DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This communication is in response to Application No. 19/226924, filed on 6/3/2025. Claims 1-12 are currently pending and have been examined. Claims 1-12 have been rejected as follows.
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.
The claims 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The claims 1-6 are a method and claims 7-12 are a system. Thus, each independent claim, on its face, is directed to one of the statutory categories of 35 U.S.C. §101.
Step 2A Prong 1: The independent claims (1 and 7, taking claim 1 as a representative claim) recite:
A method of automatically matching target product data to reference product data, the method comprising:
at a server system including one or more processers in communication with a database:
retrieving target product data corresponding to a target product including target product price and interaction data corresponding to a number of interactions with a rendering of the target product at a target user interface (UI);
determining a web-scraping frequency based on the interaction data;
executing web-scraping of a reference UI to retrieve reference product data corresponding to a reference product based on the web-scraping frequency, the reference product data including a reference product price;
matching the target product data to the reference product data meeting predefined criteria by updating the target product price a new target product price equal to the reference product price; and
automatically rendering the target product with the new target product price at the target UI.
These limitations, except for the italicized portions, under their broadest reasonable interpretations, recite certain methods of organizing human activity for managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) as well as commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). The claimed invention recites steps for matching target product data to reference product data meeting a criterion and updating a new target product price. The steps under its broadest reasonable interpretation specifically fall under sales activities. The Examiner notes that although the claim limitations are summarized, the analysis regarding subject matter eligibility considers the entirety of the claim and all of the claim elements individually, as a whole, and in ordered combination.
Prong 2: This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of
A method of automatically matching target product data to reference product data, the method comprising: at a server system including one or more processers in communication with a database: (claim 1)
A system for automatically matching target product data to reference product data, the system comprising: a server system including one or more processers in communication with a database, the server system configured to: (claim 7)
retrieving target product data corresponding to a target product including target product price and interaction data corresponding to a number of interactions with a rendering of the target product at a target user interface (UI);
determining a web-scraping frequency based on the interaction data;
executing web-scraping of a reference UI to retrieve reference product data corresponding to a reference product based on the web-scraping frequency, the reference product data including a reference product price;
matching the target product data to the reference product data meeting predefined criteria by updating the target product price a new target product price equal to the reference product price; and
automatically rendering the target product with the new target product price at the target UI.
The additional elements emphasized above are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of processing data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application – MPEP 2106.05(f).
Accordingly, these additional elements when considered individually or as a whole do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The independent claims are directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A Prong two, the additional elements in the claims amount to no more than mere instructions to apply the judicial exception using a generic computer component.
Even when considered as an ordered combination, the additional elements of claims 1 and 7 do not add anything that is not already present when they are considered individually. Therefore, under Step 2B, there are no meaningful limitations in claims 1 and 7 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (see MPEP 2106.05).
As such, independent claims 1 and 7 are ineligible.
Dependent claims 2-6 and 8-12 when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. §101 because the additional recited limitations fail to establish that the claims are not directed to the same abstract idea of Independent Claims 1 and 7 without significantly more.
Claim 2 recites wherein the interaction data includes: i) a number of selections of the rendered target product at the target UI and ii) a number of requests to purchase the rendered target product received at the target UI. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 3 recites further comprising: at the server system, preventing the new target product price from being updated for a predetermined amount of time. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 4 recites further comprising: at the server system: retrieving the target product data for a plurality of different target products; determining web-scraping frequencies for each target product of the plurality of target products based on the corresponding target product data; and for each target product of the plurality of target products: executing web-scraping of the reference UI to retrieve reference product data for reference corresponding to the target product based on the web-scraping frequency, the reference product data including a reference product price; determining that the reference product price is less than the target product price; updating the target product data to generate a new target product price equal to the reference product price; and automatically rendering the target product with the new target product price at the target UI. The limitation merely repeats the process of claim 1 for additional products and therefore the analysis of claim 1 applies to claim 4.
Claim 5 recites wherein executing web-scraping of the reference UI to retrieve reference product data includes web-scraping a plurality of reference UIs to retrieve a plurality of reference product data and the method further includes: updating the target product data to generate the new target product price equal to one of a reference product price corresponding to one of the plurality of retrieved reference product data meeting the predefined criteria. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 6 recites wherein updating the target product data includes determining at least one of: i) the lowest reference product price included in the plurality of reference product data meeting the predefined criteria, and ii) a hierarchical ranking of the plurality of reference UIs. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 8-12 recite parallel claim language and are determined to be rejected under 35 USC 101 for the same reasons set forth above.
For these reasons claims 1-12 are rejected under 35 USC 101.
Claim Rejections - 35 USC § 102
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-12 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Shpanya (US 20170109767).
