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 .
Status of Claims
This action is in response to amendment filed on 5 March 2025. Claims 1, 2, 10, 13, 14, 18 and 20 have been amended. Claims 1-20 are currently pending and have been examined.
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 claims 1-12 are a system , claims 13-19 are method and claim 20 is media. Thus, each independent claim, on its face, is directed to one of the statutory categories of 35 U.S.C. §101. However, the claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A-Proing 1: The independent claims (1, 13 and 20) recite identifying at least upcoming shopping event to be held at a futher time period, determining based on generated irrespective any shopping event, a plurality of items, form by a respective item and a respective seller such that a sell probability of successfully selling the perspective item by the respective seller in a future time period is larger than a threshold, perform an allocation of at least one of the item-seller combinations to the at least one upcoming shopping event, generate, in response to a real-time request from a seller, a customized list of items associated with the at least one upcoming shopping event based on the allocations, present the customer list of items. item. These limitations fall within “Certain Methods Of Organizing Human Activity” for commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). The identifying and determining limitation, as drafted is a process, under its broadest reasonable intepration covers, mental process such as concept performed in the human mind (including an observation, evaluation, judgment, opinion) because a human can select item that meets a specified criteria, acknowledge an agreement to recommend customized list of items). That is, other than reciting “processor,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “processor ” language, the claim encompasses the user manually calculating the amount of the probability of successful selling respective items. The mere nominal recitation of a generic processor does not take the claim limitation out of the mental processes grouping. Thus, the claim recites a mental process.
Step 2A-Proing 2: The claims recite additional limitation of a processor and memory are used to perform the identifying, performing and generating steps, machine learning for performing the determining steps and pop-up window and interface for displying data. The transmitting step is recited at a high level of generality (i.e., as general means of sending dat in exchange generating customized list of items for advertising campaign) and amount mere data gathering, which is a form of insignificant extra-solution activity. The processor, memory and machine learing model in the recited steps are recited at a high-level of generality, i.e., as a generic computer components performing a generic computer function of processing data (generating customized list of items for recommendation use) . The pop-up window and interface is also reacted at a high level of generality (i.e., general means of input/output data), which is merely invoking a tool to perform an existing process, which is displying data on generic interface/pop-up window.
The claims also recite additional limitation of the machine learing model comprising a first stage model and second sate model for performing iteratively to be trained using the first and the second training data. The machine learing model comprising a first and second stage models is recited at a high level of generality i.e, as a generic comping components performing a generic computer function of processing data (satisfy the first and second campaign data). The generic computer component is no more than mere instruction to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea.
Step 2B: As discussed with respect to Step 2A Prong Two, the additional element in the claim amounts 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 using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. The claim is ineligible. The claims does 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 element of using the claimed
computer systems amount to no more than “apply” a selection of content on the systems.
Further, the courts have consistently recognized that merely presenting the results of abstract processes of collecting and analyzing information, without more (such as identifying a particular tool for presentation), is abstract as an ancillary part of such collection and analysis. See, e.g., Content Extraction, 776 F.3d at 1347; Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014). Examiner asserts that “iteratively training the first and second stage models” do not provide specific technical solution that improves the underling machine learning technology.
Dependent claims 2 and 14, these claims recite limitation that further defines the same abstract idea noted in claims and 13. In addition, they recited additional elements of the machine learning model comprises a first stage model and a second stage model. The recited additional elements is recited at a high-level generality such it is no more identifying an item probability larger than a threshold in a future time. These claim do not contain any further additional elements per step 2A prong 2. Therefore, they are considered patent ineligible for the reason given above.
Dependent claims 3 and 15, these claims recite limitation that further defines the same abstract idea noted in claims and 13. In addition, they recited additional elements of the machine learning model comprises a first stage model and a second stage model. The recited additional elements is recited at a high-level generality such it is no more determining items sale probability. These claim do not contain any further additional elements per step 2A prong 2. Therefore, they are considered patent ineligible for the reason given above.
