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 .
Priority
Examiner acknowledges that the instant application is a continuation of US Patent No. 11,983,230, filed 08/03/2021.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 4/10/2024 has been considered by the Examiner.
Status of Claims
Applicant’s communications filed on 4/10/2024 have been considered.
Claims 1-20 have been canceled.
Claims 21-40 are newly added.
Claims 21-40 are currently pending and have been examined.
Claim Objections
Claims 21, 30, 35 and 39-40 are objected to because of the following informalities:
Regarding Claim 21, the claim recites “An artificial intelligence (AI)-based system for event predication, comprising…”. It appears that a typographical error has been made with regards to this limitation, as while the specification briefly discusses “event predications” (see at least [0005][0109]), the majority of the specification and claims discuss event prediction (see at least [0001] “The present disclosure relates to systems and methods for aggregating data, generating predictions of cyclical events, and selectively presenting generated event predictions based on user preferences”). Accordingly, for examination purposes, this limitation has been interpreted as reciting “An artificial intelligence (AI)-based system for event prediction, comprising…”. Appropriate correction is required.
Claims 30 and 40 similar recite “event predication” in their respective preambles, and accordingly, claims 30 and 40 are objected to for the same reasons with respect to claim 21.
Claim 21 further recites “in response to a determination for display the event prediction, generate a graphical user interface display…”. It appears a typographical error has been made with regards to this limitation. For examination purposes, this limitation has been interpreted as reciting “in response to a determination to display the event prediction, generate a graphical user interface display…”. Appropriate correction is required.
Claims 30 and 40 similar recite “in response to a determination for display the event prediction, generat[ing] a graphical user interface display” in their respective preambles, and accordingly, claims 30 and 40 are objected to for the same reasons with respect to claim 21.
Regarding Claim 35, the claim recites “(a vector quantization and decision tree…”. It appears that a typographical error was made with regards to the parenthesis in this limitation, as it is not required. Appropriate correction is required.
Regarding Claim 39, the claim recites “a website displaying the event prediction calendar”. It appears that a typographical error was made with regards to this limitation, as there is not a prior recitation of an event prediction calendar in claim 39, or claim 30, from which claim 39 depends. For examination purposes, this limitation has been interpreted as “a website displaying an event prediction calendar”. Appropriate correction is required.
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 21-29 are rejected because the claimed invention is directed to non-statutory subject matter. The claims does not fall within at least one of the four of categories of patent eligible subject matter because claim 21 is directed towards generating an event prediction, and does not recite structural features, but rather recites the features of an AI engine, which is primarily a software item per se, as disclosed in ([0044][Fig. 1B]) of the specification. A product claim to a software program that does not also contain at least one structural limitation has no physical or tangible form, and thus does not fall within any statutory category. See In re Ferguson, 558 F.3d 1359, 1364, 90 USPQ2d 1035, 1039-40 (Fed. Cir. 2009). Therefore this claim appears to be directed towards software per se and software is no a statutory category of patentable subject matter. Appropriate correction and/or clarification is required. The Office recommends amending the claims such that more structural features are recited in the bodies of these claims.
Claims 21-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite an abstract idea. The 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 Subject Matter Eligibility Test for Products and Processes, the claims must be directed to one of the four statutory categories. See MPEP 2106.03. Claims 30-39 are directed towards a process. Claim 40 is directed towards a machine. Therefore, claims 30-40 are directed to one of the four statutory categories (Step 1: YES, regarding claims 30-40).
Examiner has included an analysis of claims 21-29 in the 101 analysis below for the purposes of compact prosecution, in the event that the claim be amended to be directed towards a statutory category of invention under Step 1. (Step 1: NO, regarding claims 21-29).
Under Step 2A of the MPEP, it is determined whether the claims are directed to a judicially recognized exception. See MPEP 2106.04. Step 2A is a two-prong inquiry.
Under Prong 1, it is determined whether the claim recites a judicial exception. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception.
Taking Claim 30 as representative, claim 30 recites limitations that fall within the certain methods of organizing human activity groupings of abstract ideas, including:
A method for event prediction, comprising:
applying a first model to event data associated with a plurality of users to generate processed event data for one of the plurality of users;
applying a second model including matrix factorization to user preference data associated with the plurality of users to generate a user profile for the one of the plurality of users;
applying a third model to the processed event data to determine an event prediction, and
using the processed event data;
determining to display the event prediction based on the user profile;
in response to a determination for display the event prediction, generate a display with a calendar depicting the event prediction; and
presenting the display of one of the plurality of users.
Claim 21, as interpreted, and claim 40 recite the same limitations believed to be abstract as recited in claim 30.
