DETAILED ACTION
This Office action is in reply to application filed on 05/14/2025.
Claims 1-18 are pending.
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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-18 of the instant invention 19/208494 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-18 of U.S. Patent 12,327,276 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because claims of the instant application are substantially anticipated by the patent. The instant application presents broader scope compare to the patent application.
Eligibility Consideration
The claims recite eligible subject matter. Specifically, the claims do more than just apply the abstract idea using a generic computer because the recited additional elements of the claims apply or use the judicial exception in some other meaningful way beyond generally linking the user of the judicial exception to a particular technological environment, such that the claims as a whole are more than a drafting effort to monopolize the exception. Applicant’s specification discusses that the claimed invention provides a technical improvement in the field of web-scraping by abandoning rules-based approaches, which often require customization for each website. Instead, the instant invention provides a machine-learning based approach that is scalable to identifying data on a wide variety of websites (see specification pages 1-2). For example, claim 1 recites “ identifying one or more HTML blocks on a webpage for further processing, converting each of the HTML tags into a vector representation, wherein converting each of the HTML tags into a vector representation comprises: concatenating the metadata and the inner text of the HTML tag to form a string with a plurality of words, for each word in the string, retrieving a word embedding in a pretrained neural network model for generating word embeddings, and creating a sentence embedding from the word embeddings; applying a neural network model to each of the vector representations, resulting in another vector representation, which is converted into a machine-generated label prediction for each tag, wherein the neural network model is trained to predict labels, including order-related labels, corresponding to HTML tags; identifying order-related data on the webpage from the machine-generated label predictions for the HTML tags and the corresponding tag values;…” The claimed features of the neural network, the HTML blocks, the vector representation and the another vector representation do more than just apply the judicial exception and uses the judicial exception in a meaningful way beyond generally linking. The features are more than just peripherally incorporated into the claims to implement the abstract idea. These additional elements are integral part of the claimed system and method, and therefore the claims recite eligible subject matter because the claimed features integrate the judicial exception into a practical application. Independent claims 15, and 17 recite similar claim limitation as claim 1 discusses above, and are eligible under same rational as claim 1 above.
Allowable Subject Matter
Claims 1-18 are allowed over the prior art. The closest prior arts teach the technical concepts being claims, such as parsing and reading the html block, and identifying the order items. For example, Henderson et al. (US 20200334701 A1), para 0041 teaches: “[0041] To determine the present content of the shopping cart based on the data, the browser extension may identify items in the user's shopping cart, collect item data, quantity, and identifiers of items (collectively, present cart configuration information), based on the received information from the cart API or for the cart HTML page. For instance, the browser extension may invoke the automated site navigation program to perform this process, with or without being displayed to the user. The browser extension (or the automated site navigation program) may parse marked up data and/or formatted data of the received information from the cart API or for the cart HTML page to extract the present content of the user's shopping cart as the present cart configuration information.” Brady et al. (US 9563915 B2) also teaches extracting purchase related information from digital documents. Rath et al. (US 20240037631 A1) teaches use of the machine learning, training, and word embedding; for example, paragraph 0076 teaches: “[0076] In the example shown, the recommendation system 114 may generate initial item embeddings (step 504). For example, for each of the items in the input sequences received by the recommendation system, the recommendation system may generate an embedding (e.g., a real-valued vector). The number of dimensions in the embeddings may vary depending on an optimal dimensionality as determined during training or validation. In some embodiments, the recommendation system 114 may determine initial embeddings for each item of a plurality of items in a catalog as part of training the model 300. Then during an inference stage, the recommendation system 114 may, for each item of the input sequence, assign the learned embedding to the item as an initial embedding. Training the model 300 to determine weights and initial embeddings is further described below in connection with FIG. 6. In some embodiments, the initial embeddings may be generated in a different manner.” Sharshevsky et al. (US 20210312519 A1) teaches “[0038] In more detail, the automatic product information extraction module 200 includes an inventory determination sub-module 300. As a first step, the determination sub-module 300 electronically accesses each of the web pages 1-N to determine which of the web pages 1-N is actually an inventory page. In some embodiments, the inventory determination sub-module 300 makes such a determination through machine learning. As a part of the machine learning process, a human agent may browse through a few of the web pages and tag the web pages that should be considered inventory pages, for example because these pages contain a listing of products or services and their respective prices. These manually tagged web pages may be used as training data for the machine learning process. The machine learning process may identify common features in these manually tagged web pages, scan new web pages, and look for the common features in the new web pages (e.g., the web pages 1-N). The machine learning process may then predict which of the new web pages are inventory pages. In some embodiments, the machine learning process is executed using a TensorFlow platform, which is an end-to-end open source platform that includes a plurality of tools, libraries, and community resources that enable machine learning developers to build and deploy machine learning applications. The various aspects of the machine learning process are discussed in more detail below with reference to FIG. 6.” Although individually, the references teach concepts such as scraping and parsing HTML blocks, converting HTML blocks into vectors, concatenating data to form a string of words, combining word embeddings into a sentence imbedding, and detecting HTML elements that are commerce-related and outputting order-related information to fill the page, the exact sequence disclosed in the claim (i.e., scraping HTML data, converting HTML into vectors wherein the vectors are created by concatenating metadata and inner text of an HTML to form a string, retrieving a word embedding for each word in the string, and creating a sentence embedding from the word embeddings), transforming the vector representations using neural network models to create machine-generated label predictions, identifying order-related data from the web page using the machine-generated label predictions, and outputting the order-related data in the form of key-value pairs, are not taught nor rendered obvious by the prior art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WASEEM ASHRAF whose telephone number is (571)270-3948. The examiner can normally be reached Monday-Friday 09:30 A.M-06:00 P.M.
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/WASEEM ASHRAF/Supervisory Patent Examiner, Art Unit 3621
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