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 .
This non-final office action is responsive to Applicant’s filing of application No. 19/244,538 filed on June 20, 2025. Claims 1-20 are currently pending and under examination.
Double Patenting
The non-statutory 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 non-statutory double patenting rejection is appropriate where the claims at issue 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); and /n 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 a non-statutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321 (b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http:/Awww.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based e-Terminal Disclaimer may be filled out completely online using web-screens. An e-Terminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about e-Terminal Disclaimers, refer to
http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-l.jsp.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claims 1-20 are rejected on the ground of non-statutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12361361. Although the conflicting claims are not identical, they are not patentably distinct from each other because it is well settled that the omission of an element and its function is an obvious expedient if the remaining elements perform the same function as before". in re Karlson, 136 USPQ 184 (CCPA 1963).
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.
5. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Subject Matter Eligibility Standard
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
Claims 1-9 are directed to a method (i.e., a process), claims 10-15 are drawn to a computer-program product comprising a non-transitory computer readable storage medium, and claims 16-20 are drawn to a system. As such, claims 1-20 are drawn to one of the statutory categories of invention.
The claim limitations in the abstract idea have been highlighted in non-bold and the “additional elements” in bold below.
Claim 1 as a representative claim recites:
receiving, from a user interface presented on a client device, a request to view a target item for inclusion into an order; accessing a plurality of candidate pricing policies;
applying an outcome model to predict an outcome when each particular candidate pricing policy is applied to an item category that the target item is part of, wherein the outcome model is a machine-learning model trained by a process comprising:
generating a plurality of training examples from data about orders previously fulfilled, each training example including (1) a combination of a pricing policy and a category to which the pricing policy was applied and (2) a label corresponding to an outcome when the pricing policy was applied to the category; and
training the outcome model with the plurality of training examples;
selecting one of the candidate pricing policies based on the predicted outcomes for the candidate pricing policies; and
in response to the request received from the user interface presented on the device to view the target item, presenting on the user interface of the client device a price associated with the target item by applying the markup corresponding to the selected pricing policy for the category that contains the target item.
Claim 2 further recites wherein the process for training the outcome model further comprises:
applying the outcome model to each combination of the pricing policy and the category to which the pricing policy was applied to predict an outcome for the order;
scoring the predicted outcomes for the training examples based on the labels corresponding to the actual outcomes; and
updating one or more parameters of the outcome model based on the scoring.
Claim 3 further recites applying the outcome model to predict an outcome when each particular candidate pricing policy is applied to one or more other item categories, wherein selecting the pricing policy comprises selecting a set of combinations of item categories and pricing policies that maximizes predicted outcomes across item categories.
Claim 4 further recites adjusting the pricing policy to enforce one or more constraints applied to the item category.
Claim 5 further recites wherein adjusting the pricing policy comprises limiting application of the pricing policy to a threshold number of item categories.
Claim 6 further recites wherein adjusting the pricing policy comprises limiting an adjustment of price for items in the item category that the pricing policy is applied to.
Claim 7 further recites wherein adjusting the pricing policy comprises limiting an average adjustment of price for items in the item category that the pricing policy is applied to.
Claim 8 further recites wherein the average adjustment across each item category is determined by weighting a markup applied by a target revenue for the corresponding item category.
Claim 9 further recites receiving, from the user interface, user interaction with the target item;
determining an actual outcome for the markup applied to the price of the target item;
scoring the actual outcome against the predicted outcome output by the outcome model; and
retraining the outcome model based on the scoring.
Claim 10 recites a computer-program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor;:
receiving, from a user interface presented on a client device, a request to view a target item for inclusion into an order;
accessing a plurality of candidate pricing policies;
applying an outcome model to predict an outcome when each particular candidate pricing policy is applied to an item category that the target item is part of, wherein the outcome model is a machine-learning model trained by a process comprising:
generating a plurality of training examples from data about orders previously fulfilled, each training example including (1) a combination of a pricing policy and a category to which the pricing policy was applied and (2) a label corresponding to an outcome when the pricing policy was applied to the category; and
training the outcome model with the plurality of training examples;
selecting one of the candidate pricing policies based on the predicted outcomes for the candidate pricing policies; and
in response to the request received from the user interface presented on the client device to view the target item, presenting on the user interface of the client device a price associated with the target item by applying the markup corresponding to the selected pricing policy for the category that contains the target item.
