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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/20/2026 has been entered.
Claims Status
Claims 19-20 have been cancelled.
Claims 1-18 and 21-22 remain pending and stand rejected.
Suggested Amendments
Applicant’s Interview Request form is acknowledged. The Examiner attempted to schedule an interview before mailing of this correspondence. In an effort to advance prosecution, the Examiner suggests the following and invites Applicant to contact the Examiner for a future interview if desired:
[insert after “receive configuration data from a vendor user device…” and before “generate a landing webpage by”]:
for each module of a plurality of modules to be provided on a landing page:
receive, from the vendor user device, input defining a respective state for each module, the respective state for each module comprising an active state or disabled for one or more module platforms;
[insert immediately preceding the final limitation "upon a customer user interaction…"]:
activating or disabling one or more modules of the plurality of modules based on a platform of a user device accessing the landing page;
This combination of features I supported in at least 0160-0162 and Fig. 9C-9E. The platform-based activation/disabling of modules is not taught or rendered obvious over Chan, Dicker, or the remaining cited art. Attention is drawn to newly cited PTO form 892-U, which broadly discusses mobile optimization but does not teach or render obvious to specific selection of state information for each module through a vender device, and subsequent landing page generation based on a platform (e.g., App, PC) of a user device accessing the landing webpage.
Response to Arguments
I. Applicant’s arguments made with respect to the rejection under 35 USC 112(a) have been fully considered and are persuasive. Applicant emphasizes in particular paragraphs 00179-00185. Applicant specifically provides the following (emphasis added):
“[00177]. The specification further describes that "models may have category-level and product- level rankings," and gives an example where "a model may recommend 10 products in the 3 categories with the highest ranking for each customer." Id [00179]. The specification then provides an exemplary sorted hierarchy: "Category A: product 1,2,3," "Category B: product 1,3,4," and "Category C: product 1,4," and explains that "the model may output: A-1 > B-1 > C-1 > A-2 > B-3 > C-4 > A-3 > B-4." Id $[00180] - [00185].”
As best understood in light of Applicant’s arguments, generating a second data structure comprising a hierarchical data structure is performed specifically by ranking or sorting, thereby resulting in the hierarchy of the plurality of products. In view of Applicant’s arguments, the rejection under 35 USC 112(a) is withdrawn and the interpretation above has been adopted.
II. Applicant’s arguments made with respect to the rejection under 35 USC 103 have been fully considered and are persuasive.
Initially, the Examiner addresses the amended limitation of wherein the model is configured to determine at least one recommended product for the at least one targeted customer. The Examiner maintains that the combination of Chan in view of Dicker teaches this feature.
For example, Chan specifically teaches receiving configuration data from the vendor user device, the configuration data comprising a model and at least one target customer (see: col. 13 lines 23-36 & 44-47, col. 29 line 65-col. 30 line 13, Fig. 12 (Targeting), Fig. 15 (Step 2- Select widget products), Fig. 16 (Product fill algorithms)), as well as wherein the model is configured to determine at least one recommended product for the at least one targeted customer (see: col. 7 lines 17-22 & 55-60, col. 13 lines 54-57, col. 19 lines 19-21, col. 26 lines 1-12). That is, the configuration data includes targeting criteria that identifies at least one target customer (e.g., customers having intent, having certain user data (e.g., repeat customer, geography, etc.), et al.) and product fill algorithms used in selecting displayed products, the displayed products being relevant items selected for the user (i.e., determine at least one recommended product for the at least one targeted customer).
Additionally, Dicker also discloses a model explicitly used for selecting products (i.e., model is configured to determine at least one recommended product for the at least one targeted customer – see: 0021, 0216, 0224, Fig. 16).
Turning to the newly amended feature of comprising a hierarchical data structure, Dicker was relied upon for generating the second data structure. For example, Dicker teaches generating a data structure of the plurality of products based on the applied configuration data (see: 0077-0078, 0156, Fig. 5 (180-188)), such as by storing items in a similar items table (i.e., data structure) based on popularity, time period, etc. (i.e., configuration criteria).
More specifically, however, the generation of the similar items table in Dicker expressly comprises sorting, such as by highest-to-lowest commonality index (see: 0110-0111, 0131, Fig. 3A(112), Fig 3B (312)). Accordingly, the generated data structure of Dicker comprises a hierarchical data structure.