Claim 30, as exemplary, recites certain methods of organizing human activity, such as performing commercial interactions. See MPEP 2106.04(a)(2). The MPEP defines the “Certain Methods of Organizing Human Activity” grouping as including 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) (see MPEP § 2106.04(a)(2). The abstract ideas recited in representative claim 30 are certain methods of organizing human activity because event prediction, applying a first model to event data associated with a plurality of users to generate processed event data for one of the plurality of users, applying a second model including matrix factorization to user preference data associated with the plurality of users to generate a user profile, applying a third model to the processed event data to determine an event prediction, using the processed event data, determining to display the event prediction based on the user profile, generating a display with a calendar depicting the event prediction, and presenting the display is a commercial or legal interaction because it is an advertising, marketing or sales activity, or business relations. This is further supported by Applicant’s specification ([0002-0004]), disclosing that the invention generates predictions of cyclical events, such as brands and retailers offering seasonal sales events for their inventories, where consumers may potentially make purchases at inopportune times. Claims 21 (as interpreted) and 40 recite the same abstract idea as recited in claim 30.
Accordingly, under Prong One of Step 2A of the Alice/Mayo test, claims 21 (as interpreted), 30 and 40 recite an abstract idea (Step 2A, Prong One: YES).
Under Step 2A (prong 2), 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 (see MPEP 2106.04). As stated in the MPEP, when “an additional element merely recites the words ‘apply it (or an equivalent) with the judicial exception, or merely uses a computer as a tool to perform an abstract idea,” the judicial exception has not been integrated into a practical application. In this case, representative claim 40 includes additional elements such as (additional elements are bolded):
An artificial intelligence (AI)-based method for event predication, comprising:
applying, by an AI engine, a first machine learning model including natural language processing to event data associated with a plurality of users to generate processed event data for one of the plurality of users;
applying, by the AI engine, a second machine learning model including matrix factorization to user preference data associated with the plurality of users to generate a user profile for the one of the plurality of users;
applying, by the AI engine, a third machine learning model to the processed event data to determine an event prediction, wherein the third machine learning model comprises at least one of a cluster analysis machine learning model and a supervised machine learning model, and the third machine learning model is also trained using the processed event data;
determining, by the AI engine, to display the event prediction based on the user profile;
in response to a determination for display the event prediction, generating, by the AI engine, a graphical user interface display with a calendar depicting the event prediction; and
presenting, by the AI engine, the graphical user interface display on a device of the one of the plurality of users.
These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. As such, these computer-related limitations are not found to be sufficient to integrate the abstract idea into a practical application. Claims 21, 30 and 40 specifying that the abstract idea is executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer. As such, under Prong Two of Step 2A of the Alice/Mayo test, when considered both individually and as a whole, the limitations of claims 21, 30 and 40 are not indicative of integration into a practical application (Step 2A, Prong Two: NO).
Since claims 21, 30 and 40 recite an abstract idea and fail to integrate the abstract idea into a practical application, claims 21, 30 and 40 are “directed to” an abstract idea (Step 2A: YES). Accordingly, the judicial exception is not integrated into a practical application.
Next, under Step 2B, examiners should evaluate 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). In this case, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Returning to representative claims 21, 30 and 40, taken individually or as a whole the additional elements of claims 21, 30 and 40 amount to no more than mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment. For the same reason these elements are not sufficient to provide an inventive concept. Therefore when considering the additional elements alone, and in combination, there is no inventive concept in the claim, and thus the claim is not patent eligible (Step 2B: NO).
Dependent claims 22-29 and 31-39, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because they do not add “significantly more” to the abstract idea. As for dependent claims 28-33 and 37-42, these claims recite limitations that further define the same abstract idea noted in independent claims 21, 30 and 40, and do not recite any additional elements other than what is disclosed in independent claims 21, 30 and 40. Therefore, claims 28-33 and 37-42 are considered patent ineligible for the reasons given above.
As for dependent claims 24, 26, 29, 33, 35-37 and 39, these claims recite limitations that further define the abstract idea noted in independent claims 26, 35 and 44. Additionally, they recite the following additional limitations:
wherein the NLP comprises associating context around user activity data including webpages visited or emails from product brands to determine an associated user preference level with the user activity data;
wherein the event data comprises data scraped by a scraping module from a merchant website;
wherein the NLP comprises implementing word segmentation, and semantic parsing;
scraping, by the AI engine using a scraping module, additional event data from merchant websites;
wherein the third machine learning model includes at least one selected from a group of a hidden Markov model, a Gaussian mixture model, a pattern matching algorithm, a neural network, a matrix representation, a vector quantization and decision tree, a supervised learning model, an unsupervised learning model, a semi-supervised learning model, a reinforcement learning model, a self-learning model, and a feature learning model;
applying, by the AI engine, a recommendation engine implementing the matrix factorization to generate the user profile; and
transmitting, by the AI engine, a link to an email of the user or through a text to the user, which, when selected by the user, redirects the user to a website displaying the event prediction calendar.
