer protocol (SMTP), and file transfer protocol (FTP). Data exchanged over the network 120 may be represented using any suitable format, such as hypertext markup language (HTML) or extensible markup language (XML). In some embodiments, all or some of the communication links of the network 120 may be encrypted using any suitable technique or techniques.
[0021] One or more third-party systems 130 may be coupled to the network 120 for communicating with the online concierge system 102 or with the one or more client devices 110. In one or more embodiments, a third-party system 130 is an application provider communicating information describing applications for execution by a client device 110, or communicating data to client devices 110 for use by an application executing on the client device. In other embodiments, a third-party system 130 provides content or other information for presentation via a client device 110. For example, the third-party system 130 stores one or more web pages and transmits the web pages to a client device 110 or to the online concierge system 102. The third-party system 130 may also communicate information to the online concierge system 102, such as advertisements, content, or information about an application provided by the third-party system 130.
[0022] The online concierge system 102 includes one or more processors 142 configured to control operation of the online concierge system 102 by performing functions. In various embodiments, the online concierge system 102 includes a memory 144 comprising a non-transitory storage medium on which instructions are encoded. The memory 144 may have instructions encoded thereon corresponding to the modules further below that, when executed by the processor 142, cause the processor to perform the described functionality. For example, the memory 144 has instructions encoded thereon that, when executed by the processor 142, cause the processor 142 to determine attributes and attribute values for item categories. Additionally, the online concierge system 102 includes a communication interface configured to connect the online concierge system 102 to one or more networks, such as the network 120, or to otherwise communicate with devices (e.g., client devices 110) connected to the one or more networks.
[0023] One or more of a client device 110, a third-party system 130, or the online concierge system 102 may be special-purpose computing devices configured to perform specific functions as further described below, and may include specific computing components such as processors, memories, communication interfaces, and the like.
[0058] The predicted delivery metrics may be determined by any suitable computer models according to the particular metrics used in various embodiments. While TTA and late order % are metrics shown here, additional metrics and related models may be used in various embodiments. In addition, each metric may be calculated for respective geographic regions and in some cases may be based on respective computer models trained for each region that may thus learn the respective characteristics for that region
[0069] The foregoing description of the embodiments of the invention has been presented for the purpose of illustration; it is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Persons skilled in the relevant art can appreciate that many modifications and variations are possible in light of the above disclosure.
[0070] Some portions of this description describe the embodiments of the invention in terms of algorithms and symbolic representations of operations on information. These algorithmic descriptions and representations are commonly used by those skilled in the data processing arts to convey the substance of their work effectively to others skilled in the art. These operations, while described functionally, computationally, or logically, are understood to be implemented by computer programs or equivalent electrical circuits, microcode, or the like. Furthermore, it has also proven convenient at times, to refer to these arrangements of operations as modules, without loss of generality. The described operations and their associated modules may be embodied in software, firmware, hardware, or any combinations thereof.
[0071] Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In one or more embodiments, a software module is implemented with a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described.
[0072] Embodiments of the invention may also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, and/or it may comprise a general-purpose computing device selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a tangible computer readable storage medium, which include any type of tangible media suitable for storing electronic instructions and coupled to a computer system bus. Furthermore, any computing systems referred to in the specification may include a single processor or may be architectures employing multiple processor designs for increased computing capability.
[0073] Embodiments of the invention may also relate to a computer data signal embodied in a carrier wave, where the computer data signal includes any embodiment of a computer program product or other data combination described herein. The computer data signal is a product that is presented in a tangible medium or carrier wave and modulated or otherwise encoded in the carrier wave, which is tangible, and transmitted according to any suitable transmission method.
[0074] Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based hereon. Accordingly, the disclosure of the embodiments of the invention is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.
Furthermore, as an ordered combination, these elements amount to generic computer components receiving or transmitting data over a network, performing repetitive calculations, electronic record keeping, and storing and retrieving information in memory, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d).
Moreover, the remaining elements of dependent claims do not transform the recited abstract idea into a patent eligible invention because these remaining elements merely recite further abstract limitations that provide nothing more than simply a narrowing of the abstract idea recited in the independent claims.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components to “apply” the recited abstract idea, perform insignificant extra-solution activity, and generally link the abstract idea to a technical environment. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PO HAN (Max) LEE whose telephone number is (571) 272-3821. The examiner can normally be reached on Monday - Thursday, 9 AM-6:30 PM.
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/PO HAN LEE/Primary Examiner, Art Unit 3623
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