With respect to the recited icon, Applicant argues that neither Chan nor Dicker teach generating at least one user interface icon corresponding to the at least one recommended product. The Examiner disagrees. Chan specifically teaches listed products having url-based images for selection (see: Fig. 6A (602, 604), Fig. 6B (652, 654), col. 19 lines 36-40 (image_url), col. 24 line 65-col. 25 line 8, col. 25 lines 13-23). As understood, an icon “may comprise one or more of a graphic file, a symbol, text, or any other visual representation of an object or an action” (see: Specification: 0086). The listed products (including images having url) are thereby analogous to icons.
With respect to the argument of the “claimed sequence” of limitations, the Examiner asserts that is the combination of Chan and Dicker that renders obvious the claimed sequence. As articulated previously and below, Dicker remedies any deficiencies of Chan in relation to generating a landing web page. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Accordingly, the rejection under 35 USC 103 has been maintained.
Claim Rejections - 35 USC § 103
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) 1-2, 5, 7, 9, 10-11, 14, 16 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chan (US 10,534,851 B1) in view of Dicker (US 2003/0105682).
Regarding claim 1, Chan teaches a computer-implemented system for generating customizable landing webpages, the system comprising:
a memory storing instructions, and, at least one processor configured to execute the instructions (see: col. 3 lines 25-42, col. 7 lines 11-17) to:
receive input from a vendor user device to generate a first data structure, the first data structure comprising a webpage layout structure including at least one widget zone for insertion of a user interface icon (see: Fig. 14 (Step 1 – Select Widget Placement), col. 29 lines 55-64));
receive, from a customer user device, at least one user engagement associated with a plurality of products (see: Fig. 2 (216), col. 12 lines 50-54, col. 12 lines 65-col. 13 line 9);
receive configuration data from the vendor user device, the configuration data comprising a model and at least one target customer (see: col. 13 lines 23-36 & 44-47, col. 29 line 65-col. 30 line 13, Fig. 12 (Targeting), Fig. 15 (Step 2- Select widget products), Fig. 16 (Product fill algorithms)), wherein the model is configured to determine at least one recommended product for the at least one targeted customer (see: col. 7 lines 17-22 & 55-60, col. 13 lines 54-57, col. 19 lines 19-21, col. 26 lines 1-12);
Note: the configuration data includes targeting criteria that identifies at least one target customer (e.g., customers having intent, having certain user data (e.g., repeat customer, geography, etc.), et al.) and product fill algorithms used in selecting displayed products, the displayed products being relevant items selected for the user (i.e., determine at least one recommended product for the at least one targeted customer).
generate a landing webpage by:
generating a webpage using the first data structure (see: Fig. 5 (518-520), col. 24 lines 18-22, col. 25 lines 3-8);
applying the configuration data to the plurality of products associated with the at least one user engagement (see: col. 8 lines 50-53, col. 26 lines 1-12, col. 7 lines 17-22 & 55-60, col. 19 lines 19-21);
Note: the targeting criteria, selected products, and product fill algorithms are applied to generate the dynamic page.
determining at least one recommended product of the plurality of products based on the applied configuration data (see: col. 13 lines 54-57, col. 19 lines 28-34, col. 24 lines 48-58);
Note: relevant items are selected for the user in presenting the dynamic page.
generating at least one user interface icon corresponding to the at least one recommended product (see: Fig. 6A (602, 604), Fig. 6B (652, 654), col. 19 lines 36-40 (image_url), col. 24 line 65-col. 25 line 8, col. 25 lines 13-23);
Note: An icon is understood as “may comprise one or more of a graphic file, a symbol, text, or any other visual representation of an object or an action” (see: Specification: 0086). The listed products (including images having url) are analogous to icons.
and
inserting the at least one user interface icon into the at least one widget zone of the webpage layout structure (see: Fig. 6A (602, 604), Fig. 6B (652, 654), col. 19 lines 36-40 (image_url), col. 24 line 65-col. 25 line 8, col. 25 lines 13-23);
Note: the icons are inserted into the dynamic landing page at 602 and in accordance with the settings of Fig. 14, and may also be inserted into dynamic content region 604.
Though disclosing all of the above, Chan does not discloses that the configuration data received from the vendor also includes a time span,
generating a second data structure comprising a hierarchical data structure of the plurality of products based on the applied configuration data; and,
determining at least one recommended product of the plurality of products using the second data structure, and,
upon a customer user interaction with the generated landing webpage, generate a single detail page corresponding to the at least one recommended product.