Claim 11 further recites wherein the process for training the outcome model further comprises:
applying the outcome model to each combination of the pricing policy and the category to which the pricing policy was applied to predict an outcome for the order;
scoring the predicted outcomes for the training examples based on the labels corresponding to the actual outcomes; and
updating one or more parameters of the outcome model based on the scoring.
Claim 12 further recites applying the outcome model to predict an outcome when each particular candidate pricing policy is applied to one or more other item categories, wherein selecting the pricing policy comprises selecting a set of combinations of item categories and pricing policies that maximizes predicted outcomes across item categories.
Claim 13 further recites adjusting the pricing policy to enforce one or more constraints applied to the item category.
Claim 14 further recites wherein adjusting the pricing policy comprises:
limiting application of the pricing policy to a threshold number of item categories;
limiting an adjustment of price for items in the item category that the pricing policy is applied to; or
limiting an average adjustment of price for items in the item category that the pricing policy is applied to, wherein the average adjustment across each item category is determined by weighting a markup applied by a target revenue for the corresponding item category.
Claim 15 further recites receiving, from the user interface, user interaction with the target item;
determining an actual outcome for the markup applied to the price of the target item;
scoring the actual outcome against the predicted outcome output by the outcome model; and
retraining the outcome model based on the scoring.
Claim 16 recites one or more processors;
and a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by the one or more processors:
receiving, from a user interface presented on a client device, a request to view a target item for inclusion into an order;
accessing a plurality of candidate pricing policies;
applying an outcome model to predict an outcome when each particular candidate pricing policy is applied to an item category that the target item is part of, wherein the outcome model is a machine-learning model trained by a process comprising:
generating a plurality of training examples from data about orders previously fulfilled, each training example including (1) a combination of a pricing policy and a category to which the pricing policy was applied and (2) a label corresponding to an outcome when the pricing policy was applied to the category; and
training the outcome model with the plurality of training examples;
selecting one of the candidate pricing policies based on the predicted outcomes for the candidate pricing policies; and
in response to the request received from the user interface presented on the client device to view the target item, presenting on the user interface of the client device a price associated with the target item by applying the markup corresponding to the selected pricing policy for the category that contains the target item.
Claim 17 further recites wherein the process for training the outcome model further comprises:
applying the outcome model to each combination of the pricing policy and the category to which the pricing policy was applied to predict an outcome for the order;
scoring the predicted outcomes for the training examples based on the labels corresponding to the actual outcomes; and
updating one or more parameters of the outcome model based on the scoring.
Claim 18 further recites applying the outcome model to predict an outcome when each particular candidate pricing policy is applied to one or more other item categories, wherein selecting the pricing policy comprises selecting a set of combinations of item categories and pricing policies that maximizes predicted outcomes across item categories.
Claim 19 further recites adjusting the pricing policy to enforce one or more constraints applied to the item category.
Claim 20 further recites wherein adjusting the pricing policy comprises:
limiting application of the pricing policy to a threshold number of item categories;
limiting an adjustment of price for items in the item category that the pricing policy is applied to; or
limiting an average adjustment of price for items in the item category that the pricing policy is applied to, wherein the average adjustment across each item category is determined by weighting a markup applied by a target revenue for the corresponding item category.
Step 2A, Prong One:
Regarding claims 1, 10 and 16, other than reciting an user interface, a client device, a machine-learning model (claim 1), computer-program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, executed by a processor, user interface, a client device, a machine-learning model (claims 10 and 16), the claim limitations merely cover commercial interactions, including business relations, thus falling within the "Certain Methods of Organizing Human Activity" grouping of abstract ideas. Applicant is directed to In re Grams, 888 F .2d 835, 837 n.1 (Fed. Cir. 1989) in stating that ("Words used in a claim operating on data to solve a problem can serve the same purpose as a formula."); see also Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1354 (Fed. Cir. 2016) (noting that analyzing information by steps people go through in their minds, or by mathematical algorithms, without more, are essentially mental processes. Applicant is also directed to the 84 Fed. Reg. at 52 (listing exemplary mental processes including observations, evaluations, and judgments.
The claim limitations of the dependent claims also fall within the "Certain Methods of Organizing Human Activity" grouping of abstract ideas. Thus the dependent claims recite an abstract idea.