The additional elements of webpages visited or emails; data scraped by a scraping module from a merchant website; word segmentation and semantic parsing; scraping, by the AI engine using a scraping module from merchant websites; a hidden Markov model, a Gaussian mixture model, a pattern matching algorithm, a neural network, a matrix representation, a vector quantization and decision tree, a supervised learning model, an unsupervised learning model, a semi-supervised learning model, a reinforcement learning model, a self-learning model, and a feature learning model; applying a recommendation engine; and transmitting a link to an email which redirected the user to a website are all recited at a high level of generality such that they amount to no more than instructions to apply the judicial exception in a generic technological environment. Even in combination, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. Accordingly, under the Alice/Mayo test, claims 21-40 are ineligible.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
Claim(s) 21-23, 25, 28-30, 34-38 and 40 are rejected under 35 U.S.C. 103 as being unpatentable over U.S Patent Application No. 2022/0245701 A1 to Iyer et al., hereinafter Iyer, in view of U.S Patent Application No. 2022/0207430 A1 to Dickie et al., hereinafter Dickie, and further in view of U.S Patent Application No. 2017/0255985 A1 to Acott et al., hereinafter Acott.
Regarding Claim 21, Iyer discloses An artificial intelligence (AI)-based system for event prediction, comprising ([0028-0032][0049][Fig. 1] category recommender system 100):
an AI engine, wherein the AI engine is configured to ([0029-0032] category recommender system 100 includes customization computing device 116… which can include various personalization engines that can deliver customized or personalized content):
apply processing to event data associated with a plurality of users to generate processed event data for one of the plurality of users ([0059] in the first stage of processing, the data acquisition engine 302 can obtain customer information 410 [for] each of the customer segments… that characterizes a customer's interactions with the ecommerce marketplace; see [0085-0086] when a customer begins interacting with a retailer’s website… customer segments can be determined by analysis and/or observation of historical customer transaction information on the ecommerce website);
apply a second machine learning model including matrix factorization to user preference data associated with the plurality of users to generate a user profile (latent factors) for the one of the plurality of users ([0061] the discovery category engine 404… can then determine a discovery category ranking for each category of goods for each customer (or user)… indicating a likelihood that the user will purchase an item from a new category that the user has not purchased from before… including a discovery category model using matrix factorization; [0062-0065] utilizing collaborative filtering using matrix factorization, the customer information can be used to build a User Factor matrix U and a Category Factor Matrix C to determine a User-Category Matrix 502, which is factorized to obtain Latent User Factors U… discovery category rankings can then be determined using cosine similarity);
apply a third machine learning model to the processed event data to determine an event prediction, wherein the third machine learning model comprises at least one of a cluster analysis machine learning model and a supervised machine learning model ([0067] the repeat category engine includes a repeat purchase model 418, which is a random forest model used to determine the repeat category rankings by their likelihood of making a purchase of a new item (i.e., an item that the particular user has never purchased before) in the same category; see [0069] the repeat purchase model 418 uses various repeat category features 602 including the customer's historical purchase data; [0072] The repeat category model can be trained using supervised learning) (Note: rankings of categories indicating likelihoods of repeat purchases are predictions), and
the third machine learning model is also trained using the processed event data ([0072] The repeat category model can be trained using supervised learning in which training data can be compiled that consists of customers that have made repeat purchases in a category of items during a particular time period (or otherwise historically);
determine to display the event prediction based on the user profile ([0073-0075] discovery category rankings and repeat category rankings… can be input into the merging engine… to determine the final recommendations and featured categories that will be presented to the customer by the category recommender system 100; see [0061-0065]);
in response to a determination to display the event prediction, generate a graphical user interface display depicting the event prediction ([0078] The merging engine 408 can also cause a predetermined number of new categories and repeat categories to be displayed for an engaged customer… included in the final recommendations; [0079] super-engaged customers are shown mostly repeat categories… see [0077] website 700 including a display of rows, each row corresponding to a category of items, including a repeat category); and
present the graphical user interface display on a device of the one of the plurality of users ([0077] the website 700 can include a display with rows of items, each row corresponding to a category of items; [0093] the final recommendations can be provided to the customer on the retailer's website; see [0035] Each of the customer computing devices 104, 106 may be operable to view, access and interact with the websites).
Iyer discloses applying processing to event data associated with a plurality of users to generate processed event data (see at least Iyer [0059][0086]). However, Iyer does not explicitly teach applying a first machine learning model including natural language processing.
However, in the field of processing event data for prediction (see at least Dickie [abstract][0024-0028]), Dickie, on the other hand, teaches applying a first machine learning model including natural language processing ([0054] accessing all transaction data records and applying an adaptively trained, natural-language processing (NLP) model to selected portions of the elements of accessed and/or aggregated transaction data).
