DETAILED ACTION
Status of Claims
Claims 1-20 are currently pending and have been examined.
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 .
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 without significantly more. The claims recite an abstract idea. This judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Under Step 1 of the eligibility analysis the claims are directed to statutory categories. MPEP 2106.03. Specifically, the method, as claimed in claims 1-10, is directed to the process. Additionally, the computer program product, as claimed in claims 11-19 is directed to an apparatus. Finally, the system, as claimed in claim 20, is directed to a machine.
While the claims fall within statutory categories, under Step 2A, Prong 1 of the eligibility analysis (MPEP 2106.04), the claimed invention recites the abstract idea of providing product information to a user. Specifically, representative claim 1 recites the abstract idea of:
Retrieving a set of user data for a user;
Predict a measure of preference of the user associated with an item category by: receiving user data for a plurality of users, receiving, for each user of the plurality of users, a label describing the measure of preference of a corresponding user associated with the item category, and training based at least in part on the user data and the label for each user of the plurality of users;
Predict the measure of preference of the user associated with the item category based at least in part on the set of user data for the user;
Receiving, from a user client associated with the user, an order comprising a set of items, wherein the set of items comprise and item included in the item category;
Generating a prompt that comprises: the predicted measure of preference of the user associated with the item category, and a request to generate an image of the item that is consistent with the predicted measure of preference f the user associated with the item category;
Providing the prompt to a obtain an output;
Extracting, form the output the image of the item that is consistent with the predicted measure of preference of the user associated with the item category; and
Sending the image of the item to a picker client associated with a picker to which the order is assigned, wherein sending the image of the item to the picker client causes the picker client to display a set of instruction to collect the item to service the order.
Under Step 2A, Prong 1 of the eligibility analysis, it is necessary to evaluate whether the claim recites a judicial exception by referring to subject matter groupings enumerated in MPEP 2106.04(a). The abstract idea identified above is considered to be a certain method of organizing human activity. Certain methods of organizing human activity include “fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).”” MPEP 2106.04(a)(2)(II). In this case, the abstract idea recited in representative claim 1 is a certain method of organizing human activity because receiving a set of user data for a user, accessing data to predict measure of preference of user associated with an item category, receiving an order form a client for items that are included in the item category, generating a prompt based on that information, and sending an image to a picker of what item to pick based on the preference information of the item category is a commercial or legal interaction because it is a sales activity and/or relates to business relations. Thus, representative claim 1 recites an abstract idea.
Under Step 2A, Prong 2 of the eligibility analysis, if it is determined that the claims recite a judicial exception, it is then necessary to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of that exception. MPEP 2106.04(d). The courts have identified limitations that did not integrate a judicial exception into a practical application include limitations merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). MPEP 2106.04(d). In this case, representative claim 1 includes additional elements such as a computer system comprising a processor and a computer-readable medium, an online system, a machine learning model, a user client device, a generative artificial intelligence model, a picker client device. Although reciting such additional elements, the additional elements do not integrate the abstract idea into a practical application because they merely amount to no more than an instruction to apply the abstract idea using a generic computer or merely use a computer as a tool to perform the abstract idea. These additional elements are described at a high level in Applicant's specification without any meaningful detail about their structure or configuration. Similar to the limitations of Alice, representative claim 1 merely recites a commonplace business method (i.e., providing item preference information) being applied on a general-purpose computer. See MPEP 2106.05(f). Thus, the claimed additional elements are merely generic elements and the implementation of the elements merely amounts to no more than an instruction to apply the abstract idea using a generic computer. Since the additional elements merely include instructions to implement the abstract idea on a generic computer or merely use a generic computer as a tool to perform an abstract idea, the abstract idea has not been integrated into a practical application.
Under Step 2B of the eligibility analysis, if it is determined that the claims recite a judicial exception that is not integrated into a practical application of that exception, it is then necessary to evaluate the additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). MPEP 2106.05. In this case, as noted above, the additional elements recited in independent claim 1 are recited and described in a generic manner merely amount to no more than an instruction to apply the abstract idea using a generic computer or merely use a generic computer as a tool to perform an abstract idea.
Even when considered as an ordered combination, the additional elements of representative claim 1 do not add anything that is not already present when they considered individually. In Alice, the court considered the additional elements “as an ordered combination,” and determined that “the computer components...‘ad[d] nothing. ..that is not already present when the steps are considered separately’... [and] [v]iewed as a whole...[the] claims simply recite intermediated settlement as performed by a generic computer.” Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 217, (2014) (citing Mayo, 566 U.S. at 79, 101 USPQ2d at 1972). Similarly, when viewed as a whole, representative claim 1 simply conveys the abstract idea itself facilitated by generic computing components. Therefore, under Step 2B of the Alice/Mayo test, there are no meaningful limitations in representative claim 1 that transforms the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself.
As such, representative claim 1 is ineligible.
Dependent Claims 2-10 do not aid in the eligibility of independent claim 1. For example, claims 2-10 merely further define the abstract limitations of claim 1. Dependent claims 2-10 do not recite additional elements supplemental those recited in claim 1. Therefore, the additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea for the reasons described above with respect to claim 1.
Thus, dependent claims 2-10 are also ineligible.
Independent claims 11 and 20recite the same abstract idea represented in representative claim 1. In addition to the additional elements of claim 1, Independent Claim 11 recites the additional elements of a computer program product comprising a non-transitory computer-readable storage medium. In addition to the additional elements of claim 1, Independent Claim 20 recites a non-transitory computer readable storage medium. The additional elements in Independent claims 11 and 20 do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea for the reasons described above with respect to claim 1.