Notably, Chan does teach the targeting criteria, selected products, and product fill algorithms are applied to generate the dynamic page of relevant items, which are selected for specifically for the user.
To this accord, Dicker discloses configuration data including a time span (e.g., Fig. 5 (180), 0102 (specific time period), 0062 & 0156 (last six months)),
a model configured to determine at least one recommended product for the at least one targeted customer (see: 0021, 0216, 0224, Fig. 16), and
generating a data structure of the plurality of products based on the applied configuration data (see: 0077-0078, 0156, Fig. 5 (180-188)), such as by storing items in a similar items table (i.e., data structure) based on popularity, time period, etc. (i.e., configuration criteria). Further, because the generation of the similar items table comprises sorting, the table is understood to also comprise a hierarchical data structure (see: 0110-0111, 0131, Fig. 3A(112), Fig 3B (312)).
Dicker then determines, using the data structure of the plurality of products, at least one recommended product of the plurality of products (see: 0167, 0174, Fig. 5 (190-194), Fig. 6, Fig. 7 (290-294), Fig. 11 (404)).
Lastly, from the landing page of Dicker (e.g., Fig. 6, Fig. 11), the user is enabled to select a link associated with one of the recommended items to view product information (or a “detail page” – exemplified by Fig. 12, 0035) for that item (see: Fig. 6, 0168, Fig. 11 (404), 0194, 0203). Accordingly, Dicker also teaches upon a customer user interaction with the generated landing webpage, generate a single detail page corresponding to the at least one recommended product.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the invention of Chan to have utilized the know technique for using product data structures as taught by Dicker in order to provided personalized recommendations that were more highly relevant to the user's current shopping or browsing purpose (see: Dicker: 0013), thereby improving the system of Chan.
2. The computer-implemented system of claim 1, wherein the configuration data further comprises at least one of a targeted product category, an excluded product category, product category diversity, click-to rate optimization, or conversion rate optimization (see: Chan: col. 22 lines 34, col. 25 lines 11-21).
5. The computer-implemented system of claim 1, wherein the at least one user engagement comprises any one of selecting a user interface icon corresponding to a product, hovering over a user interface icon corresponding to a product, adding a product to a webpage cart, or purchasing a product (see: Chan: col. 12 line 65-col. 13 line 5, col. 13 line 32-33; Dicker: 0078).
Note: the behavior data of Chan includes products visited and purchased and add to cart actions. Similarly, Dicker utilizes users viewing activities, shopping cart activities, and item rating profiles.
7. The computer-implemented system of claim 1, wherein the landing webpage is generated based on a threshold time period or corresponding vendor marketing campaign (see: Chan: col. 11 lines 23-25, col. 15 lines 50-61, col. 23 lines 11-19).
9. The computer-implemented system of claim 1, wherein each recommended product associated with each user interface icon of the generated landing webpage comprises a discounted price or associated coupon (see: Chan: Fig. 6A-6B; Dicker: Fig. 12 (“Our Price” is discounted from “List Price”).
Note: Chan teaches a landing page having a plurality of products. Dicker teaches associating listed products with discounted prices.
Regarding claims 10-11, 14, 16 and 18, these claims recite a parallel method to claims 1-2, 5, 7, and 9 that has substantially similar limitations and scope as addressed above. Accordingly, claims 10-11, 14, 16 and 18 are rejected under at least similar rationale applied to claims 1-2, 5, 7, and 9 as would be apparent to one of ordinary skill in the art.
Claim(s) 3-4 and 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chan in view of Dicker as applied to claims 1 and 10 above, and further in view of Guan (US 2008/0120339).
Regarding claim 3 and parallel claim 12, Chan in view of Dicker teaches all of the above as noted but does not teach wherein the configuration data further comprises click-to rate optimization, wherein the click-to rate optimization corresponds at least one product of the plurality of products that maximizes user engagement with the at least on product.
Optimizing for click through rate was well-known in the art before the effective filing date of the invention, and would have been obvious to one of ordinary skill. Moreover, Dicker teaches monitoring click stream data as part of the recommendation process (see: 0179, 0182).