The steps of Dickie are applicable to the system of Iyer, as they share characteristics and capabilities, namely, they are directed to event predictions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the event prediction system as taught by Iyer, to include applying a first machine learning model including natural language processing, as taught by Dickie. One of ordinary skill in the art at the time of filing would have been motivated to expand the system of Iyer in order to identify contextual elements in transaction data to characterize changes in customer purchasing/spending (Dickie, [0054]).
Iyer discloses generating a graphical user interface display depicting the event prediction (see at least ([0077[0093]). However, Iyer in view of Dickie does not explicitly teach a graphical user interface with a calendar.
However, in the field of displaying event predictions (see at least Acott [0025][0031][0047]), Acott, on the other hand, teaches a graphical user interface with a calendar ([0047][Fig. 10] a user interface suitable for presenting upcoming gift-giving events… a calendar view is shown, with the information of elements 1010-1040 included directly on the calendar (e.g., in a box corresponding to the date of the event).
The steps of Acott are applicable to the system of Iyer in view of Dickie, as they share characteristics and capabilities, namely, they are directed to displaying event predictions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the event prediction system as taught by Iyer in view of Dickie, to include a graphical user interface with a calendar, as taught by Acott. One of ordinary skill in the art at the time of filing would have been motivated to expand the system of Iyer in view of Dickie in order to provide suggestions and reminders for upcoming events until an item has been purchased for the event (Acott, [0047-0048]).
Regarding Claim 22, Iyer, Dickie and Acott teach the limitations of claim 21.
Iyer further discloses wherein the matrix factorization comprises a matrix indicating a preference score assigned to each of a variety of products ([0062-0066] the User-Category Matrix 502 can be determined… The discovery category model 414 can attempt to predict accurate scores to fully populate the User-Category Matrix 502; see [Fig. 5]).
Regarding Claim 23, Iyer, Dickie and Acott teach the limitations of claim 21.
Iyer further discloses wherein the user preference data include products or brands of which the user has never purchased ([0060] For new users, there is no historical data available, so three months of transaction data across all customers can be used to determine popular categories of items).
Regarding Claim 25, Iyer, Dickie and Acott teach the limitations of claim 21.
Iyer further discloses wherein the matrix factorization comprises comparing user preference data between different users ([0061] the discovery category model 414 can utilize a collaborative filtering based approach using matrix factorization to determine the discovery category rankings; [0062-0064] utilizing collaborative filtering using matrix factorization, customer information can be used to build a user factor matrix U and Category Factor Matrix C… the User-Category Matrix 502 can be determined as the product of the User Factor Matrix 504 and the Category Factor Matrix 506).
Regarding Claim 28, Iyer, Dickie and Acott teach the limitations of claim 21.
Iyer further discloses wherein the event data includes information which the AI engine uses to determine event details ([0085-0086] customer segments can be determined by analysis and/or observation of historical customer transaction information on the ecommerce website). However, Iyer in view of Dickie does not explicitly teach determining event dates.
Dickie, on the other hand, teaches determining event dates ([0040] parsing transaction records 114… to obtain the corresponding temporal identifier, which specifies the transaction date or time associated with the corresponding purchase transaction).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the event prediction system as taught by Iyer, to include determining event dates, as taught by Dickie, for the same reasons discussed above with respect to claim 21.
Regarding Claim 29, Iyer, Dickie and Acott the limitations of claim 21.
Iyer does not explicitly teach wherein the NLP comprises implementing word segmentation, and semantic parsing. However, Dickie, on the other hand, teaches wherein the NLP comprises implementing word segmentation, and semantic parsing ([0062] executed NLP module 166 may apply the adaptively trained NLP algorithm or model to input data that includes each individual word (or linguistic unit) within the counterparty name (e.g., “Jamie's,” “Steak,” and “House”), either alone or in conjunction with additional words (or linguistic units) extracted from data record 162 (e.g., “Georgetown,” “Washington,” “steak,” or “seafood,” etc.)… and generates an element 168 of contextual data 167 that, among other things, specifies that “Jamie's Steak House” corresponds to a restaurant, and assigns “Jamie's Steak House” to one of the predetermined, restaurant-specific counterparty categories).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the event prediction system as taught by Iyer, to include wherein the NLP comprises implementing word segmentation, and semantic parsing, as taught by Dickie, for the same reasons discussed above with respect to claim 21.
Claim 30 is directed to a method. Claim 30 recites limitations that are substantially parallel in nature to those addressed above for claim 1 which is directed towards a system. The system of Iyer/Dickie/Acott teaches the limitations of claim 1 as noted above. Iyer further discloses An artificial intelligence (AI)-based method for event prediction, comprising (Iyer: [0028-0029][0084]). Claim 30 is therefore rejected for the reasons set forth above in claim 21 and in this paragraph.