Regarding claims 1 and 7, Shpanya discloses a method of automatically matching target product data to reference product data, the method comprising: (Claim 1)
a system for automatically matching target product data to reference product data, the system comprising: a server system including one or more processers in communication with a database, the server system configured to: (claim 7) (Shown in Figure 1 and see [0065, 0068])
at a server system including one or more processers in communication with a database: (Shown in Figure 1 and see [0065, 0068])
retrieving target product data corresponding to a target product including target product price and interaction data corresponding to a number of interactions with a rendering of the target product at a target user interface (UI) (The product catalog contains a list of the seller's product data. Examples of product data include product name, brand name, model number, product SKU, and price, para [0073] and [0053] The data gathering component is represented generally by a series of processes that are identified by reference numerals 10, 12, 14, 16, and 18. Process 10 defines a competitor. Process 12 configures a web scraper to gather specified data related to the competitor defined in process 10. Process 14 collects competitor data using the configured web scraper. The competitor data may include, for example, pricing data, internal sales velocity, number of hits or traffic per product page, conversion rate (sales of item per total traffic per page), and standard deviation for the product. And see [0048] );
determining a web-scraping frequency based on the interaction data (Process 12 configures a web scraper to gather specified data related to the competitor defined in process 10, para [0053], Fig. l);
executing web-scraping of a reference UI to retrieve reference product data corresponding to a reference product based on the web-scraping frequency, the reference product data including a reference product price (A retailer may price or re-price products dynamically in accordance with a number of variables that includes, but is not limited to competitor pricing, time of day, sales performance, traffic, or conversion rates, para [0048]);
matching the target product data to the reference product data meeting predefined criteria by updating the target product price a new target product price equal to the reference product price (If the site returns a product hit, the scraper navigates to the product page, scrapes it, and then validates that the data scraped matches given criteria, for example, an arbitrary string, UPC, Brand/Model, or expected price range, para [0086]);
and automatically rendering the target product with the new target product price at the target UI (Updated data is returned to market data 32, which is then sent to the analytics component for processing. Based on the result of the processing, feedback may be returned to the pricing automation component, and to process 20 in particular, for further price adjustment, para [0057], Fig. 1).
Regarding claims 2 and 8, Shpanya discloses wherein the interaction data includes: i) a number of selections of the rendered target product at the target UI and ii) a number of requests to purchase the rendered target product received at the target UI (However, if a competitor does not offer the same exact product, but one or more similar products are found, then at step 104 the method requests a user to manually select one or more products that may be considered competing product, para [0062], Fig. 2 and [0053] The data gathering component is represented generally by a series of processes that are identified by reference numerals 10, 12, 14, 16, and 18. Process 10 defines a competitor. Process 12 configures a web scraper to gather specified data related to the competitor defined in process 10. Process 14 collects competitor data using the configured web scraper. The competitor data may include, for example, pricing data, internal sales velocity, number of hits or traffic per product page, conversion rate (sales of item per total traffic per page), and standard deviation for the product. And see [0048]).
Regarding claims 3 and 9, Shpanya discloses at the server system, preventing the new target product price from being updated for a predetermined amount of time (In a second view of this fourth user interface, timing information is presented that allows selection of a start time upon which the rule takes effect, and an end time upon which the rule is no longer effective. Start times and end times may be provided in any currently existing or later developed means, para [0124]).
Regarding claims 4 and 10, Shpanya discloses at the server system: retrieving the target product data for a plurality of different target products (The product catalog contains a list of the seller's product data. Examples of product data include product name, brand name, model number, product SKU, and price, para [0073]);
determining web-scraping frequencies for each target product of the plurality of target products based on the corresponding target product data (Process 12 configures a web scraper to gather specified data related to the competitor defined in process 10, para [0053], Fig. l);
and for each target product of the plurality of target products: executing web-scraping of the reference UI to retrieve reference product data for reference corresponding to the target product based on the web-scraping frequency, the reference product data including a reference product price (A retailer may price or re-price products dynamically in accordance with a number of variables that includes, but is not limited to competitor pricing, time of day, sales performance, traffic, or conversion rates, para [0048]);
determining that the reference product price is less than the target product price; updating the target product data to generate a new target product price equal to the reference product price (If the site returns a product hit, the scraper navigates to the product page, scrapes it, and then validates that the data scraped matches given criteria, for example, an arbitrary string, UPC, Brand/Model, or expected price range, para [0086]);
and automatically rendering the target product with the new target product price at the target UI (Updated data is returned to market data 32, which is then sent to the analytics component for processing. Based on the result of the processing, feedback may be returned to the pricing automation component, and to process 20 in particular, for further price adjustment, para [0057], Fig. 1).
Regarding claims 5 and 11, Shpanya discloses wherein executing web-scraping of the reference UI to retrieve reference product data includes web-scraping a plurality of reference Uls to retrieve a plurality of reference product data and the method further includes: updating the target product data to generate the new target product price equal to one of a reference product price corresponding to one of the plurality of retrieved reference product data meeting the predefined criteria (The results of each rule set by the user are measured and reported on the dashboard daily or near real-time, depending on the frequency of feed updating which is set during the implementation, para [0058]).
Regarding claims 6 and 12, Shpanya discloses wherein updating the target product data includes determining at least one of: i) the lowest reference product price included in the plurality of reference product data meeting the predefined criteria, and ii) a hierarchical ranking of the plurality of reference Uls (Moreover, some embodiments may also feature a minimum price setting, below which the rule will no longer be applied. Such a feature may provide an important sanity check against unintended manipulation by third parties or lemming-style bot crashes where every competitor uses automated pricing to beat the lowest price such that the market price is driven to near zero, para [0134]).
Relevant Art Not Cited
Ouyang (US 20040249643) discloses utilizes a keyword based rule set to crawl through the web pages and fetch the related product prices. There are many algorithms involved for accurately matching and selecting the required items. The data is then sorted by the price and brand and compared with the database of a group of known products. The prices of the known products are then adjusted via a set of built-in rules to put them in a very competitive position, such as number 1 or 2 among the same product category. The new prices make the seller's products very competitive online. The new price list will then automatically be posted to the web server to replace the old prices. The whole process is automated by simply placing the program on a computer operating system scheduler and let it run periodically according to the specified date and time.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VICTORIA E. FRUNZI whose telephone number is (571)270-1031. The examiner can normally be reached Monday- Friday 7-4 (EST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Marissa Thein can be reached at (571) 272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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VICTORIA E. FRUNZI
Primary Examiner
Art Unit TC 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 8/13/2026
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