To this accord, Guan demonstrates configuration data further comprises click-to rate optimization, wherein the click-to rate optimization corresponds at least one product of the plurality of products that maximizes user engagement with the at least on product (see: 0006 (recommendation system are optimized for a specific objective function, such as click-through-rate or conversion rate), 0032, 0042-0043).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the invention of Chan in view of Dicker to have utilized the know technique for optimizing for click-through-rate or conversion as taught by Guan in order to have provided a more comprehensive recommendation system using collaborative filtering that was optimizable based on CTR or conversion rates as desired (see: Guan: 0004,
4. The computer-implemented system of claim 1, wherein the configuration data further comprises conversion rate optimization, wherein the conversion rate optimization corresponds to at least one product of the plurality of products that maximizes purchasing of the at least one product (see: Guan: 0006 (recommendation system are optimized for a specific objective function, such as click-through-rate or conversion rate), 0032, 0042-0043).
Claim(s) 6 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chan in view of Dicker as applied to claims 1 and 10 above, and further in view of Krasnikov (US 2016/0314478).
Regarding claim 6 and parallel claim 15, Chan in view of Dicker teaches all of the above including applying the configuration data to the plurality of products associated with the at least one user engagement is based on at least one user engagement by the customer user device (see: Chan: col. 12 line 65-col. 13 line 5, col. 13 line 32-33; Dicker: 0016, 0077-0078).
Though teaching the above with respect to customer engagement/interactions, the combination does not teach that the engagement occurs within a threshold time period.
To this accord, Krasnikov teaches a method for analyzing user behavior (e.g., browsing history) that occurs within a threshold time period (e.g., timescale) such as the past few minutes or past several months (see: 0029).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the invention of Chan in view of Dicker to have utilized the known technique for analyzing user behavior data that occurs within a specific time period as taught by Krasnikov in order to have provided an improved manner for estimating user interests in accordance with recent behavior to ensure relevance of selected content (see: Krasnikov: 0003, 0006, 0029).
Claim(s) 8 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chan in view of Dicker as applied to claims 1 and 10 above, and further in view of Bryson (US 2013/0124361).
Regarding claim 8 and parallel claim 17, Chan in view of Dicker teaches all of the above as noted including product recommendations based on the application of the configuration data to the plurality of products associated with the at least one user engagement (see again: Chan: col. 8 lines 50-53, col. 26 lines 1-12, col. 7 lines 17-22 & 55-60, col. 19 lines 19-21). The combination, however, does not teach wherein the at least one recommended product corresponds to a top percentile of products based on the application of the configuration data to the plurality of products associated with the at least one user engagement.
To this accord, Bryson teaches applying configuration data to a plurality of products to determine recommended products (see: 0167, 0170, 0172, Fig. 22 (705-725)), wherein the one or more recommended products corresponds to a top percentile of products (see: 0173-0174, Fig. 22 (740-745)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the invention of Chan in view of Dicker to have utilized the known technique for recommended products within a top percentile as taught by Bryson in order to ensure that only products that meet a threshold percentile are recommended, thereby ensuring more relevant product recommendations (see: Bryson: 0173, 0129).
Claim(s) 21-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chan in view of Dicker as applied to claims 1 and 10 above, and further in view of Saurav (US 2025/0094898).
Regarding claim 21 and parallel claim 22, Chan in view of Dicker teaches all of the above as noted but does not teach wherein determining the at least one recommended product of the plurality of products includes ranking the plurality of products using a two-layered model.
To this accord, Saurav teaches a recommendation system that performs determining the at least one recommended product of the plurality of products includes ranking the plurality of products using a two-layered model (see: 0108-0109, 0120-0121).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the invention of Chan in view of Dicker to have utilized the known technique for recommending products using a multi-layer model as taught by Saurav in order to have increased depth and breadth of assortment of products offered to customers (see: Saurav: 0003).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
PTO form 892-U discusses providing personalized experience in ecommerce environments, including exemplary personalized landing pages (see: “Personalized landing pages”, section (1), (2)) and mobile site design (see: “Optimization for mobile”, (3)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM J ALLEN whose telephone number is (571)272-1443. The examiner can normally be reached Monday-Friday, 8:00-4:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anita Coupe can be reached at 571-270-3614. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
WILLIAM J. ALLEN
Primary Examiner
Art Unit 3625
/WILLIAM J ALLEN/Primary Examiner, Art Unit 3619
Prosecution Insights