Similarly, the dependent claims 12-19 do not recite additional elements supplemental those recited in claims 2-10. Therefore, the additional elements to not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea for the reasons described above with respect to claims 2-10, respectively.
Thus, dependent claims 12-19 are also ineligible.
Subject Matter Free From Prior Art
Claims 1, 11, and 20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. Claims 2-10 and 12-19 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims. The following is a statement of reasons for the indication of subject matter free from prior art:
The Examiner hereby asserts that the totality of the evidence neither anticipates nor renders obvious the particular combination of elements as claimed. That is, the Examiner emphasizes the claims as a whole and hereby asserts that the totality of the evidence fails to set forth, either explicitly or implicitly, an appropriate rationale for combining or otherwise modifying the available prior art to arrive at the claimed invention. The combination of features as claimed would not be obvious to one of ordinary skill in the art because any combination of the evidence at hand to reach the combination of features as claimed would require a substantial reconstruction of Applicant’s claimed invention relying on improper hindsight bias. With respect to Independent Claims 1, 11, and 20, the prior art of record, alone or combined, neither teaches nor renders obvious the limitations, as a whole, comprising:
Accessing a machine-learning model trained to predict a measure of preference of the user associated with an item category, wherein the machine-learning model is trained by: receiving user data for a plurality of users of the online system, receiving, for each user of the plurality of users, a label describing the measure of a preference of a corresponding user associated with the item category, and training the machine-learning model based at least in part on the user data and the label for each user of the plurality of users, … generating a prompt that comprises: the predicted measure of preference of the user associated with the item category, and a request to generate an image of the item that is consistent with the predicted measure of preference of the user associated with the item category;… extracting, from the output, the image of the item that is consistent with the predicted measure of preference of the user associated with the item category; and sending the image of the item to a picker client device associated with a pricker to which the order is assigned… display the image of the item in association with a set of instructions to collect the item to service the order.
One piece of pertinent prior art is Wadhwa et al. (US 2022/0245699) disclosing a system and method for determining, using a price band prediction model of a machine learning architecture, price bands for an item type category, system and method for determining , using a price band prediction model of a machine learning architecture, price bands for an item type category, analyzing using an affinity prediction model of the machine learning architecture, price band activity data indicating interactions of a user with respective items associated and included in each of the price bands for the item type category, and generating, using the affinity prediction model of the machine learning architecture, one or more price affinity predictions for the user based, at least in part, on the price band activity data, wherein the one or more price affinity predictions predict a preference of the user for respective items associated with one or more of the price bands. See paragraphs [0024]-[0025], [0104]. Another piece of pertinent prior art is Doken et al. (US 2025/0159276) disclosing that a server generating a new image using generative artificial intelligence based on data extracted from a video content item, such as generated or externally accessed video transcripts or synopses. See at least paragraph [0043]. Another piece of pertinent prior art is Jaffery et al. (US 11,756,106) disclosing ordering of product or contents items as per current trends or content items that are shoe (or other) products, may change based on the use of machine learning logic for determining inferences about a user 117. The system 110, using machine learning logic associated with logic module 122 can predict or determine certain content item categories that align with one or more preferences of user 117 based on analysis of user preferences data about user 117. See col 7, lns 29-37. Another piece of pertinent prior art is Tate et al. (US 2022/0044299) disclosing that based on a received query from a client device, the system retrieves from a database a set of items related to the query and assigns each item to a product category that maps items to product categories. The assigned each item is input into a prediction model trained to predict a probability that an item is available at a warehouse location, and determining if a product category has low availability based on predicted probabilities for items. See Abstract. Another piece of pertinent prior art is Scaff et al. (US 20250054044) disclosing a method automatically extracting attributes of a seed clothing item from an image of the clothing item, automatically generating prompt for input to the generative intelligence based on the seed clothing item and allowing a user to interact with a single interactive element to purchase multiple clothing items that are arranged by the generative artificial intelligence into the outfit. Another piece of pertinent prior art is NPL: “Future of grocery retail shopping: challenges and opportunities in e-commerce grocery shopping” (Chua, C.S. and Yoo, C.A., Future of grocery retail shopping: Challenges and opportunities in e-commerce grocery shopping, 2018, Doctoral dissertation, Massachusetts Institute of Technology, pps. 1-31.) disclosing consumer problem of requesting perishable products being purchased that are not picked fresh enough by the picker compared to how they would self-pick themselves and being able to choose features of the item such as shape, color, thickness, marbling, etc. See pages 16, 19, 23.
However, none of these pieces of prior art, nor any others, expressly provide for Accessing a machine-learning model trained to predict a measure of preference of the user associated with an item category, wherein the machine-learning model is trained by: receiving user data for a plurality of users of the online system, receiving, for each user of the plurality of users, a label describing the measure of a preference of a corresponding user associated with the item category, and training the machine-learning model based at least in part on the user data and the label for each user of the plurality of users, … generating a prompt that comprises: the predicted measure of preference of the user associated with the item category, and a request to generate an image of the item that is consistent with the predicted measure of preference of the user associated with the item category;… extracting, from the output, the image of the item that is consistent with the predicted measure of preference of the user associated with the item category; and sending the image of the item to a picker client device associated with a pricker to which the order is assigned… display the image of the item in association with a set of instructions to collect the item to service the order.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRITTANY E BARGEON whose telephone number is (571)272-2861. The examiner can normally be reached Monday-Friday 9:00am to 6:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey A Smith can be reached at (571) 272-6763. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/B.E.B/Examiner, Art Unit 3688
/Jeffrey A. Smith/Supervisory Patent Examiner, Art Unit 3688
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