Regarding Claim 34, Iyer, Dickie and Acott teach the limitations of claim 30.
Iyer further discloses determining, by the AI engine, a confidence score indicative of a likelihood of an occurrence of the event prediction ([0061-0066] determining category rankings indicating a likelihood that the user will purchase an item from the category using matrix factorization… rankings can be determined by ranking using cosine similarity; see [0073] item recommendation engine 406 scores/ranks items utilizing matrix factorization in a similar manner previously described).
Regarding Claim 35, Iyer, Dickie and Acott teach the limitations of claim 30.
Iyer further discloses wherein the third machine learning model includes at least one selected from a group of a hidden Markov model, a Gaussian mixture model, a pattern matching algorithm, a neural network, a matrix representation, a vector quantization and decision tree, a supervised learning model ([0067] the repeat category engine includes a repeat purchase model 418, which is a random forest model used to determine the repeat category rankings; see [0072] The repeat category model can be trained using supervised learning)., an unsupervised learning model, a semi-supervised learning model, a reinforcement learning model, a self-learning model, and a feature learning model.
Note: According to the limitation “at least one selected from a group of…”, only one of the subsequent options must be taught.
Claim 36 recites a method comprising substantially similar limitations as claim 29. All limitations as recited have been analyzed and rejected with respect to claim 29, and do not introduce any additional narrowing of the scopes of the claims as analyzed.
Regarding Claim 37, Iyer, Dickie and Acott teach the limitations of claim 30.
Iyer further discloses applying, by the AI engine, a recommendation engine implementing the matrix factorization to generate the user profile ([0062-0065] using matrix factorization… determine a User-Category Matrix 502, which is factorized to obtain Latent User Factors U… the discovery category rankings can then be determined; [0074] discovery category rankings… can be input into the merging engine 408 to determine the final recommendations and featured categories).
Regarding Claim 38, Iyer, Dickie and Acott teach the limitations of claim 30.
Iyer further discloses wherein the event prediction is a predicted event ([0067] determine the repeat category rankings by their likelihood of making a purchase of a new item (i.e., an item that the particular user has never purchased before) in the same category). However, Iyer does not explicitly teach a predicted event greater than a threshold level confidence level score.
Dickie, on the other hand, teaches a predicted event greater than a threshold level confidence level score ([0117] generated elements of output data may include a numerical score (e.g., either zero or unity) indicative of a predicted likelihood that a corresponding one of the customers will be involved in a default event during the future temporal interval, e.g., with a score of zero being indicative of a predicted non-occurrence of the default event… and with a score of unity being indicative of a predicted occurrence of the default event… the output data (numerical scores) may classify the customers of the financial institution based on the predicted likelihood of their involvement in the future occurrences of the default events).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the event prediction method as taught by Iyer, to include a predicted event greater than a threshold level confidence level score, as taught by Dickie, for the same reasons discussed above with respect to claim 30.
Claim 40 is directed to a non-transitory computer-accessible medium. Claim 40 recites limitations that are substantially parallel in nature to those addressed above for claim 1 which is directed towards a method. The system of Iyer/Dickie/Acott teaches the limitations of claim 1 as noted above. Iyer further discloses A non-transitory computer-accessible medium having stored thereon computer- executable instructions for event prediction, wherein a computer arrangement comprises an artificial intelligence (AI) engine and is configured to perform procedures comprising: (Iyer: [0029-0035][0073][Fig. 1]). Claim 40 is therefore rejected for the reasons set forth above in claim 21 and in this paragraph.
Claim(s) 24, 26 and 33 are rejected under 35 U.S.C. 103 as being unpatentable over Iyer in view of Dickie in view of Acott, and further in view of U.S Patent Application No. 2020/0065425 A1 to Menguy et al., hereinafter Menguy.
Regarding Claim 24, Iyer, Dickie and Acott teach the limitations of claim 21.
Iyer does not explicitly disclose wherein the NLP comprises associating context around user activity data including webpages visited or emails from product brands to determine an associated user preference level with the user activity data.
Dickie, on the other hand, teaches wherein the NLP comprises associating context around user activity data to determine an associated user preference level with the user activity data ([0054] applying the adaptively trained NLP model to the accessed or aggregated elements of transaction data, generate one elements of contextual data that not only characterize patterns in the purchasing or spending habits of one or more of the customers of the financial institution, but also identify and characterize, in real-time, changes in the purchasing or spending of these customers).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the event prediction method as taught by Iyer, to include wherein the NLP comprises associating context around user activity data to determine an associated user preference level with the user activity data, as taught by Dickie, for the same reasons discussed above with respect to claim 30.
While Iyer in view of Dickie teaches wherein the NLP comprises associating context around user activity data to determine an associated user preference level with the user activity data, Iyer in view of Dickie in view of Acott does not explicitly teach user activity data including webpages visited or emails from product brands.