Under Step 2A Prong Two, the eligibility analysis evaluates whether the claims as a whole integrates the recited judicial exception into a practical application of the exception. This judicial exception is not integrated into a practical application. The claims include user interface, a client device, a machine-learning model, and a processor, which are recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, the 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. As a result, the claims are directed to an abstract idea.
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 a user interface, a client device, a machine-learning model, and a processor amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The dependent claims described above do not recite additional limitations that are sufficient to amount to significantly more than the abstract idea. A more detailed abstract idea remains an abstract idea.
Under step 2B of the analysis, the claims include, inter alia, one or more processors, a client and a mobile device.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
There isn't any improvement to another technology or technical field, or the functioning of the computer itself. Moreover, individually, there are not any meaningful limitations beyond generally linking the abstract idea to a particular technological environment, i.e., implementation via a computer system. Further, taken as a combination, the limitations add nothing more than what is present when the limitations are considered individually. There is no indication that the combination provides any effect regarding the functioning of the computer or any improvement to another technology.
In addition, as discussed in Paragraph 0094 of the specification, " 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 some embodiments, a software module is implemented with a computer program product comprising one or more computer-readable media storing computer program code or instructions, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described. In some embodiments, a computer-readable medium comprises one or more computer-readable media that, individually or together, comprise instructions that, when executed by one or more processors, cause the one or more processors to perform, individually or together, the steps of the instructions stored on the one or more computer-readable media. Similarly, a processor comprises one or more processors or processing units that, individually or together, perform the steps of instructions stored on a computer-readable medium.". As such, this disclosure supports the finding that no more than a general purpose computer, performing generic computer functions, is required by the claims.
Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank Int'/ et al., No. 13-298 (U.S. June 19, 2014).
As a result of the above analysis, claim 1, as well as claims 10 and 16, do not appear to be patent eligible under 101.
Dependent claims 2-9, 11-15 and 17-20 recite additional elements that merely narrow the previously recited abstract idea. When viewed as a whole, the additional elements amount to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)).
NOTE: Currently there are no outstanding prior art rejections under 35 USC § 102 or 35 USC§ 103.
Claims 1-20 would be allowable if overcome the 35 USC § 101 rejection.
Regarding claims 1, 12 and 20, prior art of record fails to teach or suggest:
“training the outcome model with the plurality of training examples; selecting one of the candidate pricing policies based on the predicted outcomes for the candidate pricing policies; and in response to the request received from the user interface presented on the client device to view the target item, presenting on the user interface of the client device a price associated with the target item by applying the markup corresponding to the selected pricing policy for the category that contains the target item” as recited in claims 1 and 16.
“training the outcome model with the plurality of training examples; selecting one of the candidate pricing policies based on the predicted outcomes for the candidate pricing policies; and in response to the request received from the user interface presented on the client device to view the target item, presenting on the user interface of the client device a price associated with the target item by applying the markup corresponding to the selected pricing policy for the category that contains the target item” as recited in claim 10.
Asaria et al (US Application 20130317642) teach a warehouse employee walks through a warehouse to pick various products in a combined pick list. By the time they have picked all the products on the combined pick list, they will have the products necessary to fulfil the orders that were used to generate the combined pick list. Asaria further teaches an itinerary generation module that determines a sequence for picking the products on the list based upon a warehouse information.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ahmann (WO2018068026 A1) teaches a successful application of an
automation of a picker-to-goods vehicle to a fully automated order fulfillment system where the goods the picker takes from the warehouse shelves in an aisle are automatically transported from that pick location to their ultimate warehouse destination, thus allowing the picker to proceed immediately to the next pick location as opposed to delivering the picked item to a minimum of the end of the aisle as current implementations dictate has, heretofore, not been addressed and that is the foundation of the present invention's solution. This includes the automated retrieval of said goods from the picker's location and the software system to coordinate all the required actions in an optimal sequence.
Jain (US Publication No. 2020/0118061) teaches a method and system for
determining the optimal packing sequence, maximum capacity utilization of the transport units is achieved.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Romain Jeanty whose telephone number is (571) 272-6732. The examiner can normally be reached M-F 9:00AM to 5:30PM.
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/RJ/
/ROMAIN JEANTY/Primary Examiner, Art Unit 3624
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