Dependent claims 4-12 and 16-20, these claims recite limitation of computing a first sale probability for each item based on a first feature set, wherein the first feature set comprise at least some of the following features related to each item.., determining the first feature importance data associated with features in the first feature set, determine first wight for twelve monthly first stage models, based on their respective first sale probabilities…, and computing a first stage probability for each item based on weighted combination of the first sale probabilities of the twelve. These limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers the performance of the limitation in the mind but for the recitation of generic computer compoints. That is, other than reciting “the first stage models” and “the first meta classifier” nothing in the claim precludes the computing and determining steps from practically being performed in the human mind. For example, but for “the first stage models” and “the first meta classifier” language, the claims encompasses the user manually calculating the amount of use of each weight for each item. This limitation is a mental process. These claim do not contain any further additional elements per step 2A prong 2. Therefore, they are considered patent ineligible for the reason given above.
The closest prior art to the applicants’ claimed invention:
Feng et al (US Pub., 2023/0136809 A1) focused on a method and a system for predicting and using customer lifetime value (CLV). The method include: providing a classifier trained using customer feature data during a first period of time as input and whether there is spending during a second period of time as classifier label; providing a regressor trained using the customer feature data during the first period of time as input and amount of spending during a second period of time as regressor label(abstract), designing marketing campaigns (paragraph [0004]) providing a classifier of a computing devise wherein the classifier is trained using customer feature data of a first plurality of customers during a first period of time as input (paragraph [0007]), provide a classifier of a computing device, wherein the classifier is trained using customer feature data of a first plurality of customers during a first period of time as input and whether there are spendings of the first plurality of customers during a second period of time as classifier labels(paragraph [0019]), the regressor is trained using customer future data a second plurlity of customers during the first period of time as input and amount of spending of the second plurlity of customer during a second period of time as regressor labels (paragraph [0020]) and targeting marketing campaign (paragraph [0078])
Koupanou (US Pub., No., 2024/0046347 A1) focused on a risk-evaluation model is trained using historical data to predict the likelihoods of future events in a future time period that impact a product. The time period may correspond to the time period over which the product is provided. On receiving a request for the product, the model is used to predict the likelihood of an event occurring and a recommendation of whether to provide the product is made to a provider of the product. The product may be provided based on the recommendation(abstract), the prediction is generated by an iteratively-trained risk-evaluation model(paragraph [0004]), and the trained risk-evaluation model may be used by the provider to evaluate requests for new loans or identify good candidates to offer new loans. In one embodiment, a loan request may identify a consumer (e.g., with a consumer ID) and data regarding the identified consumer is provided to the trained risk-evaluation model, which outputs a risk metric indicating the likelihood that the consumer will fail to make membership payments in an upcoming time period (and thus a likelihood that the consumer will default on the loan) (paragraph [0013]), apply a risk-evaluation model to consumer account data to evaluate the suitability of consumers for products ( e.g., loans or other financial products). In one embodiment, the server 110 periodically (e.g., daily) applies the risk-evaluation model to data regarding consumers to generate risk metrics…The server 110 may proactively generate recommendations for consumers to whom loans should be offered based on the risk metric ( e.g., if the risk metric is below a threshold) (paragraph [0015]) determine, based on a machine learning model generated irrespective of any shopping event, a plurality of item-seller combinations(paragraph [0052], discloses paragraph [0067], discloses the normalization model 123 is configured to upon receiving the aggregated spending of order at different time frames, such as monthly, seasonality and year ..), wherein,
Saraf (US Pub., No., 2020/0342467 A1) disclosed are systems, methods, and non-transitory computer- readable media for a geographic recommendation platform. The geographic recommendation platform receives data identifying a geographic region specified by a user and gathers data relating to the geographic region. The geographic recommendation platform determines, based on the data relating to the geographic region, an anticipated demand for an item within geographic region.