However, in the field of processing user interactions to determine user interest for various topics (see at least Menguy [abstract][0026]), Menguy, on the other hand, teaches user activity data including webpages visited or emails from product brands ([0040-0041] The crawler 208 crawls websites associated with the sanitized URLs 206, for instance by accessing the websites from service provider systems 210… the crawler 208 may crawl the corresponding content of the service provider system 210 to generate website data 218; see [0031] The service provider system 102, for instance, may be configured to support user interaction with digital content 112. User interaction data 114 is then generated (e.g., by a service manager module 116) that describes the user interaction 110).
The steps of Menguy are applicable to the system of Iyer in view of Dickie in view of Acott, as they share characteristics and capabilities, namely, they are directed to displaying event predictions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the event prediction system as taught by Iyer in view of Dickie in view of Acott, to include user activity data including webpages visited or emails from product brands, as taught by Menguy. One of ordinary skill in the art at the time of filing would have been motivated to expand the system of Iyer in view of Dickie in view of Acott in order to pool contextual data together to build a robust contextual user profile across disparate websites and more accurate and dynamic user interests (Menguy, [0026]).
Regarding Claim 26, Iyer, Dickie and Acott teach the limitations of claim 21.
Iyer further discloses wherein the event data comprises data from a merchant website ([0059-0060] the data acquisition engine 302 can obtain customer information 410 each of the customer segments… that characterizes a customer's interactions with the ecommerce marketplace. Customer information can include data regarding prior purchases on the ecommerce marketplace, and category and taxonomy data for the items that have been purchased and are otherwise available on the ecommerce marketplace). However, Iyer in view of Dickie in view of Acott does not explicitly teach data scraped by a scraping module from a merchant website.
Menguy, on the other hand, teaches data scraped by a scraping module from a merchant website ([0040-0041] The crawler 208 crawls websites associated with the sanitized URLs 206, for instance by accessing the websites from service provider systems 210… the crawler 208 may access the URL while satisfying the conditions 212, the crawler 208 may crawl the corresponding content of the service provider system 210 to generate website data 218 and update the cache 214 to reflect a new time 216 associated with the accessed URL).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the event prediction system as taught by Iyer in view of Dickie in view of Acott, to include data scraped by a scraping module from a merchant website, as taught by Menguy, for the same reasons discussed above with respect to claim 24.
Regarding Claim 33, Iyer, Dickie and Acott teach the limitations of claim 30.
Iyer further discloses obtaining, by the AI engine, additional event data from merchant websites ([0059] the data acquisition engine 302 can obtain customer information 410 each of the customer segments. Customer information is data that characterizes a customer's interactions with the ecommerce marketplace; [0060] the customer information that can be obtained is for a different period of time for each customer segment; see [0035] one or more ecommerce marketplaces on one or more websites… Each of the customer computing devices 104, 106 may be operable to view, access and interact with the websites). However, Iyer does not explicitly teach scraping, using a scraping module data from merchant websites.
Menguy, on the other hand, discloses scraping, using a scraping module data from merchant websites ([0040-0041] The crawler 208 crawls websites associated with the sanitized URLs 206, for instance by accessing the websites from service provider systems 210… the crawler 208 may access the URL while satisfying the conditions 212, the crawler 208 may crawl the corresponding content of the service provider system 210 to generate website data 218 and update the cache 214 to reflect a new time 216 associated with the accessed URL).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the event prediction method as taught by Iyer in view of Dickie in view of Acott, to include data scraped by a scraping module from a merchant website, as taught by Menguy, for the same reasons discussed above with respect to claim 24.
Claim(s) 27 and 32 are rejected under 35 U.S.C. 103 as being unpatentable over Iyer in view of Dickie in view of Acott, and further in view of U.S Patent Application No. 2017/0017970 A1 to Sarnowski et al., hereinafter Sarnowski.
Regarding Claim 27, Iyer, Dickie and Acott teach the limitations of claim 21.
Iyer further discloses wherein the event data comprises data pertaining to a multitude of items offered for sale. However, Iyer in view of Dickie in view of Acott does not explicitly teach items offered from a plurality of brands.
However, in the field of predictions based on event data (See at least Sarnowski [0010][0021]), Sarnowski, on the other hand, teaches items offered from a plurality of brands ([0018] The example ROC identifier 120 identifies a rest-of-category (ROC) grouping 206, which includes all items in the soup category exclusive of those items by the same manufacturer as the target product of interest 218… the ROC grouping 206 includes items associated with manufacturers exclusive of “Campbell's,”… [such as] Progresso 208 and Pacific Foods of Oregon 210).
The steps of Sarnowski are applicable to the system of Iyer in view of Dickie in view of Acott, as they share characteristics and capabilities, namely, they are directed to predictions based on event data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the event prediction system as taught by Iyer in view of Dickie in view of Acott, to include sales of competing entities, including competing products and/or competing brands of the products, and the duration and other characteristics of such competing events, as taught by Sarnowski. One of ordinary skill in the art at the time of filing would have been motivated to expand the system of Iyer in view of Dickie in view of Acott in order to hierarchically arrange historical market data in order to encapsulate competitor dynamics between different categories (Sarnowski, [0010-0015]).
Regarding Claim 32, Iyer, Dickie and Acott teach the limitations of claim 30.
Iyer further discloses applying, by the AI engine, to analyze the event data for sales, including the nature and frequency of such events ([0059] in the first stage of processing, the data acquisition engine 302 can obtain customer information 410 [for] each of the customer segments… that characterizes a customer's interactions with the ecommerce marketplace; see [0085-0086] when a customer begins interacting with a retailer’s website… customer segments can be determined by analysis and/or observation of historical customer transaction information on the ecommerce website; see [0058] determining customer segments based on average gaps between orders). However, Iyer does not explicitly teach applying the first machine learning model; and sales of competing entities, including competing products and/or competing brands of the products, and the duration and other characteristics of such competing events.
Dickie, on the other hand, teaches applying the first machine learning model ([0054] accessing all transaction data records and applying an adaptively trained, natural-language processing (NLP) model to selected portions of the elements of accessed and/or aggregated transaction data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the event prediction method as taught by Iyer, to include applying the first machine learning model, as taught by Dickie, for the same reasons discussed above with respect to claim 30.
While Iyer teaches applying, by the AI engine to analyze the event data for sales, including the nature and frequency of such events, and Dickie teaches applying the first machine learning mode, Iyer in view of Dickie in view of Acott does not explicitly teach sales of competing entities, including competing products and/or competing brands of the products, and the duration and other characteristics of such competing events.
Sarnowski, on the other hand, teaches sales of competing entities, including competing products and/or competing brands of the products ([0014-0015] receiving historical market sales data from data sources 106 associated with a geography of interest including competitor grouping levels; [0018] The example ROC identifier 120 identifies a rest-of-category (ROC) grouping 206, which includes all items in the soup category exclusive of those items by the same manufacturer as the target product of interest 218… the ROC grouping 206 includes items associated with manufacturers exclusive of “Campbell's,”… [such as] Progresso 208 and Pacific Foods of Oregon 210), and
the duration and other characteristics of such competing events ([0014] historical market data includes promotional price values, promotional activity associated with sales (e.g., TPRs, displays, features, etc.); [0015] manufacturers control target item parameters including promotion type, promotion intensity and promotion duration).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the event prediction system as taught by Iyer in view of Dickie in view of Acott, to include sales of competing entities, including competing products and/or competing brands of the products, and the duration and other characteristics of such competing events, as taught by Sarnowski, for the same reasons discussed above with respect to claim 27.
Claim(s) 31 is rejected under 35 U.S.C. 103 as being unpatentable over Iyer in view of Dickie in view of Acott, and further in view of U.S Patent Application No. 2013/0254013 A1 to Airani, hereinafter Airani.
Regarding Claim 31, Iyer, Dickie and Acott teach the limitations of claim 30.
Iyer further discloses deriving, by the AI engine from the event data, number of products, and a time period between similar events ([0086] determining customer segments by analysis and/or observation of historical customer transaction information on the ecommerce website… including engaged customers with an average gap between orders of greater than 7 days, and super-engaged customers with an average gap between orders of less than or equal to 7 days; see [0069] types of information that can be used as repeat category features include customer interpurchase intervals, the number or total items in a customer's order, a number of unique items in a customer’s order). However, Iyer does not explicitly teach deriving a frequency in which a particular product has been offered on sale in the past, a duration of such event, a duration of events in which more than one product from a particular brand have been offered for sale in the past, and number and nature of such products offered for sale at the events.
Dickie, on the other hand, teaches deriving a duration of such event ([0033] a default event involving the customer occurs with a corresponding past-due interval (e.g., as defined by the number of scheduled payments missed, or delayed, by the customer) that exceeds a predetermined a threshold time period (e.g., sixty days, etc.))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the event prediction method as taught by Iyer, to include deriving a duration of such event, as taught by Dickie, for the same reasons discussed above with respect to claim 30.
While Iyer teaches deriving a time period between similar events, and Dickie teaches deriving a duration of such event, Iyer in view of Dickie in view of Acott does not explicitly teach deriving a frequency in which a particular product has been offered on sale in the past, a duration of events in which more than one product from a particular brand have been offered for sale in the past, and nature of such products offered for sale at the events.