Gibson et al (US Pub., No., 2010/0131366 A1) discloses a system and methods for providing location-based upcoming event information using a client-side web application implemented on a client device are described. The location based upcoming event information may be provided to users of an online secondary ticket marketplace using a client-side web application implemented as desktop or mobile widget or within a web browser tool bar(abstract) and identify at least one upcoming shopping event to be held at a future time period (Fig. 2, discloses search event, from to dates [future time period], 214 discloses results 1-10 of about 21 for Jazz concert near San Francisco, CA for 1/12/2008, etc., and paragraph [0126], dislcies a user interface 200 presenting by a client-side web application for providing location-based upcoming event information );
Bailey et al (US Patent No., 9, 996,626 B1) discloses techniques are described for selecting content items in various manners, such as by selecting product-related content items for display to consumer users. The content items may include advertisements or other promotional materials, and the selecting may be performed as part of determining particular promotional materials to display to particular users in particular situations, such as to accompany search results. In addition, the selecting of particular content items may be based on categorization of products indicated in search results and/or based on search terms used in searches performed on retail web sites.
Pyati (US Pub., No., 2019/0311301 A1) discloses systems and methods provide real-time machine learning modeling, evaluation, and visualization. A computing system can receive input values for attributes of a new item listing. Concurrently, the system can analyze previous related item listings. The system can process the attributes and associated values of the new listing and previous listings to extract features and associated values. The system can apply the features of previous listings to machine learning algorithms to generate a machine learning model directed towards a target objective, such as maximizing a selling price or a selling probability for the new listing.
Lardeux et al (US Pub., No., 2020/0134696 A1) discloses computer-implemented methods of providing personalized recommendations to a user of items available in an online system, and related systems. First-level features including context features are computed based upon context data. A first-level machine learning model is then evaluated using the first-level features to generate predictions of user behavior in relation to a plurality of individual items available via the online system. A list of proposed item recommendations is constructed based upon the predictions.
None of the above reference failed to teach or suggest the corresponding machine learing model i) the first stage model comprising a plurality of first stage monthly models and a first meta classifier configured to receive an output from the plurality of first stage monthly models and ii) the second stage model comprising a plurality of second stage monthly models and a second meta classifier configured to receive an output from the plurlity of second stage monthly models and the first meta classifier; generation of the machine learing model comprises:
satisfying campaign data based on one or more of a camping ID and an item category, iteratively training the first stage model using first training data until a first predetermined score threshold is satisfied, wherein the first training data includes, for each first stage monthly model of the plurality of first stage monthly models, stratified first campaign data for a corresponding month of a respective first stage monthly model, and iteratively training the second stage model using second training data until a second predetermined score threshold is satisfied, wherein the second training data includes, stratified second campaign data for a corresponding month of a respective second stage monthly model, and an output of the first stage model; for processing of in items to create advertising camping for upcoming events.
Response to Arguments
Applicant's arguments of 35 .S.C 101 rejection with respect to claim 1-20 filed on 15 July 2025 have been fully considered but they are not persuasive. Applicants’ arguments of the claims do not recite i.e., are not directed to an abstract idea is not persuasive.
Applicants’ argument of Example 39, fails positive ere minimized by performing an interactive training algorithm …, claim 1 is patent eligible for the same reason as above.
Applicants’ argument is not persuasive. The method in “example 39” addresses these issues by minimizing false positives in facial detection by improving the previous facial detection methods performed poorly with distorted, rotated, or shifted faces. Simply using conventional, iterative training techniques for a machine learning model is generally not patent-eligible under 35 U.S.C. § 101, as reinforced by the 2025 Federal Circuit decision in Recentive Analytics, Inc. v. Fox Corp.. For a machine learning invention to be eligible for a patent, it must provide a specific, technical improvement to the underlying technology, not merely apply a generic process to a new field.
The patent at issue does not provide any improvement to machine learing or other technology except satisfying a campaign data. “Satisfying camping data" does not integrate an abstract idea into a practical, tangible application rather than claiming the data or an abstract concept alone. The patent eligibility of inventions involving "camping data" is determined by the two-step Alice/Mayo framework used for computer-implemented inventions. thus, the additional elements do not integrate into practical application.
Thus, the 35 U.S.C 101 rejection is updated and the rejection is maintained.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SABA DAGNEW whose telephone number is (571)270-3271. The examiner can normally be reached 9-6:45.
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/SABA DAGNEW/Primary Examiner, Art Unit 3682