However, in the field of evaluating event data for future estimations (see at least Airani [0018-0023]) Airani, on the other hand, teaches deriving a frequency in which a particular product has been offered on sale in the past ([0031-0033][0080] receiving input data corresponding to historical sales data and promotional data of the plurality of stationary sales brands… formulating of an econometric time series for the sales data and the promotional data of brand A and brand B; see [0028] the effect of the frequency of the promotions on the sales of the brands may be evaluated),
a duration of events in which more than one product from a particular brand have been offered for sale in the past ([0031-0033][0080] receiving input data corresponding to historical sales data and promotional data of the plurality of stationary sales brands… formulating of an econometric time series for the sales data and the promotional data of brand A and brand B… represents time periods for analyzing the trend of the time series function; see [0023] impacts of promotional variables on each of the plurality of brands for a fixed period of time; [0080] promotion data may include year wise promotion data), and
nature of such products offered for sale at the events ([0022] the historical data may include data pertaining to yearly sales, amount invested in promotions, amount of products manufactured, increase or decrease in price).
The steps of Airani are applicable to the method of Iyer in view of Dickie in view of Acott, as they share characteristics and capabilities, namely, they are directed to analyzing event data for future estimations. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the event prediction method as taught by Iyer in view of Dickie in view of Acott, to include deriving a frequency in which a particular product has been offered on sale in the past, a duration of events in which more than one product from a particular brand have been offered for sale in the past, and nature of such products offered for sale at the events, as taught by Airani. One of ordinary skill in the art at the time of filing would have been motivated to expand the method of Iyer in view of Dickie in view of Acott in order to identify and estimate long-term impacts of promotional (event) variables on a plurality of brands, including frequency (Airani, [0018-0023]).
Claim(s) 39 is rejected under 35 U.S.C. 103 as being unpatentable over Iyer in view of Dickie in view of Acott, and further in view of U.S Patent No. 8,355,955 B1 to Mirchandani et al., hereinafter Mirchandani.
Regarding Claim 39, Iyer, Dickie and Acott teach the limitations of claim 30.
Iyer further discloses transmitting, by the AI engine, to an email of the user or through a text to the user, information displaying an event prediction ([0093] final recommendations can be provided, displayed or presented to the customer in any suitable manner… including alerts, texts, messages). However, Iyer does not explicitly disclose transmitting a link which, when selected by the user, redirects the user to a website displaying the event prediction calendar.
Acott, on the other hand, teaches displaying an event prediction calendar ([0047][Fig. 10] a user interface suitable for presenting upcoming gift-giving events… a calendar view is shown, with the information of elements 1010-1040 included directly on the calendar (e.g., in a box corresponding to the date of the event)… the user can cause the system to present gift suggestions for the corresponding event).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the event prediction method as taught by Iyer in view of Dickie, to include displaying an event prediction calendar, as taught by Acott, for the same reasons discussed above with respect to claim 30.
While Iyer teaches transmitting, by the AI engine, to an email of the user or through a text to the user, information displaying an event prediction, and Acott teaches displaying an event prediction calendar, Iyer in view of Dickie in view of Acott does not explicitly teach transmitting a link which, when selected by the user, redirects the user to a website displaying an event prediction.
However, in the field of predictions based on behavioral data (see at least Mirchandani [Col 16 Ln 22-42]), Mirchandani, on the other hand, teaches transmitting a link which, when selected by the user, redirects the user to a website displaying an event prediction ([Col 16 Ln 22-42] based on analysis of Chris’s friends recently purchasing an item… the social networking system 50 posts a message to Chris’s news feed… including hyperlinks that can be clicked by Chris to directly access the item page on the catalog system 30)
Note: The claim recites “a link… which, when selected by the user, redirects the user to a website”. It is noted that the redirecting step is contingent upon user selection of the link, and therefore not required, as currently claimed. The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met. See MPEP 2111.04. Nevertheless, the limitation has been fully examined.
The steps of Mirchandani are applicable to the method of Iyer in view of Dickie in view of Acott, as they share characteristics and capabilities, namely, they are directed to predictions based on behavioral/event data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the event prediction method as taught by Iyer in view of Dickie in view of Acott, to include transmitting a link which, when selected by the user, redirects the user to a website displaying an event prediction, as taught by Mirchandani. One of ordinary skill in the art at the time of filing would have been motivated to expand the method of Iyer in view of Dickie in view of Acott in order to pool contextual data together to provide further integration between e-commerce sites and social networking sites, and allow a user to view or access a display page for an item (Mirchandani, [Col 1 Ln 6-30][Col 7 Ln 10-33]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S Patent Application No. 2015/0134413 A1 to Deshpande et al. – A method and system for retail forecasting, including accessing retail data for past purchases of a customer and contextual data to generate a purchase forecasting model for a customer.
U.S Patent No. 11,568,469 B1 to Zhang et al. – A recommendation model determining a probability of a future event, and generating recommendations based on the predicted future intent of a user.
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/ZACHARY RYAN DONAHUE/Examiner, Art Unit 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 6/17/2026
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